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AN EEG INVESTIGATION OF PAIN PROCESSING IN PARKINSON’S
DISEASE
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy in Faculty of Biology, Medicine and Health
2019
Sarah L. Martin
School of Biological Sciences
2
Contents
LIST OF FIGURES 5
LIST OF TABLES 7
ABBREVIATIONS 8
ABSTRACT 9
LAY ABSTRACT 10
DECLARATION 12
COPYRIGHT STATEMENT 12
ACKNOWLEDGEMENTS 13
PREFACE 15
CHAPTER 1 17
INTRODUCTION 17
1.1. PARKINSON’S DISEASE AND CHRONIC PAIN 18
1. 2. PARKINSON’S DISEASE: AN OVERVIEW 20
1.3. BRAIN REGIONS INVOLVED IN PAIN PROCESSING 24
1.4. PATHOLOGICAL PREDISPOSITION OF PAIN IN PD 29
1.5. IMAGING STUDIES SHOWING ALTERED CENTRAL PAIN PROCESSING IN PARKINSON’S DISEASE 45
1.6. CONCLUSION 47
CHAPTER 2 48
GENERAL METHODS AND INTRODUCTION TO STUDY AIMS 48
2.1. GENERAL METHODS 49
2.2. INTRODUCTION TO STUDY OBJECTIVES 54
2.3. RECRUITMENT 60
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CHAPTER 3 64
A NEUROPHYSIOLOGICAL INVESTIGATION OF PAIN ANTICIPATION IN PARKINSON’S DISEASE 64
3.1. ABSTRACT 65
3.2. INTRODUCTION 66
3.3. METHODS 68
3.4. RESULTS 76
3.5. DISCUSSION 85
3.6. CONCLUSION 89
3.7. SUPPLEMENTARY MATERIALS 90
CHAPTER 4 93
CENTRAL PAIN PROCESSING IN PEOPLE WITH PARKINSON’S DISEASE WHILST ON MEDICATION 93
4.1. ABSTRACT 94
4.2. INTRODUCTION 94
4.3. METHODS 95
4.4. RESULTS 101
4.5. DISCUSSION 104
4.6. CONCLUSION 106
CHAPTER 5 108
AN INVESTIGATION OF THE ROLE OF THE DOPAMINE D2 RECEPTOR IN PAIN PERCEPTION 108
5.1. ABSTRACT 109
5.2. INTRODUCTION 110
5.3. METHODS 113
5.4. RESULTS 120
5.5. DISCUSSION 132
5.6. CONCLUSION 139
CHAPTER 6 140
AN EEG INVESTIGATION OF AGE-RELATED DIFFERENCES IN ACUTE PAIN 140
6.1. ABSTRACT 141
6.2. INTRODUCTION 141
6.3. METHODS 144
4
6.4. RESULTS 150
6.5. DISCUSSSION 157
6.6. CONCLUSION 161
CHAPTER 7 162
GENERAL DISCUSSION 162
7.1. MAIN AIM OF THESIS 163
7.2. SUMMARY OF EXPERIMENTAL WORK 163
7.3. AN INTERPRETATION OF THESIS STUDIES 165
7.4. SUMMARY OF INTERPRETATION 171
7.5. NEUROIMAGING RESEARCH OF PAIN IN PARKINSON’S 173
7.6. FUTURE CONSIDERATIONS 175
7.7. FUTURE RESEARCH 179
7.8. FINAL CONCLUSION 181
REFERENCES 182
5
List of Figures
FIGURE 1.1: Regions of the pain network taken from Bushnell, Ceko and Low, 2013 25
FIGURE 1.2: The SN and its connections to the regions of the pain network…………… 31
FIGURE 1.3: A summary of the various roles of dopamine and how they are
involved in pain perception..................................................................... 38
FIGURE 1.4: Dopaminergic pathways…………………....................................................... 39
FIGURE 2.1: The pain rating scale that was used for the psychophysics and main
experiments………………………………………………………………………………………… 50
FIGURE 2.2: An EEG waveform of an SPN generated by a visual anticipatory cue,
auditory cues, and the LEP response following acute laser pain...……….. 53
FIGURE 2.3: A schematic diagram of the action of the dopamine D2 receptor and
how low and high doses of agonists and antagonists have different
effects on postsynaptic D2 receptor activity……………............................. 59
FIGURE 2.4: The inverted-U relationship between dopamine level and
performance………...............................................................................….. 61
FIGURE 2.5: Business cards given to participants of the D2 dopamine study……........ 63
FIGURE 3.1: A schematic diagram of a single trial of the experimental paradigm…... 71
FIGURE 3.2: The energy of the laser required to induce high (level 7) pain…............. 77
FIGURE 3.3: Graphs showing the information of the N2 and P2 components of the
laser-evoked potential (LEP)………………………………...………….………............ 79
FIGURE 3.4: Topography plots showing the average response for the mid- and late-
anticipation time windows for HC and PD…............................................ 80
FIGURE 3.5: Source estimates for significant F-contrasts at FWE correction using
MRIcroN software…………………………………………………………………............... 83
FIGURE 3.6: Diagram from Böcker et al., (2001) which reports the grand average of
the stimulus preceding negativity associated with anticipating
threatening stimuli……………………………………............................................ 87
FIGURE 4.1: The laser voltage required to induce a low and high pain
intensity…………………..…………………………………………………..……..……………... 102
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FIGURE 4.2: The effect of certainty on the pain rating scores…………………………......... 103
FIGURE 5.1: A schematic diagram of a single trial of the experimental paradigm...... 117
FIGURE 5.2: The effect of certainty on each laser intensity calculated and plotted
for each drug…………………………….................................................…………. 123
FIGURE 5.3: The eye-blink rate (EBR) calculated for each dopamine manipulation
condition highlighted a monotonic relationship between dopamine
D2R activation and EBR…………………………………………………………………….... 125
FIGURE 5.4: Topography plots showing the average response for early-, mid– and
late-anticipation for all conditions…………………………………………………....... 126
FIGURE 5.5: Source estimates during mid-anticipation for T-contrast of Control
versus agonist (Cabergoline) and antagonist (Amisulpride)……………….... 129
FIGURE 5.6: Source estimates during mid-anticipation for the Expectation T-
contrast of known High versus known Low……………………………………....... 130
FIGURE 5.7: The drug effect reported using source localisation for post-stimulus
TWOI [200 600 ms]…………………………....................................................... 131
FIGURE 5.8: A schematic diagram to signify the inverted-U relationship between
Dopamine level and performance.……………………..………………………………. 135
FIGURE 6.1: A schematic diagram of a single trial of the experimental paradigm...... 146
FIGURE 6.2: Pain rating of laser stimuli…………..………………………………......................... 151
FIGURE 6.3: The pain rating for Known High stimuli was averaged across
participants within each group at each trial number…………................... 152
FIGURE 6.4: Waveforms at Cz electrode for all participants in the Young and Old
groups……................................................................................................. 153
FIGURE 6.5: ERP waveforms and topography plots………………………………………............ 154
FIGURE 6.6: Topographic plots of the SPM F-statistics for the main group effect
during anticipation and post-stimulus time windows………………………..... 155
FIGURE 7.1: A schematic diagram of the relationship between dopamine level and
performance of dopaminergic processes…………………………………………….. 172
Word Count: 43,028
7
List of Tables
TABLE 1.1: Motor symptoms of Parkinson’s disease……………………………………………… 21
TABLE 1.2: Non-motor symptoms of Parkinson’s disease.…………………………………….. 21
TABLE 1.3: Pathological findings of Lewy body Pathology in the Parkinsonian brain outlined by Braak et al., (2004)………………………………………………….. 23
TABLE 3.1: The table displays the reported measures for the assessments for the PD and HC groups.………………………………………………………………………………. 76
TABLE 3.2: Group effect on sources of early, mid and late anticipation-evoked responses.…………………………………………………………………………………………… 82
TABLE 3.3: Scalp-level SPM results for the F-contrast of Intensity (Low v High) for post-stimulus TWOI [200 600]…………………………………………………………….. 90
TABLE 3.4: The source localisation results for the significant within-subject contrast of Intensity (Low v High) during the post-stimulus time window [200 600 ms].............................................................................. 91
TABLE 3.5: A detailed version of Table 3.2 to include all regions highlighted by the AAL2 toolbox............................................................................................ 92
TABLE 4.1: The rating of the laser stimuli for each laser condition and each group. 103
TABLE 5.1: The pain rating scores for the experimental paradigm........................... 121
TABLE 5.2: The unpleasantness rating for each laser condition for each Drug Condition................................................................................................. 128
TABLE 5.3: Source localisation significant results within mid-anticipation and the post stimulus TWOI.................................................................................. 131
TABLE 6.1: Scalp-level statistical results for whole-scalp analysis............................. 156
8
Abbreviations
AAL2 Anatomical Automatic Labelling 2 ANOVA Analysis of variance
BEM Boundary Element Model BL Baseline
CBT Cognitive Behavioural Therapy D2R Dopamine D2 Receptor EBR Eye Blink Rate EEG Electroencephalography ERP Event Related Potential
HADS Hospital Anxiety and Depression Scale HC Healthy Control
ICA Independent Component Analysis LEP Laser Evoked Potential
LORETA Low Resolution Electromagnetic Tomography MCC Mid-Cingulate Cortex
MDS-UPDRS Movement Disorder Society Unified Parkinson’s Disease Rating Scale MoCA Montreal Cognitive Assessment
PAG Periaqueductal Gray PCS Pain Catastrophising Scale PD Parkinson’s Disease
PET Positron Emission Tomography PFC Prefrontal Cortex
PwPD People with Parkinson’s Disease ROI Region of Interest
SASICA Semi-Automatic Selection of Independent Components for Artifact correction
S1/S2 Primary/Secondary Somatosensory Cortex SMA Supplementary Motor Area SPM Statistical Parametric Mapping
TWOI Time Window of Interest VAS Visual Analogue Scale
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Abstract
Chronic pain in Parkinson’s disease (PD) is significantly higher than in the general population, and is often overlooked to be a side effect of the impaired movement and stiffness associated with the disease. There is scientific rational to link the degeneration which occurs in the PD brain to the increased prevalence of chronic pain. Although acute pain thresholds have been shown to be lower in PD, there is a lack of neuroimaging research which investigates the role of central processing in PD. Therefore, this thesis documents one of the first neuroimaging studies to investigate whether central pain processing is abnormal in people with PD.
The perception of pain can be modulated by subject driven processing such as attention, salience assignment and cognition, which is regarded as top-down modulation and is often abnormal in chronic pain conditions. A technique to investigate top-down processing is to study the neural activity during the anticipation of acute pain stimulus. Hence, in this thesis, we used EEG to record the anticipatory response to acute pain stimuli delivered by a CO2 laser.
Our first experiment compared people with PD whilst off their medication to a healthy age-matched cohort and concluded that the PD patients showed an amplified anticipatory response and a higher sensitivity to the laser. In contrast, following dopamine increasing medication, no difference was shown between the PD and healthy controls.
To investigate the role of dopamine in altering pain perception in PD, the second study investigated the effect of manipulating the dopamine D2 receptor in young healthy volunteers. The study reported that the agonist and antagonist induced reductions in neural activity in the parietal and temporal lobes during pain perception, and the antagonist induced a reduction in the activity of the insula during pain perception, and intensified the effect of certainty during anticipation on the rating of pain.
The patient and the D2R manipulation studies demonstrated that dopamine may play a role in the top-down processing of pain and hence explain why chronic pain in PD is prevalent. Importantly the pain rating and sensitivity were not changed by Levodopa administration or D2R manipulation and supports the notion that dopamine is central to modulating top-down processing, rather than directly coding the intensity of pain. Therefore, together with further neuroimaging research conducted during the PhD, there is strong evidence of a disruption in the central processing of pain in PD and promotes the use of alternative therapies in the treatment of chronic pain in PD.
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Lay Abstract
It is very common that people with Parkinson’s disease also suffer from long lasting
pain, known as chronic pain. The pain can be caused by the symptoms of
Parkinson’s disease such as; rigidity, bad posture and impaired movement.
However, changes in pain perception have been seen very early in the disease
before these symptoms have occurred. In addition, there is little evidence that the
severity of the movement symptoms relates to the prevalence of chronic pain.
Therefore, our research aimed to investigate how the changes within the brain
could be involved in the increased susceptibility to chronic pain in Parkinson’s
disease. During the anticipation of pain the activity of brain regions associated with
pain have been shown to be different in people with chronic pain. Therefore, our
experiments also recorded the brain activity during the anticipation of a painful
stimulus.
Firstly, within people with Parkinson’s off their medication we recorded their brain
activity whilst they anticipated pain. In comparison to a cohort of healthy
participants, we discovered that the participants with Parkinson’s disease were
more sensitive to the pain, plus a region of the brain known as the midcingulate
cortex showed increased activity during anticipation. The midcingulate cortex is
involved in pain sensitivity and therefore shows that the region is working
differently in people with Parkinson’s. After the people with Parkinson’s had taken
their normal medication, they remained more sensitive to the painful stimulus;
however, the medication had stopped the increased brain activity within the
midcingulate cortex during anticipation that was seen whilst they were off their
medication.
The medication for Parkinson’s disease improves the activity of dopamine, a
chemical within the brain. Therefore, our second investigation aimed to understand
the role of dopamine in the anticipation and perception of pain within young
healthy people by using drugs which increase and decrease the level of dopamine
activity in the brain. The participants completed the same experiment which was
11
used in the Parkinson’s study. Our results showed that dopamine did not change an
individual’s pain sensitivity; however it does alter the way we rate the pain
depending on the information we provided during anticipation. This may indicate
that long-term changes in dopamine in the brain within Parkinson’s disease could
alter how an individual feels pain.
Our final aim was to investigate the difference in brain activity during the
anticipation and perception of pain between young and old people. Our
comparison showed that the young cohort showed a much higher level of brain
activity during the experiment. This may have been due to a difference in pain
processing and our relationship with pain changing over time between the two
groups. The difference could also be due to changes in the quality of brain
recordings in young and older brains.
In summary, the research highlighted an increased sensitivity to pain in people with
Parkinson’s disease and some evidence for altered brain activity in Parkinson’s
disease. With better understanding of the source of the pain, there will be better
treatment for people with Parkinson’s disease and pain. Therefore, the evidence of
altered brain activity during anticipation of pain indicates that treatments aimed at
improving how the brain processes pain, such as meditation and cognitive
behavioural therapy (CBT), could be beneficial for people with Parkinson’s disease.
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Declaration
No portion of the work included in this thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.
Copyright Statement
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ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made.
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iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=24420), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses.
Acknowledgements
I have been immensely lucky for the support that I have received throughout my
PhD from my supervisors, colleagues, family and friends. Firstly I would like to
extend huge thanks to my supervision team; Dr Monty Silverdale, Professor
Anthony Jones, Dr Christopher Kobylecki, Dr Christopher Brown and Professor Wael
El-Deredy, and my advisor Dr Joanna Neil. Together they have helped me to
develop as a researcher and overcome challenges that I have faced.
I am hugely thankful for the teaching and guidance I received from my supervisors,
including Anthony’s knowledge of all things pain related, Chris Brown’s EEG analysis
training & support, and Chris Kobylecki’s Parkinson’s knowledge & recruitment.
Special thanks go to Monty, who is without a doubt, scarily intelligent, both in
neuroscience and as a supervisor. His support has given me the confidence to be
independent in my research and excitement to continue research in Parkinson’s
disease.
Working in The Human Pain Research Group has been incredibly important to my
enjoyment of my PhD. Anthony has created a supportive and team-focused lab
where hard work and commitment are expected, yet a good work-life balance is
expected in equal measure. An immense thank you to the irreplaceable Tim Rainey;
I truly would have been lost without your EEG training, technical support, running
and cycling commentary, and of course the amazing cups of teas. I also want to give
huge thanks to Kate Lees, a fountain of knowledge and the greatest supplier of
kitten and puppy photos! And finally to Grace Whitaker and Emily Hird who guided
and trained me in my first couple of years. To be part of the group gave me an
identity that I am proud of, and truly hope that the saying “You never leave The
Human Pain Research Group” is true.
I am also indebted to the people at Salford Royal who have provided technical
support from Medical Physics; Stuart Watson, Prawin Samraj, and Donald Allan and
IT support from Adam Lounsbach.
Acknowledgements
14
It goes without saying that my family have provided fantastic support throughout
my PhD – as they do in everything that I do. Special thanks go to my dad, Ian
Martin, for his support. An extra thank you goes to my brother Paul Martin, for all
of his help with coding throughout the early stages of my PhD.
I am immensely grateful for the friendships outside the world of PhD. Special thanks
go to the South Manchester Mafia; Grace Bennett, Alex Bennett and Jon Robinson –
a home away from home, with Friday night film and food, and our superior skills of
escape rooms keeping me sane throughout my PhD. Huge thanks go to Ruth Ingram
– the dream of a house-mate and more-so greatest friend and PhD ally.
Further thanks must be extended to my Girlguiding family in Manchester who have
provided me with a break from the PhD bubble. Running the 1st Height Brownie unit
is a joy and I have been so lucky to have made so many friends, and had amazing
experiences through Girlguiding.
This research could not have happened without the funding from Parkinson’s UK,
who I am incredibly grateful for. And finally, I am immensely thankful to my
examiners, Dr Donna Lloyd and Dr Ellen Poliakoff, for taking the time to read and
assess my thesis.
This thesis is dedicated to my mum, Carran Martin, a fun -loving,
courageous and inspiring woman.
Preface
This thesis contains the research which stems from Monty Silverdale’s observations
of Parkinson’s disease patients suffering from chronic pain which did not appear to
correlate with the severity of their motor and peripheral symptoms. For better
understanding and thus better treatment, Monty began a collaboration with The
Human Pain Research Group at Salford Royal Hospital to further understand the
brain’s role in pain in Parkinson’s disease. Together with Professor Anthony Jones,
Dr Christopher Kobylecki, , Dr Christopher Brown and Professor Wael El-Deredy, a
series of studies were designed using EEG and laser-induced pain. The aim of the
PhD was to investigate the processing of pain within the Parkinson’s disease in
comparison to a healthy age-matched cohort, and to investigate the impact of the
altered neurotransmitter levels within Parkinson’s disease could have on pain
processing. I was lucky enough to be selected to undertake this research and knew
the projects were very exciting for a PhD student to carry out.
Initially, two studies were outlined for the thesis, including a comparison of
Parkinson’s patients with age-matched controls, and a second study investigating
the role of Dopamine and Serotonin in pain perception within a young healthy
cohort via the use of acute amino acid depletion. At the start of my PhD, an exciting
collaboration was set up with Dr Grace Whitaker who would be recruiting
participants for a pharmacological modulation of Dopamine. The research linked
perfectly with our research aims and the study was carried out in my second year of
the PhD.
Unfortunately, the building which the research took place, was being demolished
throughout my second and third year, and caused significant delays to data
collection of the amino acid depletion study due to the displacement, noise,
vibration and dust. Therefore, for the submission of this thesis, the second study
included in the thesis was switched to be the dopamine pharmacological
modulation study carried out in collaboration with Dr Grace Whitaker, instead of
the amino-acid depletion study. The theories behind the amino acid modulation
Preface
16
and dopamine modulation studies were both to investigate how altered
neurotransmitters in Parkinson’s disease impacts pain perception, and hence,
provide a similar narrative to the progression of this thesis.
Chapter 1
Introduction
1. Introduction
18
1.1. PARKINSON’S DISEASE AND CHRONIC PAIN
In comparison to the general population, the prevalence of chronic pain in
Parkinson’s disease (PD) has been reported to be significantly higher at between
40% and 85% (median = 57.78 %) (Valkovic et al., 2015; Allen et al., 2015; Beiske et
al., 2009; Lee et al., 2006; Goetz et al., 1986; Tinazzi et al., 2006; Nègre-Pagès et al.,
2008a; Hanagasi et al., 2011; Rana et al., 2013; Defazio et al., 2008; Silva, Viana and
Quagliato, 2008; Silverdale et al., 2018) whilst the prevalence within the general
population has been reported to be 22.75% [averaged from (Blyth et al., 2001b;
Johannes et al., 2010; Breivik et al., 2006)]. There is clearly a higher prevalence of
chronic pain reported in the PD population compared to the general population
which indicates that the development of PD may increase an individual’s
susceptibility to developing chronic pain.
The development and persistence of chronic pain is thought to be associated with
dysfunctional bottom-up and top-down mechanisms of pain modulation. The term
bottom-up refers to stimulus-driven modulation (i.e. stimulus intensity, stimulus
novelty etc) and modulation of ascending pain signals in the dorsal horn of the
spinal cord. In contrast, top-down modulation refers to subject-driven manipulation
of the perception of pain (i.e. emotional state, cognition, attention orientation,
memories etc). Both processes can either facilitate or inhibit pain perception and
abnormalities within these mechanisms are associated with chronic pain conditions
and pain hypersensitivity.
The presence of chronic pain in PD patients has often been considered to be a
consequence of the musculoskeletal symptoms, and to be primarily driven by
excessive nociceptive input due to bottom-up processes. Although musculoskeletal
symptoms are likely to contribute to chronic pain, the fact that pain can precede
the musculoskeletal symptoms and that the pain can be idiopathic, demonstrates
that heightened pain may be a syndrome caused by the pathological changes within
the brain associated with PD. There is a lack of neuroimaging research which
1. Introduction
19
investigates the role of impaired top-down processing. However, the degeneration
within the PD brain provides a scientific rationale to suggest that altered top-down
processing, rather than merely atypical peripheral nociceptive transmission, may be
the cause of the increased likelihood of chronic pain in PD.
To dispel theories that the pain is solely a result of peripheral musculoskeletal
symptoms a large scale investigation of 1957 PD patients reported that there was
no correlation between pain and motor impairments during early stage PD
(Silverdale et al., 2018). In addition, Valkovic et al., (2015) reported that a high
proportion of pain in PD is deemed to be central neuropathic pain which was
widespread and not associated with the musculoskeletal or dystonic symptoms.
Additionally, pain has been reported to be located in areas that are unlikely to be
affected by the PD muscular symptoms such as oral, genital, (Ford et al., 1996) and
abdominal pain (Nègre-Pagès et al., 2008a).
Finally, pain perception has been shown to be abnormal in PD patients irrespective
of whether they have chronic pain or not. For instance, PD patients have an
increased pain-induced cortical activation as measured by positron emission
tomography (PET) (Brefel-Courbon et al., 2005), reduced pain thresholds and
tolerance (Zambito Marsala et al., 2011), and reduced electrical and muscle
withdrawal reflex to pain (Mylius et al., 2008). Furthermore, numerous studies
which selectively investigate PD patients suffering from chronic pain have reported
reduced pain thresholds and an inability to habituate to stimuli compared to
healthy controls (Schestatsky et al., 2007a; Djaldetti et al., 2004a).
Chronic pain negatively impacts on daily life and has been considered to be more of
an impairment than the motor symptoms (Silverdale et al., 2018). Therefore, there
is clear evidence that further research is required to understand the cause of
chronic pain in people with PD to provide an improvement in treatment. Here, I
briefly review the pathology of PD, pain processing and the impairments within
shared central process which shed light on a potential link between PD and chronic
pain.
1. Introduction
20
1. 2. PARKINSON’S DISEASE: AN OVERVIEW
PD is a neurodegenerative disease affecting 127, 000 people in the UK and 1 million
in the US and is progressively more common with increasing age (Dorsey et al.,
2007). It is the second biggest neurological disease after Alzheimer’s disease,
affecting approximately 100/100,000 (Seidel et al., 2015b). PD is classically
regarded to be due to the loss of dopaminergic neurons located in the basal
ganglia, specifically the substantia nigra pars compacta (SNpc), however, there is a
multitude of additional pathological changes. The basal ganglia are the main
processors of voluntary motor movements and hence the main symptoms of PD are
regarded to be motor related. The characteristic features of PD are the presence of
a tremor, bradykinesia, and rigidity with symptoms including, shuffling gait, bad
posture and reduced facial expression. Nevertheless, PD is not solely a movement
disorder, and non-motor symptoms are seen to precede the motor aspects. The
widespread degeneration throughout the brain also leads to non-motor symptoms
including; cognitive impairments, dementia, pain, sleep disturbances, depression
and anxiety (Barone, 2010).
The three main motor symptoms of PD are tremor, bradykinesia and rigidity and
are normally the first point at which the disease can be clinically recognised. Below
Table 1.1 defines the motor related symptoms seen in PD. The severity of each
symptom varies between patients. The non-motor symptoms associated with PD
are outlined in Table 1.2. It is important to state that not all PD patients experience
all of the symptoms discussed, and the severity of the condition varies between
each person. The presence of cognitive decline has been related to the altered
neurotransmitter activity in the PD brain.
1. Introduction
21
Table 1.1: Motor symptoms of Parkinson’s disease.
Symptom Definition Location Reference
Tremor
Uncontrolled, somewhat rhythmic movements. The amplitude of the tremor can be as large as more than 10cm, as
well as being as low as below 1cm.
Upper and lower limbs.
Jaw/Lips
(Geraghty, Jankovic and Zetusky, 1985; Jankovic,
Schwartz and Ondo, 1999)
Bradykinesia Slow movement and freezing n/a (Berardelli et al., 2001)
Rigidity Impaired movement of the joints Joints (Berardelli, Sabra and
Hallett, 1983; Prochazka et al., 1997)
Akinesia Impaired control of voluntary movement (Pascual-Leone et al.,
1994a; b)
Dystonia Muscle spasms and abnormal muscle tone causing uncontrolled movements
Widespread (Tolosa and Compta, 2006;
Poewe, Lees and Stern, 1988)
Abnormal gait Gait is shorter than normal, and can
develop to become shuffling. n/a
(Bloem et al., 2004; Rogers, 1996; Olson, Lockhart and Lieberman, 2019; Morris et
al., 2019)
Impaired posture
A consequence of the rigidity, dytonia and akinesia
n/a (Benatru, Vaugoyeau and
Azulay, 2008; Dietz, Berger and Horstmann, 1988)
Table 1.2: Non-motor symptoms of Parkinson’s disease.
Symptom Definition Reference
Dementia Cognitive decline including memory and attention, and increased confusion. Reduced independence.
(Aarsland, Zaccai and Brayne, 2005; Gomperts et al., 2016)
Speech and communication
problems
Their speech may become slurred and lack of expression within their speech. Handwriting may
also become smaller and less-defined.
(Ho et al., 1999; Canter, 1963; Drotár et al., 2016)
Chronic pain Chronic pain is persistent pain for more than 3
months. Pain can be caused by musculoskeletal problems and can also be idiopathic.
(Silverdale et al., 2018; Nègre-Pagès et al., 2008b; Beiske et
al., 2009)
Fatigue Prolonged tiredness (Lou et al., 2001; Friedman and
Friedman, 1993; Friedman et al., 2007)
Anxiety Excessive and idiopathic sense of dread, worry and
with physical symptoms of breathlessness and heart palpitations
(Richard, Schiffer and Kurlan, 1996; Broen et al., 2016)
Depression Prolonged intense feeling of sadness and lack of
optimism. (Thobois et al., 2017; Maillet et
al., 2016; Hu et al., 2015)
Hallucinations Hallucinations can be visual, auditory, tactile and
illusions.
(Fenelon et al., 2000; Sanchez-Ramos, Ortoll and Paulson, 1996; Inzelberg, Kipervasser
and Korczyn, 1998; Barnes and David, 2001)
Delusions Delusions include paranoia, jealousy and
extravagance (Schrag, 2004)
Memory problems
Impaired storage of new memories or retrieval of old memories.
(Morris et al., 1988; Harrington et al., 1990; Lehrner et al., 2015)
Sleep problems Impaired sleep patterns. Can include insomnia,
nightmares, nocturia and sleep-apnoea.
(Faludi et al., 2015; Albers, Chand and Anch, 2017; Lin et
al., 2017)
1. Introduction
22
The development of PD is typically characterised by the loss of dopaminergic
neurons and in some cases, the presence of Lewy body pathology within the brain.
Although PD is considered a dopamine disorder, there is also prominent
dysfunction of other neurotransmitters such as serotonin, noradrenaline,
acetylcholine and glutamate (Xu et al., 2012; Mann and Yates, 1983; Reisine et al.,
1977; Hornykiewicz, 1981). They are discussed in further detail in regards to the
effect on pain perception in section 1.4.
In addition to the loss of dopamine and other neurotransmitters, a pathological
characteristic in PD is the presence of Lewy body pathology throughout the brain,
which are a type of protein aggregate and results in the reduction of
neurotransmitter release or neuronal death (Sulzer and Surmeier, 2013). The
presence of Lewy body pathology increases over time and has been classified by
Braak et al., (2004) to be in six stages which is outlined in Table 1.3.
Therefore, the pathological and neurotransmitter changes in the Parkinsonian brain
result in the diverse list of symptoms. Whilst the motor symptoms have been
researched extensively, the non-motor aspect of PD has been investigated less. One
of the least researched non-motor symptoms in PD is pain, yet the impact of
chronic pain on quality of life is substantial and contributes to the development of
depression, sleep disturbances and anxiety. The widespread pathophysiology and
diverse range of symptoms in PD therefore may predispose patients to
dysfunctional central processes including pain perception. First, section 1.3 will
briefly summarise the brain’s involvement in pain processing, and section 1.4 will
highlight how PD pathology could predispose patients to the development of
chronic pain.
1. Introduction
23
Table 1.3: Pathological findings of Lewy body Pathology in the Parkinsonian brain outlined by Braak et al.,
(2004)
Stage Brain location
Sub-region (if applicable)
Lewy body Pathology More details
Stage 1
Brain stem
Medulla Dorsal motor nucleus of the
vagal nerve Long unmyelinated preganglionic
fibers
Intermediate reticular zone (adjoins the dorsal motor
nucleus)
Stage 2
Brain stem
Reticular formation
Lower raphe nuclei Responsible for the synthesis of
serotonin Magnocellular portions of the
reticular portion (especially the gigantocellular reticular
nucleus)
Pons Coeruleus-subcoeruleus
complex
Descending tracts ‘Gain-setting’ system nuclei
Sparingly myelinated tracts. Involved in the pain control system
involved in the inhibition of somatosensory and viscerosensory
input.
Stage 3
Basal ganglia
Substantia Nigra pars compacta
Postero-lateral subnucelus
Melanised projection neurons. Neurons have little myelination and
are thin. Eventually 70% neurons lost. Lose
almost all melononeurons
Temporal lobe
Amygdala Central subnucleus
LNs extends throughout the central subnucleus of the amygdala and isolates it from the neighbouring
structures Basolateral nuclei
Brainstem
Pedunculopontine nucleus
Cholinergic tegmental nucleus Connections to the SN
Oral raphe nuclei Use serotonin and projects to the
striatum Basal
forebrain
Cholinergic magnocellular nuclei
Stage 4
Cerebral Cortex
The bridge between the allocortex and
the neocortex.
Anteromedial temporal mesocortex
Thick layer of LNs appear. Sparsely myelinated fibers.
Hippo-campus
Second sector Can be a marker of PD that a clinician
can use for diagnosis.
Stage 5
Cerebral cortex
Neocortex
Prefrontal High-order sensory association
regions
Premotor region
Stage 6
Cerebral cortex
Neocortex First order sensory association
regions
Primary fields (In some cases)
1. Introduction
24
1.3. BRAIN REGIONS INVOLVED IN PAIN PROCESSING
There are multiple regions of the brain involved in pain processing and which
constitute the pain network (see Figure 1.1). The main regions are regarded to be
the insula, anterior cingulate cortex (ACC), thalamus, somatosensory cortex (S1/S2),
prefrontal cortex (PFC) and the amygdala. The network integrates the
somatosensory, emotional and contextual components of the pain input. It is
important to note that these regions have multiple roles and are not unique to
nociceptive processing. The network is considered to be important for the
detection of salient stimuli within the environment (Legrain et al., 2011).
One theory of the organisation of the pain network within the brain is of two
defined pathways; the medial and lateral systems. Firstly, the medial pathway
ascends via the spinoreticulothalmic pathway and terminates in the amygdala,
hypothalamus, insula and the ACC, and is responsible for the affective and
emotional aspect of pain perception (Vogt and Sikes, 2000; Treede et al., 2000;
Scherder et al., 2005). The medial pain pathway also has projections to the PFC,
basal ganglia, and periaqueductal grey (PAG), which allows integration of the
nociceptive transmission and a link to the descending pain pathway. Secondly, the
lateral pathway ascends via the spinothalamic tract and terminates in the S1 and
S2, parietal operculum and the posterior insula. The lateral pathway reports the
intensity, duration and location of the pain stimuli (Sewards and Sewards, 2002;
Treede et al., 2000; Vogt and Sikes, 2000). However, the division of the pain
network into medial and lateral systems which have distinct roles in the perception
of pain is an oversimplification, and both systems have been shown to be involved
in the processing both sensory and affective aspects of pain.
In addition, there are descending modulatory tracts which originate in the
brainstem and can be modulated by higher cortical regions (Millan, 2002). The
descending modulation of pain is considered to be a type of top-down processing
(Pertovaara, 2013; Donaldson and Lumb, 2017). The descending tracts begin from
1. Introduction
25
the brainstem within the structures such as PAG and rostral ventromedial medulla
(RVM), with descending targets including the dorsoreticular nucleus, parabrachial
nucleus and the rostraventral medulla (Millan, 2002). The end target of the
descending pathways is the dorsal horn of the spinal cord, a site for modulation of
ascending pain transmission. The descending tracts are not independent of the
higher brain areas and receive modulatory input from the cortex, hypothalamus
and the amygdala and are therefore modulated by central processes (ie. Top-down
modulation).
Figure 1.1: Regions of the pain network taken from Bushnell, Ceko and Low, 2013. Abbreviations: PFC;
Prefrontal Cortex, ACC; Anterior Cingulate cortex, S1/S2; Primary/Secondary Somatosensory cortex, BG;
Basal Ganglia, AMY; Amygdala, PAG; Periaqueductal grey, PB; Parabrachial nucleus. The ascending tracts
from the spinal cord travel via the brainstem and terminate in regions of the midbrain, which connect to
cortical regions for the integration of pain processing.
1. Introduction
26
1.3.a. Top-Down modulation of pain
The focus of this thesis is regarding the top-down processing of pain in people with
PD. Here I will briefly summarise the different types of top-down processing and
their potential impact on the perception of pain and involvement in the
development of chronic pain.
The processing of pain is often divided into bottom-up and top-down aspects.
Bottom-up perception is stimulus-driven, whereby the stimulus influences our
perception. For example, the salience of a stimulus or the novelty of a noxious
stimulus can modulate the perceived pain intensity (Hauck et al., 2015a). In
contrast, top-down processing is subject-driven where prior knowledge influences
perception. An example of top-down processing in regards to pain perception is
attentional orientation or emotional state, or prior experience of pain. Top-down
pain processes are considered to be central to the development of chronic pain, yet
are also believed to be the key to treating chronic pain. For instance, whilst
negative expectation can cause an increase in pain (Bjørkedal and Flaten, 2012),
positive expectation such as the placebo effect (Dobrila-Dintinjana and Nacinović-
Duletić, 2011), and improving control by meditation (Zeidan et al., 2012; Brown and
Jones, 2010) can modulate pain perception via top-down processes.
For instance, the activity of the dorsolateral prefrontal cortex has been linked to the
ability to cope with chronic pain, and a reduced activity within the region has been
associated with chronic pain conditions such as FM and osteoarthritis (Brown, El-
Deredy and Jones, 2014). Abnormalities within this region are thought to contribute
to depression, Schizophrenia and other neurological mood disorders, which also
have an impact on pain management (Narasimhan and Campbell, 2010).
