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Faculty of Psychology and Educational Sciences
Department of Experimental and Theoretical Psychology
Academic Year: 2015-2016
2nd Examination Session
White matter integrity and cognition in young Traumatic Brain Injury patients:
a diffusion tensor imaging study
Master thesis in aspiration for the degree of Master of Science in Psychology,
option Theoretical and Experimental Psychology
Louise Puttevils
01010789
Promotor: Guy Vingerhoets
Co-promotor: Karen Caeyenberghs
Mentor: Helena Verhelst
Acknowledgements
This master thesis is the final realization before obtaining my master’s degree in Theoretical
and Experimental Psychology. It has been a hell of a ride, by all means. It hasn’t always been
easy, but on the way I have learned a lot of valuable skills and obtained interesting knowledge
that I will carry with me in the future.
First of all, I would like to thank my promotor Guy Vingerhoets, and my co-promotor Karen
Caeyenberghs for giving me the opportunity to work on this fascinating project.
Additionally, I am very grateful for the excellent supervision of my mentor, Helena Verhelst.
For encouraging me to keep going whenever I lost track, guiding me through the intriguing
jungle of TBI and DTI, and supporting me in the writing process with patience and much
appreciated feedback.
Furthermore, I would also like to express my gratitude towards my friends, and a special
thanks goes to a few people in particular. To Gang 1 of Home Fabiola, for sharing simple
moments of joy, for entertaining conversations that took my mind off my thesis for just a
moment, and for enduring my whining (a special thanks goes to Daan for his delightful
meals!). To Niki, for enduring and understanding my procrastination, and for standing by me
in times of need. To Anaïs, not only for checking my thesis most carefully, but for being there
no matter what and for giving me the courage to keep going. And to Linde and Annelien, for
their continuous support and entertainment at all times, and for keeping me motivated
throughout the whole process.
Last but not least, the biggest thanks goes out to my family. To my sweethearts Cheyenne,
Trésor and Kweepetje, for their unconditional love. But most of all to my mum, without
whom I would never have been able to get here. Thank you for providing a warm and cosy
home, for your everlasting belief in my abilities, and simply for making me a better person.
You’re the best mum I could wish for and I could not be more grateful.
Abstract
Traumatic Brain Injury (TBI) is a leading cause of disability in young children and
adolescents, and often involves the occurrence of diffuse axonal injury (DAI). DAI can lead
to neurological impairment, and this affects higher cognitive functions such as cognitive
control, attention and memory. Previous studies have already revealed a link between certain
white matter structures and performance on cognitive tests. The current study aims to
investigate whether there is a link between attention and working memory, and the Superior
Longitudinal Fasciculus (SLF) and cingulum as white matter tracts of interest in adolescents
with chronic TBI. Compared to other neuroimaging techniques, Diffusion Tensor Imaging
(DTI) is a more suitable tool to detect DAI and to predict functional outcome. DTI parameters
(fractional anisotropy and mean diffusivity) were calculated and subsequently correlated with
performance on neuropsychological tests (spatial span and flanker task). The data acquired in
nine adolescents who sustained moderate-to-severe TBI revealed no significant relationship
between working memory and white matter bundles, and the correlation with attention turned
out to be non-significant as well. These results are not consistent with previous findings, that
found that the decreased structural connectivity and integrity of the SLF and cingulum in TBI
patients have a significant impact on certain aspects of cognition. Our findings could be
explained by issues such as methodological choices and biological confounds.