One of the main top-down processes which can modulate pain perception is the
orientation of attention (Miron, Duncan and Bushnell, 1989a). For instance, a
simple distraction can result in reduced pain, and focused attention can cause an
increase in pain intensity (Ruscheweyh et al., 2011). In addition, an unpleasant
1. Introduction
27
distraction has shown to alter the activity within the ACC during painful stimuli
(Phillips et al., 2003; Roy et al., 2009; Berna et al., 2010). Attention orientation also
modulates the descending pain pathways which can change the perception of pain.
The descending pain pathways receive input from regions associated with attention
processing such as the amygdala, PFC and ACC, and modulate the descending pain
systems, either facilitating or inhibiting pain transmission (Ong, Stohler and Herr,
2019)(Ossipov and Limitado, 2012). Therefore, dysfunction within the cortical and
subcortical regions, influences the descending modulatory system and can alter
pain perception (Ossipov and Limitado, 2012).
An example of how attention can alter the perception of pain is via the study of
anticipation of a painful stimulus. Anticipation results in the pain network to be
activated for the forthcoming pain as attention is oriented towards the stimulus,
and can modulate the resultant perception of the pain (Koyama et al., 2005; Wang
et al., 2008). For instance, when expectation of intensity is greater than what is
delivered, the pain perceived is closer to the expected rather than the stimulus
intensity. In contrast, a decreased pain expectation can reduce the pain intensity
perceived, thereby emphasising the influence of a positive outlook may have on
reducing pain (Schmid et al., 2013). The specific regions that are activated during
anticipation include the ACC, insula, S1/S2, PFC and the PAG (Koyama et al., 2005).
Within chronic pain conditions, such as FM and osteoarthritis, heightened
anticipation is common and correlated to increased pain sensitivity (Brown, El-
Deredy and Jones, 2014).
The mechanism by which expectation or anticipation of painful stimuli can alter the
perception of pain is yet to be fully understood. A functional magnetic resonance
imaging (fMRI) study has highlighted that the pain network regions including the
ACC, insula, thalamus, PFC were increased relative to the level of pain intensity
expected (Koyama et al., 2005). The study reported that the expectation of a lower
pain than administered resulted in a decreased level of activation of the insula, ACC
and S1, which is similar to that of a placebo response. The anticipation of a painful
1. Introduction
28
stimulus is a technique used to investigate central processing of pain, and has
highlighted abnormalities within chronic pain cohorts, and is therefore used in the
research conducted within this thesis.
1.3.b. Chronic pain
Finally, I will briefly discuss the central processing associated with chronic pain
conditions. Chronic pain is defined as persistent pain for at least 3 months and can
be linked to a primary condition such as osteoarthritis, or can have an idiopathic
cause. Living with chronic pain can be debilitating, with severity ranging from mild
to excruciating, and with persistent or relapsing states (Von Korff et al., 1992). The
development of chronic pain has been associated with atypical ascending and
descending pathways, along with abnormalities in top-down processing of
nociceptive information. For instance, the anti-nociceptive descending pathway of
pain is altered in many chronic pain states (Porreca, 2002; Banic et al., 2004;
Gebhart, 2004) and may be important in the persistence of the chronic pain
(Gebhart, 2004).
Numerous experiments have investigated pain processing within different pain
conditions and it is well-established that patients with chronic pain conditions such
as Fibromyalgia (FM), arthritis and back pain, have reduced pain thresholds
compared to controls (Wolfe et al., 1995; Huskisson and Hart, 1972; Imamura et al.,
2013). The establishment of reduced pain thresholds in chronic pain conditions, and
the persistence of the pain, is indicative of aberrant top-down processing. For
instance, FM, which is characterised by widespread pain with no pathophysiological
explanation, is considered to be a partly due to central sensitisation and abnormal
top-down processing (Guymer and Littlejohn, 2007). FM is the most prominently
researched chronic pain condition in regards to the role of top-down processing in
its development – therefore, it is important to review the current research for
comparison with pain processing in PD. The top-down processes include an
imbalance of neurotransmitters and neuroendocrine factors, and dysfunction of the
1. Introduction
29
hypothalamic-pituitary-axis (HPA) (Guymer and Littlejohn, 2007). FM patients have
also shown augmented brain activity within regions associated with pain perception
during the anticipation and perception of pain (Brown, El-Deredy and Jones, 2014).
Further evidence of central dysfunction includes; reduced dopamine release within
the basal ganglia (Wood et al., 2007b), reduced activation of the ACC and
descending inhibitory pain pathway (Jensen et al., 2009), and increased rates of
pain catastrophising within the FM population which correlates with altered brain
activity (Gracely et al., 2004). The treatment of FM is limited and an increasing
focus is being placed on the positive effects of alternative, non-pharmacological,
treatments such as meditation and cognitive behavioural therapy due to their
ability to improve top-down processes (Kaplan, Goldenberg and Galvin-Nadeau,
1993; Pleman et al., 2019; Montero-Marín et al., 2018; Aman et al., 2018; Amutio et
al., 2018). This emphasises the role of the brain in the manifestation of persistent
pain and why treatments for chronic pain which are targeted at central processing
are promising.
To summarise, the baseline state of the brain, be it healthy or negatively affected
by a condition such as depression or catastrophizing pain, effects the perception of
pain. Additional to the baseline state, the expectation and level of attention can
alter the pain perception, as does the context of the pain. Chronic pain states can
alter the brain’s structure and neurotransmitter levels which further impact on the
processing of pain, producing a negative cycle that can progress to worsening of the
chronic pain state. Therefore, irrespective of whether the central dysfunction is the
cause or consequence of the chronic pain state, the top-down modulation of pain is
central to deterioration, and yet also very important in the treatment of pain.
1.4. PATHOLOGICAL PREDISPOSITION OF PAIN IN PD
The pathophysiology of PD has widespread affects throughout the brain and
includes regions of the pain network and alteration in the level of
neurotransmitters which are also evident in chronic pain conditions. This section
1. Introduction
30
will highlight how the abnormalities seen in the PD brain can cause alteration in
pain processing and hence contribute to the development of central sensitisation
and chronic pain.
1.4.a. Pain network and Parkinson’s disease
Firstly, the regions associated with pain perception outlined in Figure 1.1 have been
shown to be abnormal in PD patients. For instance, the activation of the insula and
the ACC, regions of the medial pain system, are increased in PD patients following a
painful stimulus compared to healthy participants (Brefel-Courbon et al., 2005). The
augmented activation was shown to normalise following the administration of
Levodopa, a common drug used to treat PD, which increases the level of dopamine
and highlights the potential role of dopamine. The lateral pain pathway involves the
somatosensory cortex and has been associated with abnormal somatosensory
processing in PD patients, and affects their sensorimotor integration (Conte et al.,
2013; Lewis and Byblow, 2002; Rossini, Filippi and Vernieri, 1998).
Furthermore, the impaired function of the basal ganglia is central to PD motor
symptoms, however, there is extensive evidence of the basal ganglia’s involvement
pain processing and chronic pain states (see review: Borsook et al., 2010). The basal
ganglia receive input from the ascending pain spinothalamic tract and have diverse
connections with regions associated with pain processing; and is important for the
integration of motor, cognitive, emotional and autonomic aspects of pain
processing (Borsook et al., 2010). Additionally, there is an abundance of opioids and
their receptors, which are responsible for endogenous analgesia, within the basal
ganglia (Barceló, Filippini and Pazo, 2012). The basal ganglia also have roles in
attention (Van Schouwenburg, Den Ouden and Cools, 2010), emotion (Lanciego,
Luquin and Obeso, 2012), and avoidance behaviours (Hormigo, Vega-Flores and
Castro-Alamancos, 2016), all of which play a part in the perception of pain.
Aberrant function of the basal ganglia has been associated with chronic pain
conditions such as FM (Shokouhi et al., 2016), chronic regional pain syndrome
1. Introduction
31
(CRPS) (Azqueta-Gavaldon et al., 2017) and burning mouth syndrome (BMS)
(Jääskeläinen, 2012).
This section will summarise studies which have investigated the role of the basal
ganglia nuclei in pain processing, and hence highlight possible explanations of the
high prevalence of chronic pain in PD.
Firstly, the degeneration of the dopaminergic neurons in the substantia nigra pars
compacta (SNpc) within PD patients is the most significant change within the basal
ganglia and an early investigation reported that the SN consisted of approximately
50% nociceptive neurons (Pay and Barasi, 1982). The SN is connected to regions of
the pain network and is shown in Figure 1.2. The degeneration of the SNpc is
therefore likely to affect pain processing
Figure 1.2: The SN and its connections to the regions of the pain network. Figure taken from (Wasner and
Deuschl, 2012). S1: Primary Somatosensory Cortex, S2: Secondary Somatosensory Cortex. The Substantia
nigra is connected, either directly (black line) or indirectly (red lines), to regions of the pain matrix. The red
and yellow boxes highlight the pain-processing regions.
1. Introduction
32
The SN has also been associated with the descending inhibition of pain. The
stimulation of the SN has been shown to increase the descending inhibition, as did
the selective stimulation of PAG and the raphe nuclei (Barnes, Fung and Adams,
1979). The study also investigated the role of dopamine and serotonin on the
inhibitory effect and highlighted that it relied on dopaminergic neurons of the SN
innervating the serotonergic neurons within the raphe nuclei. Therefore, the
substantial loss of dopamine neurons within the SN in people with PD indicates a
possible impaired function of the descending inhibition of pain.
Within the basal ganglia, the striatum, made up of the putamen and caudate
nucleus, has been linked to pain transmission. The striatum is involved in cortical
and subcortical loops which are involved in both bottom-up and top-down
processing. A study using anaesthetised rats showed that the activation of the
peripheral nociceptors caused an increase in the activity of the nociceptive neurons
in the caudate nucleus and the putamen (Chudler, Sugiyama and Dong, 1993). The
caudate nucleus and the putamen of the rat have been analysed to show that there
are different types of nociceptors ranging from; wide-dynamic range, nociceptive
specific, and inhibited neurons (Chudler, Sugiyama and Dong, 1993). The blood
flow in the striatum has also been shown to increase when the contralateral hand
receives a painful stimulus (Barceló, Filippini and Pazo, 2012).
As previously mentioned, the basal ganglia is home to a high level of opioid activity;
this being especially high in the striatum. This leads to a strong belief that the
striatum is required for the endogenous analgesia generated by opioid activity.
Patients with arthritis have been shown to have a high abundance of opioid
receptors and a low binding availability within the striatum, which is thought to be
an adaptive mechanism (Brown et al., 2015).
1. Introduction
33
A high degree of dopaminergic transmission occurs within the striatum, yet is
impaired within people with PD. Further detail of how this impacts on pain
processing is discussed in section 1.4.
A study by Starr et al., 2011, highlights the putamen’s role in pain processing and
provides evidence that the putamen is involved in sensory processing related to
pain. The study reported that stroke patients with selective lesions of the putamen
had a reduced activation of the pain matrix and alterations of pain thresholds
depending on the type of stimulus. Within the healthy controls, it was shown that
the putamen was involved in the cortical-basal-ganglia-thalamic-cortical loop and
connections to the ACC, insula and the thalamus during painful stimulation. These
connections proposed the theory that the putamen was involved in the memory,
attention and emotional aspects of pain transmission (Starr et al., 2011). Therefore,
the putamen appears to play an important role in pain processing within the brain,
including the pain threshold levels and the cognitive aspects of pain perception.
Hence, the atypical striatal activity in PD may impact on the role of the putamen in
pain regulation.
The second region within striatum is the caudate nucleus and there is evidence that
the caudate nucleus is involved in both acute and chronic pain. Firstly, the
conscious act of suppressing electrically induced acute pain was shown to trigger a
consistent bilateral activation of the caudate along with contralateral activation of
the insula (Wunderlich et al., 2011). In contrast, heat pain did not show this strong
activation pattern (Wunderlich et al., 2011). The caudate nucleus’ role in pain
modulation is further supported by a study reporting its role in pain-avoidance
(Koyama, Kato and Mikami, 2000). The study involved macaque monkeys and
focused on the ACC, a well-known region of the pain network, and the caudate
nucleus (Koyama, Kato and Mikami, 2000). Both regions were activated during the
task, resulting in the idea that the caudate nucleus is functionally related to the ACC
during pain transmission. Hence, the caudate nucleus might be associated with the
ACC role of processing the affective component of pain (Besson, Guilbaud and Ollat,
1. Introduction
34
1995). Furthermore, there was early research within rats that demonstrate that the
caudate nucleus is involved in avoidance processing (Kirkby and Kimble, 1968;
White and Rebec, 1993).
Finally, the globus pallidus, a region of the striatum affected by PD, is involved
primarily in the control of voluntary movement and has been shown to be involved
in pain transmission (Braz et al., 2005; Chudler, 1998). By using a tracing agent, the
globus pallidus was shown to be a target for peripheral non-peptide nociceptors,
whereas the peptide nociceptors did not terminate within the globus pallidus,
indicating two separate ascending pathways depending on nociceptor type. There is
evidence that the globus pallidus within the PD brain responds to thermal and
mechanical noxious stimuli (Belasen et al., 2016). The deep brain stimulation of the
globus pallidus (Loher et al., 2002), or the bilateral removal of the region in PD
patients results in pain relief (Favre et al., 2000).
1.4.b. Basal ganglia connections to regions of the pain matrix
The basal ganglia have numerous connections to other regions which include those
that are part of the pain network such as; the thalamus, insula, ACC and the S1/S2.
There are also regions that are involved in the descending modulation of pain
transmission which the basal ganglia is connected with. The following section
discusses the important connections that the basal ganglia make with these regions
of the pain network, and how they can be disrupted in PD.
The thalamus, a main output of the basal ganglia, is dysfunctional in PD and hence
important to discuss its role in pain processing (Halliday, 2009; Pifl, Kish and
Hornykiewicz, 2012). The thalamus receives input from the globus pallidus and
substantia nigra pars reticulatar (SNpr), and is a major output of the basal ganglia.
The basal ganglia afferents innervate the ventral nuclear and the intralaminar group
(centromedian) of the thalamus. The ventral nuclear group projects to the motor,
pre-motor and supplementary motor area (SMA), whereas the intralaminar group
projects to the motor cortex and the striatum within the basal ganglia. The
1. Introduction
35
thalamus receives input from the ascending nociceptive tract such as the
spinothalamic tract which terminates in the lateral nuclei of the thalamus, and is
deemed to be part of both the medial and lateral pain pathways within the brain.
The thalamus innervates higher cortical regions including the insula, ACC and the
S1/S2, along with the striatum of the basal ganglia and descending afferents to the
brainstem. Thus, due to the thalamus being a major output of the basal ganglia and
the altered activity of the thalamus in PD patients (Halliday, 2009) may impact on
the thalamic role in nociception.
The basal ganglia is also connected to the insula which is central pain perception
(Flynn, 1999). The insula projects to the basal ganglia for the integration of motor,
somatosensory and vestibular processing (Flynn, 1999). The insula specifically
targets the striatum and the globus pallidus, and the afferents which terminate in
the dorsolateral striatum are deemed to be for somatosensory integration which is
involved in pain processing (Starr et al., 2009; Kim et al., 2017). Hence, the reduced
connectivity between the substantia nigra pars compacta (SNc), a region of the
basal ganglia, and the insula in PD patients is likely to have an impact on the ability
to integrate information during pain processing (Flynn, 1999; Wu et al., 2012). In
addition, the pain processing region of the ACC innervates the ventral striatum
(Alexander, DeLong and Strick, 1986) and has shown to be functionally correlated
with the basal ganglia (Margulies et al., 2007).
The basal ganglia is also connected to the periaqueductal gray, a region of the
midbrain and the main control centre for descending pain modulation (Northoff,
2013; Behbehani, 1995). Within a rat model of PD, a defective pathway between
the Substantia nigra, PAG and rostral medulla oblongata, has been shown (Lima et
al., 2018) and indicates that the disruption of the basal ganglia afferents may lead
to altered nociceptive modulation via the descending pain pathway.
1. Introduction
36
1.4.c. The Brainstem changes in PD and pain processing
The brainstem is one of the earliest regions to be affected by Lewy body pathology
in PD (Braak et al., 2004), and as pain has been reported to precede motor
symptoms, the brainstem could be involved in the development of chronic pain in
PD.
Firstly, descending tracts are affected by Lewy body pathology during Braak stage 2
which has been linked with pain hypersensitivity and indicates that the descending
pain modulation could be altered in PD patients (Latremoliere and Woolf, 2009).
The pons, medulla (Gebhart, 2004) and reticular formation (Casey, 1980; Bowsher,
1976) are also involved in the descending modulation pain system and thus the
Lewy body pathology in these regions may contribute to atypical pain processing.
Additionally, the nucleus raphe magnus (NRM) is highly populated with
serotoninergic neurons and is involved in both movement and endogenous
analgesia within the healthy brain. However, in PD, the NRM has been shown to
degenerate and thus likely to inhibit the endogenous analgesic response to
nociceptive input (Gai, Blessing and Blumbergs, 1995). For instance, a lesion within
the regions of the brainstem can alter pain sensitivity and rodent studies have
shown how lesions impact the action of opiate related analgesia (Abbott and
Melzack, 1982; Basbaum and Fields, 1984). For instance, the lesion of the median
raphe nucleus or central tegmental nucleus induced a potentiation of the morphine
response; whereas the loss of the nucleus raphe magnus lead to an attenuation of
the morphine response (Abbott and Melzack, 1982).
Additionally, an interesting link between pain and PD is how the activation of the
motor cortex is believed to disinhibit the PAG by inhibiting the GABAergic
interneurons and thus stopping their inhibition on the descending anti-nociception
pathway (Pagano et al., 2012). Therefore, this interesting finding implies that the
reduced motor cortex activity in PD patients may lead to a reduced disinhibition of
the PAG, and thus a reduced activation of the descending anti-nociception
1. Introduction
37
pathway. This would also help to explain the reason why Levodopa improves pain
states in PD as it increases the motor cortex output.
1.4.d. Cortical changes and pain processing
The final stages of PD involves the deterioration of the cerebral cortex leading to
cognitive decline and damage to the hippocampus contributing to loss of memory
and the development of PD dementia (Braak et al., 2003). The changes in the higher
cortical regions will impact on the complex emotional and attention related aspects
of nociception processing. As discussed in section 1.3, the top-down processes
involved in pain have a significant impact on the development of chronic pain.
1.4.e. Neurotransmitter changes in Parkinson’s disease linked with
pain
There are prominent changes in the levels of neurotransmitters in PD and abnormal
neurotransmitter levels are related to chronic pain states such as Fibromyalgia
(FM), Burning Mouth Syndrome (BMS) and Chronic Regional Pain Syndrome (CRPS)
(Stahl, 2009). As such, this section will look at the role of neurotransmitters in pain
processing and how abnormalities in neurotransmitter levels within PD may impact
pain transmission.
1.4.e.i. Dopamine
PD is characterised by the degeneration of the SNpc, a highly dopamine abundant
region, and the resulting decrease in dopamine produces both the motor and non-
motor symptoms. In addition to dopamine’s role in regulating movement, there are
theories of dopamine being central to the precision of prediction and the salience
value of stimuli, and is considered to be involved in the integration of bottom-up
and top-down processing (Friston et al., 2012; FitzGerald, Dolan and Friston, 2015;
Shiner et al., 2015). Dopamine is therefore important in the top-down modulation
of pain perception and dysfunctional transmission in PD may lead to impaired pain
processing. Figure 1.3 summaries the importance of dopamine in pain perception.
1. Introduction
38
Figure 1.3: A summary of the various roles of dopamine and how they are involved in pain perception. DA:
Dopamine, BG: Basal ganglia, SN: Substantia Nigra.
Dopamine is a modulatory neurotransmitter due to its role in both excitation and
inhibition of neural activity. There are two main post-synaptic dopaminergic
receptors, the D1 (subtypes D1 and D5) (excitatory) and D2 (subtypes D2, D3 and D4)
(inhibitory) receptors, plus pre-synaptic D2 receptors which regulate the release of
dopamine into the synapse. Dopamine receptors are found widespread, however,
there are most abundant in the basal ganglia. Within a human study, the D1Rs were
shown to be more widespread than the D2Rs and more abundant within the
dopaminergic pathways, especially in the cortex and limbic system (Hall et al.,
1994). The D2Rs have a higher abundance in the thalamus in comparison to the
D1Rs, whilst both receptors are co-localised and highly abundant in the striatum
1. Introduction
39
(Hall et al., 1994). Furthermore, a rodent study revealed higher levels of D2Rs in the
midbrain compared to D1Rs (Meador-Woodruff et al., 1991). There are four well
defined dopaminergic pathways within the brain, which originate from the SN,
ventral tegmental area (VTA) and hypothalamus (see Figure 1.4).
Figure 1.4: Dopaminergic pathways. [Anatomical locations not precise]. Blue: Mesocortical; Ventral
Tegmental area (VTA) to Prefrontal cortex (PFC). Orange: Mesolimbic; Ventral Tegmental area (VTA) to
Nucleus Accumbens (NAc). Pink: Nigrostriatal; Substantia Nigra pars compacta (SNpc) to the Striatum (STR).
Purple: Tuberinfundibular; Arcuate nucleus (Arc) of the Hypothalamus (Hyp) to the Pituitary gland.
Within healthy subjects, a PET study detected an increased D2R dopaminergic
activity in the basal ganglia and connecting pathways during pain perception (Scott
et al., 2006). The study identified different roles of the nigrostriatal and mesolimbic
pathways; the nigrostriatal pathway was associated with the pain intensity
perceived, whilst the mesolimbic pathway was associated with the affective
processing such as unpleasantness ratings and the emotional aspect of the pain
(Scott et al., 2006). In addition, the binding capacity of D2R/D3Rs within the
1. Introduction
40
putamen of the striatum have been shown to be inversely correlated to individual
pain thresholds (Pertovaara et al., 2004). Therefore, dopamine has the potential to
modulate the pain intensity perceived whilst also being susceptible to affective
aspects on pain perception.
The dopaminergic nigrostriatal pathway has been regarded to be involved sensory
gating of nociceptive stimuli and the development of chronic pain (Jääskeläinen et
al., 2001). The nigrostriatal pathway originates in the substantia nigra and
terminates in the dorsal striatum; both regions are within the basal ganglia. Altered
dopaminergic transmission within the nigrostriatal pathway has been shown to
contribute to burning mouth syndrome (BMS) (Hagelberg et al., 2003b) and atypical
facial pain (Hagelberg et al., 2003a). BMS patients have also been reported to have
a reduced level of presynaptic dopaminergic function within the right putamen, a
nuclei of the striatum (Jääskeläinen et al., 2001).
Chemical lesions of the substantia nigra to reduce dopamine levels in rats has
shown to lead to an increase in pain sensitivity (Saadé et al., 1997). Furthermore,
lesions of the substantia nigra in rats was shown to induce allodynia following a
mild stimulation to the orofacial region (Dieb et al., 2014), and subsequent
administration of a D2R agonist (Bromocriptine) remediated the allodynia. The
study also demonstrated how the loss of the dopaminergic nuclei of the substantia
nigra caused a reduced level of neurons within the striatum and the VTA (Dieb et
al., 2014). Therefore, the degeneration of the dopaminergic neurons of the
substantia nigra pars compacta within PD has an impact on the abundance of
neurons within other regions, and there is evidence that this will lead to pain
sensitivity.
Furthermore, the dopaminergic mesolimbic and mesocortical pathways have been
linked to nociceptive processing. The main focus of research of the mesolimbic and
mesocortical pathways in PD is in regards to their role in regulating reward and
gambling behaviours (Berridge and Robinson, 1998; Anselme and Robinson, 2013).
1. Introduction
41
It is common that PD patients have impaired hedonistic regulation and suffer from
pathological gambling due a dysfunction of the pathways (Torta and Castelli, 2008).
However, chemical lesions of the VTA (the origin of both pathways) have also
proven to increase the pain sensitivity in rats during standard pain tests (Saadé et
al., 1997). Analgesics which increase pain thresholds, such as opioids and
amphetamine, increase the abundance of dopamine in the nucleus accumbens
(Altier and Stewart, 1999). Hence, a low endogenous level of dopamine in PD may
result in the opposite and lower pain thresholds.
The mesolimbic pathway is also known as the ‘reward pathway’ and has also been
reported to be involved in pain. The naming of the pathway as the ‘reward
pathway’ is an oversimplification as the pathway is involved in the regulation of
both aversive and pleasurable stimuli (Leknes and Tracey, 2008; Taylor et al., 2016).
The mesolimbic pathway has been shown to activate during noxious thermal stimuli
(Leknes and Tracey, 2008). An fMRI study highlighted that the activation of regions
within the mesolimbic pathway preceded the somatosensory activation during
thermal pain (Becerra et al., 2001). Furthermore, the mesolimbic pathway is
involved in the placebo response as the dopaminergic reward system is involved in
the successful placebo effect (Borsook et al., 2010) and dopaminergic activity within
the ventral basal ganglia has been reported to increase during a successful placebo
response of pain (Scott et al., 2008; Schweinhardt et al., 2009). There is also a
positive correlation between the level of dopamine activity and effectiveness of the
placebo (Scott et al., 2008).
The final dopaminergic pathway, the mesocortical pathway, has reduced dopamine
levels in PD (Ford, 2010). The mesocortical dopaminergic pathway is associated
with emotional, attention and motivational processing (Levy, 1991; Bertolucci-
D’Angio, Serrano and Scatton, 1990; Bromberg-Martin, Matsumoto and Hikosaka,
2010). All three aspects are involved in pain processing and can manipulate the
perception of pain. For instance, a rodent study concluded that the mesocortical
1. Introduction
42
pathway increased the release of dopamine within the PFC via D2Rs mechanisms,
and resulted in a reduction in pain (Sogabe et al., 2013).
Therefore, dopamine transmission within the four pathways has been associated
with a negative effect on pain perception. Thus, the reduced production of
dopamine in PD is likely to contribute to the dysfunctional pain processing and a
susceptibility of chronic pain in the disease.
In addition to the altered activity within the dopaminergic pathways, the altered
level of dopamine leads to an altered abundance and activation of the
dopaminergic receptors within PD (Ryoo, Pierrotti and Joyce, 1998; Brooks et al.,
1992) and further disrupts the role of dopamine in pain processing.
For instance, the D2 subtype of dopamine receptors has been associated with pain
transmission and the administration of D2R agonists and antagonists have been
shown to modulate pain perception. For example, pain was reduced in rats
following an injection of a D2R agonist (Quinpirole) into the nucleus accumbens
(Taylor, Joshi and Uppal, 2003), which was blocked if preceded by a selective D2
antagonist (Raclopride) (Taylor, Joshi and Uppal, 2003). In addition, the abundance
of D2 receptors within the putamen and ratio of D1 and D2 receptors within the
striatum have been suggested to have a relationship with atypical facial pain
syndrome (Hagelberg et al., 2003a).
The abundance of D2-receptors is altered in PD, with studies reporting variability
(Brooks et al., 1992), and a 15% increase in abundance (Ryoo, Pierrotti and Joyce,
1998). The level of D2 binding potential in the putamen and the right medial
temporal cortex has shown an inverse correlation to cold pain thresholds
(Hagelberg et al., 2002). Participants who had a low D2 binding potential reported a
high pain threshold, whereas subjects with high D2 binding potential reported low
pain thresholds. In contrast, when heat and cold induced pain was administered
concurrently, low binding potential correlated with a low pain threshold and vice
versa (Hagelberg et al., 2002). Although the study’s conclusions were speculative,
1. Introduction
43
they suggested that low D2 binding potential was due to a high level of dopamine,
and the high dopamine abundance correlated with a high pain threshold.
Therefore, the study supports the fact that in PD, a low level of dopamine would be
correlated with subsequent low pain threshold for cold stimuli.
Further evidence of how abnormal dopamine signalling may contribute to chronic
pain is how patients with FM have atypical dopamine signalling. A PET study which
recorded D2 and D3 receptor activity reported that the FM patients did not show
dopamine release within the basal ganglia in response to pain, whilst the control
subjects showed a release of dopamine which was positively correlated to the
intensity of pain (Wood et al., 2007b). This study highlights how dopamine
transmission within the basal ganglia is abnormal in FM patients, and this provides
strong evidence that the low dopamine levels and dysfunctional basal ganglia
activity in the PD basal ganglia contributes to the development of persistent pain.
1.4.e.ii. Serotonin
Similar to dopamine, serotonin is a modulatory neurotransmitter with effects seen
in a diverse range of processes such as cognition, mood and stress (see review:
Olivier, 2015). Serotonin dysfunction is associated with the manifestation of
depressive and anxiety related disorders (Young et al., 1985; Cannon et al., 2006,
2007). It is well-known that low mood is correlated with chronic pain and
exacerbates the intensity and unpleasantness of pain (Bair et al., 2003; Giesecke et
al., 2005).
Serotonin dysfunction is present in PD and has been associated with the
development of non-motor symptoms (Poewe and Luginger, 1999; Politis and
Niccolini, 2015; Langston, 2006; Kish, 2003; Bernheimer, Birkmayer and
Hornykiewicz, 1961). The raphe-nuclei which produces serotonin degenerates in PD
and the neuronal loss contributes the low serotonin levels in the PD brain
(Hornung, 2003; Braak et al., 2003; Halliday et al., 1990). A PET study has shown
that serotonin dysfunction is widespread and amongst other regions, was present
1. Introduction
44
in regions associated with the pain network, including the thalamus, ACC, PFC,
insula and amygdala (Politis et al., 2010). PD patients also have a lower serotonin
level within the cerebrospinal fluid which has been correlated to the presence of
depression (Mayeux et al., 1988). Serotonin is involved in the modulation of pain
via the descending inhibitory pain pathways (Ossipov, Morimura and Porreca, 2014;
Millan, 2002; Bowker et al., 1983) and the development of chronic pain has been
associated with reduced serotonergic activity within the descending pathways
(Kwon et al., 2013; Ossipov, Morimura and Porreca, 2014). Serotonin has a
modulatory role on pain processing, and depending on the receptor subtype, can
induce either analgesia or algesia (Sommer, 2004). For instance, selective-
serotonergic reuptake inhibitors (SSRIs), a common anti-depressant, increases
serotonin transmission and can evoke an analgesic effect (Sansone and Sansone,
2008). Additionally, a lower level of serotonin has been linked with FM (Wolfe et al.,
1997).
Therefore, the dysfunction of serotoninergic transmission within PD is likely to
impair the anti-nociceptive descending pain pathways, and negatively affect the
contextual-affective aspect of pain processing. Importantly, there is a link between
depression and the prevalence of chronic pain PD, however, it is difficult to
differentiate between cause or consequence (Ehrt, Larsen and Aarsland, 2009).
1.4.e.iii. Noradrenaline
In addition to Dopamine and Serotonin, the level of noradrenaline is reduced in PD
(Scatton et al., 1983) due to degeneration of the locus coeruleus, the main source
of noradrenaline production (Reches and Meiner, 1992; Mavridis et al., 1991;
Mann, 1983; Benarroch, 2009; Del Tredici and Braak, 2013). In addition, there is
widespread Lewy body pathology in the brain stem nuclei including the medulla,
pons and descending tracts which are involved in descending pain inhibition (Braak
et al., 2003). A reduction in the abundance of noradrenaline due to the presence of
Lewy body pathology in the locus coeruleus has been reported in the PD brain (Del
Tredici and Braak, 2013).
1. Introduction
45
Noradrenaline is involved in nociceptive processing within the spinal cord,
brainstem nuclei and higher brain regions (see review: Pertovaara, 2006).
Noradrenaline is present in the nociceptive processing nuclei within the brainstem
which are important for descending pain pathways, and noradrenergic efferents to
the amygdala are involved in the effect of emotion on pain perception (Strobel et
al., 2014). Within the spinal cord, noradrenaline can suppress the nociceptive input
via the descending pain pathways (Pertovaara, 2006).
Noradrenaline is theorised to mainly play a role in persistent pain, whereby
sustained pain activates noradrenergic mechanisms to inhibit pain transmission.
Research has also highlighted how chronic pain can induce atypical noradrenergic
transmission (Alba-Delgado et al., 2013). Furthermore, the role of noradrenaline is
involved with the modulation of behavioural states and as such is considered to be
involved in top-down modulation of pain (Strobel et al., 2014; Pertovaara, 2006;
Hirata, Aguilar and Castro-Alamancos, 2006; Onur et al., 2009). The combination of
chronic pain and noradrenaline dysfunction can lead to negative emotional state
such as anxiety and depression (Alba-Delgado et al., 2013), which also increases
pain sensitivity and reduces the ability to cope with chronic pain (Geisser et al.,
1994).
Hence, the abnormal noradrenergic transmission triggered by PD results in a
disruption in its normal role in the pain processing and predisposes PD patients to
be more susceptible to develop chronic pain.
1.5. IMAGING STUDIES SHOWING ALTERED CENTRAL PAIN
PROCESSING IN PARKINSON’S DISEASE
The changes in the PD brain discussed in Section 1.4. demonstrate how PD patients
may be predisposed to the development of chronic pain. Therefore, there has been
a clear scientific rationale to research the mechanisms of pain in the Parkinsonian
1. Introduction
46
brain via imaging techniques. Here I will review the current neuroimaging results of
research into central pain processing in PD.
Firstly, PET imaging research has shown that PD patients had a higher activation
within the prefrontal cortex, insula and ACC during thermal pain (Brefel-Courbon et
al., 2005, 2013). PET has also highlighted reduced activity within the PFC and insula
during the cold pressor test, and an increase within the ACC (Brefel-Courbon et al.,
2013). Within both experiments, the differences in activation were normalised
after taking L-DOPA.
fMRI has also been useful in understanding pain processing in PD. A recent fMRI
study investigating the descending pain pathway in drug-naive PD patients and
reported that the PD patients had dysfunctional activity and connectivity during
anticipation of pain. Regions affected included the PFC, ACC and MCC which are
associated with the top-down modulation of descending pain inhibition (Forkmann
et al., 2017). The study by Forkmann supports the theory that the descending pain
pathway is impaired in PD. Furthermore, the latest investigation by Tessitore et al.,
(2018) showed significant BOLD signal changes in the drug-naïve PD brain following
heat stimulation. The study concluded that there was an increased activation within
the cerebellum, somatosensory cortex and pons of the brainstem compared to
healthy controls (Tessitore et al., 2018).
Finally, a study used EEG to assess the brain response to a range of nociceptive
stimuli in PD patients, off and on their medication, in comparison to healthy
controls (Priebe et al., 2016). Priebe showed that brain activations did not correlate
with pain rating in the PD group, and concluded that amplitude and frequencies of
brain responses were lower in PD. In contrast, the latency of brain activation was
increased in the PD cohort and together with increased pain sensitivity, supports
the theory that central pain processing is dysfunctional in the PD.
1. Introduction
47
1.6. CONCLUSION
Although the general consensus is often that chronic pain is driven by peripheral
nociceptive dysfunction, there is strong evidence that many chronic pain states are
due to disruption to central mechanisms. The high prevalence of chronic pain in PD
patients and the presence of idiopathic and widespread pain unrelated to the
musculoskeletal symptoms present a foundation to investigate the central
mechanisms involved in PD pain. There are numerous regions that are mutually
affected by PD pathology and are involved in pain processing within the healthy
brain. Furthermore, the development of pain in PD has been shown to precede the
onset of motor symptoms which may be correlated to the early Lewy body
pathology within the brainstem. Research has shown that the perception of pain is
significantly modulated by central processing and disruption to the somatosensory,
emotional and cognitive networks have shown to lead to hyperalgesia,
catastrophizing states and hence, the development of chronic pain. The fact that
neurotransmitters such as dopamine, serotonin and noradrenaline are involved in
pain processing and are also irregular in PD, strengthens the theory that central
mechanisms are likely to be involved in PD pain. Therefore, to conclude, the
combination of Lewy body pathology and neurotransmitter changes within regions
involved in pain processing are likely to increase the likelihood of a chronic pain
state in PD patients.
Chapter 2
General Methods and Introduction to study aims
2. General Methods and Introduction to Study Aims
49
This section will discuss the rationale behind the choice of methodology and outline
the studies which will be discussed in this thesis.
2.1. GENERAL METHODS
The research included in this thesis used electroencephalography (EEG) to record
brain activity during the anticipation and perception of laser induced acute pain.
The study design of the research was based on the protocol carried out in Brown et
al., (2014) and Brown et al., (2008). Firstly, the Brown et al., (2014) investigated the
brain activity during the anticipation and perception of pain in people with
Fibromyalgia, Osteoarthritis and healthy age-matched participants. The research
used EEG source localisation analysis to conclude regional differences in the patient
groups during the anticipation of pain, and hence demonstrated that the protocol
was sensitive to distinguishing differences in the processing of pain within patients
groups. Secondly, the Brown et al., (2008) study demonstrated the effect of
certainty on the rating of pain and neural activity. There is evidence that dopamine
is involved in the processing of uncertainty and hence was included as an aspect of
the protocol (Fiorillo, 2017).
The same experimental protocol was applied in each investigation, and as such, the
rationale for the study design is introduced in the following sections, with further
detail included in the individual papers of the thesis.
2.1.a. Experimental pain
The common techniques used to induce pain in research are via thermal,
mechanical, chemical and electrical stimulation. The choice of stimulation is
dependent on the experimental design and for the studies included in this thesis,
thermal stimulation via a CO2 laser was used (Brown, El-Deredy and Jones, 2014)
due to its quick pulse duration, small stimulation area and selective activation of
the nociceptive A and C-fibres. The laser induced pain has been used to evaluate
pain thresholds in healthy and patient groups (Gibson et al., 2001; Brown, El-Deredy
and Jones, 2014; Brown et al., 2008a; Derbyshire et al., 2002). In addition, laser
2. General Methods and Introduction to Study Aims
50
induced pain stimuli have been used in numerous pain experiments investigating
pain anticipation (Brown, El-Deredy and Jones, 2014; Clark et al., 2008a; Wang et
al., 2008; Brown and Jones, 2010, 2008; Forkmann et al., 2017). The fast onset and
offset of the laser produces well-defined evoked responses which can be time-
locked in neuroimaging techniques and are referred as laser evoked potentials
(LEPs).
A limitation of the laser is the potential of skin sensitivity and damage due to the
heating of the skin. To limit the potential, the laser was moved in a pattern within a
grid on the forearm of the participant. The laser was also limited to a maximum
voltage that could be delivered to avoid skin damage. Nevertheless, the skin
sensitivity of the individual must be considered and the maximum voltage adjusted
accordingly, or stimulation stopped altogether in rare instances.
Pain is a subjective experience and the rating of pain can therefore be variable
between individuals. To limit the subjective interpretation of how to rate the laser
stimuli, a pain rating scale was presented to the participants and was explained
using a script by the researcher (see figure 2.1).
Figure 2.1: The pain rating scale that was used for the psychophysics and main experiments. The explanation
of the rating scale was identical for all participants to limit subjective interpretation.
2.1.b. EEG
EEG is a method used to record the neural activity by monitoring the voltage
oscillations generated by the brain. The post-synaptic activity of neighbouring
neurons produces local dipoles which are recorded by electrodes placed on the
Moderate
Pain
Severe
Pain
No
Sensation
Just
Painful
Unbearable
pain
2. General Methods and Introduction to Study Aims
51
scalp (Baillet, Mosher and Leahy, 2001). The pyramidal neurons are the main
generators of the EEG signal due to their uniform orientation to the surface and
their long length allowing dipoles to form. EEG detects the synchronised activity of
neurons with a high temporal resolution that is significantly higher than other
imaging techniques.
As such, EEG has allowed for a deeper understanding of cognitive processing and
disease states, along with providing a diagnostic tool for epilepsy, sleep disorders
and many other diseases. The non-invasive nature of EEG allows researchers to
record the activity in a broad range of people, irrespective of their health or age.
EEG has also helped to compare the differences of electrical activity between
disease states and healthy volunteers.
2.1.b.i. The Forward and Inverse EEG Problems
EEG has two main confines, the forward and inverse problem. The forward problem
consists of how the activity within the brain is recorded on the scalp electrodes,
whilst the inverse problem is in regards to calculating the precise source of the
potential generated at the scalp. Over decades of research, more complex models
have been developed to address the forward problem, which take into
consideration; head shape, tissue heterogeneity and the difference in conductivity
(Hallez et al., 2007). There are also several techniques to resolve the inverse
problem [see review: (Grech et al., 2008)]. The LORETA (Low resolution
electromagnetic tomography) technique which is used within the research of this
thesis has been reviewed to be able to accurately identify sources, even of deep
regions (Grech et al., 2008).
The LORETA technique overcomes the inverse problem using a tomographic
solution whereby it calculates the current source density in a large cortical area to
produce a 3D volume image of neural activity. The solutions created by LORETA are
based on the assumption that neighbouring neurons respond similarly, such as the
same orientation, latency and activation. An advantage of using the LORETA
technique is that it does not require a priori estimates of sources, which are a
2. General Methods and Introduction to Study Aims
52
requirement in other inverse solutions such as the equivalent current dipole (ECD)
technique. Therefore, LORETA is not restricted by a limited number of expected
sources.
Research into the accuracy of the LORETA technique has shown that the source
estimates are analogous to other imaging techniques which have a higher spatial
resolution. Concurrent recording of EEG and fMRI has shown that LORETA
successfully detects current source densities which are consistent and are on
average 14.5/16 mm within the fMRI loci (Mulert et al., 2004; Vitacco et al., 2002).
Similar validation has been shown using PET and LORETA (Zumsteg et al., 2005), as
have intra-cerebral recordings (Seeck et al., 1998; Trébuchon-Da Fonseca et al.,
2005). LORETA is also able to localise activity within deep brain structures such as
the insula (Mulert et al., 2004).
2.1.b.ii. Anticipatory-evoked potentials
A cue indicating a forthcoming stimulus can evoke an anticipatory response which is
detected in EEG as a negative waveform known as the stimulus-preceding
negativity (SPN) (Figure 2.2). The SPN can be triggered by both innocuous and
noxious stimuli. There is evidence to show that the SPN induced by a noxious
stimulus may be generated within the cingulate cortex, especially the anterior- and
mid- regions (Böcker et al., 2001). Therefore a more negative response during the
anticipation of noxious stimuli indicates a higher anticipatory response. The
characteristic topographic map of anticipation is a negative response over the
dorsal regions and is shown in Figure 2.2.
For the research conducted within this thesis two baselining methods were applied
to investigate the neural activity during the anticipation of pain. Initially, a single
baseline of -3500 -3000 ms was used to compare with the Early (-2500 -2000ms),
Mid (-1500ms -1000ms) and Late (-500 0ms) time windows of interest. However, as
this time window used for the single baseline was between trials and no cue was
presented yet, observations of participants highlighted that they were using this
time period to readjust their position, relax or stretch. Furthermore, the tremor of
2. General Methods and Introduction to Study Aims
53
the participants with PD was more prominent during the inter-trial periods.
Therefore, the single baseline of -3500 to -3000ms was potentially variable and
affected by artefacts, and a secondary baselining method was applied.
Therefore, primary baselining method was adjusted to use individual baselines for
each time window of interest set as -500ms prior to the auditory beeps. The
baselines (BL) for each time window of interest (TWOI) were as follows: Early (BL: -
3500 -3000ms, TWOI: -2500 -2000ms), Mid (BL: -2500 -2000, TWOI: -1500 -
1000ms), Late (BL: -1500 -1000ms, TWOI: -500 0ms). An aim of baselining uniquely
for each anticipation window was to reduce variability in the data as the
anticipation phase progressed, such that the analysis of each phase of anticipation
was unique to that phase and not subject to variability arising from neural activity
occurring in the previous phase.
The analysis using single baseline of -3500 -3000ms was also conducted to enable
comparison to previous studies (Brown et al., 2008a; Brown, El-Deredy and Jones,
2014) that used the same baselining method. All statistical analysis for the
anticipation phase was adjusted for multiple comparisons.
Figure 2.2: (Left) An EEG waveform of an SPN generated by a visual anticipatory cue, auditory cues, and the
LEP response following acute laser pain. Diagram from (Jones, Brown and El-Deredy, 2013). (Right) An
example of a characteristic anticipatory response recorded via EEG displayed on a topographic map. Plot
displays the grand mean response of a healthy cohort during the late SPN (-500 ms) preceding a noxious laser
stimulus.
2. General Methods and Introduction to Study Aims
54
2.1.b.iii. Suitability of EEG for research aims
EEG was chosen as the method to record brain activity for the two studies due to its
suitability in assessing pain perception which was used in Brown et al., (2014) and
other pain research studies (Clark, Brown, Jones, et al., 2008; Shao et al., 2012;
Jones et al., 2013; Hauck et al., 2015).
A benefit of EEG is that the setup is quick and does not require restricted
movement compared to fMRI. Therefore, the technique was suitable for volunteers
with PD as movement was allowed between trials and did not present possible
complications of claustrophobia. The movement constraints of other imaging
techniques meant that EEG was the optimal tool for recording brain activity in
people with Parkinson’s disease. In addition the use of EEG for pain processing has
been used extensively within pain research due to its good temporal resolution. The
research conducted in The Human Pain Research Group has developed protocol
and analysis methods using EEG to investigate the top-down modulation of pain via
the anticipation signal.
The good temporal resolution of EEG is in contrast to its poor spatial resolution in
comparison to fMRI and PET. Therefore, source localisation was a key focus for the
research to establish the sources of the brain activity recorded that would allow a
deeper interpretation of the EEG data. The LORETA source localisation technique is
used to solve the inverse problem with EEG recording (Pascual-Marqui, 2002). The
technique was selected due to its accuracy in localisation activity. In addition, due
to the LORETA technique being used in previous research conducted by The Human
Pain Research Group, the findings can be comparable to prior research.
2.2. INTRODUCTION TO STUDY OBJECTIVES
The research within this thesis was planned to investigate central pain processing in
Parkinson’s disease with the aim to improve understanding of the cause of the
higher rate of chronic pain in the disease. The first study explored pain processing
in PD patients and age-matched healthy volunteers (Chapter 3 & 4). This study was
2. General Methods and Introduction to Study Aims
55
followed by an investigation into the role of dopamine in pain processing and was
carried out in young healthy volunteers (Chapter 5). An additional analysis was
carried out between young and old healthy volunteers to establish the age-related
differences in the response to the protocol (Chapter 6).
2.2.a. Chapters 3 & 4: Study 1: PD vs Healthy Controls
The first study investigated pain processing in volunteers with PD and age-matched
healthy volunteers. We wanted to investigate pain processing both on and off
medication within the PD group. The benefit of investigating the ‘off’ state of PD
was to remove any effect of the medication and to understand the ‘normal’ state of
the brain in PD patients. The additional study of ‘on’ medication was to highlight
whether the dopaminergic medication used for PD changed pain processing as L-
DOPA had previously been shown to have analgesic qualities. The experimental
paradigm explained below was carried out in the morning following the withdrawal
of PD medication and repeated on the same day 1 hour after PD patients had taken
their medication. The order of the two recordings could not be randomised due to
the need of 12 hours withdrawal of the medication within the ‘off’ condition. The
healthy controls also repeated the study in the afternoon after a 1 hour break. The
decision to complete the two recordings on the same day was after discussions with
potential PD volunteers and their limited willingness to complete two separate
visits. Furthermore, although the order of the visits could have been randomised,
there would have been no blinding to the study due to the use of personal
medication and the researcher’s interaction with the participant. Therefore, the
two groups completed the paradigm twice on the same day, and the healthy
control group was a control for each time-point.
The limitations of this study design became apparent after data collection as a high
proportion of the EEG datasets needed to be excluded due to high levels of noise in
the data. The reason is likely to be due to the length of the EEG cap being
connected, and the reduced quality of EEG data in the aged population. In addition,
there was a clear difference in both the PD and HC groups’ tolerance to the laser
due to the repetition of the paradigm. For this reason, the morning ‘off’ and
2. General Methods and Introduction to Study Aims
56
afternoon ‘on’ sessions were analysed and discussed individually, without
comparison between time-points. Therefore, Chapter 3 includes the main report of
PD ‘off’ state, and Chapter 4 summarises the results of the ‘on’ state.
The protocol was based on the previous research carried out in The Human Pain
Research Group within chronic pain groups and healthy volunteers (Brown, El-
Deredy and Jones, 2014; Brown et al., 2008a). The original protocol used a CO2 laser
to induce acute pain to the forearm. The laser stimuli were preceded by a visual cue
of Low, Mid, High or Unknown which informed the participant of the forthcoming
intensity of the laser. The Unknown visual cue indicated that either intensity would
be given and allowed investigation into the effect of certainty on pain perception.
The visual cue was also concurrently presented with auditory beeps for an accurate
3 second countdown to the stimulus. The participants completed the paradigm
whilst EEG recording was taken, and both sensor level and source localisation was
used to analyse the data.
The original laser paradigm was slightly adjusted to be suitable for the PD patient
group and the protocol was consistent between studies to allow for comparison.
The number of conditions was reduced to be Low, High and Unknown, by removing
the Mid intensity condition, to reduce the time needed for the PD participants to be
sat still. The reduced time was also required to allow time to repeat the study once
the PD patients had taken their PD medication. The three main conditions were
sufficient for analysis as the inclusion of the Low and High conditions allowed for
analysis on for the effect of expectation on brain activity, and the Unknown
condition was used to establish an effect of certainty on brain activity and pain
rating. The protocol is outlined in full within the papers of the thesis.
2.2.b. Chapter 5: Study 2: D2 Dopamine Study
The second study investigated the manipulation of striatal dopamine due to its
strong links with pain and PD (discussed in section 1.4). Therefore, an agonist and
antagonist of dopamine D2Rs were selected to study both high and low levels of
dopamine transmission. The D2R was selected as the target of modulation due to
2. General Methods and Introduction to Study Aims
57
the high abundance in the striatum in comparison to the more widespread
distribution of other dopamine receptors. The use of D2 modulation via drugs has
been shown to be successful in numerous studies (Nandam et al., 2013; Mehta et
al., 2005; Becker et al., 2013). A D2R agonist and antagonist were selected via Dr
Grace Whitaker for their selectivity, safety and efficacy. The agonist Cabergoline
and the antagonist Amisulpride were selected. The in depth comparison between
other drugs and the reason for the final choice is reported in her thesis (Whitaker,
2017).
Cabergoline is often used as a Parkinson’s treatment and has one of the highest
binding affinity to D2Rs compared with other agonists (Gerlach et al., 2003). Also,
Cabergoline has shown to be highly tolerable and safe to administer in patient
groups and experimental studies (Vilar et al., 2018; Webster et al., 1993; Gibson et
al., 2012; Nomoto et al., 1998).
Amisulpride is a selective antagonist of D2 receptors (subtypes D2 and D3) and
prescribed as an anti-psychotic for conditions such as schizophrenia (Silveira da
Mota Neto, Soares and Silva de Lima, 2002). Amisulpride has a good tolerability and
safety profile (Rosenzweig et al., 2002; Juruena, de Sena and de Oliveira, 2010),
however, Amisulpride been associated with prolongation of the QTc interval in an
electrocardiogram (ECG) (Wenzel-Seifert, Wittmann and Haen, 2011; Isbister et al.,
2010; Isbister and Page, 2013). The risk of QTc prolongation is low and will only
occur in combination with additional risk factors such as a pre-existing heart
condition or concurrent administration with other drugs (Wenzel-Seifert, Wittmann
and Haen, 2011). Therefore, all participants recruited to the study underwent an
ECG assessment to ensure that no underlying heart condition was present.
The D2R is a G-coupled receptor and activation induces an inhibitory response to
the post-synaptic neuron. The D2R is mainly located on non-dopaminergic neurons,
however, is also present on dopaminergic neurons and acts as an autoreceptor
which regulate the activity of the dopaminergic neurons (see Figure 2.3). The
activation of the autoreceptors causes a reduction in dopamine transmission and
dopamine production, and previous studies have demonstrated that low doses of
2. General Methods and Introduction to Study Aims
58
D2R drugs favours the activation of the autoreceptors. Therefore, due to the
possible conflicting outcome of the activation of the autoreceptors (reducing
dopamine transmission), rather than the desired activation of the post-synaptic
receptors, the two drugs were administered at a relatively high dose. On the basis
of previous research, the agonist, Cabergoline was given at 1.25mg (Nandam et al.,
2013; Frank and O’Reilly, 2006), and the antagonist, Amisulpride, was given at
400mg (Rosenzweig et al., 2002).
The modulation of the dopamine levels was assessed via eye-blink rate (EBR) which
has previously been positively correlated to striatal dopamine activity and
associated with the D2R (Taylor et al., 1999; KARSON, 1983). The method to
quantify the blink rate was via the ICA function within the EEGLAB toolbox in
MATLAB. The upward and downward deflection, shape and topography of the blink
was searched for within the resting state data. The blink-rate has been shown to
reduce in Parkinson’s disease and is hypothesised to be due to the reduced
dopaminergic activity within the nigrostriatal pathway. Therefore, the blink rate of
each participant was calculated using the EEG recording during a period of resting.
In addition to the selection of the drugs, the recruitment of participants and health-
screening was completed by Dr Whitaker. The running of the study was carried out
by Dr Whitaker and myself in equal measure. All data presented in this thesis
related to the D2 Dopamine study were collected and analysed by myself. The
collaboration was organised due to the central role of dopamine in PD and the thus
a suitable follow-on to the Parkinson’s study.
2.2.c. Chapter 6: Age comparison
The same protocol was completed in Study 1 (PD v age-matched healthy controls)
and Study 2 (D2 dopamine modulation in young healthy volunteers). Therefore, a
comparison was made between the brain activity from the older healthy controls in
Study 1, and the young volunteers in the control condition in Study 2. The rationale
to compare the two age-groups was that there is limited research in the effect of
age on pain processing, especially top-down modulation.
2. General Methods and Introduction to Study Aims
59
R
R
R
opam
ine
D2R ac va on
in ibits
postsynap c
ac va on
A to
-
re eptor
D2 Auto
-receptor
ac va on decreases
dopam
ine synthesis
and release
Presynap
ne ron
Postsynap
Ne ron
A onist
Anta onist
o doses favours
presynap c D2
auto
-receptor ac va on
i doses ac vate
postsynap c D2Rs in
addi on to presynap c
auto
-receptors
A onist
Decrease in dopam
ine
release and decrease in
postsynap c D2R
ac va on
Anta onist
Increase in dopam
ine
release and increase in
postsynap c D2R
ac va on
A onist
Increase in postsynap c
D2R ac va on despite
presynap c dopam
ine
down
-regula on.
Anta onist
Decrease in postsynap c
D2R ac va on despite
presynap c dopam
ine
up
-regula on.
Figure 2.3: A schematic diagram of the action of the dopamine D2 receptor and how low and high doses of
agonists and antagonists have different effects on postsynaptic D2 receptor activity.
2. General Methods and Introduction to Study Aims
60
2.3. RECRUITMENT
2.3.a. Recruitment: Study 1: PD vs Healthy Controls
The recruitment of the PD group was carried out by Dr Monty Silverdale and Dr
Christopher Kobylecki at their clinics held at Salford Royal NHS Foundation Trust.
The study was introduced to people who could be temporarily withdrawn from
their PD medication and pain relief on the study visit. Patients presenting a severe
tremor or dystonia were not recruited to the study due to the EEG recording being
sensitive to movement. The contact details of those interested in taking part were
passed over to me for a follow-up phone-call. Approximately 50% of the patients
who showed interest took part in the research.
The recruitment for the HC group was via the Citizen Scientist website and adverts
within local golf clubs. The HC group were age-matched to the PD group. The
recruitment of the partners of PD patients was avoided due to previous research
demonstrating an altered pain perception within partners who had a caring role
(Romano et al., 2000).
The PD study recruited age-matched controls to act as the health control group for
the study. The HC group carried out the experiment twice to replicate the
experience of the PD group completing the study off and on their medication. The
HC also repeated the study and acted as a control for the ‘on’ condition of the PD
group.
2.3.b. Recruitment: Study 2: D2 Dopamine Study
The recruitment of the Dopamine D2 Study was via the email announcements at
The University of Manchester, and advert posters circulated in the University
buildings. The age range was selected to be 18-35 years old to limit age-related
changes which may affect the results. In addition, the criteria for the study required
a healthy cohort and as such a younger age range was appropriate. Prior to
selection for the study, the participants completed the Barratt impulsiveness scale
(BIS-11 (provided by Dr Grace Whitaker) to establish their baseline level of striatal
2. General Methods and Introduction to Study Aims
61
dopamine as the (BIS-11) has previously been correlated with individual dopamine
levels, specifically striatal dopamine (Costa et al., 2013; Buckholtz et al., 2010;
Korponay et al., 2017). Final recruitment to the experiment was restricted to the
participants who scored in the mid-range on the questionnaire as this indicated that
their dopamine levels were also mid-range and within the peak of the inverted-u
relationship of dopamine and performance (see figure 2.4). The inclusion of this
assessment was aimed to reduce the range of the variability in baseline dopamine
levels in the participants which may have affected the outcome of the D2R
manipulation. A study by Cools et al (2009) demonstrated the importance of
baseline dopamine and D2R manipulation. The study assessed the relationship
between reward- and punishment-based learning and baseline dopamine level and
concluded using PET imaging that participants’ with high striatal baseline dopamine
performed better in the paradigm than those with low dopamine levels. They
further highlighted that the baseline dopamine level affected the result of a D2R
agonist on punishment driven outcomes; such that performance was improved by
the agonist in participants’ with low baseline dopamine, whilst impairing
performance in those with high baseline dopamine (Cools et al., 2009).
Figure 2.4: The inverted-U relationship between dopamine level and performance. There is an optimal range
of dopamine level at the peak of the curve, and a low or high level of dopamine has been shown to impair
performance in cognitive responses. The BIS-11 score of impulsivity has been correlated with baseline
dopamine levels and was used as a screening technique to recruit participants with dopamine levels within
the optimal range. This recruitment criterion aimed to limit the variability in the effect of the D2R
manipulation on performance output.
Per
form
ance
Dopamine level/BIS-11 score
2. General Methods and Introduction to Study Aims
62
Furthermore, the Dopamine D2R antagonist Amisulpride required pre-health
screening due to it being dangerous for people with extended QTc intervals (Täubel
et al., 2017). Therefore, an electrocardiogram (ECG) was performed on all potential
volunteers by either Dr Monty Silverdale or Dr Christopher Kobylecki. All ECGs were
assessed prior to recruitment to the study and if a volunteer showed abnormalities
they would have been withdrawn from the study and their GP informed.
The study was designed to be repeated-measures and all participants completed
the experiment three times over a six week period. Each visit was separated by 10-
days to allow for the skin the recover between each laser session. The study was
pseudo-randomised and double-blinded. The randomisation was pseudo-
randomised so that near equivalent numbers were in each of the six possible orders
of the drug conditions.
The benefit of a repeated-measures design is that there is less variability within
condition groups and the highly subjective and individuality of pain perception
would have increased the variability within a between-subject design. Therefore, a
between-subject design would require a much higher n number to account for the
high degree of variability.
After each study visit the participants were given specially made business cards
with information about the study. The participants were advised to keep it on their
person as it provided information to medical personnel if an emergency arose
(Figure 2.5).
2. General Methods and Introduction to Study Aims
63
Figure 2.5: Business cards given to participants of the D2 dopamine study. The participants were instructed to
carry the cards on them for 24 hours in the unlikely case that they needed medical attention and were not
able to explain their involvement in the study.
Chapter 3
A neurophysiological investigation of pain anticipation in Parkinson’s
disease
Sarah Martin, Anthony Jones, Christopher Brown,
Christopher Kobylecki, Wael El-Deredy, Monty Silverdale
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
65
3.1. ABSTRACT
Chronic pain is common in people with Parkinson’s disease, and is often considered
to be caused by the motor impairments associated with the disease. Altered top-
down processing of pain characterises several chronic pain conditions and occurs
when the cortex modifies nociceptive processing in the brain and spinal cord. This
contrasts with bottom-up modulation of pain whereby nociceptive processing is
modified on its way up to the brain. Although several studies have demonstrated
altered bottom-up pain processing in Parkinson’s, the contribution of enhanced
pain anticipation and enhanced top-down processing of pain has not been fully
explored.
In the present study, EEG was recorded whilst noxious stimuli were delivered by a
carbon dioxide laser to the forearm. Participants with Parkinson’s disease were
compared with a Healthy Control group.
EEG source localisation reported an increased activation in the mid-cingulate cortex
and supplementary motor area in the Parkinson’s disease group compared to the
healthy control group during mid [-1500 -1000 ms] and late anticipation [-500 0
ms], indicating enhanced cortical activity before noxious stimulation. The
Parkinson’s disease group was also more sensitive to the laser and required a lower
energy level to induce pain.
This study provides evidence supporting the hypothesis that enhanced top-down
processing of pain may contribute to the development of chronic pain in
Parkinson’s. With further research to confirm these findings, our results inform a
scientific rationale for novel treatment strategies of pain in Parkinson’s disease,
including mindfulness, cognitive therapies and other approaches targeted at
reducing top down processing of pain.
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
66
3.2. INTRODUCTION
3.2.a. Pain in Parkinson’s disease
Chronic pain is a highly prominent symptom in people with Parkinson’s disease
(PwPD), yet there is a limited understanding of whether the pain is primarily a
consequence of motor impairment, including muscle rigidity, or whether
Parkinson’s disease (PD) causes a centrally produced heightened sensitivity to pain.
Whilst the percentage of the general population living with chronic pain is
approximately 20% (van Hecke, Torrance and Smith, 2013; Breivik et al., 2006),
there is a significantly higher prevalence within the PD population of approximately
two-thirds (Ozturk et al., 2016; Nègre-Pagès et al., 2008a; Skogar and Lokk, 2016;
Silverdale et al., 2018). There is evidence that PwPD have lower threshold and
tolerance of pain compared to age-matched healthy cohorts (Brefel-Courbon et al.,
2005; Chaudhuri and Schapira, 2009; Djaldetti et al., 2004a; Schestatsky et al.,
2007a), and EEG and functional imaging studies have demonstrated an altered
central response to pain in PD (Tinazzi et al., 2009; Schestatsky et al., 2007b; Brefel-
Courbon et al., 2005). These abnormalities provide strong evidence that altered
central pain processing contributes to the development of pain in PD (Silverdale et
al., 2018). Therefore, an increased understanding of the pathophysiological
mechanisms causing the chronic pain is imperative to make an informed
improvement in the treatment of pain in Parkinson’s.
3.2.b. Top down alteration in pain processing
Research into chronic pain has largely focused on bottom-up mechanisms
amplifying the pain signal on its way from the peripheries to the brain (Reicherts et
al., 2017; Watson et al., 2009). However, there is a developing field in the role of
top-down modulation of pain, whereby the cortical activity modulates the incoming
pain signal as it reaches the brain.
Previous research within our group has used EEG source localisation to investigate
the anticipatory processing of painful stimuli. Our previous research has shown that
the anticipation of pain leads to changes in the activity of the same brain regions
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
67
that subsequently respond to nociception (Brown and Jones, 2008; Brown, El-
Deredy and Jones, 2014; Clark et al., 2008b; Brown et al., 2008a). Examples of
regions which have been seen to activate during anticipation include the cingulate
cortex, insula, primary and secondary somatosensory cortex (SI/SII), prefrontal
cortex (PFC) and the periaqueductal gray (PAG) (Koyama et al., 2005; Babiloni et al.,
2004). The degree of anticipation has been shown to be correlated with subsequent
pain perception and the EEG laser-evoked potential (LEP) (Brown et al., 2008a).
Hence, we have previously demonstrated that the cortex can modulate the
perception of pain of the incoming pain signal via top-down modulation. Within
chronic pain conditions, such as fibromyalgia and osteoarthritis, heightened
anticipation within the insula cortex is correlated with the extent and severity of
chronic pain symptoms (Brown, El-Deredy and Jones, 2014).
Unlike fMRI and PET, the higher temporal resolution of EEG allows for a more
accurate recording of the anticipatory processes. Prior to a noxious stimulus, during
anticipation, there is cortical activity from a network of regions including the
cingulate cortex, basal ganglia and thalamic structures (Brunia and van Boxtel,
2001; Vogt, Finch and Olson, 1992). In this study we used EEG with source
localisation analysis because of the temporal advantage over neuroimaging
techniques reliant on slow haemodynamic responses. The accuracy and reliability of
EEG source localisation has been verified by research which shows high similarity to
other neuroimaging techniques such as PET (Christoph M. Michel, 2004; Lantz et al.,
2003), fMRI (Mulert et al., 2004; Vitacco et al., 2002) and intracerebral recordings
(Seeck et al., 1998; Trébuchon-Da Fonseca et al., 2005). EEG source localisation has
reliably reported activations within the pain network during anticipation (Brown
and Jones, 2012; Brown et al., 2008a; b). For instance, impaired processing within
the parietal and frontal regions during anticipation has been reported using source
localisation in patients with the chronic pain associated with Fibromyalgia and
Osteoarthritis (Brown, El-Deredy and Jones, 2014).
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
68
3.2.c. Aim of study
Here we investigated anticipatory processing in PwPD in the “off” medication state
in comparison to age-matched healthy volunteers. We used a CO2 laser to induce
acute noxious stimuli and monitored brain activity throughout via EEG. We
hypothesised that the anticipatory phase would be abnormal in the PD group and
would provide evidence that top-down mechanisms are key to explaining the
mechanisms of chronic pain in PD.
3.3. METHODS
The study was approved by the local ethics committee and all participants gave
informed consent according to the Declaration of Helsinki to participate in the
study.
3.3.a. Participants
Twenty-four participants with Parkinson’s were recruited (sixteen males). PD
patients were recruited via correspondence with their neurologist (MS or CK).
Participants were screened to ensure safe withdrawal of medication for the study,
and to exclude cases of severe tremors which might interfere with EEG recording.
Clinically significant peripheral neuropathy was excluded by clinical examination
and neuropathy scale. Symptom duration ranged from 1-month to 16 years, with a
mean duration of 5.13 years. One PD participant was unable to complete the study
due to severe symptoms after medication withdrawal. Twenty-three participants
with PD were included for analysis of behavioural measures. Twenty PD participants
were included for EEG analysis due to two datasets being removed because of noisy
data and one for a low MoCA (Montreal Cognitive Assessment) score (12/30).
Twenty-five age-matched healthy controls (HCs) were recruited (fourteen males).
One HC participant was unable to complete the study due to the laser failing to
induce a sufficient pain level, and one HC dataset was removed due to noisy data.
Twenty-four HCs were included in the behavioural measures and twenty-three HC
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
69
participants were included for EEG data analysis. The participants with PD [age
range; 63.3 ± 8.27] were age matched to the HCs [age range; 65.5 ± 8.59].
3.3.b. Medication
The PD group omitted their evening medication and were studied in the practically-
defined OFF state (after 12 hours withdrawal of anti-Parkinsonian medication). The
motor section of the Movement Disorder Society Unified Parkinson's Disease Rating
Scale (MDS-UPDRS) was completed to report the current severity of their
parkinsonian disability off their medication.
3.3.c. Assessments
The participants were assessed for; PD motor severity, pain, mood and cognitive
state. PD motor severity: The motor section (III) of the MDS-UPDRS was completed
to evaluate the motor disability due to PD (Goetz et al., 2008). The assessment in
the PD group was completed off their medication. Pain: All participants rated their
current pain state via a number rating scale (NRS) prior to starting the laser
protocol. The PD participants with chronic pain reported their minimum and
maximum degree of pain over the last 6 months via a visual analogue scale (VAS).
All participants completed the pain catastrophising scale (PCS) (Sullivan et al., 1995)
to report their psychological coping ability when experiencing pain. Mood: All
participants completed the Hospital Anxiety and Depression Scale (HADS) (Zigmond
& Snaith, 1983) to dissociate results from anxiety or depression. Cognitive state:
The Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005) test was
carried out to assess the participants’ cognitive ability. Participants with low scores
(<25) were removed from the study to avoid cognitive decline affecting the EEG
signal.
3.3.d. Experimental design
3.3.d.i. Pain stimuli
A CO₂ laser [50W Synrad 48-5 J-series (J-48-5(S)W) Wavelength: 10600nm] was
used to deliver acute pain to the dorsal surface of the right forearm. The CO₂ laser
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
70
delivered a beam with a diameter of 15 mm and 150 ms duration. The voltage (V) of
the laser is linearly related to the laser energy delivered to the forearm. For each
test, the stimuli were delivered in an area measuring 4 x 5cm and was delivered in a
predetermined randomised path (Brown et al., 2008a). This was to avoid
habituation, sensitization, or skin damage.
3.3.d.ii. Psychophysics
Before starting the experimental protocol, psychophysics was used to calibrate the
laser to the individual’s pain sensitivity. An ascending method of limits procedure
was used, starting from 0.6 V with 0.06 increments each time. The participant used
an eleven point NRS (0 - 10) to rate the intensity of the pain perceived for each
stimulus and the following description was provided; 0=no sensation, 4=pain
threshold, 7=moderately painful, 10=unbearably painful. The rating scale was
introduced to the participant via these standardised descriptives to ensure that no
explanation altered their interpretation of the scale. The procedure was repeated
three times to allow participants to get used to the laser and was used to calculate
the average voltage to induce level 4 (low) and level 7 (moderate) pain. These two
levels provided ‘low’ and ‘high’ stimuli intensity for the main laser experiment
protocol.
3.3.d.iii. Main experiment
The participants received 120 laser stimuli at the two intensities (low and high)
separated into four conditions; Low (level 4), High (level 7), Unknown Low (level 4)
and Unknown High (level 7). To investigate the anticipation of a painful stimulus, a
3 second auditory countdown preceded the laser stimuli (see Figure 3.1). The first
auditory cue was presented concurrently with an anticipatory cue to indicate the
forthcoming laser stimuli and to maintain attention. The participant was either
shown ‘Low’, ‘High’ or ‘Unknown’. The presentation of the word ‘Unknown’
indicated that the laser stimulus has an equal chance of being low or high. This was
to investigate the importance of certainty in the anticipation of the laser stimuli.
The image was also used as a visual fixation cue to discourage eye movements.
After the laser stimuli, the 0-10 numerical rating scale was shown on the screen and
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
71
the participant rated the intensity of the pain. The order of the stimuli was
randomised and separated into three blocks with short breaks in-between.
Figure 3.1: A schematic diagram of a single trial of the experimental paradigm. A computer monitor showed
the participant a visual cue of; Low, High or Unknown from -3s to +2s. A three second countdown of beeps at
-3, -2 and -1 allowed accurate anticipation of the laser stimuli at time 0s (red bar). The presentation of the
visual cue at -3s was concurrent with the first auditory cue. The visual cue was consistent throughout the
anticipation and laser stimulus. At +2s, an eleven-point number rating scale (NRS) was presented for the
participant to rate the laser stimulus. For each condition (Low, High, Unknown Low and Unknown High) there
were thirty trials. The trials were presented in a randomised order and divided into three blocks of forty
trials.