Keywords: Traumatic Brain Injury, Diffuse Axonal Injury, Attention, Working Memory,
Diffusion Tensor Imaging, Superior Longitudinal Fasciculus, Cingulum
List of abbreviations
TBI Traumatic Brain Injury
DAI Diffuse Axonal Injury
GCS Glasgow Coma Scale
LOC Loss of Consciousness
PTA Post-Traumatic Amnesia
WM Working Memory
CT Computed Tomography
MRI Magnetic Resonance Imaging
DWI Diffusion Weighted Imaging
DTI Diffusion Tensor Imaging
ADC Apparent Diffusion Coefficient
FA Fractional Anisotropy
MD Mean Diffusivity
SLF Superior Longitudinal Fasciculus
CANTAB Cambridge Neuropsychological Test Automated Battery
SSP Spatial Span
TR Repetition Time
TE Echo Time
FOV Field of View
ROI Region of Interest
Table of contents
Introduction...............................................................................................................................1
Neuropathology of TBI...................................................................................................1
Diagnosis and classification of TBI................................................................................2
Consequences of TBI......................................................................................................4
Working Memory................................................................................................4
Attention..............................................................................................................6
Structural Imaging of TBI..............................................................................................7
Standard neuroimaging of TBI...........................................................................7
Diffusion Weighted Imaging..............................................................................8
DTI based measures..........................................................................................9
Fractional anisotropy.............................................................................9
Mean diffusivity....................................................................................10
DTI studies in TBI.............................................................................................10
Current study.....................................................................................................12
Methods....................................................................................................................................12
Participants....................................................................................................................12
Tasks and procedure......................................................................................................13
Neuropsychological assessment........................................................................14
Working memory task...........................................................................14
Attention task.........................................................................................15
MRI data acquisition.....................................................................................................16
DTI preprocessing.........................................................................................................16
Statistical analysis.........................................................................................................17
Results......................................................................................................................................17
Relationship Between DTI Measures And Behavioural Outcome...........................18
Fractional anisotropy.........................................................................................18
Mean diffusivity................................................................................................18
Discussion.................................................................................................................................19
Limitations and future directions..................................................................................21
Conclusion.....................................................................................................................23
Nederlandstalige samenvatting..............................................................................................24
References................................................................................................................................25
1
Introduction
Traumatic Brain Injury (TBI) can be defined as “an alteration in brain function, or other
evidence of brain pathology, caused by an external force” (Menon, Schwab, Wright, & Maas,
2010, p. 1637). External forces (due to a traffic accident, fall, sports injury, …) may include
direct impact of the head with another object, indirect forces from acceleration/deceleration,
or a blast injury (Blyth & Bazarian, 2010). TBI is a major cause of death and disability,
especially in children and young adults (Kraus & McArthur, 1996; Bruns & Hauser, 2003). In
industrialized countries, the incidence is approximately 1/2000, and TBI is most common in
young children between zero and four years old, older adolescents between 15 and 19 years
old, and adults aged 65 and older (Faul, Xu, Wald, & Coronado, 2010). Falls are responsible
for half of the incidences of TBI among children between 0 and 14 years old, followed by
motor vehicle accidents, collisions and assaults (Faul et al., 2010).
Neuropathology of TBI
Different lesion types can occur after sustaining TBI, and these types of brain damage
can co-occur. A first distinction can be made between primary and secondary brain damage.
Primary brain damage is the degree of damage which is measured at the moment of the
impact (skull fracture and bleeding). The damage that evolves over time after the trauma, is
called secondary brain damage (e.g. swelling and increased intracranial pressure; Baethmann
et al., 1998). These processes have their origin at the start of the injury, but have a delayed
clinical manifestation.
Another way to differentiate between different types of TBI includes the
location of the brain damage (Povlishock & Katz, 2005): in focal brain damage, the lesion is
manifested locally, and this includes incidents such as (superficial) brain contusions, skull
fractures, and intracranial hematomas. In addition to focal damage, closed head injuries
frequently cause diffuse brain injuries or damage to several other areas of the brain. Diffuse
(also known as multifocal) brain injury on the other hand is more spread out, and may result
in diffuse axonal injuries and diffuse brain swelling (Graham, Adams, Nicoll, Maxwell, &
Gennarelli, 1995). Diffuse axonal injury (DAI) is caused by rotational acceleration (the head
suddenly accelerates and the stationary brain is struck by the accelerated cranium at the site of
the blow)/deceleration (rapidly moving skull is abruptly stopped (e.g., an auto accident),
while the brain continues forward and impacts directly below the site where the skull stops)
forces that arise at the time of the injury (Gentry, 1994). DAI is defined by microscopic
2
lesions that occur throughout regions that contain white matter such as corpus callosum, upper
brain stem and internal capsule (Levin et al., 1997).
Diagnosis and classification of TBI
There are different ways to classify the severity of TBI (see Figure 1). In most TBI
research, TBI is classified according to single indicators such as the Glasgow Coma Scale
(GCS, Teasdale & Jennett, 1974), Loss of consciousness (LOC) and Post Traumatic Amnesia
(PTA). The GCS exists of three subscales (eye opening, motor response and verbal response)
and is commonly used to define the severity of a TBI within 48 hours after the injury has
taken place. The score on the GCS is used to distinguish between mild (GCS 13-15),
moderate (GCS 9-12) and severe (GCS 3-8) TBI. Loss of consciousness (LOC) and post
traumatic amnesia (PTA) are more recent classification methods to define TBI. Based on the
LOC criteria, one can make an alternative discrimination between mild (the patient has been
unconscious for 30 minutes or less), moderate (more than 30 minutes but less than 24 hours)
and severe (more than 24 hours) TBI. PTA can be defined as “a period of mental confusion
immediately following head trauma during which disorientation as to place, time and person
is present, and in addition the inability to retain (new) experiences” and is the interval
between injury and the return of continuous recall (Rujis, Keyser, & Gabreels, 1994). For
mild TBI, the PTA lasts 0-1 days, up to seven days for moderate TBI and one speaks of
severe TBI if the PTA lasts for longer than seven days.
Fig. 1. Classification of TBI based on the Glasgow Coma Scale (GCS), Post-
Traumatic Amnesia (PTA) and Loss of Consciousness (LOC).