3.3.d.iv. EEG recording:
A BrainVision MR EEG cap was used to record from 63 scalp electrodes using a
BrainVision-cap system [Standard BrainCap-MR with Multitrodes]. The arrangement
of the electrodes was modelled on the extended 10-20 system. Recording
parameters were set at: Filter (DC to 70 Hz), Sampling rate (1000 Hz), Gain (500). To
reduce electrical interference, a 50Hz notch filter was applied. Prior to starting the
laser protocol, resting states were recorded with eyes open and closed for two
minutes in all participants. This ensured that the experience prior to the experiment
was identical. The three experimental blocks were recorded separately to allow for
better artefact rejection of the EEG data.
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
72
3.3.e. Analysis Methods
3.3.e.i. Statistical analysis of behavioural data
Statistical analyses of the behavioural measures were carried out using IBM SPSS
Statistics 22 software. The questionnaires were all investigated for significant group
differences. Prior to using statistical tests, the data was assessed for normality using
a combination of Q-Q plots, histograms, and the values of skew and kurtosis.
Normally distributed data was analysed using independent t-tests and ANOVA tests,
whilst data reported to be not normally distributed, the appropriate non-
parametric test was utilised. Specific statistical tests for each analysis step are
reported in the results section.
3.3.e.ii. EEG analysis method
EEG pre-processing was carried out using EEGLAB toolbox (Delorme and Makeig,
2004) in MATLAB version R2015a (The Mathworks Inc) whilst statistical analysis was
carried out using SPM12 toolbox (Wellcome Department of Imaging Neuroscience,
Institute of Neurology, UCL, London, United Kingdom) running in MATLAB. The EEG
data was pre-processed for scalp and source localisation analysis. The main
motivation of the analysis was to establish the anatomical origin of the brain
activity using (Low Resolution Electromagnetic Tomography) LORETA source
localisation.
3.3.e.iii. EEGLAB Pre-processing
Pre-processing consisted of; removal and interpolation of bad channels, down-
sample to 500, low-pass filter of 20Hz and re-reference to the common average.
The four conditions were separated and -3500ms to 2000ms epochs extracted and
Linear detrend applied. Independent Component Analysis (ICA) was carried out on
all datasets using the SemiAutomatic Selection of Independent Components for
Artifact correction (SASICA) toolbox to select components to remove via pre-
determined thresholds. The thresholds were set to; Autocorrelation (threshold =
0.35 r, lag = 20 ms), Focal (threshold = 3.5 z), Focal trial (threshold 5.5 z), Signal to
noise (period of interest (POI) = [0 Inf], baseline (BL) [-Inf 0], threshold ratio = 0.5),
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
73
and Adjust Selection enabled. The thresholds were sufficient to remove artefacts
from the majority of the datasets; however, a number of datasets required further
manual removal of eye-blink components where not picked up by SASICA.
3.3.e.iv. SPM EEG Analysis
The pre-processed datasets were converted to Statistical Parametric Mapping
(SPM) compatible files. Statistical analysis was carried out to investigate the
anticipation evoked potentials and the post-stimulus LEPs using scalp-level and
source localisation analysis techniques available in the SPM toolbox.
SPM scripts for batch processing were used to analyse the EEG data at the scalp
level and source localisation. Two baselining methods were applied to the data
analysis for the anticipation phase and were applied for scalp-level and source
localisation analysis (see 2.1.b.ii). Primary analysis applied distinct baselines (BLs) of
500 ms, occurring prior to each of the three auditory cues respectively, to analyse
each of the three anticipation phases. The BLs and time window of interest (TWOI)
were as follows; Early [BL: -3500 ms -3000 ms: TWOI: -2500 ms -2000 ms], Mid [BL:
-2500 ms -2000 ms: TWOI: -1500 -1000 ms] and Late [BL: -1500 -1000 ms: TWOI: -
500 0 ms] anticipation phases. The secondary analysis method applied a single
baseline of 500 ms prior to the first auditory cue [BL: -3500 ms -3000 ms] that was
common to every TWOI anticipation phase (early, mid and late). This second
analysis was conducted to enable comparison to previous studies (Brown et al.,
2008a; Brown, El-Deredy and Jones, 2014). All statistical analysis for the
anticipation phase was adjusted for multiple comparisons.
The SPM whole-head analysis for the post-stimulus phase TWOI [200 ms 600 ms],
centred on the LEP, was baseline corrected to -500 ms prior to the laser stimulus
[BL: -500 ms 0 ms].
Two further analysis steps were carried out to investigate the LEP further. Firstly,
the N2 and P2 components of the LEP were calculated for the Cz electrode at scalp
level (chosen for showing highest amplitude during LEP). The time window was
restricted to 0 600 ms and the amplitude and latency of the most negative (N2-
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
74
peak) and positive (P2-peak) peaks were extracted for statistical analysis. All laser
conditions were averaged. The values extracted for the N2-peak was restricted to
be prior to the P2-peak due to the N2 peak occurring prior to the P2 peak.
Secondly, a focussed analysis of the P2 peak using source localisation and individual
time windows was carried out. A time window of 80ms centred on each
participant’s P2 peak latency was used to calculate source estimates, and was
assessed for group differences.
We also calculated the SD (Standard Deviation) of the EEG potential over trials for
every time sample across the whole-epoch [-3500 1500 ms] and compared the
results between the two groups to evaluate for possible differences in variability of
the data over trials. This was to test whether any group differences found in ERP
amplitudes and sources from the main analyses might have resulted from
differences in data variability; such variability can arise from noise in the EEG signal
(including motion and other artefact) rather than from neural signals. Such noise
was expected to be greater in the PD group and therefore required assessing in
order to interpret the results.
3.3.e.v. Source Localisation analysis parameters
SPM12 EEG and MATLAB scripts were used to estimate the sources of the
anticipation- and laser-evoked potentials using LORETA. The forward model was
created using an 8196 vertex template cortical mesh coregistered to the electrode
positions of the standard 10-20 EEG system. A three-shell boundary element model
(BEM) EEG head model available in SPM12 was used to compute the forward-
model. The images were smoothed with a 12mm full-width-at-half-maximum
(FWHM).
3.3.e.vi. EEG Analysis Statistical analysis
For the analysis of the anticipatory TWOIs, a three way repeated measures ANOVA
was applied, with one between-subject factor of Group (HC vs PD) and two within-
subject factors of Certainty [Known (Low/High) v Unknown] and Expectation [Low v
High (Known)]. For the analysis of the post-stimuli TWOI, a three way repeated
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
75
measures ANOVA was applied, with one between-subject factor of Group (HC v PD)
and two within-subject factors of Certainty [Known (Low/High) v Unknown] and
Intensity [Low v High (Known and Unknown)].
Source localisation results were reported as follows. To control for multiple
comparisons, a cluster-forming threshold of p<0.001 was used and resulting
clusters were considered significant at FWE (p<0.05). Significant clusters were also
restricted to >100 voxels in size and regions labelled using the Anatomical
Automatic Labelling (AAL2) toolbox in SPM. We also extracted the eigenvariate data
from source estimates to investigate possible correlations with behavioural
measures including MDS-UPDRS (PD group only), HADS, PCS, chronic pain maximum
VAS score and laser voltage for high pain (V). The p value was adjusted for multiple
comparisons, p<0.01.
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
76
3.4. RESULTS
3.4.a. Behavioural Results
3.4.a.i. Questionnaires
Group comparisons were carried out using an independent t-test between
questionnaire scores and are reported in Table 3.1. There was a significant
difference reported in the PCS, such that the PD group reported a higher level of
catastrophising behaviours towards pain compared to the HC group. In contrast,
there was no significant difference seen in the HADS, although a trend is seen
towards higher scores in the PD group compared to the HC group. The OFF MDS-
UPDRS-III motor score recorded in the PD group ranged from 15 to 78, with a mean
of 38.3±16.56. For reference, a high MDS-UPDRS-III score indicates more severe
movement impairments.
Table 3.1: T e table displays t e reported meas res for t e assessments for t e P and C ro ps. ^ evene’s
Test for Equality of variance p<0.05 hence equal variances not assumed and significance adjusted. * =
Si nifi ant res lt p<0.05. P : Parkinson’s disease, C: ealt y Control, PCS: Pain Catastrop isin S ale, A S:
Hospital Anxiety and Depression Scale, SD: Standard deviation.
3.4.a.ii. Laser behavioural results
The psychophysics ramping procedure was used to calculate the participants’
individual energy level required to induce a High pain score. A Welch t-test was run
to determine if there were differences between the HC and PD groups in the energy
required to induce a High pain score due to the assumption of homogeneity of
variances being violated, as assessed by Levene's test for equality of variances (p =
Assessment Group N Mean SD Significance
(2-tailed T-test)
PCS PD 23 11.59 10.85 0.019^*
HC 24 5.29 5.67
HADS PD 23 13.83 9.89 0.070
HC 24 9.21 6.76
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
77
0.010). There was a statistically significant difference in energy required to induce a
High pain between HC and PD (Figure 3.2), with PD requiring a lower energy level
(1.99±0.44 V) compared to HC (2.20±0.25 V), (95% CI, 0.003 to 0.432), t(34.75) =
2.060, p = 0.047.
Figure 3.2: The energy of the laser required to induce high (level 7) pain. The PD group required a
lower energy level compared to the HC group to induce the equivalent pain. The blue and red
boxes show the median (bold horizontal line) (HC: 2.32V, PD: 2.24V), lower and upper quartiles.
The vertical lines show the minimum (HC: 1.68V, PD: 1.30V) and maximum (HC: 2.50V, PD: 2.48V)
values. Laser voltage was delivered from 0.8V to a maximum of 2.5V in increments of 0.06V. * =
p<0.05. C: ealt y Control, P : Parkinson’s disease.
The mean and standard error (SE) of the pain rating scores for all conditions were
calculated; Low (PD: 2.54±0.23, HC: 2.0±0.18), High (PD: 4.94±0.30, HC: 4.7±0.26),
Unknown Low (PD: 3.06±0.25, HC: 2.4±0.22) and Unknown High (PD 4.53±0.33, HC:
4.1±0.26). The pain rating scores for the laser stimuli during the main experiment
were investigated using a three-way mixed ANOVA with a between-subject factor
of group, and within-subject factors of certainty (Known v Unknown) and Intensity
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
78
(Low v High). The data met homogeneity threshold (p>0.05) calculated by the
Levene’s Test of Equality of Variances. There was no effect of group F(1, 43)=2.347,
p = 0.133, η2 = 0.52. There was an effect of intensity F(1, 43)=290.992, p=0.000, η2 =
0.871, yet no effect of certainty F(1, 43)=0.006, p= 0.937, η2 = 0.000. However,
there was a significant two-way interaction between certainty and intensity F(1,
43)=59.188, p=0.000, η2 = 0.579. A follow-up paired t-test comparing the difference
between known and unknown for Low and High pain rating reported a significant
result; T(1, 43)=-7.820, p<0.000, d=42, such that the pain ratings of unknown low
were higher (+0.52) compared to known low, whilst ratings of unknown high were
lower (-0.46) compared to known high. This result acts as a manipulation check that
demonstrates that the experiment was successful in inducing expectations (via
anticipation cues) that modulated pain in the expected direction as seen in previous
research (Brown et al., 2008b). There was no difference in the effect of intensity or
certainty on pain ratings between the two groups (HC v PD), nor was there any
interaction between intensity, certainty and group.
3.4.b. EEG Results
3.4.b.i. LEP component analysis
The N2 and P2 components of the LEP at the Cz electrode were assessed for
differences between the two groups using independent t-tests. Outliers were
assessed via boxplot function in SPSS and one data point was excluded from the
statistical analysis due to being more than 1.5 box-lengths away from the box edge.
The statistical analysis concluded that there were no significant differences
between the HC and PD groups in the amplitude or latency of the N2 and P2 peaks
(see Figure 3.3).
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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Figure 3.3: Graphs showing the information of the N2 and P2 components of the laser-evoked
potential (LEP). The N2 peak was deemed to be the most negative amplitude prior to the P2 peak
latency and within the post-stimulus time-window. The P2 peak was the most positive amplitude
within the time window of [0 600ms]. A) and B) show the amplitude of the N2 and P2 peaks
respectively, whilst C) and D) show the latency of the N2 and P2 peaks respectively. All data is
presented as mean and error bars of standard deviation. Statistical analysis to assess group
differences between HC and PD, reported no significant differences.
3.4.b.ii. Scalp-level Whole head analysis
SPM whole-head statistical analysis of the early [-2500 -2000 ms], mid [-1500 -1000
ms] and late [-500 0 ms] anticipatory phases, reported no significant difference of
group, certainty or expectation. This was true for both baseline methods (see
Methods section). Similarly, the post-stimulus TWOIs [200 600 ms] reported no
significant group or certainty differences. The intensity factor in the post-stimulus
A)
B)
C)
D)
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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TWOI reported increased activity in high pain condition compared to low pain (SPM
results reported in Supplementary materials Table 3.3).
The anticipatory response over time was calculated for each group and is displayed
as topography plots in Figure 3.4. The difference between HC and PD group was
also determined by subtracting the HC amplitudes from the PD data. Despite the
SPM scalp-level analysis not reporting a significant difference, an anticipation
response is seen in the central scalp region which is more prominent in PD than HC.
Because scalp responses are a summation of deeper brain events from different
sources, the scalp-level analysis was hence followed up by source localisation
analysis.
Figure 3.4: Topography plots showing the average response for the mid- and late-anticipation time
windows for HC and PD. Mid-anticipation is baseline corrected to [-2500 -2000 ms] and late-
anticipation of [-1500 -1000]. The difference between the PD and the HC group was calculated via
subtracting the HC data from the PD data. The same scale has been used for all topography plots.
In the group difference plot, the red highlights a more positive response in the PD participants,
whilst blue indicates a more negative amplitude in PD.
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
81
3.4.b.iii. Source EEG analysis
Source localisation analysis using the distinct baselines prior to the auditory cues
revealed that the PD group showed a higher degree of activity compared to the HC
group during mid and late anticipation phases (see Table 3.2). In addition, a trend
was also seen in the early anticipation phase. Cluster-level statistics which showed
a trend within regions previously associated with pain anticipation including the
hippocampus (Reicherts et al., 2017), the postcentral gyrus (Yang, Jackson and
Huang, 2016), and the cingulate cortex (Shackman et al., 2011) and so these were
also reported at the uncorrected p value, 0.001. Figure 3.5, shows the significant
clusters which are significant F-contrasts after FWE and adjustment of the p value
(p<0.025) due to multiple analysis techniques. The clusters are denoted with ‘◊’ in
Table 3.2 and were used for correlation analysis with behavioural parameters.
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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Table 3.2: Group effect on sources of early, mid and late anticipation-evoked responses. A threshold of
clusters >100 voxels was set and results were restricted to FWE correction and the p value adjusted for
m ltiple omparisons (p<0.0 5). Res lts labelled it ‘◊’ are used for figure 3.5 and correlation analysis.
Res lts s o in trend are reported and si nified it †, and t e n orre ted val e (0.001) reported belo .
The Anatomical Automatic Labelling (AAL2) atlas was used to report the regions within the significant cluster
for the group effect. The region with the highest percentage overlap is shown, unless an equivalent share of
per enta e overlap as seen. A label of ‘Unkno n’ as not reported. T e f ll report of all of t e per enta e
overlaps can be seen in the Supplementary Materials Table 3.5.
Group Difference
Brain Region Cluster-Level Peak-Level MNI
coordinates
P(FWE) K F/T Z x y z
Earl
y
F-contrast 0.092†
(0.033 Uncorr) 413 13.79 3.45 16 -22 -16 R Hippocampus (47.5%)
T-contrast
PD>HC 0.028 757 3.71 3.63 16 -22 -16 R Hippocampus (46.8%)
Mid
F-contrast
0.021◊ 763 27.41 4.89 14 -22 52
R
R
Supplementary Motor Area (52.7%)
Mid Cingulate (33.6%)
0.003◊ 1233 19.77 4.16 -16 -38 38
L
L
Mid Cingulate (41.0%)
Supplementary Motor Area (35.2%)
0.059†
(0.022 Uncorr) 529 19.02 4.08 -26 -34 38 L Postcentral (67.5%)
T-contrast
PD>HC 0.019 922 5.24 5.03 14 -22 52 R
L
Supplementary Motor Area (52.5%)
Mid Cingulate (42.1%)
0.002 1706 4.45 4.31 -16 -38 38 L
L
Mid Cingulate (42.1%)
Supplementary Motor Area (31.4%)
0.022 877 4.36 4.24 -26 -34 38 L Postcentral (66.5%)
0.074†
(0.033 Uncorr) 457 4.13 4.03 14 -36 -14
R
R
R
Lingual (19.2%)
ParaHippocampal (16.3%)
Posterior Cingulate (12.1%)
0.068†
(0.031 Uncorr) 568 4.05 3.95 14 -60 36 R Precuneus (56.0%)
Late
F-contrast 0.000
◊ 4001 22.15 4.40 -2 -22 54
L+R
L+R
Mid Cingulate (32.6%)
Precuneus (17.0%)
T-contrast
PD>HC 0.000 6223 4.71 4.55 -2 -22 54 R
R
Mid Cingulate (27.7%)
Precuneus (17.7%)
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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Figure 3.5: Source estimates for significant F-contrasts at FWE correction using MRIcroN software.
Red represents the clusters of increased activity in PD compared to HC. A) Shows the increased
activation in the PD group compared to the HC group within the mid-anticipation phase: i) Clusters
are within the Right Supplementary Motor Area and Right Mid Cingulate Cortex. ii) Clusters are
within the Left Mid Cingulate Cortex and Left Supplementary Motor Area. B) Shows the increased
activation within the PD group compared to the HC group during the late anticipatory phase: i)
Clusters are within the bilateral Mid Cingulate and Precuneus. The image is aligned by the peak-
voxel at peak-level and reported as x y z (mm).
The certainty (Known v Unknown) contrast reported a trend within the early
anticipatory TWOI, [(F-contrast: p = 0.053 F=19.87, kE = 520), (T-contrast:
Unknown>Known: p = 0.013, T= 4.32, kE = 967)], with peak voxel at x:48 y:16 z:-16
(mm). The T-contrast indicated an increased activity within the cluster in the
unknown condition in contrast to the known condition. The cluster is within the
right superior temporal pole (37.5%), right mid temporal pole (18.3%), right
superior temporal (14.6%) and the right insula (3.7%). Contrasts for expectation
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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(Low v High) during anticipation, and certainty (Known v Unknown) within the post-
stimulus TWOI, showed no differences in source estimates.
During the post-stimuli TWOI there was a significant difference in the intensity,
such that a greater response was seen in the High intensity condition compared to
Low, (see supplementary materials Table 3.4). The additional source localisation
analysis of the P2 component which applied individual time-windows centred on
the P2-peak latency, reported no group differences.
3.4.b.iv. Source correlations with behavioural measures
The eigenvariate data from the source estimates (denoted with ‘◊’ in Table 3.2) for
the mid and late anticipation group effects were extracted for correlation analysis.
Spearman rank correlation of mid-anticipation clusters (A: i & ii) and the late
anticipation cluster (B: i) were investigated for correlation with MDS-UPDRS motor
score (PD only), HADS, PCS, maximum chronic VAS score and the energy of the
laser. The P value was adjusted to account for multiple comparison and no
significant correlations were highlighted.
3.4.b.v. Standard Deviation analysis
The analysis of the SD-over-trials across the whole epoch at sensor-level EEG
revealed no significant differences between the HC and PD groups, nor within-
subject contrasts, certainty or expectation.
ii) -16 –38 38
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
85
3.5. DISCUSSION
In the present study, we used EEG source localisation techniques to investigate the
pain processing in PwPD in the practically-defined OFF state (after 12 hours
withdrawal of anti-Parkinsonian medication). There were three main findings.
Firstly, the PD group were more sensitive to the laser and required a lower energy
level to evoke painful stimuli. Secondly, EEG source localisation showed that the PD
group had a heightened anticipatory signal during the anticipation phase prior to
acute heat pain. And finally, the heightened anticipatory response was independent
of PD motor severity (MDS-UPDRS), mood (HADS), pain coping mechanisms (PCS)
and severity of chronic pain (max chronic VAS score). This present study provides
evidence to support the hypothesis that there is an augmented top-down
processing during the anticipation of pain in PwPD which may help us to
understand the high prevalence of pain in PwPD.
3.5.a. Altered Top-Down control
The amplified anticipatory signal seen in the PD group is evidence of altered top-
down modulation within the pain network prior to the pain signal reaching the
brain. This opens the possibility that the heightened pain sensitivity in PD is not
solely due to impaired peripheral, bottom-up, factors, but may also be due to the
modulation within the pain network of the brain.
The significance of altered top-down processing is well established in the pain field:
different attentional states can directly and substantially alter pain intensity and
unpleasantness (Miron, Duncan and Bushnell, 1989b); anxious anticipation of
aversive stimuli activates regions of the pain network, resulting in altered
perception of the stimuli (Reicherts et al., 2017). Furthermore, the degree of
anticipation of a noxious stimulus has been shown to directly correlate with the
perceived stimuli intensity (Pfingsten et al., 2001; Fairhurst et al., 2007), and
abnormal anticipation of a painful stimulus has been shown in a number of chronic
pain conditions such as Fibromyalgia (Burgmer et al., 2011; González-Roldán et al.,
2016; Brown, El-Deredy and Jones, 2014).
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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Here we have shown increased activation in the PD group within the pain network,
including, the Mid-cingulate cortex (MCC), Supplementary Motor Area (SMA) and
Precuneus. Importantly, the group effects seen during anticipation were
independent of the MDS-UPDRS, PCS and HADS emphasising that it is likely that the
underlying PD pathological process is causing the greater anticipatory activity
rather than the consequence of motor impairment, coping mechanisms and mood
state. The fact that the abnormal processing is independent of the MDS-UPDRS
supports the hypothesis reported in Silverdale et al., (2018) that the high
prevalence of chronic pain in PD is independent of the severity of the motor
impairments and could be due to top-down modulation of the pain signal.
Our results are consistent with a single previous fMRI study, using a very different
protocol which demonstrated that PwPD showed a significantly reduced activation
within the inhibitory descending pain pathway during the anticipation of pain and
an increased activation within the MCC during pain perception (Forkmann et al.,
2017). In combination with the results presented in this current study, we reason
that PwPD display maladaptive top-down modulation during pain processing which
may help to explain the high prevalence of pain within the PD community.
3.5.b. Regions activated during the anticipatory response
The location of the amplified anticipatory signal within the PD group during mid and
late anticipation was mainly, but not limited to, the SMA and the MCC. The SMA
and MCC have a role in the selection and planning of movements (Russo et al.,
2002; Morecraft and Van Hoesen, 1998; Rushworth et al., 2004), and are indicated
to be involved in the prediction of aversive events and the movement response
required (Morrison, Peelen and Downing, 2007). The SMA and the MCC have shown
overlapping functional connectivity during pain-processing and motor control
(Misra and Coombes, 2015), and enhanced activation within the SMA has been
associated with patients with phantom limb pain (Dettmers et al., 2001).
The MCC is a region involved in cognitive processing and is associated with the fear
response, pain processing and specific motor outcomes to painful stimuli (Vogt,
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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Berger and Derbyshire, 2003; Shackman et al., 2011; Böcker et al., 2001). The MCC
has previously been shown by Böcker et al., (2001) to be activated during
anticipation of threats and suggested to be the source of generating the slow-wave
anticipatory response (see figure 3.6.
Figure 3.6: Diagram from Böcker et al., (2001) which reports the grand average of the stimulus preceding
negativity associated with anticipating threatening stimuli. The data is representative of eleven participants
and show conclusions drawn from a 1-dipole source model. Böcker et al., (2001) concluded that the dipoles
clustered within the posterior of the medial frontal cortex, and show high similarity with loci of the LORETA
source localisation results shown in this study. Thereby validates the role of the MCC in the anticipation of
noxious stimuli.
Abnormal connectivity within the MCC is associated with patients with migraines
(Hubbard et al., 2014) and a heightened response within the MCC has been
reported in PwPD during pain perception (Forkmann et al., 2017). In addition, in an
extensive rodent study, the MCC showed to be central to the development of pain
hypersensitivity in the absence of peripheral noxious stimuli (Tan et al., 2017). The
study also demonstrated that silencing the MCC via optogenetic techniques,
partially reversed inflammatory hypersensitivity, thus highlighting the role of MCC
in development of chronic pain conditions (Tan et al., 2017).
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
88
Additionally, the MCC is also associated with reward processing (Hayden et al.,
2008; Pearson et al., 2009; Shackman et al., 2011), a process highly dependent on
dopaminergic signalling and hence may be important due to the depletion of
dopamine seen in PD. The reward pathway is linked to pain perception as it
encodes the inverse of a reward signal and is closely correlated to the opioid
system. Abnormal reward processing is a possible predictor of the development of
increased pain sensitivity (Nees and Becker, 2017; Nees et al., 2017), and provides a
potential explanation of why abnormalities in the MCC during pain perception are
present in PwPD.
Thus, the MCC and SMA are important for the processing of the pain signal and
provide a putative anatomical substrate for enhanced top-down modulation of
incoming pain signals.
3.5.c. Laser Evoked Potential
The LEP was not significantly different between the two groups. The subjective
experience of pain was standardised in both groups, such that participants
experienced what they subjectively considered to be a low and high pain. We
therefore did not expect a group difference in the LEP.
Although research has previously shown that an altered degree of anticipation can
modulate the LEP amplitude and pain unpleasantness, there is research by Clark et
al., (2008) which reported that the characteristics of the LEP, specifically the P2
peak, was not affected by the duration of the anticipation period, nor correlated to
the behavioural pain ratings. They concluded that the anticipatory response is more
closely aligned with attentional processing rather than intensity coding of the
stimulus. Hence the research is a potential explanation of how a difference during
the anticipatory period was reported, yet not the LEP.
3.5.d. Considerations of interpretation
The present study has used source localisation to successfully quantify the
anatomical origin of the activity recorded at scalp electrodes. These differences
were not directly correlated to significant changes at scalp-level EEG and thus needs
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
89
to be considered when interpreting these results. It is known that there is no simple
relationship between scalp and source activity, as a single electrode summates the
potentials generated within neural sources and is dependent on the combination of
the orientation, strengths and location of these potentials. Hence, as scalp
topographies are the product of the addition of multiple brain sources, it is difficult
to interpret them directly in relation to source localisation estimates. The
comparison of SD-over-trials between the groups did not report any differences,
and indicates that group differences in data variability (e.g. due to noise) is unlikely
to be a factor driving group effects at the source level. Hence we are confident that
despite not seeing scalp-level group differences, the significant differences reported
via source localisation are valid.
3.5.e. Clinical impact
The current perception of pain in PwPD is often considered to be a consequence of
peripheral symptoms such as rigidity and stooped posture. However, our findings
suggest the possibility that the treatment for chronic pain in PD could beneficially
incorporate alternative treatments such as mindfulness and cognitive behavioural
therapy (CBT). These treatments have been shown to reduce pain anticipation
(Brown and Jones, 2010), improve cognitive control of pain over time and reduce
the severity of chronic pain (Kabat-Zinn, Lipworth and Burney, 1985; Kabat-Zinn,
1982). Nevertheless, further investigations are essential to establish whether
cognitive therapies are a suitable treatment for pain in Parkinson’s disease.
3.6. CONCLUSION
In conclusion, Parkinson’s patients demonstrated enhanced anticipatory activity in
the pain network before an acute pain stimulus. Thus we have provided evidence
for enhanced top-down processing of pain in Parkinson’s disease, increasing the
evidence for abnormal central processing in this and other chronic pain conditions.
Our results contribute to the building knowledge of the relationship between
chronic pain and Parkinson’s disease; and inform a possible scientific rational for
novel treatment strategies in Parkinson’s pain, including mindfulness, cognitive
therapies and other treatments targeted at reducing top down processing of pain.
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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3.7. SUPPLEMENTARY MATERIALS
Table 3.3: Scalp-level SPM results for the F-contrast of Intensity (Low v High) for post-stimulus
TWOI [200 600]. Significant results were restricted to clusters of >100 voxels and significant
following FWE-correction, During the TWOI centred on the laser evoked potential (LEP) the data
shows an increased activation in the High condition compared to the low condition. Results are
presented at cluster- and peak-level, plus the coordinates of the peak-voxel within the cluster.
Cluster-Level Peak-Level Coordinates
P(FWE-corr) kE p(unc) p(FWE-corr) F Z p(unc) x,y,z {mm}
0.000 30153 0.000 0.000 86.31 Inf 0.000 8.5 -40.88 458
0.000 82.53 Inf 0.000 12.75 -40.88 478
0.000 81.25 Inf 0.000 -8.5 -51.63 538
0.000 2869 0.000 0.000 49.48 6.48 0.000 -42.5 -8.63 256
0.004 28.54 5.00 0.000 -51 -30.13 216
0.004 28.32 4.98 0.000 -55.25 -24.75 210
0.000 3096 0.000 0.000 45.45 6.23 0.000 -68 7.50 488
0.000 43.41 6.10 0.000 -68 7.50 472
0.000 42.59 6.05 0.000 -68 7.50 462
0.000 1833 0.000 0.000 43.34 6.10 0.000 4.25 -73.13 264
0.000 879 0.006 0.000 41.76 5.99 0.000 55.25 -67.75 428
0.000 37.83 5.72 0.000 63.75 2.13 434
0.000 35.11 5.52 0.000 59.5 -24.75 434
0.005 272 0.094 0.000 35.84 5.58 0.000 63.75 2.13 536
0.001 32.82 5.35 0.000 55.25 18.25 536
0.001 729 0.010 0.000 34.68 5.49 0.000 -51 34.38 898
0.000 1090 0.003 0.001 31.98 5.28 0.000 -59.5 18.25 740
0.001 31.49 5.24 0.000 -59.5 18.25 698
0.000 1174 0.002 0.001 31.58 5.25 0.000 -12.75 -73.13 954
0.003 29.65 5.09 0.000 -8.5 -73.13 976
0.002 493 0.030 0.001 31.57 5.25 0.000 -55.25 23.63 638
0.011 143 0.214 0.005 28.09 4.96 0.000 -55.25 29.00 796
0.007 211 0.136 0.006 27.69 4.93 0.000 55.25 18.25 674
0.020 24.48 4.64 0.000 63.75 2.13 704
0.005 275 0.092 0.007 27.30 4.89 0.000 59.5 7.50 772
0.008 186 0.159 0.010 26.29 4.80 0.000 55.25 18.25 832
0.011 139 0.221 0.010 26.19 4.79 0.000 17 -8.63 192
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
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Table 3.4: The source localisation results for the significant within-subject contrast of Intensity
(Low v High) during the post-stimulus time window [200 600 ms]. A threshold of clusters >100
voxels was set and results were restricted to FWE correction. The Anatomical Automatic Labelling
(AAL2) atlas was used to report the regions within the significant cluster for the group effect. All
re ions are reported in l din ‘Unkno n’. T e T-contrast shows that High intensity evoked a
higher activation in comparison to Low intensity.
Cluster-Level Peak-Level Brain Region
P(FWE) K F/T Z mm mm mm
Po
st-S
tim
ulu
s [2
00
60
0 m
s]
F-contrast
0.000 70986 55.34 6.80 20 -60 -12
Unknown (17.0%), Occipital_Mid_L (3.3%), Precuneus_R (3.0%), Fusiform_L (2.9%), Parietal_Inf_L (2.8%), Lingual_R (2.8%),
Lingual_L (2.6%), Fusiform_R (2.5%), Calcerine_L (2.2%), Postcentral_L(2.2%),
Parietal_Sup_L (2.2%), Calcerine_R (1.9%), Parietal_Sup_R (1.9%), Postcentral_R (1.8%),
Cingulate_Mid_L (1.7%), Temporal_Inf_L(1.7%), Occipital_Sup_R
(1.6%), Occipital_Sup_L (1.6%), Cuneus_L(1.5%), Occipital_Mid_R (1.5%),
SupraMarginal_L (1.5%), Precentral_L (1.5%), Angular_L (1.4%), Temporal_Sup_L
(1.4%), Insula_L (1.4%), Rolandic_Oper_R(1.3%), Cingulate_Mid_R (1.3%), Frontal_Inf_Tri_L (1.2%), Cuneus_R
(1.2%), Temporal_Inf_R(1.1%), Hippocampus_L (1.1%), Parietal_Inf_R (1.1%), Paracentral_Lobule_L (1.0%),
Rolandic_Oper_L (1.0%)
0.002 1290 21.29 4.32 26 24 28
Frontal_Mid_2_R (59.2%), Unknown (22.1%), Frontal_Sup_2_R (13.3%),
Precentral_R(5.3%)
T-contrast High>Low
0.000 43912 5.63 5.38 -32 -38 12
Unknown (20.0%), Precuneus_L (4.7%), Parietal_Inf_L (4.1%), Lingual_R (4.0%), Postcentral_L(3.8%), Fusiform_L (3.0%),
Postcentral_R (2.8%), Parietal_Sup_L (2.7%), Preentral_R (2.7%), Frontal_Inf_Tri_L (2.6%),
Calcerine_R (2.6%), Insula_L (2.4%), Lingual_L (2.3%), Precentral_L (2.2%), Cingulate_Mid_L (2.2%), Precuneus_R
(1.9%), Fusiform_R (1.9%), Angular_L(1.8%), Calcerine_L (1.7%), Paracentral_Lobule_L
(1.7%), Frontal_Inf_Oper_L (1.7%), Occipital_Mid_L(1.7%), Cingulate_Mid_R
3. A neurophysiological investigation of pain anticipation in Parkinson’s disease
92
Table 3.5: A detailed version of Table 3.2 to include all regions highlighted by the AAL2 toolbox.
The table displays the group effect on sources of early, mid and late anticipation-evoked
responses. A threshold of clusters >100 voxels was set and results were restricted to FWE
orre tion and t e p val e adj sted for m ltiple omparisons (p<0.0 5). Res lts labelled it ‘◊’
are used for figure 3.5 and correlation analysis. Results showing a trend are reported and signified
it †, and the uncorrected value (0.001) reported below. The Anatomical Automatic Labelling
(AAL2) atlas was used to report the regions within the significant cluster for the group effect. All
re ions are reported in l din ‘Unkno n’. = eft, R = Ri t, S p = Superior, Inf = Inferior.
Group Difference
Cluster-Level Peak-Level Brain Region
P(FWE) K F/T Z mm mm mm
Ea
rly
F-contrast 0.092
†
(0.033 Uncorr) 413 13.79 3.45 16 -22 -16
Unknown (48.4%), Hippocampus_R (47.5%),
Parahippocampal_R (4.1%)
T-contrast
PD>HC 0.028 757 3.71 3.63 16 -22 -16
Hippocampus_R (46.8%), Unknown (46.1%),
Amygdala (4.0%), Parahippocampal_R (2.9%),
Linqual_R (0.3%)
Mid
F-contrast 0.021◊ 763 27.41 4.89 14 -22 52
Supp_Motor_Area_R (52.7%) Cingulate_Mid_R
(33.6%), Paracentral_Lobule_R (10.1%),
Unknown (2.6%), Precuneus_R (1,0%)
0.003◊ 1233 19.77 4.16 -16 -38 38
Cingulate_Mid_L (41.0%) Supp_Motor_Area_L
(35.2%), Unknown (23.1%), Precuneus_L (0.2%),
Paracentral_Lobule_L (0.2%), Frontal_Sup_2_L
(0.1%), Cingulate_Ant_L (0.1%)
0.059†
(0.022 Uncorr) 529 19.02 4.08 -26 -34 38
Postcentral_L (67.5%), Unknown (19.3%),
Parietal_Inf_L (12.3%), Supramarginal_L (0.9%)
T-contrast
PD>HC 0.019 922 5.24 5.03 14 -22 52
Supp_Motor_Area_R (52.5%) Cingulate_Mid_L
(42.1%), Paracentral_Lobule_R (11.0%),
Unknown (3.7%), Precuneus (2.8%)
0.002 1706 4.45 4.31 -16 -38 38
Cingulate_Mid_L (42.1%) Supp_Motor_Area_L
(31.4%), Unknown (24.6%), Precuneus_L (0.7%),
Cingulate_Ant_L (0.4%), Frontal_Sup_Medial_L
(0.4%), Parecentral Lobule (0.2%),
Frontal_Sup_2_L (0.2%)
0.022 877 4.36 4.24 -26 -34 38 Postcentral_L (66.5%), Unknown (18.9%),
Parietal_Inf_L (13.0%), SupraMarginal_L (1.6%)
0.074†
(0.022 Uncorr) 457 4.13 4.03 14 -36 -14
Unknown (30.3%), Lingual_R (19.2%)
ParaHippocampal_R (16.3%) Cingulate_Post_R
(12.1%) Cerebellum_R (9.7%),
Hippocampus_R(4.9%), Precuneus_R (3.5%),
Thalamus_R (2.2%), Cerebelum_3_R (1.3%),
Vermis_4_5 (0.5%)
0.068†
(0.022 Uncorr) 568 4.05 3.95 14 -60 36
Precuneus_R (56.0%) Cingulate_Mid_R (10.4%)
Cingulate_Post_R (9.9%) Cuneus_R (7.4%),
Unknown (6.0%), Calcerine_R (5.6%),
Precuneus_L (3.0%), Cingulate_Post_L (0.7%),
Cuneus_L (0.7%), Calcerine_L (0.2%),
Occipital)Sup_R (0.2%)
La
te
F-contrast
0.000◊ 4001 22.15 4.40 -2 -22 54
Unknown (17.2%), Cingulate_Mid_R (16.5%)
Cingulate_Mid_L (16.1%) Precuneus (11.4%)
Supp_Motor_Area_L (10.3%) Cingulate_Post_L
(5.8%) Precuneus_L (5.6%) Paracentral_Lobule_L
(4.5%) Supp_Motor_Area_R (4.3%)
Paracentral_Lobule_R (4.1%) Cingulate_Post_R
(3.8%), Postcentral_R (0.2%), Parietal_Sup_R
(0.0%).
T-contrast
PD>HC
0.000 6223 4.71 4.55 -2 -22 54
Unknown (21.0%), Cingulate_Mid_R (14.5%)
Cingulate_Mid_L (13.2%) Precuneus_R (9.6%)
Precuneus_L (8.1%) Supp_Motor_Area_L (7.5%)
Cingulate_Post_L (4.7%), Calcerine_L (4.6%),
Supp_Motor_Area_R (4.1%),
Parecentral_Lobule_L (4.0%),
Paracentral_Lobule_R (2.9%), Cingulate_Post_R
(2.8%), Postcentral_R (1.3%), SupraMarginal_R
(0.8%), Cuneus_L (0.5%), Thalamus_L (0.2%),
Occipital_Sup_L (0.1%), Lingua_L (0.1%),
Parietal_Inf_R (0.0%), Parietal_Sup_R (0.0%).
Chapter 4
Central pain processing in people with Parkinson’s disease whilst on
medication
Sarah Martin, Anthony Jones, Christopher Brown,
Christopher Kobylecki, Wael El-Deredy, Monty Silverdale
4. Central pain processing in people with Parkinson’s disease whilst on medication
94
4.1. ABSTRACT
The administration of dopaminergic medication for Parkinson’s disease has
previously been shown to have analgesic qualities. Here we investigated the pain
processing of people with Parkinson’s disease whilst on their medication via EEG
recording and receiving acute noxious stimuli. We have previously shown that in
the same participants, whilst off their medication, there was a significant
amplification of the anticipatory processing within the mid-cingulate cortex
compared to healthy age-matched volunteers. This study reported that following
medication administration, the people with Parkinson’s disease remained
significantly more sensitive to the noxious stimulus in comparison to a healthy
cohort. However, no difference was seen in the brain activations between the PD
and healthy groups post medication administration. Therefore, in the PD
medication normalised the augmented anticipatory processing that was reported in
the patient cohort whilst off their medication.
4.2. INTRODUCTION
There is a higher prevalence of chronic pain in Parkinson’s disease (PD) than the
general population (Nègre-Pagès et al., 2008; Ozturk et al., 2016; Skogar & Lokk,
2016; Silverdale et al., 2018). Pain is often considered to be secondary to muscle
stiffness and reduced movement characteristic to PD, however, there is growing
evidence that altered brain activity during pain processing contributes to the
development of chronic pain.
The severity of pain in Parkinson’s has been shown to be worse in the ‘off’ state,
when medication is wearing off, and improved after taking medication (Skogar and
Lokk, 2016). Hence, the stabilisation of the dopamine levels following Parkinson’s
medication may regulate the dopamine-dependent processes involved in pain
processing. L-DOPA, a commonly used drug in PD, has been associated with
analgesic qualities (Shimizu et al., 2004a; Kernbaum and Hauchecorne, 1981; Ertas
et al., 1998; Nixon, 1975; Minton, n.d.; Sandyk et al., 1987; Cotzias et al., 1970). The
4. Central pain processing in people with Parkinson’s disease whilst on medication
95
role of dopamine in the brain is widespread and contributes to the affective and
attentional processing in pain (de Wied and Verbaten, 2001; Tiemann et al., 2014).
Dopamine has also shown to have analgesic properties (Wood, 2014) and is
considered to be a potential treatment of chronic pain conditions (Lawson, 2016;
Haddad et al., 2018).
Hence, we aim to investigate the processing of acute pain in people with
Parkinson’s disease (PwPD) whilst on their medication in comparison to healthy
age-matched controls. This investigation follows the report that PwPD whilst off
their medication were more sensitive to the noxious stimuli and showed
augmented anticipatory processing prior to acute pain (see Chapter 3). On the basis
that dopamine has shown analgesic qualities, we hypothesised that the Parkinson’s
medication, which increases dopamine, would reduce pain sensitivity and normalise
the amplified central processing seen whilst off their medication.
4.3. METHODS
4.3.i. Participants
Nineteen PD participants and twenty-one HC participants repeated the experiment
outlined in Chapter 3. However, within the PD cohort, five datasets were removed
due to noisy EEG data, and within the HC cohort, two datasets were removed due
to habituation to the laser, and eight datasets removed due to noisy EEG data. The
high degree of noise seen in the EEG was likely to be due to the EEG cap being kept
on for a long time. Therefore, fourteen PD participants and eleven HC participants
were included in the final analysis.
4.3.ii. Medication
The PD group were studied in their ON state after taking their medication at least 1
hour prior to experimental assessment. All participants were prescribed their usual
levodopa medication which increases dopamine activity.
4. Central pain processing in people with Parkinson’s disease whilst on medication
96
4.3.ii. Assessments
The motor section (III) of the Movement Disorder Society Unified Parkinson’s Rating
Scale (MDS-UPDRS) was completed to assess the PD participants’ symptom severity
whilst on their medication (Goetz et al., 2008). Other assessments also carried out
were the Hospital Anxiety and Depression HADS, Pain Catastrophizing Scale (PCS),
Montreal Cognitive Assessment (MoCA) and PD participants used a visual analogue
scale (VAS) to report their minimum and maximum intensity of chronic pain over
the last 6 months.
4.3.iii. Experimental design
4.3.iii.a. Pain stimuli
A CO₂ laser [50W Synrad 48-5 J-series (J-48-5(S)W) Wavelength: 10600nm] was
used to deliver acute pain to the dorsal surface of the right forearm. The CO₂ laser
delivered a beam with a diameter of 15 mm and 150 ms duration. The voltage (V) of
the laser is linearly related to the laser energy delivered to the forearm. For each
test, the stimuli were delivered in an area measuring 4 x 5cm and were delivered in
a predetermined randomised path (Brown et al., 2008a). This was to avoid
habituation, sensitization, or skin damage.
4.3.iii.b. Psychophysics
Before starting the experimental protocol, psychophysics was used to calibrate the
laser to the individual’s pain sensitivity. The procedure was carried out 1 hour post
medication administration. An ascending method of limits procedure was used,
starting from 0.6 V with 0.06 increments each time the laser was delivered. The
participant used an eleven point NRS (0 -10) to rate the intensity of the pain
perceived for each stimuli and the following description was provided; 0=no
sensation, 4=pain threshold, 7=moderately painful, 10=unbearably painful. The
rating scale was introduced to the participant via these standardised descriptives to
ensure that no explanation altered their interpretation of the scale. The procedure
was repeated three times to allow participants to get used to the laser and was
4. Central pain processing in people with Parkinson’s disease whilst on medication
97
used to calculate the average voltage to induce level 4 (just painful) and level 7
(moderate pain) . These two levels provided low and high stimuli intensity for the
main laser experimental protocol.
4.3.iii.c. Main experiment
The participants received 120 laser stimuli at the two intensities (low and high)
separated into four conditions; Low (level 4), High (level 7), Unknown Low (level 4)
and Unknown High (level 7). The unknown condition was to investigate the effect of
certain or uncertain anticipatory cues on neural activity and pain perception. To
investigate the anticipation of a painful stimulus, a 3 second auditory countdown
preceded the laser stimuli (see Figure 1). The first auditory cue was presented
concurrently with an anticipatory cue to indicate the forthcoming laser intensity
and to maintain focused attention. The participant was either shown ‘Low’, ‘High’
or ‘Unknown’. The presentation of the word ‘Unknown’ indicated that the laser
stimulus has an equal chance of being low or high. This was to investigate the
importance of certainty in the anticipation of the laser stimuli. The image was also
used as a visual fixation cue to discourage eye movements. After the laser stimuli,
the 0-10 numerical rating scale was shown on the screen and the participant rated
the intensity of the pain. The order of the stimuli was randomised and separated
into three blocks with short breaks in-between.
4.3.iii.d. EEG recording:
A BrainVision MR EEG cap was used to record from 63 scalp electrodes using a
BrainVision-cap system [Standard BrainCap-MR with Multitrodes]. The arrangement
of the electrodes was modelled on the extended 10-20 system. Recording
parameters were set at: Filter (DC to 70 Hz), Sampling rate (1000 Hz), Gain (500). To
reduce electrical interference, a 50Hz notch filter was applied. Prior to starting the
laser protocol, resting states were recorded with eyes open and closed for two
minutes in all participants. This ensured that the experience prior to the experiment
was identical. The three experimental blocks were recorded separately to allow for
better artefact rejection of the EEG data.
4. Central pain processing in people with Parkinson’s disease whilst on medication
98
4.3.iv. Analysis Methods
4.3.iv.a. Statistical analysis of behavioural data
Statistical analyses of the behavioural measures were carried out using IBM SPSS
Statistics 22 software. The questionnaires were all investigated for significant group
differences. Prior to using statistical tests, the data was assessed for normality using
a combination of Q-Q plots, histograms, and the values of skew and kurtosis.
Normally distributed data was analysed using independent t-tests and ANOVA tests,
whilst data reported to be not normally distributed, the appropriate non-
parametric test was utilised. Specific statistical tests for each analysis step are
reported in the results section.
4.3.iv.b. EEG analysis method
EEG pre-processing was carried out using EEGLAB toolbox (Delorme and Makeig,
2004) in MATLAB version R2015a (The Mathworks Inc) whilst statistical analysis was
carried out using SPM12 toolbox (Wellcome Department of Imaging Neuroscience,
Institute of Neurology, UCL, London, United Kingdom) running in MATLAB. The EEG
data was pre-processed for scalp and source localisation analysis. The main
motivation of the analysis was to establish the anatomical origin of the brain
activity using LORETA (Low Resolution Electromagnetic Tomography) source
localisation.
4.3.iv.c. EEGLAB Pre-processing
Pre-processing consisted of; removal and interpolation of bad channels, down-
sample to 500, low-pass filter of 20Hz and re-reference to the common average.
The four conditions were separated and -3500ms to 2000ms epochs extracted and
Linear detrend applied. Independent Component Analysis (ICA) was carried out on
all datasets using the SemiAutomatic Selection of Independent Components for
Artifact correction (SASICA) toolbox to select components to remove via pre-
determined thresholds. The thresholds were set to; Autocorrelation (threshold =
0.35 r, lag = 20 ms), Focal (threshold = 3.5 z), Focal trial (threshold 5.5 z), Signal to
4. Central pain processing in people with Parkinson’s disease whilst on medication
99
noise (period of interest (POI) = [0 Inf], baseline (BL) [-Inf 0], threshold ratio = 0.5),
and Adjust Selection enabled. The thresholds were sufficient to remove artefacts
from the majority of the datasets; however, a number of datasets required further
manual removal of eye-blink components where not picked up by SASICA.
4.3.iv.d. SPM EEG Analysis:
The pre-processed datasets were converted to Statistical Parametric Mapping
(SPM) compatible files. Statistical analysis was carried out to investigate the
anticipation evoked potentials and the post-stimulus LEPs using scalp-level and
source localisation analysis techniques available in the SPM toolbox.
SPM scripts for batch processing were used to analyse the EEG data at the scalp
level and source localisation. Two baselining methods were applied to the data
analysis for the anticipation phase and were applied for scalp-level and source
localisation analysis (see 2.1.b.ii). Primary analysis applied distinct baselines (BLs) of
500 ms, occurring prior to each of the three auditory cues respectively, to analyse
each of the three anticipation phases. The BLs and time window of interest (TWOI)
were as follows; Early [BL: -3500 ms -3000 ms: TWOI: -2500 ms -2000 ms], Mid [BL:
-2500 ms -2000 ms: TWOI: -1500 -1000 ms] and Late [BL: -1500 -1000 ms: TWOI: -
500 0 ms] anticipation phases. The secondary analysis method applied a single
baseline of 500 ms prior to the first auditory cue [BL: -3500 ms -3000 ms] that was
common to every TWOI anticipation phase (early, mid and late). This second
analysis was conducted to enable comparison to previous studies (Brown et al.,
2008a; Brown, El-Deredy and Jones, 2014). All statistical analysis for the
anticipation phase was adjusted for multiple comparisons.
All analysis for the post-stimulus phase TWOI [200 ms 600 ms], centred on the LEP,
was baseline corrected to -500 ms prior to the laser stimulus [BL: -500 ms 0 ms].
4.3.iv.e. Source Localisation analysis parameters
SPM12 EEG and MATLAB scripts were used to estimate the sources of the
anticipation- and laser-evoked potentials using LORETA. The forward model was
4. Central pain processing in people with Parkinson’s disease whilst on medication
100
created using an 8196 vertex template cortical mesh co-registered to the electrode
positions of the standard 10-20 EEG system. A three-shell boundary element model
(BEM) EEG head model available in SPM12 was used to compute the forward-
model. The images were smoothed with a 12mm full-width-at-half-maximum
(FWHM).
4.3.iv.f. EEG Analysis Statistical analysis
For the analysis of the anticipatory TWOIs, a three way repeated measures mixed
ANOVA was applied, with one between-subject factor of Group (HC vs PD) and two
within-subject factors of Certainty [Known (Low/High) v Unknown] and Expectation
[Low v High (Known)]. For the analysis of the post-stimuli TWOI, a three way
repeated measures ANOVA was applied, with one between-subject factor of Group
(HC v PD) and two within-subject factors of Certainty [Known (Low/High) v
Unknown] and Intensity [Low v High (Known and Unknown)].
Source localisation results were reported as follows. To control for multiple
comparisons, a cluster-forming threshold of p<0.001 was used and resulting
clusters were considered significant at FWE (p<0.05). Significant clusters were also
restricted to >100 voxels in size and regions labelled using the Anatomical
Automatic Labelling (AAL2) toolbox in SPM.
4. Central pain processing in people with Parkinson’s disease whilst on medication
101
4.4. RESULTS
4.4.i. Behavioural Results
4.4.i.a. Questionnaires
The PCS was significantly higher in the PD group (11.18±10.66, mean±SD) compared
to the HC group (4.36±4.11), a statistically significant difference of 6.81(95% CI,
13.35 to 0.28), t(23)=-2.194, p=0.042. Hence, indicating a higher level of pain
catastrophizing behaviours in PD. In contrast, there were no statistically significant
differences in the scores of the HADS or MoCA between the HC and PD groups.
The MDS-UPDRS Motor score (III) was recorded 1 hour after medication
administration within the PD group to be 20.75±7.70 (mean±SD). The score had
reduced from the score recorded during the PD participants’ ‘off’ state
(36.42±15.84) demonstrating symptom improvement.
Within the PD group, the level of current pain recorded via a VAS was compared to
their baseline current pain level whilst they were off their medication. A 2-tailed
paired t-test showed that within those with chronic pain n=8, there was a
significant reduction in their current pain levels (Mean±SD: 15.85±23.15 to
8.75±19.63) T(1, 7)=2.486, p=0.041. .
4.4.i.b. Laser Sensitivity
The voltage of the laser was calibrated to the participants’ subjective perception of
the intensity. An independent-samples t-test was carried out on the two pain
intensities to determine any differences in sensitivity between the HC and PD group
(Figure 4.1). The voltage to induce low pain was lower in PD (1.48±0.36; mean±SD)
than the HC group (1.75±0.24), a statistically significant difference of 0.27 (95% CI,
0.01 to 0.52), t(23)=2.13, p=0.045. The voltage to induce high pain was assessed via
a Welch test due to equal variances not assumed assessed by Levene’s Test for
Equal Variances (p=0.033). The result showed that the voltage required to induce
high pain was also lower in PD (mean±SE: 1.95±0.44) compared to HC (2.25±0.25).
4. Central pain processing in people with Parkinson’s disease whilst on medication
102
The comparison was a statistically significant difference of 0.14 (95% CI, 0.01 to
0.59), t(21.32)=2.12, p=0.046.
Figure 4.1: The laser voltage required to induce a low and high pain intensity. The PD group were more
sensitive to the laser stimuli as they required a significantly lower voltage level of the laser to induce a high
pain. The data is separated into HC (blue) and PD (red), and for Low and High pain. The boxes show the
median (bold horizontal line), and lower and upper quartiles. The vertical lines show the minimum and
maximum values. * denotes significance p<0.05.
4.4.i.c. Pain rating
The rating of the laser stimuli (Table 4.1) was assessed using a mixed-ANOVA to
determine any effect of the certainty of the visual cues and the intensity of the
laser, and any differences between the PD and HC group. Firstly, there was no
difference in pain rating between the PD and HC groups [F(1, 23)=0.738, p=0.992],
nor an effect of certainty on pain ratings. As expected, the rating of the high
intensity laser stimuli was significantly higher than low intensity [F(1, 23)= 108.7,
p<0.000].
There was a significant effect of Certainty (Known v Unknown) on Intensity (Low v
High), [F(1, 23)=34.91, p<0.000], such that the “Unknown” visual cue resulted in a
higher rating of the Low intensity, yet a lower rating of the high intensity of the
0
1
2
3
L a s e r v o lta g e to in d u c e L o w a n d H ig h
in te n s ity in H C a n d P D
La
se
r V
olt
ag
e (
V)
L o w H ig h
*
*
H C
P D
4. Central pain processing in people with Parkinson’s disease whilst on medication
103
laser (Figure 4.2). This manipulation of pain rating has previously been shown
(Chapter 3, (Brown et al., 2008a)) and is thus a good validation of the experimental
design. There was no difference seen between the two groups in the effect of
Certainty and Intensity.
Table 4.1: The rating of the laser stimuli for each laser condition and each group.
Figure 4.2: The effect of certainty on the pain rating scores. The graph shows the averaged pain
ratings for both HC and PD groups. The effect of the uncertain visual cue ‘Unknown’ resulted in a
higher rating of the low intensity compared to the known ‘Low’ visual cue. In contrast, the rating
of the high laser intensity was lower when preceded by the ‘Unknown’ cue rather than the certain
‘ i ’ vis al e. T is manip lation of pain ratings by certainty has been reported previously and
is a good validation of experiment design. The boxes show the median (bold horizontal line), and
lower and upper quartiles. The vertical lines show the minimum and maximum values. The
patterned boxes denote the Unknown cues which denote uncertain anticipation. UnLow =
Unknown Low, UnHigh = Unknown High. *** denotes significance p<0.001.
0
2
4
6
8
T h e c o n tra s t in g e ffe c t o f c e r ta in ty o n lo w a n d h ig h in te n s ity
La
se
r V
olt
ag
e (
V)
L o w H ig hU n L o w U n H ig h
* * *
HC PD
Laser Condition Mean SD Mean SD
Pai
n R
atin
g
Low 2.34 0.55 2.17 0.99
High 4.92 1.09 4.90 1.87
Unknown Low 2.76 0.81 2.56 1.16
Unknown High 4.25 1.03 4.63 1.65
4. Central pain processing in people with Parkinson’s disease whilst on medication
104
4.4.ii. EEG Results
The EEG recording was analysed to determine whether there were any differences
between the HC and PD whilst anticipating and perceiving the laser stimuli. During
anticipation, scalp-level analysis reported no difference in Group (HC v PD),
Certainty (Known v Unknown) or Expectation (Known Low v Known High). During
the post-stimulus time window centred on the LEP, there was no difference
between Group (HC v PD), or an effect of Certainty (Known v Unknown). However,
there was a significant effect of Intensity (Low v High), such that the higher laser
intensity evoked a higher degree of activity [kE=5101, p(FWE)=0.000, t=5.99, [-4 -52
mm], 518ms]. The cluster was located on the top of the head over dorsal regions.
The source localisation analysis showed no group differences in brain activity
between the PD and HC groups during both the anticipation and perception of the
laser stimuli. An expectation effect was seen in both PD and HC groups, such that a
higher degree of activity was seen during anticipation of high stimuli compared to
low stimuli. The increased activity during expectation of high stimuli was seen
within the left superior parietal lobe [kE=656, p(FWE)=0.042, t=4.63 [-26 -54
64mm]] during early anticipation, and higher activity within the left postcentral
gyrus during mid anticipation [kE=1620, p(FWE)=0.002, t=4.24 [-36 -22 36mm]]
(baselined to -2500 -2000 ms).
4.5. DISCUSSION
The results presented in this report contribute further to the findings described in
the Chapter 3. Here we have shown that the PD patients whilst on their medication
did not show any significant differences in central pain processing compared to
healthy controls, yet were more sensitive to the laser stimuli than controls.
The results in this study indicate that the Parkinson’s disease medication could
normalise the amplified anticipatory response seen following withdrawal of their
medication (see Chapter 3). The reduction in their current pain levels demonstrate
a potential analgesic quality and supports the theory that the lack of dopamine
4. Central pain processing in people with Parkinson’s disease whilst on medication
105
results in heightened pain sensitivity. Nevertheless, the direct comparison of
behavioural and neural responses whilst PD participants were off and on mediation
was not completed due to the confounding effect of protocol repetition alongside
medication administration. Thereby caution must be taken when suggesting
normalisation of the PD response to the HC response. The result was in agreement
with our prediction that the Parkinson’s medication, which increases dopamine,
would normalise the amplified central processing seen in the off state. This finding
suggests that the amplification of the anticipatory processing seen in the mid-
cingulate cortex whilst PD patients are off their medication is due to the reduced
level of dopamine.
Interestingly, we have shown that the PD participants were significantly more
sensitive to the laser stimuli as they required a lower laser voltage to induce both
low and high pain. This further supports the same finding in Chapter 3, and
contributes to the evidence of increased pain sensitivity in Parkinson’s and that the
increased pain sensitivity in PwPD is due to the dysfunction of other
neurotransmitter systems (Silverdale et al., 2018; Tinazzi et al., 2009). In contrast,
previous research on the effect of PD medication on pain tolerance of
experimentally induced pain has shown a reduction of sensitivity. Assessments of
cold pain tolerance and the nociceptive flexion reflex was shown to be lower in PD
patients off their medication, yet equal to healthy controls following taking their
medication (Brefel-Courbon et al., 2005; Gerdelat-Mas et al., 2007).
Dopamine’s role in pain processing is not considered to be solely specific to
intensity coding, instead it is known to be fundamental to the precision of
prediction coding of changing sensory inputs (Schwartenbeck et al., 2015; Friston et
al., 2014, 2012), which includes cognitive processing (Nieoullon, 2002) and salience
assignment (Borsook et al., 2013; Shiner et al., 2015; Mitsi and Zachariou, 2016).
The role of dopamine in the development of chronic pain has been shown to be due
to dysfunctional dopaminergic pathways affecting these processes (Wood, 2004;
Jarcho et al., 2012; Bushnell, Ceko and Low, 2013; Hagelberg et al., 2004). The
anticipatory response to the pain is highly likely to be dependent on dopaminergic
4. Central pain processing in people with Parkinson’s disease whilst on medication
106
transmission due to the role of attention, cognition and salience assignment
processes occurring prior to pain perception. Therefore, the repletion of the
dopamine via PD medication has shown to be sufficient to normalise the
anticipatory response seen in the ‘off-state’ (Chapter 3)
The high prevalence of chronic pain in the PD population, and the consistent
increase in pain sensitivity that we have seen in this study, demonstrates that PD
medication is not sufficient to prevent or reverse chronic pain in PD. It could be
argued that the fluctuating dopamine levels caused by PD physiology and the on-off
states caused by the PD medication causes long-term changes within key brain
regions involved in pain processing, predisposing and maintaining chronic pain.
Nevertheless, a confident conclusion is not possible when interpreting the results
from this study due to the limitations of the numerous uncontrolled variables which
may have resulted in a lack of a significant difference between the PD and HC
groups. Firstly, the type of PD medication was variable within the PD participants,
and no placebo medication was administered to the HC group to account for a
placebo effect of medication administration. In addition, the current level of pain
within the PD cohort was reduced following medication and may have had an effect
on neural activation. Furthermore, the number of participants who successfully
completed the study on their medication was lower than the cohort within the off-
medication study (Chapter 3). One reason of not taking part of the on-medication
aspect of the study was tiredness due to being off their medication. Hence, the final
cohort for the on-medication aspect of the study may have been biased to patients
with less severe symptoms as they did not tire as much as those with severe off-
state symptoms. This will have resulted in an imperfect representation of the
disease within the PD group.
4.6. CONCLUSION
The anticipation of pain is regarded to be a useful technique to investigate top-
down modulation of pain and has been shown to be dysfunctional in chronic pain
4. Central pain processing in people with Parkinson’s disease whilst on medication
107
conditions. The processes associated with top-down modulation of pain include
processes such as salience assignment, the orientation of attention, and cognitive
processing, which are all modulated be dopaminergic transmission. Therefore, it
would be expected that PD patients show altered anticipatory neural activity to
pain whilst off their medication, which is normalised following dopaminergic
medication. This study provides potential evidence to support this theory and
further highlights the role of dopamine dysfunction in the development of
persistent pain in PD. This study also concludes that the PD patients had a reduced
tolerance to the pain stimuli, even with being on their medication, and contributes
to the prior knowledge of increased sensitivity in the PD population.
Chapter 5
An investigation of the role of the dopamine D2 receptor in pain
perception
Sarah Martin, Anthony Jones, Christopher Brown,
Christopher Kobylecki, Monty Silverdale
5. An investigation of the role of the dopamine D2 receptor in pain processing
109
5.1. ABSTRACT
Striatal dopamine dysfunction has been shown in a number of chronic pain
conditions and is associated with altered top-down modulation of pain processing.
Unlike the widespread abundance of the dopamine D1 receptor, the dopamine D2
receptor is confined primarily to the striatum and therefore, a focus within our
research. Here we investigated the effect of modulating striatal dopamine D2
receptor activity on the anticipation and perception of acute pain stimuli whilst
brain activity was recorded by EEG. Participants completed the experiment under
three conditions; control (Sodium Chloride, 20 mg), D2 receptor agonist
(Cabergoline, 1.25 mg) and D2 receptor antagonist (Amisulpride, 400 mg) in a
repeated-measures, double-blind study. The main experiment included the
presentation of cues which allowed for certain or uncertain prediction of two pain
intensity levels. Our results demonstrate that the agonist and antagonist did not
affect pain sensitivity. However, the antagonist amplified the effect of certainty on
pain rating. The EEG recording showed that the agonist and antagonist induced a
significant reduction in brain activation within the right temporal and parietal
regions during the anticipation of the pain. In addition, during the perception of
pain, the antagonist evoked a reduction in activity within the right insula and
inferior temporal lobe in comparison to the control and agonist conditions. This
study highlights that the D2 receptors are involved in the anticipatory processing
which occurs prior to the receipt of pain stimuli, and supports the notion that
dopamine has a role in the processing of uncertain predictions.
5. An investigation of the role of the dopamine D2 receptor in pain processing
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5.2. INTRODUCTION
5.2.a. Dopamine and pain
There is evidence to show that there is a relationship between pain processing and
dopaminergic transmission. However, the role of dopamine in coding the intensity
of acute pain is unlikely to be its main role in pain perception. The majority of
dopaminergic neurons do not respond directly to aversive stimuli, with only a small
percentage (5-15%) reportedly activated during pain perception (Taylor et al.,
2016). Instead, the role of dopamine has been theorised to be involved in
processing salient stimuli within the environment (Cooper and Knutson, 2008; Wulff
et al., 2019; Gentry, Schuweiler and Roesch, 2018; Roughley and Killcross, 2019;
Nieoullon, 2002), assignment of emotional valence (Jensen et al., 2003), and to
modulate sensorimotor and motivation-related processes (Horvitz, 2002;
Bromberg-Martin, Matsumoto and Hikosaka, 2010).
There is evidence that dopamine has endogenous analgesic qualities (Bhagyashree
et al., 2017; Wood, 2014; Esposito et al., 1986). However, although drugs which
increase dopamine have been demonstrated to have analgesic properties (Shimizu
et al., 2004b; Nixon, 1975; Cobacho, de la Calle and Paíno, 2014), they are rarely
prescribed to treat pain, especially not for acute pain. On the basis of dopamine’s
widespread modulatory role in higher cortical processing, it is likely that the
analgesic qualities of dopamine are related to the improvement in the cognitive and
affective aspects of pain processing, rather than directly reducing pain intensity.
Central processing (i.e. attention, emotional state, and motivational state) can
manipulate pain perception and are regarded as top-down processes, whereby
subjective factors can modulate pain perception. The top-down modulation
(subject-driven) occurs in parallel with the bottom-up factors of pain processing
(stimulus-driven), including stimulus novelty and physical saliency. A prominent
theory of dopamine function outside of pain processing, is that it acts within a
Bayesian framework which codes the precision in predicting desired or expected
outcomes based on prior beliefs, thereby serving a top-down function (Friston et
5. An investigation of the role of the dopamine D2 receptor in pain processing
111
al., 2014; FitzGerald, Dolan and Friston, 2015). Altered top-down processing has
been associated with chronic pain conditions such as Fibromyalgia (Miron, Duncan
and Bushnell, 1989a; Burgmer et al., 2011; Brown, El-Deredy and Jones, 2014;
González-Roldán et al., 2016; Reicherts et al., 2017). Dopamine plays a central role
in modulating a wide variety of processes such as attention flexibility (van Holstein
et al., 2011; Klanker, Feenstra and Denys, 2013; Whitaker, 2017), salience
assignment (Nieoullon, 2002; Kapur, Mizrahi and Li, 2005; Howes and Nour, 2016;
Shiner et al., 2015; Parr and Friston, 2017), motivational states (Pultorak et al.,
2018; Elliot and Covington, 2001) and emotional processing (Salimpoor et al., 2011;
Baixauli, 2017; Peciña et al., 2013), and thus demonstrates the potential
detrimental affect dopamine dysfunction would have on top-down modulation of
pain. For example, impaired dopaminergic transmission has been associated with
chronic pain conditions (Jääskeläinen et al., 2001; Wood et al., 2007b; a) such as
Fibromyalgia (Häuser, 2016; Albrecht et al., 2016) and atypical facial pain
(Hagelberg et al., 2003a), plus it is likely to contribute to pain in Parkinson’s disease
(Blanchet and Brefel-Courbon, 2018; Silverdale et al., 2018).
5.2.b. Dopamine Receptors & Pain Perception
The dopaminergic activity within the striatum has been associated with modulation
of pain perception, especially the activity of the D2 dopamine receptor (D2R)
(Barceló, Filippini and Pazo, 2012; Hagelberg et al., 2002). For instance, direct
injection of the D2R agonists apomorphine or quinpirole into the striatum has
shown to reduce pain related behaviours within rodents, whilst a D1R agonist did
not induce any change in response (Barceló, Filippini and Pazo, 2012; Cobacho, de
la Calle and Paíno, 2014). Within humans, PET imaging has shown that striatal D2R
binding potential is inversely correlated with an individuals’ pain tolerance of the
cold pressor test (CPT) (Hagelberg et al., 2002) and heat thermode (Martikainen et
al., 2005) and their ability to suppress pain (Hagelberg et al., 2002).
The research into D2R’s role in pain has primarily been focused on pain tolerance
and brain activity during the receipt of noxious stimuli. Therefore, the role of the
D2R in the top-down modulation of pain processing is yet to be investigated. While
5. An investigation of the role of the dopamine D2 receptor in pain processing
112
the role of the D2Rs during the anticipation of noxious stimuli has not been
investigated, their role in anticipating rewarding stimuli has been conducted.
Research by Ye et al., (2011), targeted the striatal presynaptic D2/D3 autoreceptors
to reduce dopamine release via the agonist Pramopexole1, and highlighted an
increase in striatal activity during the anticipation of rewarding stimuli. They also
reported a reduction in the connection between the striatum and prefrontal cortex,
and concluded that manipulation of dopamine induced a reduction in top-down
control of impulsive behaviours.
5.2.c. Aim of study
In summary, the purpose of this study was to establish whether the D2R had a role
in top-down modulation of pain perception. To investigate this, a D2R agonist
(Cabergoline) and antagonist (Amisulpride) was chosen for their selectivity for the
D2R. Using EEG source localisation analysis we aimed to investigate changes in
brain activity during the anticipation and perception of a noxious stimuli following
D2R manipulation. Due to the role of dopamine in salience assignment, predicting
outcomes and attentional flexibility, the experimental paradigm was configured to
evaluate the effect of expectation of both low and high pain stimuli, and certain
and uncertain anticipation of the forthcoming stimuli intensity.
1 Pramopexole was given at a dose which activated the presynaptic auto-receptors and resulted in a
reduction of dopamine synthesis and release from the presynaptic neuron. In turn, there was a reduction in the activation of the post-synaptic dopamine receptors.
5. An investigation of the role of the dopamine D2 receptor in pain processing
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5.3. METHODS
5.3.a. Ethical approval
The study was approved by the University of Manchester Ethics Committee and was
the UK Health Research Authority for the use of a National Health Service (NHS) site
(Salford Royal NHS Foundation Trust Hospital).
5.3.b. Drug selection
To target the striatum, we selected a D2R agonist, Cabergoline, and a D2R
antagonist, Amisulpride, which both have a high affinity for D2Rs. Previous studies
have shown that at low doses of D2R drugs, the binding of the presynaptic D2
autoreceptor is favoured and produces the opposite of the desired results (Maruya
et al., 2003; Ford, 2014). Therefore, a sufficiently high dose was selected to
modulate the postsynaptic D2R despite the action of the autoreceptors.
5.3.c. Participant recruitment
A total of twenty-nine healthy participants (16 females) were recruited for the
study (mean age 22.4 years, SD 3.2 years). All subjects gave written informed
consent. The exclusion criteria of the study was as follows; history of significant
head injury or seizures, diagnosed or taking medication for any neurological or
psychiatric condition, history of drug or alcohol dependence, use of psychotropic
medication within the past 6 months, use of dopaminergic drug within the past
month or lifetime use exceeding 3 months, pregnant or breastfeeding or
attempting to conceive (females only), suffering from chronic pain.
All participants also completed the Barratt Impulsivity Scale (BIS-11, Patton et al.,
1995) subscale of ‘cognitive instability’. The degree of impulsivity has shown to be
related to dopaminergic transmission and receptor abundance within the striatum
(Bucker and Theeuwes, 2014; Costa et al., 2013; Buckholtz et al., 2010; Dalley and
Robbins, 2017). The participants’ were only recruited if their score was within 1.5
standard deviations of the mean reported from a large control sample (1,577
healthy adults) (Stanford et al., 2009).
5. An investigation of the role of the dopamine D2 receptor in pain processing
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We used a repeated-measures, double-blind and triple-crossover design such that
all participants were recruited to attend three visits and complete the experimental
protocol following ingestion of the agonist, antagonist and control substance. Two
participants did not complete all three visits of the study, and one participant was
removed due to an adverse reaction to Amisulpride. Therefore, twenty-six datasets
were included for behavioural and EEG analyses (mean age 22.2 years, SD 3.7 years,
14 females).
5.3.d. Health Screening
Before the first experimental visit, the participants attended a health screening to
deem them safe to take part in the study. A medical doctor assessed the
participants’ heart rate, blood pressure, temperature and recorded an
electrocardiogram (ECG). Due to the potential risk of Amisulpride causing
arrhythmias, all participants were assessed for QTc abnormalities in the ECG prior
to inclusion in the study. No participant presented with any abnormalities resulting
in the exclusion from the study. A letter to the participants’ general practitioner
(GP) was also organised to inform them of the participants’ involvement in the
study.
5.3.e. Prior to experimental visits
Participants were instructed to not consume alcohol for 24 hrs prior to the visit, to
only drink their normal intake of coffee or tea on the morning of each visit, and to
refrain from consuming other caffeinated drinks 2 hours prior to each visit.
Participants were also informed to not consume any psychoactive substances for
the duration of the study, or any over-the-counter medicine 48 hrs prior to each
visit.
5.3.f. Experimental Visit Protocol
5.3.f.i. Drug administration
One of three substances was administered to the participant at each experimental
visit; Cabergoline (Agonist) (1.25 mg), Amisulpride (Antagonist) (400 mg), or Sodium
5. An investigation of the role of the dopamine D2 receptor in pain processing
115
Chloride (Control) (20mg). All drugs hold a full product licence (EU). The
administration of the drug2 was double-blinded and each participant had each drug
condition once over the three visits in a randomised order. The three visits were
separated by at least ten days. The administration of the drug was recorded as time
0 hr and experimental testing commenced at 3 hr 30 mins post drug administration.
5.3.f.ii. Resting state
Previous research has indicated a potential correlation between the level of striatal
dopamine and the rate of blinks per minute (KARSON, 1983; Elsworth et al., 1991).
The blink rates of the participants were recorded using frontal EEG electrodes
during a resting state of nine minutes at +2 hours after taking the drug and prior to
any experimental testing. The participants were not informed that their blink rate
was being assessed to avoid affecting their spontaneous blink rate.
5.3.f.iii. Pain stimuli
A CO₂ laser [50W Synrad 48-5 J-series (J-48-5(S)W) Wavelength: 10600nm] was
used to deliver acute pain to the dorsal forearm surface (Brown, El-Deredy and
Jones, 2014). The CO₂ laser delivered a beam with a diameter of 15 mm and 150 ms
duration. For each test, the stimuli were delivered in an area measuring 4 x 5cm
and were delivered in a predetermined randomised path (Brown et al., 2008a). This
was to avoid habituation, sensitization, or skin damage.
5.3.f.iv. Psychophysics
Before starting the experimental protocol, psychophysics was used to calibrate the
laser to the individual’s pain sensitivity. An ascending method of limits procedure
was used, starting from 0.6 V with 0.06 increments each time. The participant used
an eleven point number rating scale (NRS) (0 - 10) to rate the intensity of the pain
perceived for each laser stimuli (0=no sensation, 4=pain threshold, 7=moderately
painful, 10=unbearably painful). The rating scale was introduced to the participant
2 The term drug or drug condition is in reference to all three substances; Control, Agonist and
Antagonist, unless specified.
5. An investigation of the role of the dopamine D2 receptor in pain processing
116
via these standardised descriptives to ensure that no explanation altered their
interpretation of the scale. The procedure was repeated three times to allow
participants to get used to the laser and was used to calculate the average voltage
to induce level 4 (low) and level 7 (moderate) pain. These two levels provided ‘low’
and ‘high’ pain stimuli for the laser protocol.
5.3.f.v. Main experiment
The participants received 120 laser stimuli at the two intensities (low and high)
separated into four conditions; Low (level 4), High (level 7), Unknown Low (level 4)
and Unknown High (level 7). To investigate the anticipation of a painful stimulus, a
3 second auditory countdown preceded the laser stimuli (see Figure 5.1). The first
auditory cue was presented concurrently with a visual anticipatory cue to indicate
the forthcoming laser stimuli. The participant was either shown ‘Low’, ‘High’ or
‘Unknown’. The presentation of the word ‘Unknown’ indicated that the laser
stimulus has an equal chance of being low or high. This was to investigate the
importance of certainty in the anticipation of the laser stimuli. The image was also
used as a visual fixation cue to discourage eye movements. After the laser stimuli,
the 0-10 numerical rating scale was shown on the screen and the participant rated
the intensity of the pain via a numerical keypad. The order of the stimuli was
randomised and separated into three blocks with short breaks in-between.
Following each block, the participants rated the unpleasantness on average for
each laser condition. Unpleasantness was scored using an 11-point NRS whereby 0
is not unpleasant and 10 is most unpleasant sensation.
5. An investigation of the role of the dopamine D2 receptor in pain processing
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Figure 5.1: A schematic diagram of a single trial of the experimental paradigm. A computer monitor showed
the participant a visual cue of; Low, High or Unknown from -3s to +2s. A three second countdown of beeps at
-3, -2 and -1 allowed accurate anticipation of the laser stimuli at time 0s (red bar). The presentation of the
visual cue at -3s was concurrent with the first auditory cue. The visual cue was consistent throughout the
anticipation and laser stimulus. At +2s, an eleven-point number rating scale (NRS) was presented for the
participant to rate the laser stimulus. For each condition (Low, High, Unknown Low and Unknown High) there
were thirty trials. The trials were presented in a randomised order and divided into three blocks of forty
trials.
5.3.f.vi. EEG recording
A BrainVision MR EEG cap was used to record from 63 scalp electrodes using a
BrainVision-cap system [Standard BrainCap-MR with Multitrodes]. The arrangement
of the electrodes was modelled on the extended 10-20 system. Recording
parameters were set at: Filter (DC to 70 Hz), Sampling rate (1000 Hz), Gain (500). To
reduce electrical interference, a 50Hz notch filter was applied. Prior to starting the
laser protocol, resting states were recorded with eyes open and closed for two
minutes in all participants. This ensured that the experience prior to the experiment
was identical. The three experimental blocks were recorded separately to allow for
better artefact rejection of the EEG data.
5.3.g. Analysis Methods
5.3.g.i. Statistical analysis of behavioural data
Statistical analyses of the behavioural measures were carried out using IBM SPSS
Statistics 22 software. Prior to using statistical tests, the data was assessed for
normality using a combination of Q-Q plots, histograms, and the values of skew and
kurtosis. Normally distributed data was analysed using independent t-tests and
ANOVA tests, whilst for data reported to be not normally distributed, the
Visual Cue: Low / High / Unknown
-3 -2 -1 0 2 8
Rating Scale
Time (s)
5. An investigation of the role of the dopamine D2 receptor in pain processing
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appropriate non-parametric test was utilised. Specific statistical tests for each
analysis step are reported in the results section.
5.3.g.ii. EEG analysis method
EEG pre-processing was carried out using EEGLAB toolbox (Delorme and Makeig,
2004) in MATLAB version R2015a (The Mathworks Inc) whilst statistical analysis was
carried out using SPM12 toolbox (Wellcome Department of Imaging Neuroscience,
Institute of Neurology, UCL, London, United Kingdom) running in MATLAB.
5.3.g.iii. Blink rate
For each participant and visit, the number of blinks per minute was calculated using
a MATLAB script3. The shape of waveform and topography was used to detect
blinks within the resting state data. A script which used the ICA function within the
EEGLAB toolbox was applied to count the blinks. The blinks per minute were
reported as the blink rate and were assessed for differences via repeated measures
ANOVA.
5.3.g.iv. EEGLAB Pre-processing
Pre-processing consisted of: removal and interpolation of bad channels, down-
sample to 500, low-pass filter of 20Hz and re-reference to the common average.
The four conditions were separated and -3500ms to 2000ms epochs extracted and
Linear detrend applied. Independent Component Analysis (ICA) was carried out on
all datasets using the SemiAutomatic Selection of Independent Components for
Artifact correction (SASICA) toolbox to select components to remove via pre-
determined thresholds. The thresholds were set to; Autocorrelation (threshold =
0.35 r, lag = 20 ms), Focal (threshold = 3.5 z), Focal trial (threshold 5.5 z), Signal to
noise (period of interest (POI) = [0 Inf], baseline (BL) [-Inf 0], threshold ratio = 0.5),
and Adjust Selection enabled. The thresholds were sufficient to remove artefacts
3 The MATLAB script was written and executed by Dr Grace Whitaker and Professor Wael El-Deredy.
All participants within this experiment were part of a wider study in collaboration with Dr Whitaker. The resting state data and assessment of the blink rate was a validation step in the all elements of the study and hence the data shared.
5. An investigation of the role of the dopamine D2 receptor in pain processing
119
from the majority of the datasets; however, a number of datasets required further
manual removal of eye-blink components where not picked up by SASICA.
5.3.g.v. SPM EEG Analysis
The pre-processed datasets were converted to Statistical Parametric Mapping
(SPM) compatible files. Statistical analysis was carried out to investigate the
anticipation-evoked potentials and the post-stimulus LEPs using scalp-level and
source localisation analysis techniques available in the SPM toolbox.
SPM scripts for batch processing were used to analyse the EEG data at the scalp
level and source localisation. Two baselining methods were applied to the data
analysis for the anticipation phase and were applied for scalp-level and source
localisation analysis (see 2.1.b.ii). Primary analysis applied distinct baselines (BLs) of
500 ms, occurring prior to each of the three auditory cues respectively, to analyse
each of the three anticipation phases. The BLs and time window of interest (TWOI)
were as follows; Early [BL: -3500 ms -3000 ms: TWOI: -2500 ms -2000 ms], Mid [BL:
-2500 ms -2000 ms: TWOI: -1500 -1000 ms] and Late [BL: -1500 -1000 ms: TWOI: -
500 0 ms] anticipation phases. The secondary analysis method applied a single
baseline of 500 ms prior to the first auditory cue [BL: -3500 ms -3000 ms] that was
common to every TWOI anticipation phase (early, mid and late). This second
analysis was conducted to enable comparison to previous studies (Brown et al.,
2008a; Brown, El-Deredy and Jones, 2014). All statistical analysis for the
anticipation phase was adjusted for multiple comparisons.
All analysis for the post-stimulus phase TWOI [200 ms 600 ms], centred on the LEP,
was baseline corrected to -500 ms prior to the laser stimulus [BL: -500 ms 0 ms].
5.3.g.vi. Source Localisation analysis parameters
SPM12 EEG and MATLAB scripts were used to estimate the sources of the
anticipation- and laser-evoked potentials using Low Resolution Electromagnetic
Tomography (LORETA). The forward model was created using an 8196 vertex
template cortical mesh coregistered to the electrode positions of the standard 10-
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120
20 EEG system. A three-shell boundary element (BEM) EEG head model available in
SPM12 was used to compute the forward-model. The images were smoothed with
a 12mm full-width-at-half-maximum (FWHM).
Source localisation results were reported as follows; to control for multiple
comparisons, a cluster-forming threshold of p<0.001 was used and resulting
clusters were considered significant at FWE (p<0.05). Significant clusters were also
restricted to >100 voxels in size and regions labelled using the Anatomical
Automatic Labelling (AAL2) toolbox in SPM. Small volume correction (SVC) was
applied using a 25 mm sphere to further investigate effects within regions of the
pain matrix, specifically the Insula, Anterior Cingulate Cortex (ACC), Thalamus and
Amygdala, using coordinates from the Brede Database (Årup Nielsen, 2003).
5.3.g.vii. EEG Analysis Statistical analysis
The anticipatory time windows of interest (TWOIs) were analysed using a repeated-
measure three-way ANOVA, with within subject factors of Drug (Control v Agonist v
Antagonist), Certainty (Known v Unknown), and Expectation (Known Low v Known
High). The analysis of the post-stimuli TWOI was conducted using a repeated
measure three-way ANOVA, with within subject factors of Drug (Control v Agonist v
Antagonist), Certainty (Known v Unknown), and Intensity (Low v High).
5.4. RESULTS
5.4.a. Behavioural results
5.4.a.i. Laser Sensitivity
To assess whether dopamine modulation evoked changes in sensitivity to the laser
stimuli, the participants’ tolerance to the laser was compared between each drug
condition. The voltage (V) required to induce a high (level 7) pain rating that was
determined by the psychophysics procedure was analysed for within-subject
differences using a repeated measures one-way ANOVA. The mean ±SD laser
energy (V) was calculated for control (1.93±0.43 V), agonist (1.98±0.49 V) and
5. An investigation of the role of the dopamine D2 receptor in pain processing
121
antagonist (1.97±0.41 V). There was no significant effect reported between drug
conditions F(2, 50)=0.465, p=0.631, signifying no drug-induced effect on sensitivity
to the nociception.
5.4.a.ii. Pain rating
Throughout the experiment, the participants rated the pain of the laser stimuli to
establish whether dopaminergic manipulation and anticipatory cues affected the
rating of the stimuli (see Table 5.1). The mean pain rating score for each drug
condition was calculated and analysed using a repeated measures three-way
ANOVA with within-subject factors of drug (control, agonist and antagonist),
certainty (known and unknown), and intensity (low and high). The pain rating scores
were normally distributed, as assessed by Shapiro-Wilk's test of normality (p > .05).
The within-subject comparison of the three Drug Conditions (Control, Agonist &
Antagonist) reported no effect on pain rating F(2, 50) = 0.60, p=0.552. This outcome
was expected as the laser intensity was calibrated to the participants’ individual
pain sensitivity on each visit to induce a score of 7 on the pain NRS. The calibration
was completed +3hr30 mins post drug administration and thus pain rating was not
expected to differ between drug conditions.
Table 5.1: The pain rating scores for the experimental paradigm. The rating is separated into laser conditions
and reported as mean and SD.
As we expected, there was a significant effect of intensity [F(1, 25)=262.98, p<
0.001], such that high intensity laser stimuli were rated more painful than low
intensity. Although the assessment of the effect of Certainty (Known v Unknown) on
5. An investigation of the role of the dopamine D2 receptor in pain processing
122
the pain rating did not show a significant result F(1, 25)= 3.33, p=0.80, a two-way
interaction between Certainty and Intensity was shown [F(2, 50)=47.82, p<0.001].
Pain ratings increased from Low (2.95±0.94) < Unknown Low (6.31±0.70) <
Unknown High (5.73±0.70) < to High (6.31±0.70). This shows that when participants
received a certain cue of Low, they rated the pain less than when they received the
Unknown cue and received a low intensity stimulus. In contrast, when the
participants were presented with a certain cue of High, they rated the pain higher
than when they received the Unknown cue and a high intensity stimulus was
delivered. This demonstrates how the rating of the low and high intensity stimuli
was affected differently by the certainty during the anticipation period. This
manipulation of pain perception by anticipatory cues replicates previous findings
(Brown et al., 2008b) and acts to validate the experimental paradigm.
Interestingly, there was a three-way interaction between drug, certainty and
intensity [F(2, 50)=5.60, p=0.006]. This indicates that the modulation of the D2
dopamine receptors via the agonist and antagonist affected the interaction
between certainty and intensity. Follow up two-way ANOVAs established how the
drug condition affected the interaction between certainty and intensity. Firstly, the
comparison of control and agonist revealed no three way interaction between drug,
certainty and intensity [F(1,25)=2.658, p=0.116]. However, the comparison of
control versus the antagonist did reveal a significant three way interaction, [F(1,
25)=9.535, p<0.005], such that the effect of uncertainty was increased within the
antagonist condition. The significant interaction was followed-up with paired t-tests
to establish the differences observed between the placebo and antagonist
condition. To investigate the degree of modulation via certainty, the Unknown pain
rating was deducted from the Known pain rating for low and high laser intensity. In
contrast to the placebo condition, the Antagonist showed a significantly higher
degree of certainty modulation for the high laser stimuli T(1,25)=3.525, p=0.002,
and did not show evidence of a difference within the low laser condition
T(1,25)=1.009, p=0.322. The degree of effect that the certainty condition had on the
pain rating for each Drug Condition, is shown in Figure 5.2.
5. An investigation of the role of the dopamine D2 receptor in pain processing
123
Figure 5.2: The effect of certainty on each laser intensity was calculated and plotted for each drug. The
difference between Known and Unknown pain rating of each laser intensity was calculated by subtracting the
Known rating from the Unknown rating. A positive value is indicative of a higher rating in the Unknown
condition, whereas a negative value is indicative of a lower rating in the Unknown condition. The bigger the
number, the bigger the effect of certainty had on pain rating. Therefore, the graph shows that for Low
intensity, there was an increase in pain rating following the Unknown visual cue within all drugs. In contrast,
for High intensity, there was a decrease in pain rating following the Unknown visual cue within all drugs. Both
the agonist and antagonist caused a bigger effect of certainty on pain rating compared to the control
condition, with the antagonist showing a significantly bigger difference in comparison to the Control
condition. **=p<0.005.
5.4.a.iii. Unpleasantness rating
The rating of unpleasantness was recorded for each laser condition (Low, High,
Unknown Low & Unknown High) and documented at three time points during the
laser experiment. The mean unpleasantness rating for each drug condition (Control,
Agonist & Antagonist) (Table 5.2) was compared and reported no significant
difference [F(2, 30) = 2.766, p=0.079]. In contrast, there was an expected effect of
Intensity, such that high intensity stimuli were rated more unpleasant than low
intensity stimuli [F(1, 15) = 197.75, p=0.000]. There was also an effect of Certainty
Low High
-2
-1
0
1
2
The effect of certainty on Low and High laser
intensity within each drug cond ition.
Laser Intensity
Un
kno
wn -
Kn
ow
n P
ain
Ratin
g
**
Agonist
Control
Antagonist
5. An investigation of the role of the dopamine D2 receptor in pain processing
124
(Known v Unknown) [F(1, 15) = 11.924, p=0.004] on unpleasantness ratings which
did not differ between the three drug conditions. The effect demonstrated that the
presentation of the Unknown cue caused a higher rating in unpleasantness
compared to the Known cues (‘Low’ and ‘High’).
Table 5.2: The unpleasantness rating for each laser condition for each Drug Condition. There was a significant
difference between low and high laser intensity, and the unknown conditions were rated significantly more
unpleasant than the known conditions. There was no significant difference between the three drug
conditions. Unpleasantness score is reported as mean and SD.
5.4.b. EEG results
5.4.b.i. Blink rate analysis
Blink rate has been shown to have a potential relationship with striatal dopamine
activity. As such, we analysed blink rate in order to test whether the agonist and
antagonist had modulated striatal dopamine. The analysis of the blink rate during
the resting state recording highlighted a linear relationship between the eye blink
rate and the expected dopamine D2R activation (Figure 5.3). The eye blink rate
(mean blinks per minute ±SD) was lowest in the Antagonist condition (23.58±13.13)
and highest in the Agonist condition (28.70±12.27), with the control eye blink rate
(26.77±13.35) in the middle of the Agonist and Antagonist. This monotonic
relationship was not significant, F(2, 46)=1.806, p=0.176. Nevertheless, the
monotonic relationship between the dopamine level and eye blink rate is consistent
with previous reports (Karson et al., 1984; Shukla, 1985; Taylor et al., 1999; Kleven
Unpleasantness Rating (VAS score /10)
Low High Unknown Low Unknown High
Drug Condition
Mean SD Mean SD Mean SD Mean SD
Control 2.39 1.30 6.49 0.91 2.56 1.44 7.08 1.30
Agonist 2.25 1.07 6.49 0.67 2.35 1.02 6.98 0.90
Antagonist 1.94 1.22 5.42 2.37 2.22 1.55 5.91 2.67
5. An investigation of the role of the dopamine D2 receptor in pain processing
125
and Koek, 1996) and demonstrated that the dosage of the agonist and antagonist
was sufficient to induce an increase and decrease of striatal dopamine respectively.
Figure 5.3: The eye-blink rate (EBR) calculated for each dopamine manipulation condition highlighted a
monotonic relationship between dopamine D2R activation and EBR. The D2 receptor agonist increased the
EBR and the antagonist reduced the EBR in comparison to the control condition. Data is presented as mean
and 95% confidence-intervals (cf. Cousineau, 2005).
5.4.b.ii. Scalp-level EEG analysis
The analysis of the EEG recording via whole head scalp-level analysis of the
anticipation TWOIs (early, mid and late) reported no main effect of Drug,
Expectation or Certainty for both baselining methods. The mean activity at each
electrode during anticipation is plotted as topographies for all drug conditions in
Figure 5.4.
Ag o n is t C o n tr o l An ta g o n is t
1 0
2 0
3 0
4 0
Bli
nk
s p
er m
inu
te
D o p a m in e M a n ip u la t io n
5. An investigation of the role of the dopamine D2 receptor in pain processing
126
Figure 5.4: Topography plots showing the average response for early-, mid– and late-anticipation for all
conditions. Data is presented as µV. All plot baselined to a common baseline of [-3500 –3000 ms] pre laser
stimulus.
In addition, the analysis of the post-stimulus TWOI [200 600 ms] at scalp-level, also
reported no significant effects of drug or certainty. A main effect of intensity
produced two clusters (p<0.000, kE =20418, x:-13mm y:-36mm, 464ms, and
p<0.000, kE = 3510, x:55mm y:-68mm, 450ms) with the T-contrast indicating an
enhanced activation during high pain in contrast to low pain (p<0.000, kE = 17732, x:
-13mm y: -41mm, 460ms).
5.4.b.iii. Source EEG analysis
The source localisation analysis reported novel results which we did not predict.
There were significant contrasts within the anticipatory and post-stimulus time
5. An investigation of the role of the dopamine D2 receptor in pain processing
127
window only when using pre-auditory cue baselines. Firstly, the mid-anticipation
TWOI [-1500 -1000 ms] reported significant clusters within the main contrasts of
Drug and Expectation (see Table 5.2). The subsequent T-contrasts of the drug effect
during mid-anticipation reported a lower degree of activity within both the agonist
and antagonist in comparison to the control condition. The clusters in both the
agonist and antagonist condition were located within the right hemisphere. The
lower activity in the agonist condition was reported in the right mid-temporal
region and angular gyrus, whilst the antagonist condition induced reduced activity
within the postcentral, mid temporal and inferior parietal regions (see Figure 5.5).
In addition to the main effect of drug condition, all drug conditions showed an
effect of Expectation (Known Low v Known High) during mid-anticipation. The
significant differences in activity were located in the left hemisphere, contralateral
to the pain, and showed augmented activity when participants were presented with
the visual cue of High in comparison to the visual cue of Low. The clusters were
located in the contralateral mid-temporal, mid-occipital and insula regions and are
shown in Figure 5.6.
The post-stimulus TWOI [200 600 ms] produced a main effect of intensity (Low v
High) and drug condition (control v agonist v antagonist) (see Table 5.3). The effect
of intensity during the post-stimuli time window was as expected such that the high
condition induced a higher degree of activation in contrast to the low condition (T-
contrast: High>Low: p = 0.000 F=8.44, kE =98792, x: 8, y: -14, z: -16). The main effect of
the drug condition highlighted a difference within the right insula, a main region of
pain perception. Subsequent T-contrast tests specified that the differences were
between the control and antagonist conditions, and the agonist and antagonist
conditions. Firstly, the antagonist was reported to cause a reduced level of
activation within the insula in comparison to the control condition (see Figure 5.7).
The agonist condition showed a significantly higher activation than the antagonist
within multiple regions; right hippocampus, mid- and inferior temporal region,
insula and the Heschl (auditory processing) (see Figure 5.7).
5. An investigation of the role of the dopamine D2 receptor in pain processing
128
Table 5.3: Source localisation significant results within mid-anticipation and the post stimulus TWOI. The mid-
anticipation TWOI is baseline corrected to the distinct pre-auditory cue time window. The significant clusters
were restricted to >100 voxels, reported at FWE correction with a threshold of p<0.025 to account for
multiple comparison. Results are divided into main F-contrast and post-hoc T-contrasts. The MNI coordinates
are reported as the peak-voxel response within the cluster. The brain regions were labelled using the
Anatomical Automatic Labelling (AAL2) atlas. The percentage overlap of the significant cluster with the brain
region is reported. The region with the highest percentage overlap is shown, unless an equivalent share of
per enta e overlap as observed. A label of ‘Unkno n’ as not reported. FWE=Family Wise Error,
K=number of voxels, F/T=F-contrast/T-contrast, Z=Z-score, Expectation=Known Low v Known High visual
cues, Antag = Antagonist, R = Right, L = Left.
Cluster-Level Peak-Level MNI coordinates
Brain Region P(FWE) K F/T Z x y z
Mid
An
tici
pa
tio
n
F-contrast
Drug 0.004 909 11.62 4.20 32 -44 34 R Inferior Parietal (48.0%)
0.013 700 9.79 3.79 34 -54 14 R Mid Temporal (56.3%)
Expectation
0.000 4098 25.90 4.85 -44 -48 6 L
L
Mid Temporal (35.3%)
Mid Occipital (23.1%)
0.013 783 18.64 4.09 -56 2 -16 L Mid Temporal (69.1%)
0.007 931 15.28 3.69 -14 -86 2 L
L
Calcarine (52.8%)
Lingual (37.2%)
0.005 390 15.65 3.73 -30 -18 8 L Insula (60.7%)
T-contrast
Drug:
Control > Agonist 0.031 693 3.92 3.87 52 -58 10
R
R
Mid Temporal (49.9%)
Angular gyrus (29.4%)
Drug:
Control > Antag 0.000 4184 4.60 4.52 34 -40 40
R
R
R
Postcentral (17.2%)
Mid Temporal (16.9%)
Inferior Parietal (15.0%)
Expectation:
High > Low
0.000 8555 5.09 4.98 -44 -48 6 L
L
Mid Temporal (30.4%)
Mid Occipital (17.9%)
0.003◊ 641 3.96 3.90 -30 -18 8 L Insula (58.8%)
Po
st S
tim
ulu
s
F-Contrast
Drug 0.022◊ 164 10.82 4.02 28 -26 14 R Insula (40.9%)
T-Contrast
Drug:
Control > Antag 0.033
◊ 131 3.73 3.68 28 -26 14 R Insula (45.0%)
Drug:
Agonist > Antag
0.022 748 3.93 3.88 30 -20 -16 R Hippocampus (54.3%)
0.010 940 3.80 3.75 64 -30 -6 R
R
Mid Temporal (52.0%)
Inferior Temporal (38.7%)
0.008◊ 347 4.14 4.07 28 -26 14
R
R
Insula (32.6%)
Heschl (18.9%) ◊
Small volume correction (SVC) has been applied using a sphere of 25 mm radius
> Symbolises that the left-sided condition showed a greater response in comparison to the right-sided condition.
5. An investigation of the role of the dopamine D2 receptor in pain processing
129
Figure 5.5: Source estimates during mid-anticipation for T-contrast of Control versus agonist (Cabergoline)
and antagonist (Amisulpride). The time window of mid anticipation [–1500 –1000 ms] was baseline corrected
to [–2500 –2000 ms]. Clusters are shown at FWE correction using MRIcroN software and regions labelled
using the AAL2 atlas. The agonist (blue) reported reduced activity within the right mid-temporal and angular
regions in contrast to the control. The antagonist (pink) reported reduced activity within the right
postcentral, mid-temporal and inferior parietal regions. The image is aligned via the peak-voxel at peak-level
and reported as x y z (mm).
5. An investigation of the role of the dopamine D2 receptor in pain processing
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Figure 5.6: Source estimates during mid-anticipation for the Expectation T-contrast of known High versus
known Low. The time window of mid anticipation [–1500 –1000 ms] was baseline corrected to [–2500 –2000
ms]. Clusters are shown at FWE correction using MRIcroN software and regions labelled using the AAL2 atlas.
Two clusters of enhanced activity were seen in the high condition versus the low condition and were
contralateral to the noxious stimuli. The regions included the left mid temporal and the occipital lobe, and
the left insula. The image is aligned via the peak-voxel at peak-level and reported as x y z (mm).
5. An investigation of the role of the dopamine D2 receptor in pain processing
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Figure 5.7: The drug effect reported using source localisation for post-stimulus TWOI [200 600 ms]. The TWOI
was baseline corrected to -500ms prior to the noxious stimuli. There was a significant difference between the
control and antagonist conditions, showing a lower degree of activity within the right insula in the antagonist
condition. The antagonist condition also reported a lower activity in comparison to the agonist within the
right hippocampus, mid/inferior temporal lobe, insula and Heschl. The alignment of the images is fixated on
the peak-voxel MNI coordinates from the peak-level results.
5. An investigation of the role of the dopamine D2 receptor in pain processing
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5.5. DISCUSSION
In this study, we aimed to address the role of D2 dopaminergic receptors during
pain anticipation and pain perception by using EEG source localisation. The D2
receptor agonist Cabergoline and the antagonist Amisulpride were used to
modulate striatal dopaminergic activity within healthy volunteers. The agonist and
antagonist amplified the effect of anticipation on pain ratings, with the antagonist
inducing a significantly larger effect in comparison to the control condition. Using
EEG source localisation, we have shown that both an increase and decrease in
striatal dopamine D2R activity results in a reduced degree of activity during mid-
anticipation within regions of the right mid-temporal and parietal lobes in
comparison to the control condition. In addition, EEG source localisation
highlighted that the modulation of D2 receptor activity induced altered processing
during the perception of the laser stimuli, yet did not change behavioural outcomes
of pain sensitivity or pain rating. This latter result is comparable to the research by
Becker et al., (2013), whereby dopamine manipulation did not change the rating of
acute pain.
Importantly, the manipulation of striatal dopaminergic activity via the Agonist
(Cabergoline) and Antagonist (Amisulpride) was shown via the calculation of the eye
blink rate during resting state. Although the differences between the drug
conditions were not significant, the eye blink rate showed a monotonic relationship
with the level of striatal dopamine D2R activity and indicates that the dose of the
agonist and antagonist were sufficient to increase and decrease striatal dopamine
respectively (Karson et al., 1984; Shukla, 1985; Taylor et al., 1999; Kleven and Koek,
1996). Nevertheless, caution should be taken when interpreting eye blink rate as
there is recent evidence that eye blink rate does not correlate with dopamine
synthesis capacity or D2R availability, or that it is an inverse relationship (Sescousse
et al., 2018; Dang et al., 2017).
5. An investigation of the role of the dopamine D2 receptor in pain processing
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5.5.a. Behavioural results
5.5.a.i. Pain sensitivity
Subjective pain intensity within this study was deliberately maintained at similar
levels across all drug conditions in order to maintain similar levels of anticipation.
Adjusting the laser intensity to each condition allowed us to identify that the
dopamine manipulation did not affect the participants’ sensitivity to the laser,
consistent with a previous study by Becker et al., (2013).
5.5.a.ii. Pain unpleasantness
The unpleasantness rating was not affected by dopamine manipulation. This result
is in contrast to previous research which has shown that acute reduction in global
dopamine results in the increase in unpleasantness ratings (Tiemann et al., 2014).
Furthermore, an investigation of dopamine neurotransmission in chronic back pain
has shown that a low abundance of D2/D3 receptors within the striatum is
correlated with the unpleasantness rating. Therefore, we would have expected that
the agonism and antagonism of the D2Rs would modulate the rating of
unpleasantness. Several reasons may explain the difference reported in this study.
Firstly the method of recording the rating of unpleasantness may not have been
sufficiently accurate due to it being recorded as a mean after each experimental
block. Also, the acute action of the D2R agonist and antagonist within this study
could have been insufficient to evoke changes in the affective processing and a
more persistent change in dopamine may be required to alter affective processing.
Another explanation could be that the feeling of unpleasantness is not a D2R-
dependent process.
5.5.a.iii. Pain rating
The rating of the pain intensity within this study was also not altered by dopamine
manipulation. As mentioned, this was expected as the stimulus intensity was
calibrated to the individuals’ subjective perception after drug administration.
However, the dopamine manipulation did cause a difference on the effect of
5. An investigation of the role of the dopamine D2 receptor in pain processing
134
Certainty (known v unknown) on Intensity (low v high), such that the effect of
uncertainty was amplified within both the agonist and antagonist condition, with
the largest difference seen between the control and antagonist conditions. Thus,
following an ‘Unknown’ cue, participants demonstrated reduced attentional
flexibility during both the antagonist condition when compared to placebo (i.e. a
reduced ability to perceive the strength of the subsequent stimulus following the
‘Unknown’ cue).
The activity of the midbrain dopaminergic neurons has previously been shown to be
involved in the anticipation of uncertain cues of motivational-related outcomes
(Bromberg-Martin, Matsumoto and Hikosaka, 2010). Thus one population of
dopamine neurons has been demonstrated to code the uncertainty of a
forthcoming reward whereas a different population codes the reward prediction
errors (Fiorillo, Tobler and Schultz, 2003). By coding uncertainty as well as reward
prediction errors, phasic and tonic increases in dopamine neurotransmission may
encourage exploratory ‘risk-taking’ behaviour as well as attentional flexibility
(Whitaker 2017). We propose that the cue of Unknown evoked a more uncertain
state which requires a higher salience value (Seidel et al., 2015a). It could then be
predicted that the coding of both uncertainty and reward prediction errors is
impaired in the antagonist condition (due to blocking dopamine responses). This
impairment in both the coding of uncertainty and of reward prediction might
explain the reduced attentional flexibility of our participants in the antagonist
condition (Whitaker 2017). Hence, the interaction of the dopamine manipulation
with the effect of Certainty on pain rating provides further evidence for a role of
dopamine in salience processing.
5.5.b. EEG results
5.5.b.i. Drug induced effect during anticipation
One of the present study’s main findings was a drug-induced reduction in neural
activity during the mid-anticipation phase. The agonist and antagonist both induced
a region-specific reduction in activity during the anticipation of pain when
5. An investigation of the role of the dopamine D2 receptor in pain processing
135
compared to the control condition. Although the result being in the same direction
could be considered to be unexpected, this result could be explained by the fact
that dopamine signalling is well-known to have an inverted-U type relationship
(Cools and D’Esposito, 2011; Vijayraghavan et al., 2007; Levy, 2009; Floresco, 2013;
Beggs and Plenz, 2003; Finke et al., 2010; Li and Backman, 2010; Goto, Otani and
Grace, 2007) (See Figure 5.8). The peak of the inverted-U curve is indicative to be
the optimum level of dopamine, with both low and high levels of dopamine being
sub-optimal. This relationship has been demonstrated in cognitive and memory
tasks (Cools and D’Esposito, 2011; Floresco, 2013), and understood further with
help from research into Parkinson’s disease and Schizophrenia.
Figure 5.8: A schematic diagram to signify the inverted-U relationship between Dopamine level and
performan e. T e verti al das ed lines are representative of t e ‘optim m’ ran e of dopamine for many
cortical processes.
During mid-anticipation, both the agonist and antagonist conditions induced a
reduced activity in the right mid-temporal lobe, a region involved in sensory
integration and semantic processing (Herath, Kinomura and Roland, 2001; Ishai et
al., 1999; Chao, Haxby and Martin, 1999; Tranel, Damasio and Damasio, 1997).
D2Rs are located within the mid-temporal lobe (Goldsmith and Joyce, 1996) and the
5. An investigation of the role of the dopamine D2 receptor in pain processing
136
binding potential of D2Rs within the right medial temporal cortex has been
inversely correlated with cold pain tolerance (Hagelberg et al., 2002). In addition, a
reduced abundance has been proposed to explain the aberrant information
processing in cognitive tasks in dementia (Joyce, Myers and Gurevich, 1998).
Therefore, the region has previously been demonstrated to have a role in the
integration of sensory and cognitive processing, and be central to an individual’s
pain tolerance. Therefore, this study’s finding of altered processing prior to pain
perception following dopamine D2R manipulation may indicate a potential role of
the mid-temporal lobe in top-down mechanisms associated with pain processing.
We have also highlighted differences within regions of the parietal lobe in both the
agonist and the antagonist conditions. The agonist condition showed a reduced
activity within the right angular gyrus, a region located within the parietal lobule
and connected with the mid-temporal lobe. Whilst the left angular gyrus is
responsible for language processing (Seghier, 2013; Binder et al., 1996; Pugh et al.,
2000), the right angular gyrus is associated with body representation (Spitoni et al.,
2013), attentional reorientation (Taylor et al., 2011), and the integration of
vestibular and somatosensory input (Blanke et al., 2002). Hence, we propose that
the dopamine manipulation may have altered these processes.
The differences seen in the parietal and temporal regions following dopamine
modulation may be linked with the orienting attention which is associated with
salience detection (Legrain et al., 2011). The network of regions associated with
pain processing is often referred to a salience detection system, and includes the
temporal and parietal regions (Legrain et al., 2011). The participants of this study
were also assessed for their attentional reorientation in a separate assessment
(Whitaker, 2017). The results of this assessment showed that both the agonist and
antagonist reduced attentional flexibility, and this aligns with the reduced
activation seen within the right parietal lobe in this study. The results also support
the inverted-u relationship between dopamine and performance. Hence, our
results indicate a potential disruption in the salience and orientating attention
network which is activated during the anticipation of the noxious stimuli.
5. An investigation of the role of the dopamine D2 receptor in pain processing
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The antagonist induced a lower activity within the right postcentral gyrus, located
within the inferior parietal lobule and the location of the primary somatosensory
cortex. This area is responsible for tactile and pain processing (Backonja, 1996;
Bushnell et al., 1999; Corkin, Milner and Rasmussen, 1970) and has previously been
seen to be involved in the anticipatory response to tactile and painful stimuli
(Roland, 1981; Brown, El-Deredy and Jones, 2014). A reduced activation within the
right somatosensory cortex has been shown in Fibromyalgia patients during the
anticipation of pain (Brown, El-Deredy and Jones, 2014; Loggia et al., 2014). In
addition, an fMRI study has previously shown a reduced BOLD response in the right
somatosensory cortex within Fibromyalgia patients in comparison to controls
during painful pressure (Gracely et al., 2002). Hence a reduced activity within the
right somatosensory cortex has been associated with abnormal pain processing and
we have shown that the D2Rs could be involved in this process.
5.5.b.ii. Drug induced effect during laser perception
During the post-stimulus time window, the antagonist induced a reduced degree of
activity in the right insula in comparison to the control condition, and when
compared to the agonist, also showed reduced activity in the right hippocampus,
mid- and inferior-temporal lobe, and the Heschl. The change in activity during the
perception of pain was not correlated to any change in pain rating of intensity or
unpleasantness. Thus indicates that the processes affected were not involved in the
coding of these aspects of pain, and instead could be related to the integration of
sensory information.
The activation of the contralateral insula during pain perception has previously
been shown to be increased in patient groups whom have a reduced level of
baseline dopamine; Fibromyalgia (FM) patients (Gracely et al., 2002; Bradley et al.,
2000), Parkinson’s disease (Brefel-Courbon et al., 2005) and people with cluster
headaches (Hsieh et al., 1995). It would be predicted that the D2R antagonist,
would induce a response in healthy controls that is similar to that seen in these
patient groups. However, we have shown a contrasting result whereby the
antagonism of the D2Rs reduced the activity within the ipsilateral (right) insula
5. An investigation of the role of the dopamine D2 receptor in pain processing
138
during nociception. The specific manipulation of D2Rs is thus affecting the insula’s
processing during pain differently than the effect of global loss of dopamine. Our
results may be explained by the differences between acute manipulation of
dopamine receptors as seen in our study compared with the chronic reduction in
dopamine levels seen in PD and FM. It is well recognised that chronic, but not
acute, alteration in dopamine levels can lead to long term changes in synaptic
plasticity and may explain this difference (Jay, 2003; Shen et al., 2008; Arbuthnott,
Ingham and Wickens, 2000).
The insula has been considered to be involved in intensity coding (Derbyshire et al.,
1997), however, there is more evidence to show that the insula may play a much
more complex role in integrating both the sensory and affective aspects of pain
(Mazzola et al., 2010; Lu et al., 2016; Singer et al., 2004a), and integrating cognitive
and emotional processing. This parallels with the developing story for the role of
dopamine and D2Rs in pain, such that dopamine is important for correct
interpretation and integration of sensory stimuli. Hence, a persistent dysfunction of
the D2R (Martikainen et al., 2015) could amplify the altered insula activity observed
in this study and result in an impaired integration of sensory processing.
5.5.c. Future Considerations
An important consideration of the interpretation of this study is whether the
changes in the activity during anticipation are directly linked with pain processing
and whether long term dopamine dysfunction would impair the top-down
modulation of pain. The reduction in the activity within the aforementioned regions
could be independent of pain processing. Therefore, a future investigation which
uses salient (i.e. noxious) and non-salient (i.e. visual) stimuli could further decipher
D2Rs role in pain perception. In addition, the acute administration of D2R agonist
and antagonist does not fully enable us to understand how long term dopaminergic
transmission (with resultant changes in synaptic plasticity) can impact pain
processing. The fact that we have shown significant differences following acute D2R
manipulation within multiple aspects of pain processing, suggests that prolonged
changes in dopamine might magnify these changes.
5. An investigation of the role of the dopamine D2 receptor in pain processing
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5.6. CONCLUSION
In summary, these data demonstrate that manipulating the activity of D2Rs alters
aspects of pain processing. Firstly, we have shown for the first time, that D2R
antagonism induced a greater effect of uncertainty on pain rating, and that neither
agonism nor antagonism affected pain sensitivity, pain unpleasantness or pain
rating. The EEG recording highlighted that sub-optimal dopamine activity following
the agonist and antagonist evoked a reduction in activity within the right parietal
and temporal lobes during the anticipation of pain. The antagonist also induced a
reduction in the activity within the right insula during the perception of pain.
Therefore, we propose that the dopaminergic activity via the D2Rs is likely to be
involved in the top-down processes associated with pain perception, rather than
directly coding the intensity of pain. The strong role of dopamine neurons in
attention and salience, plus the participants’ showing impaired attentional
flexibility (Whitaker, 2017), and the importance of these factors in top-modulation
supports D2R neurotransmission as a strong candidate for involvement in the top-
down modulation of noxious stimuli.
Chapter 6
An EEG investigation of age-related differences in acute pain
Sarah Martin, Anthony Jones, Christopher Brown,
Christopher Kobylecki, Monty Silverdale
6. An EEG investigation of age-related differences in acute pain
141
6.1. ABSTRACT
The tolerance to experimentally induced pain has previously been shown to
increase with age, however, chronic pain rates increase with age. Therefore, within
this study we investigated whether age-related changes in central processes of pain
can help to explain the increased prevalence of chronic pain in the older
population. Previous research has shown that pain tolerance increases with age,
however, there is limited information on changes within the central processing of
pain. Participants were divided into a Young and Old group and underwent an EEG
recording whilst acute heat stimuli were delivered to their forearm. Our results
concluded that the Older cohort were significantly more tolerant of the pain
stimuli, and showed a reduced degree of brain activity during the anticipation and
perception of the pain stimuli. We have highlighted a possible age-related change in
central processing of nociception which will help to further understand the natural
changes in pain processing over time. In addition, the age-related difference in pain
processing highlights the importance for chronic pain conditions to be researched
within age-groups in which the disease is prevalent. Nevertheless, additional
research is required to confirm our findings due to differing protocol efficacy
between the two age groups.
6.2. INTRODUCTION
There is a substantial increase of chronic pain with age (Andersson et al., 1993;
Blyth et al., 2001a; Rustøen et al., 2005). Whilst the increase in chronic pain in the
aged population can be partly explained due to an accumulation of negative
lifestyle choices, such as a sedentary lifestyle, and age-related diseases such as
arthritis, there is emerging evidence of the deterioration of the pain system leading
to a heightened susceptibility to chronic pain (see review: Yezierski, 2012).
Studies investigating the age-induced changes in pain perception have mainly
focused on acute pain tolerance. In contrast to the increased rates of chronic pain,
the sensitivity of acute pain has been shown to reduce with age in the majority of
6. An EEG investigation of age-related differences in acute pain
142
studies demonstrating that with age comes a higher pain tolerance (see review:
Gibson and Farrell, 2004). The cause of altered pain tolerance could be explained by
the age-related changes in the peripheral nervous system such as slower
conductance of sensory nerves (Voitenkov et al., 2017; Palve and Palve, 2018), and
changes in the Aδ- and C-fibres (Chakour et al., 1996). Therefore, the development
of chronic pain within the older population is not merely due to increased
sensitivity to noxious stimuli, and is likely to be linked with altered central top-down
processing.
The processing of pain is multidimensional and depends on the integration of a
number of cortical processes. Abnormalities in central processing have been
associated with chronic pain conditions (Brown, El-Deredy and Jones, 2014; Dick,
Eccleston and Crombez, 2002; Gracely et al., 2004; Baliki et al., 2006; Latremoliere
and Woolf, 2009) which demonstrate the importance of cortical processing prior
and during pain processing. There is evidence that maladaptive top-down
modulation of pain, which takes into account the subjective aspect of pain
processing, is central to the development of chronic pain conditions. Hence, the
age-related changes seen in the central nervous system may negatively affect the
top-down modulation of pain and help to explain the increased susceptibility of
chronic pain.
Age-related changes have been demonstrated within regions associated with pain
processing, including a reduced brain volume within the prefrontal cortex,
hippocampus and brain stem structures, whilst the insula and putamen have shown
to reduce in activity during pain perception (Farrell, 2012). In addition, there is
evidence that there is an age-related deterioration in the cortical networks
associated with sensory processing, cognition and salience (Onoda, Ishihara and
Yamaguchi, 2012; La Corte et al., 2016; Eckert et al., 2012). Furthermore, the
efficacy of the endogenous pain control systems such as the descending pain
pathway and the diffuse noxious inhibitory controls (DNICs) reduce with age
(Lariviere et al., 2007; Grashorn et al., 2013) and potentially contributes to the
6. An EEG investigation of age-related differences in acute pain
143
development of chronic pain (Ossipov, Dussor and Porreca, 2010). These findings
indicate an age-related alteration in central top-down modulation of pain.
The investigation of the top-down modulation can be investigated by studying the
anticipation of noxious stimuli (Brown et al., 2008b; Brown, El-Deredy and Jones,
2014; Brown and Jones, 2008; Clark et al., 2008a; Jones, Brown and El-Deredy,
2013). The anticipation of pain has previously shown to be abnormal within chronic
pain conditions (Brown, El-Deredy and Jones, 2014; Jones et al., 2012) and is
indicative of a maladaptive pain network. In addition, the activity during the
anticipation of a noxious stimulus is associated with the assignment of a salience
value to the forthcoming stimulus (Seidel et al., 2015a; Wiech et al., 2010; Menon
and Uddin, 2010) and has been shown to be correlated to the pain perceived.
Therefore, for the first time, we have compared the anticipatory processing to a
noxious stimulus in young and old cohorts via EEG recording and the delivery of
noxious stimuli. Any differences observed aim to improve our understanding of how
pain perception changes over time. Also, it will improve the translatability of pain
studies using EEG conducted in young healthy volunteers to chronic pain conditions
which are more prevalent in the older population.
Importantly, we will address methodological considerations when using laser
induced pain stimuli and EEG recordings in the different age groups. We will also
discuss the possible limitations of interpretation of EEG recordings between age
groups due the changes seen in the aging nervous system.
6. An EEG investigation of age-related differences in acute pain
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6.3. METHODS
The study was approved by the local ethics committee and all participants gave
informed consent according to the Declaration of Helsinki to participate in the
study.
6.3.a. Participants
A total of fifty healthy participants took part in the study and were divided into two
age groups. The young age group consisted of twenty-six participants aged between
18 and 32 years old (mean age 22.2 years, SD 3.7 years). The older age group
included twenty-four participants aged between 45 and 82 years old (mean age
62.92 years old, SD 8.76 years).
The datasets included in this report were from two separate studies using identical
experimental methods. The young group reported in this paper are the participants
within Chapter 5 of this thesis during the control condition of the study4. The old
group in this study are the healthy control participants reported in Chapter 3 of this
thesis. The data used is from their first session completing the experimental
protocol. A difference within the recruitment of the participants is that the Young
cohort was selected for a middle score of the BIS-11 questionnaire with the aim of
limiting baseline dopamine variability, whereas the Old cohort did not complete the
questionnaire. Therefore, there is a potential impact of different variability within
the baseline dopamine levels within the two groups.
6.3.b. Experimental design
6.3.b.i. Pain Stimuli
A CO₂ laser was used to deliver acute pain to the dorsal forearm surface. The CO₂
laser delivered a beam with a diameter of 15 mm and 150 ms duration. For each
4 Participants were part of randomised, repeated measure, double-blinded study design. The study
involved the administration of a D2 dopamine receptor agonist (Cabergoline) or antagonist (Amisulpride), or an inactive control (Sodium Chloride) (20mg) on three separate visits. The data collected from the control condition was used in this analysis. The control visit was visit 1 for 9 participants, visit 2 for 11 participants and visit 3 for 8 participants.
6. An EEG investigation of age-related differences in acute pain
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test, the stimuli were delivered in an area measuring 4 x 5cm and was delivered in a
predetermined randomised path (Brown et al., 2008a). This was to avoid
habituation, sensitization, or skin damage.
6.3.b.ii. Psychophysics
Before starting the experimental protocol, psychophysics was used to calibrate the
laser to the individual’s pain sensitivity. An ascending method of limits procedure
was used, starting from 0.6 V with 0.06 increments each time. The participant used
an eleven point NRS (0 - 10) to rate the intensity of the pain perceived for each
stimuli (0=no sensation, 4=pain threshold, 7=moderately painful, 10=unbearably
painful). The rating scale was introduced to the participant via these standardised
descriptives to ensure that no explanation altered their interpretation of the scale.
The procedure was repeated three times to allow participants to get used to the
laser and was used to calculate the voltage used for the Low and High laser
intensities in the main experiment.
The High laser intensity was calibrated to be the voltage required to induce level 7
(moderate pain) and the Low laser intensity was calculated to be level 4 (just
painful) in both groups.
6.3.b.iii. Main experiment
The participants received 120 laser stimuli at the two intensities (low and high)
separated into four conditions; Low (level 4), High (level 7), Unknown Low (level 4)
and Unknown High (level 7). To investigate the anticipation of a painful stimulus, a
3 second auditory countdown preceded the laser stimuli (see Figure 6.1). The first
auditory cue was presented concurrently with an anticipatory cue to indicate the
forthcoming laser stimuli. The participant was either shown ‘Low’, ‘High’ or
‘Unknown’. The presentation of the word ‘Unknown’ indicated that the laser
stimulus has an equal chance of being low or high. This was to investigate the
importance of certainty in the anticipation of the laser stimuli. The image was also
used as a visual fixation cue to discourage eye movements. After the laser stimuli,
the 0-10 numerical rating scale was shown on the screen and the participant rated
6. An EEG investigation of age-related differences in acute pain
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the intensity of the pain. The Young group used a handheld keypad to enter a pain
rating, whilst the Old group verbally reported their pain rating5. The order of the
stimuli was randomised and separated into three blocks with short breaks in-
between.
Figure 6.1: A schematic diagram of a single trial of the experimental paradigm. A computer monitor showed
the participant a visual cue of; Low, High or Unknown from -3s to +2s. A three second countdown of beeps at
-3, -2 and -1 allowed accurate anticipation of the laser stimuli at time 0s (red bar). The presentation of the
visual cue at -3s was concurrent with the first auditory cue. The visual cue was consistent throughout the
anticipation and laser stimulus. At +2s, an eleven-point number rating scale (NRS) was presented for the
participant to rate the laser stimulus. For each condition (Low, High, Unknown Low and Unknown High) there
were thirty trials. The trials were presented in a randomised order and divided into three blocks of forty
trials.
6.3.b.iv. EEG recording
A BrainVision MR EEG cap was used to record from 63 scalp electrodes using a
BrainVision-cap system [Standard BrainCap-MR with Multitrodes]. The arrangement
of the electrodes was modelled on the extended 10-20 system. Recording
parameters were set at: Filter (DC to 70 Hz), Sampling rate (1000 Hz), Gain (500). To
reduce electrical interference, a 50Hz notch filter was applied. Prior to starting the
laser protocol, resting states were recorded with eyes open and closed for two
minutes in all participants. This ensured that the experience prior to the experiment
5 The difference in pain rating methodology was due to the participants within the Old group were the control group for the investigation of pain processing in people with Parkinson’s disease. As the Parkinson’s patients had limited movement, the pain rating was completed verbally in both groups. The addition of the keypad for pain rating within the Young cohort was the return to standard practice of the experimental protocol.
6. An EEG investigation of age-related differences in acute pain
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was identical. The three experimental blocks were recorded separately to allow for
better artefact rejection of the EEG data.
6.3.c. Analysis Methods
6.3.c.i. Statistical analysis of behavioural data
Statistical analyses of the behavioural measures were carried out using IBM SPSS
Statistics 22 software. Prior to using statistical tests, the data was assessed for
normality using a combination of Q-Q plots, histograms, and the values of skew and
kurtosis. Normally distributed data was analysed using independent t-tests and
ANOVA tests, whilst data reported to be not normally distributed, the appropriate
non-parametric test was utilised. Specific statistical tests for each analysis step are
reported in the results section.
6.3.c.ii. EEG analysis method
EEG pre-processing was carried out using EEGLAB toolbox (Delorme and Makeig,
2004) in MATLAB version R2015a (The Mathworks Inc) whilst statistical analysis was
carried out using SPM12 toolbox (Wellcome Department of Imaging Neuroscience,
Institute of Neurology, UCL, London, United Kingdom) running in MATLAB.
6.3.c.ii.a. EEGLAB Pre-processing
Pre-processing consisted of; removal and interpolation of bad channels, down-
sample to 500, low-pass filter of 20Hz and re-reference to the common average.
The four conditions were separated and -3500ms to 2000ms epochs extracted and
Linear detrend applied. Independent Component Analysis (ICA) was carried out on
all datasets using the SemiAutomatic Selection of Independent Components for
Artifact correction (SASICA) toolbox to select components to remove via pre-
determined thresholds. The thresholds were set to; Autocorrelation (threshold =
0.35 r, lag = 20 ms), Focal (threshold = 3.5 z), Focal trial (threshold 5.5 z), Signal to
noise (period of interest (POI) = [0 Inf], baseline (BL) [-Inf 0], threshold ratio = 0.5),
and Adjust Selection enabled. The thresholds were sufficient to remove artefacts
6. An EEG investigation of age-related differences in acute pain
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from the majority of the datasets; however, a number of datasets required further
manual removal of eye-blink components where not picked up by SASICA.
6.3.c.ii.b. SPM EEG Analysis
The pre-processed datasets were converted to Statistical Parametric Mapping
(SPM) compatible files. Statistical analysis was carried out to investigate the
anticipation evoked potentials and the post-stimulus LEPs using scalp-level and
source localisation analysis techniques available in the SPM toolbox.
SPM scripts for batch processing were used to analyse the EEG data at the scalp
level and source localisation. Two baselining methods were applied to the data
analysis for the anticipation phase and were applied for scalp-level and source
localisation analysis (see 2.1.b.ii). Primary analysis applied distinct baselines (BLs)
of 500 ms, occurring prior to each of the three auditory cues respectively, to
analyse each of the three anticipation phases. The BLs and time window of interest
(TWOI) were as follows; Early [BL: -3500 ms -3000 ms: TWOI: -2500 ms -2000 ms],
Mid [BL: -2500 ms -2000 ms: TWOI: -1500 -1000 ms] and Late [BL: -1500 -1000 ms:
TWOI: -500 0 ms] anticipation phases. The secondary analysis method applied a
single baseline of 500 ms prior to the first auditory cue [BL: -3500 ms -3000 ms] that
was common to every TWOI anticipation phase (early, mid and late). This second
analysis was conducted to enable comparison to previous studies (Brown et al.,
2008a; Brown, El-Deredy and Jones, 2014). All statistical analysis for the
anticipation phase was adjusted for multiple comparisons.
All analysis for the post-stimulus phase TWOI [200 ms 600 ms], centred on the LEP,
was baseline corrected to -500 ms prior to the laser stimulus [BL: -500 ms 0 ms].
6.3.c.ii.c. Source Localisation analysis parameters
SPM12 EEG and MATLAB scripts were used to estimate the sources of the
anticipation- and laser-evoked potentials using Low Resolution Electromagnetic
Tomography (LORETA). The forward model was created using an 8196 vertex
template cortical mesh coregistered to the electrode positions of the standard 10-
6. An EEG investigation of age-related differences in acute pain
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20 EEG system. A three-shell boundary element model (BEM) EEG head model
available in SPM12 was used to compute the forward-model. The images were
smoothed with a 12mm full-width-at-half-maximum (FWHM).
6.3.c.ii.d. EEG Analysis Statistical analysis
The EEG analysis was analysed via an analysis of covariance (ANCOVA) with
covariates of laser energy and average pain rating. All data was assessed for outliers
using SPM’s boxplot function and only outliers labelled as extreme outliers were
removed. The linear relationship between the covariates and the EEG data was
assessed via extraction of non-adjusted Eigenvariate data from the significant
clusters reported in the SPM statistics output. The homogeneity of regression
slopes was assessed between age and each covariate for each group. The data had
no outliers and the assumptions required for an ANCOVA were met.
For the analysis of the anticipatory TWOIs, a three way repeated measures ANOVA
was applied, with one between-subject factor of Group (Young vs Old) and two
within-subject factors of Certainty [Known (Low/High) v Unknown] and Expectation
[Low v High (Known)]. For the analysis of the post-stimuli TWOI, a three way
repeated measures ANOVA was applied, with one between-subject factor of Group
(Young vs Old) and two within-subject factors of Certainty [Known (Low/High) v
Unknown] and Intensity [Low v High (Known and Unknown)].
Source localisation results were reported as follows. To control for multiple
comparisons, a cluster-forming threshold of p<0.001 was used and resulting
clusters were considered significant at FWE (p<0.05). Significant clusters were also
restricted to >100 voxels in size and regions labelled using the Anatomical
Automatic Labelling (AAL2) toolbox in SPM.
6. An EEG investigation of age-related differences in acute pain
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6.4. RESULTS
6.4.a. Laser behavioural results
The previous research showing an age-related reduction in sensitivity to
experimentally induced pain was investigated via comparing the sensitivity to the
laser in the Young and Old groups. An independent t-test reported a significantly
higher tolerance in the older group (Mean ±SD: 2.20±0.25) in comparison to the
young group (Mean ±SD: 1.93±0.43), F(1,48)=9.52, p=0.009.
To assess any differences in the rating of pain between the two groups, a mixed
three way ANOVA was carried out on the pain rating with a between factor of Age
(Young v Old), and within factors of Certainty (Known v Unknown) and Intensity
(Low v High). A group difference was reported showing that the old group had a
lower rating of the pain stimuli than the young group [F(1, 48)=34.90, p<0.000]
(Figure 6.2). This indicates that although the laser was calibrated to induce a
subjective moderate pain prior to the main experiment via the psychophysics
procedure, the older group significantly reduced their rating for the same intensity
when it was delivered in the main protocol.
The effect of certainty on the rating of the Low and High pain stimuli was consistent
between the two groups [F(1,48) = 55.27, p<0.000] such that the certainty (Known
(Low or High) v Unknown) of the visual cue affected the rating of Low and High
stimulation with contrasting results. The known Low intensity was rated lower than
the unknown Low condition, whilst known High intensity was rated higher than the
unknown High condition (Figure 6.2).
6. An EEG investigation of age-related differences in acute pain
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Figure 6.2: Pain rating of laser stimuli. A) The pain rating of the laser stimuli is shown for each laser condition.
The Young (blue) group rated the pain significantly higher than the Old (red) group. B) The effect of certainty
on the rating of Low and High was consistent in both groups, such that the Unknown Low was rated higher
than known Low, whilst Unknown High was rated lower than known High. NRS: Number rating scale, Low:
‘ o ’ vis al e and lo laser intensity, i : ‘ i ’ vis al e and i laser intensity, Un o : ‘Unkno n’
visual cue and lo laser intensity, Un i : ‘Unkno n’ vis al e and i laser intensity. ata is plotted as
mean ± SD.
To assess whether either group habituated to the laser stimuli within the main
experiment, a linear regression was applied to establish whether the rating of the
Known High stimuli could be predicted by trial number. The known high stimuli was
selected for the habituation analysis due to it being calculated by identical
protocols within the psychophysics in each group, and so that the effect of
uncertainty did not need to be accounted for. We concluded that trial number did
not predict pain rating for the Young [F(1,28) = 1.042, p=0.316] or Old [F(1, 28) =
3.325, p=0.079] (see Figure 6.3).
Lo
w
Un
Lo
w
Hig
h
Un
Hig
h
0
2
4
6
8
T h e c o n tra s t in g e ffe c t o f c e r ta in ty
o n lo w a n d h ig h in te n s ity
Pa
in
Ra
tin
g (
VA
S /
10
)
* * *
Lo
w
Un
Lo
w
Hig
h
Un
Hig
h
0
2
4
6
8
D iffe re n c e in p a in ra tin g b e tw e e n
th e Y o u n g a n d O ld g ro u p s
Pa
in
Ra
tin
g (
VA
S /
10
)
Y o u n g
O ld
***
A) Difference in pain rating between the Young and Old groups
B) The contrasting effect of certainty on low and high intensity
Pai
n R
atin
g (N
RS/
10
)
Pai
n R
atin
g (N
RS/
10
)
6. An EEG investigation of age-related differences in acute pain
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Figure 6.3: The pain rating for Known High stimuli was averaged across participants within each group at each
trial number. There was no association between the trial number and pain rating within either group which is
indicative of no habituation to the laser stimuli during the main experiment. NRS: Number rating Scale.
6.4.b. EEG Results
6.4.b.i. Sensor-level
The waveforms at Cz are presented in Figure 6.4 and present the individual
waveforms for participants in the Young and Old groups. The plots present the
spread of the data and the average for each group (black line). The averaged
waveform for each group is shown in Figure 6.5 and shows that the Old group had a
distinctively lower degree of activity during the stimulus preceding negativity (SPN),
a representation of anticipation, and a smaller LEP. This difference in the activity
between the Young and Old groups can also be seen in the topography plots in
Figure 6.5.
No habituation of pain rating was reported for high stimuli across trials
Pai
n R
atin
g (N
RS/
10
)
6. An EEG investigation of age-related differences in acute pain
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Figure 6.4: Waveforms at Cz electrode for all participants in the Young and Old groups. Both plots
show the stimulus preceding negativity (SPN) during the anticipation of the laser stimuli (0 ms)
and t e s bseq ent laser evoked potential ( EP). Ea aveform represents a parti ipant’s
waveform averaged across all trials and the solid black line shows the averaged response within
each group. Data is baselined to -3500 -3000.
6. An EEG investigation of age-related differences in acute pain
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Figure 6.5: ERP waveforms and topography plots. A) The average waveform at Cz electrode of the
anticipatory and post-stimulus response. Red: Old, Blue: Young. The data is presented as an average of all
laser-conditions and baselined to [–3500 –3000 ms]. The waveforms of the young and old groups are
distinctively different, with the young group showing a higher stimulus preceding negativity (SPN), and larger
laser-evoked potential (LEP). B) The topoplots represent the average activity at each electrode for all
participants in each group. The difference between Young and Old is also shown.
) rand avera e topo rap i plots for ea ro p
o n Old o n - Old
Early
Mid
ate
Time (ms)
-3000 -2000 -1000 0 1000
Pote
ntia
l (
V)
-4
-2
0
2
4
Old Young
A) rand avera e aveforms for ea ro p
6. An EEG investigation of age-related differences in acute pain
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6.4.b.i.a. SPM analysis
Both baselining methods reported the same sensor-level differences between the
young and old groups. For clarity and to allow reference to results from Chapters 3,
4 and 5, here we will show statistical results using the primary baseline method
using baselines prior to auditory cues.
The Young group showed a significantly higher degree of activity during
anticipation. The characteristic topography of anticipatory processing is a negative
response over dorsal regions and a positive response over posterior regions. Our
results of comparing the Young and Old groups show two distinct locations of
significant clusters which are located in the centre of the top of the head (dorsal)
and the back of the head (posterior (Figure 6.6). Hence, the statistical results along
with the topographic plots (Figure 6.5) show that the Young group have a
significantly higher anticipatory response compared to the Old group. This
interpretation is supported by the SPM T-contrasts which report which factor (i.e.
Age group) had a more positive EEG amplitude in the significant clusters (Table 6.1).
The Young group showed a more negative dorsal response (as the Old group
showed a more positive response: Old>Young), and a more positive response over
posterior regions (Young>Old).
Figure 6.6: Topographic plots of the SPM F-statistics for the main group effect during anticipation and post-
stimulus time windows. The results are restricted to FWE correction and clusters of >100 voxels. The red
arrow indicates the highest peak value.
6. An EEG investigation of age-related differences in acute pain
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Table 6.1: Scalp-level statistical results for whole-scalp analysis. Group differences were found within all
time-windows. The significant clusters were reported at FWE correction with a threshold of p<0.025 to
account for multiple comparison (due to two baseline methods). The results are divided into main F-contrast
and post-hoc T-contrasts. The MNI coordinates are reported as the peak voxel response within the cluster
and described with x and y coordinates and time (ms). The interpretation of the T-contrast comparisons are
such that the > sign denotes which factor (i.e. Age group) had a more positive EEG amplitude within the
cluster. Group = Young v Old, FWE = Family-wise error K = Number of voxels, F/T = F-contrast/T-contrast, Z =
Z-score.
6.4.b.ii. Source localisation
The source localisation analysis using the LORETA technique did not highlight any
significant contrasts between the two groups.
Cluster-Level Peak-Level
MNI coordinates
(mm)
Time
(ms)
P(FWE) K F/T Z x y
Early
F-contrast
Group 0.000 42426 47.99 6.42 13 13 -2420
0.000 29119 42.06 6.04 -9 -79 -2450
T-contrast
Group:
Young>Old 0.000 31969 6.49 6.15 -9 -79 -2450
Group:
Old>Young 0.000 47905 6.93 6.53 13 13 -2420
Mid
F-contrast
Group
0.000 40878 73.28 7.76 -9 2 -1462
0.000 14418 34.50 5.50 0 -79 -1480
0.000 5027 21.02 4.31 -4 -89 -1058
T-contrast
Group:
Young>Old
0.000 16324 5.87 5.62 0 -79 -1480
0.000 6607 4.58 4.46 -4 -89 -1058
Group:
Old>Young 0.000 44388 8.56 7.84 -9 2 -1462
Late
F-contrast
Group 0.000 39799 72.23 7.71 -4 2 -232
0.000 10846 28.02 4.97 -4 -95 -240
T-contrast
Group:
Young>Old 0.000 13905 5.29 5.10 -4 -95 -240
Group:
Old>Young 0.000 42859 8.50 7.80 -4 2 -232
Post-
Stim
F-contrast
Group 0.000 12166 49.16 6.49 0 -30 376
Intensity 0.000 21047 74.71 7.82 9 -36 462
0.000 4951 25.90 4.78 55 -68 492
Group*Intensity 0.013 1829 16.97 3.86 -4 -25 368
T-contrast
Group:
Young>Old 0.000 13636 7.01 6.60 0 -30 376
Intensity:
Low>High 0.000 6354 5.09 4.92 55 -68 492
Intensity:
High>Low 0.000 22348 8.64 Inf 9 -36 462
6. An EEG investigation of age-related differences in acute pain
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6.5. DISCUSSSION
Here we have indicated that there may be an age related difference in pain
processing. We have shown that the younger cohort were more sensitive to the
laser stimuli and rated the pain higher than the older group. We have also shown
via scalp-level EEG analysis that the younger cohort showed a higher degree of
anticipation related activity and larger response during perception of pain.
Although caution must be taken when interpreting the results due to several
limitations of the investigation (see section 6.4.c) the results are indicative that the
central processing of pain may change with age.
6.5.a. Pain sensitivity
Firstly, the older group were less sensitive to the laser stimuli during the
psychophysics procedure and required a higher voltage of the laser. The finding
that the older cohort was less sensitive to the laser stimulation supports previous
knowledge of increased tolerance of a range of experimentally induced stimuli. The
experiments are reviewed in detail by Gibson and Farrell, 2004, and collates
research to show either an age related increase, or no change, in pain threshold to
heat, electrical and mechanical stimuli.
The psychophysics procedure allowed for the calibration of the laser voltage to the
individuals’ subjective sensitivity. Although the psychophysics protocol was
successful within both groups to induce a high (level 7) pain, the Old group reduced
their rating of the pain when the main experiment was started using the previously
calibrated voltage. Therefore, we have demonstrated that age can affect the
efficacy of the psychophysics protocol used to calibrate to a subjective experience.
This highlights the benefit of using a strict age range in pain research to limit the
variability. The reduction in the rating of the laser stimuli must be considered when
interpreting the results and are discussed in Section 6.4.c.
6. An EEG investigation of age-related differences in acute pain
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6.5.b. Interpretation of anticipation phase in EEG results
Here we have shown a reduced degree of activity during anticipation in the older
group compared to the younger group. One potential explanation of this finding is
to consider the age-related changes in an individual’s relationship with pain
stimulation. The relationship with pain changes with the experiences that we have,
and as such our response to pain will ultimately change as we grow older. The top-
down processes such as context, prior experience and emotional state, are all able
to modulate the perception of pain and are likely to change with age. There is a
theory coined as the ‘positivity effect’ whereby older people are more likely to
favour positive stimuli over negative stimuli within cognitive tasks (Reed and
Carstensen, 2012). An fMRI study reported a reduced activation within the striatum
and insula during the anticipation of monetary loss in the older cohort in
comparison to the younger group (Samanez-Larkin et al., 2007). This age-related
reduced activity was not observed during the anticipation of monetary gain.
Therefore, the reduction in brain activity during anticipation of the laser stimuli that
we have seen in this study may be analogous to the age-related difference seen
associated with the ‘positivity effect’ (Reed and Carstensen, 2012; Samanez-Larkin
et al., 2007), i.e. older subjects have a tendency to pay less attention to negative
cognitive input. In addition, there is evidence to show that psychological resilience
is increased within the older population (Cicchetti and Cohen, 2006; Ong, Bergeman
and Boker, 2009). As such, within the older population, the noxious stimuli may
have been assigned a lower salience value in comparison to the younger cohort,
and resulted in a reduced level of activity during anticipation.
The development of chronic pain has been associated with dysfunctional brain
activity during the anticipation of pain (Gracely et al., 2004; Brown, El-Deredy and
Jones, 2014; Loggia et al., 2014). Therefore, in people with chronic pain, the normal
age-related reduction in the anticipatory activity to pain stimuli could be different
and represent abnormal top-down processing of pain. It would be interesting to
establish whether older people with chronic pain show the same ‘positivity effect’
seen in the healthy population. It could be hypothesised that older people with
6. An EEG investigation of age-related differences in acute pain
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chronic pain would not show the typical disfavour for negative stimuli which is seen
in the healthy population. As such, if a higher salience was assigned to the negative
stimuli, and a higher degree of activity occurred, this may help to explain the
persistence of chronic pain.
6.5.c. Limitation of interpretation
The interpretations of these results are constrained by several limitations
associated with the comparison. Here we will outline the limitations and provide
suggestions of interpretation.
6.5.c.i. Recruitment
One constraint of interpretation includes the recruitment of the participants being
from two different studies. The participants within the Young age group were part
of a repeated-measures, double-blind and placebo-controlled study which
investigated the role of dopamine D2 receptors in pain perception. The data used
for this comparison is taken from the control condition when they received the
inactive compound Sodium Chloride (20 mg). Although this would have no direct
physiological effect, there is potential that the knowledge that it could be a
dopamine modulating drug could induce a difference in their response to the
experiment. In contrast, the Old group were the control group in an investigation of
pain perception in Parkinson’s disease, and no intervention was administered.
6.5.c.ii. Difference in Pain Rating
Although the psychophysics procedure successfully calibrated the laser voltage to
induce a high pain in both age groups, the resulting pain rating during the main
experiment was significantly lower within the older group and demonstrates that
the older participants habituated to the laser stimuli between the psychophysics
and main experiment. Importantly, the rating of the stimuli within the main
experiment did not show any habituation within either group. The drop in pain
rating was not shown in the Young group and the pain rating was hence used as a
covariate within the EEG data analysis. Although the covariate was included, it is
6. An EEG investigation of age-related differences in acute pain
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difficult to conclude whether the decrease in activity during anticipation in the Old
group was not merely due to the participants anticipating a lower intensity of pain.
Whilst the young group rated the pain via a numeric keypad, the older group
reported their ratings verbally. The different techniques may have led to the
difference in pain rating due to the impact of psychological and social factors when
rating verbally in comparison to via a personal keypad. There is evidence that the
environment and social context of pain experiments can influence pain rating. For
instance, males are likely to reduce their pain rating when the researcher is female
in comparison to a male researcher (Levine and De Simone, 1991). Furthermore,
stoic behaviours of hiding pain severity have been shown to be a good predictor of
reductions in self-reported pain intensity (Yong, 2006; Cantwell et al., 2013).
Therefore, the verbal pain rating in the Old group may have been sensitive to the
additional psychological factors such as resilience (Ong, Bergeman and Boker,
2009), stoicism and rapport with the researcher, in comparison to the Young groups
use of the keypad.
6.5.c.iii. Age-related changes in EEG recording
Another consideration of comparing younger and older participants is the
differences in the EEG recording. A study by Duffy et al., (1984) reported no age-
related change in alpha frequency or amplitude, however, did report a dysfunction
of EEG desynchronization. In addition, a study which investigated the visual evoked
potential (VEP) in EEG recordings of young and middle aged adults concluded that
the older cohort showed a lower VEP amplitude and delayed latency (Dustman et
al., 1990). Although there is limited information regarding EEG age-related changes,
there are widely discussed differences in fMRI recording. A comparison in the
overall functional response to cerebral activity in fMRI highlighted that the older
participants showed a lower amplitude in the BOLD response compared to the
younger participants (Ross et al., 1997; Madden et al., 2004; Juckel et al., 2012).
Therefore, an age-related decline in neuroimaging may have produced the reduced
response seen in the older cohort. Further investigation is needed to establish
6. An EEG investigation of age-related differences in acute pain
161
whether the reduced response is a consequence of reduced anticipation of pain, or
a result of a decline in neuroimaging quality.
6.6. CONCLUSION
Our investigation further strengthens the knowledge that tolerance to
experimentally induced pain is increased with age. We have also highlighted a
potential age-related reduction in brain activity during the anticipation and
perception of pain, and highlighted methodological considerations for experimental
paradigms in different ages. Although there are limitations which impair a confident
conclusion of the comparison, the reduced response may be highlighting the
‘positivity-effect’ within older cohorts whereby they show a reduced neural
response to negative outcomes. We have also shown that the current
psychophysics procedure is sensitive to age and is an important protocol choice for
future studies. Further investigations are required to understand the differences in
pain perception in young and older cohorts.
Chapter 7
General Discussion
7. General Discussion
163
7.1. MAIN AIM OF THESIS
The main aim of the research conducted in this thesis was to further understand
pain processing in Parkinson’s disease and to establish whether abnormal top-down
processing could help to explain the high prevalence of chronic pain seen in
Parkinson’s disease. The main focus of the research was to investigate the brain
activity during anticipation of pain as a technique to research top-down modulation
during pain processing.
The first study investigated pain perception in people with Parkinson’s disease
whilst off (Chapter 3) and on (Chapter 4) their medication. The second study
explored the role of dopaminergic transmission in pain perception in young healthy
volunteers (Chapter 5). And finally, age-related changes in pain perception were
explored (Chapter 6).
7.2. SUMMARY OF EXPERIMENTAL WORK
Chapters 3 and 4 describe the Parkinson’s disease patient study which investigated
the brain activity during anticipation and perception of pain in the patient group
and healthy age-matched controls.
Firstly, Chapter 3 reviews the results from the study conducted whilst the patients
were withdrawn from their medication. The behavioural results reported that the
PD group, compared to the healthy age-matched controls, were more sensitive to
the laser stimuli, showed higher scores in pain catastrophising, and yet showed no
difference in their mood or cognitive states (assessed via HADS and MoCA
respectively). Brain activity was assessed using LORETA source localisation and
concluded that there was an increased activation in the midcingulate region within
the PD group during mid and late anticipation in comparison to the control group.
This amplified response was independent of the PD motor severity, PCS, HADS and
chronic pain severity.
Chapter 4 (on) summarises the results from when the experimental protocol was
repeated in participants from the study reported in Chapter 3 (off) whilst the PD
7. General Discussion
164
patients were on their Parkinson’s medication. The behavioural results showed a
higher sensitivity to the laser in the PD group, which is consistent with conclusions
drawn in Chapter 3 (off). The EEG data reported no differences between the PD
group on their medication and the healthy controls. Caution must be taken in
drawing firm conclusions due to the following limitations of the study: no
randomisation of experiment order, repetition of the experiment on the same day,
a potential time-of-day effect and a low n number. However, the study indicates
that the higher pain sensitivity seen in the PD participants is not affected by
increasing dopamine and suggests that dysfunction of other neurotransmitters such
as Serotonin or Noradrenaline may be to blame. The lack of difference in the EEG
results between the HC and PD on their medication could indicate that the increase
in dopamine ‘normalised’ the anticipatory response in PwPD seen following
withdrawal of their medication, but it did not ‘normalise’ their pain sensitivity.
Chapter 5 (D2R) reviews the pharmacological manipulation of dopamine D2
receptors to assess the role of dopamine in pain processing. The investigation was
carried out in young healthy volunteers and conducted using a repeated-measures,
randomised, double-blind, placebo-controlled protocol. The dopamine
manipulation did not change sensitivity to the laser, but EEG source localisation
analysis did show a significant effect on brain activity during the anticipation and
the perception of pain. The agonist (Cabergoline) and antagonist (Amisulpride)
induced similar reductions in the parietal and temporal lobes during anticipation in
comparison to the control condition. In addition, the antagonist induced a reduced
activation in the insula during the receipt of pain stimuli in comparison to the
control and agonist. Interestingly, the dopamine manipulation evoked a larger
effect of certainty on pain rating, with the antagonist inducing a significantly
amplified effect of the Unknown cue on the rating of the pain.
Chapter 6 (Age) reviews the differences in the behavioural and neural responses to
the experimental protocol. The aim of the analysis was to shed light on the age-
related changes in pain processing due to the higher degree of chronic pain within
the aging population. The comparison highlighted a higher pain sensitivity and
7. General Discussion
165
anticipatory response in the Young group compared to the Old group. The reduced
pain rating in the Old group and age-related difference in EEG recording may have
influenced the results, and caution is needed when interpreting the results.
Nevertheless, the study indicated that there is an age-related reduction in
anticipatory processing to noxious stimuli.
The conclusions drawn from the EEG analysis were mainly from the pre-auditory
cue baselining method. The method was carried out in conjunction with a single
baselining method which did not highlight significant differences within the studies
(except for the Age comparison: Chapter 6). A potential reason for this is due to the
timing of the single baseline period (-3500 -3000 ms) being when a higher degree of
movement of the participants would be expected which increased variability in the
baseline time window. The PD participants especially were likely to use the interval
between the trials to relax their suppression of tremors or to move into more
comfortable condition, prior to the visual/auditory cue. The pre-auditory cue
baselining method, which highlighted significant differences, may have shown
differences due to the mid- and late- baselines being during focused attention on
the experiment (i.e. low variability). Due to two baselining methods being used, the
statistical significance was adjusted accordingly, and ensured reliability of the
results.
The conclusions drawn from each chapter and how they contribute to the
understanding of pain processing, both in Parkinson’s disease and the general
population, will be discussed in the following sections. Considerations for future
research and details of potential further research will also be discussed.
7.3. AN INTERPRETATION OF THESIS STUDIES
We propose that the dysfunctional dopaminergic system within the Parkinson’s
brain results in a disruption in the top-down modulation of pain, which predisposes
PwPD to develop chronic pain.
7. General Discussion
166
The three main studies (Chapters 3, 4 & 5) which investigate pain in Parkinson’s
patients off and on dopamine medication, and the effect of dopamine modulation
on healthy participants, contribute to the understanding that dopamine plays a role
in top-down modulation in pain processing, whilst demonstrating that it is unlikely
to be central in coding the intensity of pain. This thesis can draw this conclusion
because we have shown no change in pain sensitivity following dopamine
manipulation, yet have shown changes in brain activity prior to pain stimulation, a
representation of top-down processing, following dopamine modulation. Top-down
modulation encompasses various neural processes associated with processing
nociceptive information such as: cognition, attentional orientation and emotional
aspects. Therefore, we conclude that changes in dopamine levels present in
Parkinson’s disease is likely to cause disruption within these processes and may
result in long-term changes in the brain’s ability to integrate nociceptive
information accurately and increase susceptibility to the development of chronic
pain.
7.3.a. Behavioural response
Firstly, the sensitivity to the laser stimuli was not shown to be modulated by
dopamine levels in the PD study or D2R study. The PD participants had a higher
sensitivity to the CO2 laser whilst both off and on medication. This finding was also
shown by Tinazzi et al., (2008) using CO2 laser stimulation, and Djaldetti et al.,
(2004) using a heat thermode. In contrast, (Brefel-Courbon et al., 2005) showed a
reduction in sensitivity to the cold pressor test (CPT) in PwPD following Levodopa
medication. The difference in the effect of dopamine medication may be due to the
differences in the method of inducing experimental pain. The CO2 laser stimulation
is comparable to an acute pain, whereas the CPT is regarded to be more analogous
to chronic pain (Rainville et al., 1992). Therefore, due to dopamine’s potential role
in modulating top-down processes during pain perception, it would be more likely
that the PD dopaminergic medication would improve CPT tolerance in contrast to
acute CO2 laser stimulation.
7. General Discussion
167
In addition, the manipulation of D2Rs, via Cabergoline and Amisulpride, in healthy
participants also did not modulate the individual’s sensitivity to the laser. The
higher sensitivity to the laser in the PD participants (both off and on medication),
and no differences seen in the D2R study participants, indicates that the sensitivity
in PD patients is likely to be associated with other neurotransmitters such as
serotonin, noradrenaline and/or opioids.
Furthermore, the D2R study concluded that the manipulation of dopamine changed
the effect of certainty on pain rating of the two pain intensities. This shows that
there is dopamine-dependent processing during the anticipation of pain which
results in the alteration of the pain rating and strengthens our theory that
dopamine is involved in the cognitive/top-down processes related to pain
perception. However, it is important to state that the PD group did not show
evidence of an altered effect of certainty on pain ratings which was present
following the D2R manipulation. The translatability of results from a young healthy
cohort to Parkinson’s disease is limited due to age-related differences in
nociceptive processing, and the multitude of neurological changes present in the PD
brain. Yet a better understanding of dopaminergic transmission in nociceptive
processing will help to better understand, and treat, dysfunctions of pain
processing.
7.3.b. Neural response
A difference in brain activity during the anticipation of the laser stimuli was present
in the PD patients off their medication, and the D2R agonist and antagonist
conditions, and thus indicates that there are dopamine dependent processes prior
to the perception of pain. As previously discussed in Chapter 2, brain activity which
occurs prior to the perception of pain can represent top-down modulation of pain
perception, whereby factors such as cognition, attention and emotional state can
manipulate the perception of the pain.
7. General Discussion
168
7.3.b.i. Neural response in PD
The increased anticipatory activity within the MCC/SMA of the PD patients whilst
off their medication is hence highlighting an underlying dysfunction of pain-related
processing. The amplified response was not observed following levodopa and
suggests that dopaminergic hypofunction may be responsible for the amplified
response. Therefore, the degeneration and long-term disruption in the
dopaminergic pathways seen in the PD brain are likely to result in persistent
disruption of top-down processes. In addition to the deterioration of the
dopaminergic systems potentially impairing central processing of pain, a possible
‘side-effect’ of dopamine treatments for patients in the early stages of PD is
dopamine hyper-function following L-DOPA administration (Poletti, 2018).
Dopamine hyper-function has been linked with aberrant salience assignment
whereby a neutral stimulus is attributed as a salient event (Poletti, 2018; Nagy et
al., 2012; Poletti et al., 2014). Therefore, the fluctuations between hypo- and hyper-
function of dopaminergic processes are likely to disrupt the interpretation of pain.
7.3.b.ii. Neural response following D2R manipulation
The manipulation of the dopamine D2R in young healthy participants also induced
changes in central processing during the anticipation of the laser stimuli and
highlights a potential role of dopamine activity in the central processes related to
pain. A common compensatory response to the loss of dopamine in PD is a
significant increase in the abundance of striatal D2 (and D1) receptors (Hassan and
Thakar, 1988; Ryoo, Pierrotti and Joyce, 1998), and drug-naïve PD patients have
shown supersensitivity in the D2R binding. A study by Christopher et al., (2014)
have also shown a reduction in D2R in the striatum and insula, within PwPD who
also presented with mild cognitive impairment (MCI). In theory, the change in the
anticipatory activity within regions associated with sensory integration and pain
perception in young healthy volunteers following D2R manipulation may highlight
that the pathophysiological changes in the D2R in the PD brain could also disrupt
pain processing within these regions. The potential reasons for why the neural
7. General Discussion
169
response during anticipation was different in the PD patients and young healthy
volunteers following D2R manipulation are discussed in the forthcoming section.
The D2R study also showed a reduction in the Insula during the perception of the
laser stimuli within the antagonist group. The Insula is a region sometimes
considered to be involved in coding pain intensity, however, there is also evidence
that it has a wider role in the integration of sensory and affective processing in pain
(Derbyshire et al., 1997; Mazzola et al., 2010; Lu et al., 2016; Singer et al., 2004b).
The fact that the altered activity within the Insula within the antagonist condition
did not alter pain rating or pain sensitivity, supports the theory that the Insula is not
solely intensity coding, and further supports our theory that dopamine is not
central to intensity coding.
7.3.c. Parkinson’s Disease vs D2R manipulation in healthy participants
The D2R manipulation in the young healthy group induced a different alteration in
neural activity that was shown in PD patients whilst off their medication (Chapter
3). Therefore, the amplified response in the MCC/SMA within PwPD is likely to not
be solely due to altered D2R activity. This would be expected as PD is not a disorder
of D2Rs, and in reality is the combination of impaired signalling of numerous
neurotransmitters and structural changes across the brain. In addition, the acute
manipulation of D2Rs in healthy volunteers cannot be directly compared to the
chronic changes that have occurred in the PD brain. In addition, the action of taking
long-term medication, and the action of medication withdrawal will have also
produced unique differences in the PD group.
The PD participants off their medication have low striatal dopaminergic processing
and Chapter 3 concluded that they have an increased anticipatory activity within
the MCC/SMA prior to the pain stimuli. In contrast, the dopamine D2R antagonist in
young healthy volunteers evoked a reduction in activation in the temporal/parietal
regions during the anticipation of pain stimuli. The global loss of dopamine and the
antagonism of the D2R hence resulted in opposing effects on the degree of activity
during anticipation, and within different regions. There are numerous reasons for
the differences observed. Firstly, the global loss of dopamine in PwPD is different to
7. General Discussion
170
the specific agonism/antagonism of the D2R, and therefore, cannot be directly
compared. In addition, the neurodegeneration associated with the PD brain such as
the deterioration of the dopaminergic, serotonergic and noradrenergic systems,
along with any age-related differences, mean that a direct comparison cannot be
carried out. Nevertheless, as stated previously, the D2R-related changes in the
healthy brain during anticipation of pain, and the changes in the D2R within the PD
brain, may further suggest an impaired central processing of pain in the PD brain.
7.3.d. Age, Pain and Dopamine
Chapter 6 discussed the finding of reduced brain activation in the older cohort
compared to the young control participants. Due to the focus of this thesis being on
the dopamine’s role in pain perception, an extra consideration to discuss is the age-
related decline in dopamine (Volkow et al., 2000, 1996b; a; Suhara et al., 1991).
There is evidence that dopaminergic activity changes with age. For instance, the D2
(Volkow et al., 1996b) and D1 (Suhara et al., 1991) receptors, and dopamine
transporters (Volkow et al., 1996a) decline with age, and the positive correlation
between midbrain dopamine level and frontal cortex activity changes to a negative
correlation (Dreher et al., 2008). In addition, within healthy aging, there is a
reduction in the D2R within the putamen and caudate, which correlates with a
reduction in motor and cognitive tasks (Volkow et al., 1998). Thus demonstrates
that the age-related reduction in the dopaminergic system can result in the changes
in the performance of dopamine-dependent processes.
We can only speculate that the natural reduction in dopamine and the decline of
dopaminergic processes may indicate that if the dopamine level in the healthy aged
population becomes too low, and drops below the ‘optimal’ level for performance,
there is an impairment in the top-down aspects of pain processing. Theoretically,
this could explain why chronic pain is more prevalence in the older population
however, dopamine is only one of many age-related changes in the brain and no
evidence as yet has investigated this relationship.
7. General Discussion
171
To the best of my knowledge, there are no investigations which specifically
investigate the relationship of age-related changes in dopamine and its effect on
pain processing. Future investigations would be valuable to establish whether age-
related decline in dopamine contributes to the higher susceptibility of chronic pain
in the older population.
7.4. SUMMARY OF INTERPRETATION
In summary, acute modulation of dopamine transmission affects the processing
prior to pain perception. And although no change was seen in the pain rating, long
term changes in dopaminergic transmission and the top-down processes which are
dependent on dopamine, is likely to result in an impaired pain system which creates
persistent and augmented perception of pain. Previous research has shown an
inverted-u relationship between dopamine and the performance of central
processes. Therefore, Figure 7.1 summarises the potential effect that dopamine
level has had on central processing.
7. General Discussion
172
Figure 7.1: A schematic diagram of the relationship between dopamine level and performance of
dopaminer i pro esses. It is important to state t at t e ‘performan e’ is not dire tly orrelated to t e brain
activity during the anticipation of t e laser stim li; rat er t at ‘performan e’ is t e
effectiveness/efficacy/accuracy of dopamine neurotransmission. We suggest that a dopamine level outside
of the optimal may induce an impaired top-down processing of pain.
Firstly, t e lo dopamine level in t e Parkinson’s patients red es t e optimal performan e of t e
dopaminergic processes, such as the precise prediction of stimuli, which may have resulted in the
a mentation of t e anti ipatory a tivity prior to pain stim lation ( an’t be for s re ntil e r le o t
serotonin, noradrenaline etc). However, following the administration of the dopaminergic medication, the
dopamine levels were increased and anticipatory processing did not differ to the HC group. The age-related
decline in dopamine has been taken into account such that the older Healthy controls (Blue Square) within
t e Parkinson’s st dy (C apters 3 & 4) are positioned on the lower side of the optimal dopamine level. We
propose that a de novo PD patient group would show a similar response to our off-medication group; yet
variability would be expected based on the severity of their diagnosis, and the unknown impact of
medication withdrawal and compensation mechanisms has had on our results.
The modulation of dopamine level via the D2 receptor agonist and antagonist is likely to have resulted in an
increase and decrease of dopamine respectively. Both low and high dopamine has been reported to reduce
performance in cognition, memory, attention due to sub-optimal dopamine levels. Therefore, the sub-
optimal dopamine level induce a change in the central top-down processing of the forthcoming stimuli and
also alter the effect of certainty on pain ratings.
Dopamine Level
Pe
rfo
rman
ce
Optimum range PD Off medication
PD On medication
Healthy Controls (Old)
Young Healthy Controls
Agonist
Antagonist
7. General Discussion
173
7.5. NEUROIMAGING RESEARCH OF PAIN IN PARKINSON’S
The motivation of the research within this thesis was to address the concerning
high prevalence of chronic pain in people with Parkinson’s disease, and to establish
whether dysfunctional central processing within PD contributes to the persistence
of pain. To establish the potential significance of the research within this thesis in
the clinical setting, we need to understand where it fits in with the wider research
and how together the research could be clinically important. Therefore, here we
will summarise the research studies which shaped our theory of altered central
processing prior to the commencement of the PhD (pre-2015), and also discuss the
research which has been published during the PhD (post-2015).
At the beginning of the PhD, there was limited neuroimaging research published on
central pain processing in PD. An early publication by Schott, 1985 highlighted the
potential role of the dysfunctional dopaminergic pathways in altered pain in
Parkinson’s disease, however, focused research on central pain processing is limited
until the last decade.
7.5.a. Pre-PhD
Prior to starting the research within this thesis (pre-2015), there were few
neuroimaging studies which focused on the central processing of pain in PwPD. A
PET imaging study by Brefel-Courbon et al., (2005) showed that PwPD had an
increased activity within the right insula and PFC, and the left ACC, during the cold
pressor test compared to the healthy age-matched group. The augmented activity
was attenuated following Levodopa administration. The research conducted by
Schestatsky et al., (2007) and Tinazzi et al., (2008) investigated the LEP response to
CO2 laser stimulation using single or pairs of electrodes on the scalp. Schestatsky et
al., (2007) concluded that the LEP response at the electrode position of Cz was
amplified in PD patients who suffered from chronic pain, but not in those who did
not have pain and controls. The difference was attenuated or disappeared following
L-Dopa medication. In contrast, Tinazzi et al., (2008), recorded LEPs from electrodes
at Cz and Fz positions, from pain-free PD patients, and reported a reduction in LEP
7. General Discussion
174
amplitude which was not affected by Levodopa administration. Therefore, the
study by Brefel-Courbon et al., (2005) highlighted a difference in central pain
processing in PD patients during the perception of pain. The research by
(Schestatsky et al., 2007a) and (Tinazzi et al., 2008) applied similar techniques to
conclude differences in the LEP in PD, however, the LEP is unlikely to shed-light on
alterations in top-down processing.
7.5.b. Post-PhD start
Following the start of the PhD (2015) there have been several neuroimaging studies
published which strengthen the theory that PD pathophysiology alters pain
processing and increase the likelihood of developing chronic pain. fMRI has been
the most widely used technique and has highlighted differences in regions
associated with pain processing in PwPD. Tan et al., (2015a) showed a reduced
functional connectivity in the putamen and midcingulate cortex, and between the
basal ganglia network and the salience network, during heat pain stimuli in drug-
naïve PwPD without pain. Also a reduced activation of the temporal gyrus and
insula was shown in subsequent analysis (Tan et al., 2015b). An fMRI and DTI
investigation of PwPD with chronic pain indicated a thinning in several regions
which are associated with pain processing (i.e. PFC, cingulate cortex & parietal
areas), and an amplified response within the cerebellum and right inferior temporal
areas, plus a reduction in the activity within the left frontal inferior orbital cortex
(Polli et al., 2016). There was one study which also investigated the anticipatory
response to laser pain in PD, and concluded that PD patients showed a reduced
activation in the ACC and dorsal lateral PFC during anticipation and an augmented
response in the MCC during the receipt of the pain (Forkmann et al., 2017). And
finally, a study by Tessitore et al., (2018) showed that drug-naïve, pain-free PD
patients showed an increase in neural activity within the left somatosensory cortex,
left cerebellum and right pons, during heat pain stimulation.
Therefore, the research within this thesis in combination with other research also
concluding altered central processing in PwPD (with or without pain) strongly
indicates a role of altered central processing in chronic pain in PwPD. There is
7. General Discussion
175
therefore, a scientific rationale to promote the use of treatments which aim to
improve central processing (i.e. meditation, cognitive behavioural therapy etc) in
the treatment of pain in PwPD. For example, mindfulness meditation has
successfully been used to reduce pain and is considered to improve cognitive
processing important in modulating pain (Zeidan et al., 2012). Furthermore, EEG
imaging has shown that meditation can alter anticipatory neural responses to
painful stimuli and reduce the resultant unpleasantness ratings of the pain (Brown
and Jones, 2010). In addition, there is increasing evidence of Cognitive Behavioural
Therapy (CBT) as a treatment option for chronic pain. Along with improvements in
pain severity, fMRI has highlighted the beneficial changes in connectivity between
regions associated with nociceptive processing (Shpaner et al., 2014). Hence there
is converging evidence that non-pharmacological ‘alternative’ treatments may be a
valid treatment option for chronic pain which is considered to be due to
dysfunctional top-down processes such as cognition, expectations, attention and
emotion.
7.6. FUTURE CONSIDERATIONS
The limitations within the studies have been discussed within the individual
chapters, however, there are general considerations which should be taken into
account for future research.
7.6.a. Selection of pain stimuli
The use of the CO2 laser for pain stimulation was chosen for its optimal delivery
speed and activation of nociceptive fibres and previous use in pain research (Brown
and Jones, 2008; Clark et al., 2008b; Brown, El-Deredy and Jones, 2014). However,
the technique does not come without limitations. Although the laser selectively
activates the Aδ- and C-fibres which process pain, the activation of the C-fibres can
sometimes result in a dull burning sensation which reduces the ability to distinguish
the laser stimuli resulting in habituation. In contrast, due to the laser heating up the
skin over time, there is also the potential development of sensitisation to the laser.
The movements within a grid aim to avoid the development of sensitisation,
7. General Discussion
176
however, due to the limited size of the grid, it is likely that the same area of skin will
receive more than one laser stimulus.
A further limitation of the laser which needs to be considered is the potential to
cause long-lasting pigment change or skin damage. All participants included in laser
experiments are informed of the likelihood of temporary pigment changes which
last up to 6 weeks. However, there is a small chance that the pigment changes can
last longer than this in a very small number of cases. Although there is a maximum
of energy that the laser can be set to, the individual differences in skin sensitivity
results in different reactions to the laser. There were no long-term changes in
pigment change or skin damage within the studies included in this thesis.
There are several other options to induce pain experimentally which could be
considered for future research. Firstly, the use of an electric shock allows for event
related potentials (ERPs) analogous to the laser evoked potentials (LEPs). However,
the stimulation activates all nerves and muscles below the electrodes and is not
specific to pain fibres. Also, the anticipatory response to electric pain is different to
that evoked by laser stimuli (Hird et al., 2018), plus the habituation of electrical
stimuli is common (Bingel et al., 2007).
Another consideration is whether the use of acute pain stimulation is optimal to
investigate chronic pain. A benefit of using acute pain is that it allows for the
investigation of anticipation prior to the stimuli, a technique to assess the top-down
processes that occur prior to pain which have been shown to be abnormal in
chronic pain conditions (Brown, El-Deredy and Jones, 2014). In addition to acute
pain, tonic pain induced via the cold pressor test (CPT) is also beneficial to research
chronic pain due to it inducing persistent pain which requires motivational,
attentional and emotional control (Verhoeven et al., 2010; Ahles, Blanchard and
Leventhal, 1983), and can investigate coping strategies of individuals (Berntzen,
1987; Efran et al., 1989; Geisser, Robinson and Pickren, 1992). The CPT involves the
submersion of the hand into cold water and has been used for the investigation of
affective and cognitive factors which influence pain perception (Birnie et al., 2014).
However, there is a high variability in the protocol of CPT between researchers and
7. General Discussion
177
leads to a limited ability to compare results between studies. The CPT has
previously shown that Parkinson’s patients have a difference cardiovascular
response during the CPT, and the tolerance of the CPT has been shown to be
reduced in PD patients. However, to the best of our knowledge there have been no
investigations of neural activity during CPT in Parkinson’s disease. Therefore, it
would be beneficial to include the CPT in further neuroimaging investigations into
chronic pain in PD.
7.6.b. Protocol Design
The laser paradigm has been used in both experiments and no changes were made
to allow direct comparison between studies. The laser paradigm is a replication of
previous research into chronic pain conditions; however, improvements could be
made to the paradigm.
The current laser paradigm would be improved by including both innocuous and
noxious stimuli to allowing conclusions to be drawn that the neural response is
unique to the anticipation of noxious stimuli. For instance, the innocuous stimuli
could be a tactile stimulus and be cued by a visual cue of ‘No Pain’.
In addition, the reduction in the pain rating observed in the older participants
highlighted that an improvement to the protocol is needed so that the perception
of the pain is maintained throughout the experiment so that the anticipation is to a
consistent perceived intensity. Firstly, the psychophysics procedure could include a
more randomised up/down method to calibrate the laser to the participants’
subjective level 7. Also, the voltage of the laser could be updated throughout the
main experiment. Experience with the laser has led to the development of a
formula (see below) which allows for a standardised change in laser voltage that is
related to the average pain rating within each block of 30 laser stimulations.
𝒘 ∗ (𝒙 − 𝒚) = 𝒛 𝑤 = 𝑇𝑤𝑜 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑠 𝑜𝑓 𝑙𝑎𝑠𝑒𝑟 𝑣𝑜𝑙𝑡𝑎𝑔𝑒 (0.12 𝑉) 𝑥 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑝𝑎𝑖𝑛 𝑟𝑎𝑡𝑖𝑛𝑔 𝑜𝑓 𝐻𝑖𝑔ℎ 𝑦 = 𝐷𝑒𝑠𝑖𝑟𝑒𝑑 𝑝𝑎𝑖𝑛 𝑟𝑎𝑡𝑖𝑛𝑔 (𝑖. 𝑒. 7) 𝑧 = 𝑉𝑜𝑙𝑡𝑎𝑔𝑒 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒
7. General Discussion
178
7.6.c. Study Recruitment
7.6.c.i. Parkinson’s patients
For PD versus HC study, a possible improvement of the study design would have
been to use additional control groups which divided the PD group into people with
and without chronic pain, and healthy control group with no pain. This would allow
any differences observed in the PD group to be independent of the presence of
chronic pain and any abnormal brain response would hence be unique to PD
physiology. An additional comparison with a chronic pain group would also be
interesting to establish whether any maladaptive central processing within PD is
common to all chronic pain conditions (such as FM) or whether the abnormalities
are unique to PD.
In addition, the PD group could be better controlled by solely recruiting drug-naïve
participants, single medication usage, and/or similar disease progression or
duration. However, to remove the variability within the PD group would remove the
true representation of the variety seen in the PD population. The recruitment of a
small/specific participant is timely and often very difficult at a single-site
experiment. The recruitment criteria being restricted to people able to be
withdrawn from their medication already created a limited recruitment pool, and
hence a further restriction of recruitment would have resulted in delays and a false
representation of the PD population.
7.6.c.ii. D2R study
Within the Dopamine D2R modulation study, the participants attended three visits
and carried out the same laser protocol. The order of the drug condition (agonist,
antagonist or control) was randomised; however, there is a potential effect on
neural processing when a protocol is repeated. A possible effect is that the
participants’ response to the laser stimulation would be different on each visit,
especially due to the stimuli being novel to the participant on their first visit. This
potential effect was controlled by the participants receiving the three drug
conditions in a randomised order. However, for future research, we propose the
7. General Discussion
179
use of a pre-study assessment to introduce the pain stimuli to the participants. This
may reduce potential chance of a higher sensitivity to the laser being seen on their
first visit compared to subsequent visits.
The choice to use a within-subject repeated-measure study design was selected
over a between-subject design as the positives outweighed the negatives. For
instance, a significantly higher n number would have been required for a between-
subject study design. Plus there is a high degree of variability within individuals in
their baseline dopamine, D2R properties, and pain perception, which could not
have been controlled for and could have led to high variability within and between
drug condition groups.
7.7. FUTURE RESEARCH
7.7.a. The role of Dopamine, Serotonin and Noradrenaline in pain
perception
The focus of the research within this thesis is on dopamine’s role in pain processing
due to PD being characterised by low dopamine levels. However, PD is also a result
of other neurotransmitter changes, such as serotonin, noradrenaline, choline, and
glutamate transmission. Therefore, understanding of the role of these
neurotransmitters in pain perception will increase our understanding of what
causes the potential dysfunction of central processes in PD.
Future research to compliment the findings within this thesis includes the
investigation of tryptophan (precursor of Serotonin) and tyrosine (precursor of
Dopamine & Noradrenaline) depletion in healthy volunteers. The design will be a
repeated-measure, triple-crossover, placebo-controlled, double-blind study. The
aim is to use the same laser and EEG protocol to investigate the effect of low levels
of Serotonin and Dopamine/Noradrenaline, with additional testing of the CPT
response due to its potential translatability to central processing associated with
chronic pain. Results of the study will hopefully help to explain how low levels of
7. General Discussion
180
serotonin and noradrenaline (in addition to low dopamine), could contribute to
impaired central pain processing in PD.
Preliminary findings of the study indicates that the depletion of serotonin and
dopamine via their precursors Tryptophan and Tyrosine respectively, does not
change pain tolerance to the laser stimuli or the cold pressor test. The lack of
difference induced in pain perception in dopamine depletion is in line with our
results presented in the D2 study within this thesis. However, the serotonin
depletion has previously been shown to result in a decreased pain tolerance to
laser stimuli (Martin et al, 2017). Therefore, our results may indicate that there is a
degree of variability in the role of serotonin pain tolerance. EEG imaging indicated
that serotonin depletion evokes amplification in the anticipation-evoked neural
response to pain during the late anticipation time-window, plus increased activity
during the receipt of the laser. In addition, there was evidence that serotonin and
dopamine depletion reduced expectation- and certainty-evoked neural responses
compared to the control condition. Therefore, preliminary findings are in
agreement with our conclusions drawn within this thesis that dopamine and
serotonin are involved in the central processing associated with processing
nociceptive information, rather than coding the intensity of acute pain.
7. General Discussion
181
7.8. FINAL CONCLUSION
In summary, the research conducted within this thesis supports the theory of
altered central pain processing in PD; and is likely to contribute to the high
prevalence of pain in PD. The role of dopamine in causing the abnormalities is not
confirmed, however, a possible normalisation of anticipation following dopamine
medication, and altered pain anticipation during D2R manipulation, may indicate
that dopamine is involved in the disruption in top-down processing seen in PD. Our
conclusions are strengthened by the recent neuroimaging studies investigating pain
in PD which have also confirmed altered central processing. Further investigations
of global loss of dopamine, noradrenaline and serotonin will help to further
understand the cause of these abnormalities. A better understanding of pain in PD
is essential to provide better treatment and improve the quality of life for people
with PD.
182
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