3
The Mayo Classification system for TBI severity (Malec et al., 2007) makes use of a
combination of the GCS, PTA, and LOC. According to this system, TBI patients can be
categorized into three levels of injury severity: Moderate-Severe (Definite) TBI, Mild
(Probable) TBI, and Symptomatic (Possible) TBI. The criteria for each category can be found
in Figure 3. One advantage of the Mayo Classification system over single indicator systems is
that it can classify a larger number of cases with reasonable accuracy (Malec et al., 2007). It
also makes use of a positive classification method, which implies that as soon as one or more
of the TBI criteria apply (as stated in Figure 2), an individual can already be classified as (A)
Moderate-Severe TBI, (B) Mild (Probable) TBI or (C) Symptomatic (Possible) TBI,
according to outcome severity.
Fig. 2. The Mayo TBI Severity Classification System (Malec et al., 2007).
4
Consequences of TBI
Different types of TBI severity can result in a variety of cognitive, neurological,
emotional and behavioural changes, and there is a lot of variability in outcome across
individuals. Most progress is made during the first six months following the trauma, and most
of the recovery is made between the first and the second year after injury (Babikian &
Asarnow, 2009; Babikian, Merkley, Savage, Giza, & Levin, 2015). Both acute critical care
variables and premorbid psychosocial variables can predict the degree of recovery during the
first years post-injury (Woodwarth et al., 1999; Green et al., 2008).
Even several years after post-injury, patients still experience impairment on a lot of
different ‘high-level’ domains, such as intelligence, memory, attention, executive function,
learning, language, (psycho)motor skills, and social judgment (Langlois, Rutland-Brown, &
Wald, 2006; McAllister, 2011; Babikian et al., 2015). Other deficits can be located on the
behavioural level, such as poor impulse control, anxiety, irritability, agitation, confusion, loss
of spontaneity, frustration, stress, denial, and affective disturbances (e.g. Trenchard, Rust, &
Bunton, 2013). Cognitive impairment is one of the most frequent consequences of TBI, and
they persist to exist even several years after sustaining TBI (Chen & D’Esposito, 2010;
Whitnall, McMillan, Murray, & Teasdale, 2006). The information of these cognitive functions
is spread out across several spatially divergent brain regions. Since DAI damages long-
distance white matter tracts that connect several regions, cognitive functions will be impaired
(Mesulam, 1998). Disruption in large-scale intrinsic connectivity networks can be an indicator
for damage on the functional level, which often leads to cognitive impairment (Sharp, Scott,
& Leech, 2014). Because TBI has an influence on a widespread variety of domains, some
cognitive functions are more vulnerable than others to develop impairments due to DAI.
Working memory and attention are two of the most frequent domains that are affected by TBI
(McAllister et al., 1999; Levin et al., 2002; Levin et al., 2007; Wassenberg, Max & Lindgren,
2004). Although these cognitive domains have often been studied as independent cognitive
constructs, more recent studies have shown that these two concepts are strongly related, and
that attention is a critical component for WM capacity, and WM is a valid predictor of
attentional control (Kane, Bleckley, Conway, & Engle, 2001).
Working Memory. Memory impairment is not only one of the most frequent
complaints, but also among the most severe deficits following TBI (e.g. McAllister,
Flashman, McDonald, & Saykin, 2006; Sanchez-Carrion et al., 2008). One of the components
of explicit memory that can be damaged in TBI patients, is working memory (WM). WM can
5
be defined as a limited capacity system that accounts for the temporary maintenance and
processing of task-relevant information that is necessary for reasoning, language
comprehension, memory updating and learning (Baddeley, 1992). According to the model of
Baddeley & Hitch (1974), WM consists of three components: the central executive,
the phonological loop and the visuo-spatial sketchpad (separate systems for verbal and spatial
information). The central executive acts as a supervisor and coordinates its two slave systems
(phonological loop & visuo-spatial sketchpad). The phonological loop is one of the slave
systems of WM that processes and stores verbal information, and consists of two
subcomponents: a phonological store (holds speech-based information for a few seconds) and
an articulatory control process (related to inner speech, this process registers and maintains
verbal material in the phonological store). The visuo-spatial sketchpad on the other hand
temporarily holds and manipulates spatial and visual information, and it is also involved in
navigation processes. In a more recent version, Baddeley (2000) added a fourth component to
the model: the episodic buffer (Figure 3). This component acts as another slave system that
integrates the processed information of the other slave systems. The episodic buffer is
probably linked to long-term memory as well as to semantic meaning. If we look at the
number of information units that can be stored in WM, there is some evidence for the fact that
the maximum span contains about seven units (Miller, 1956).
Fig. 3. The updated version of the working memory model (Baddeley, 2000).
WM is a complex system and is associated with multiple brain regions (Gazzaley, Rissman, &
D’Esposito, 2004). Critical regions for WM typically include prefrontal cortex, anterior
cingulate cortex, and medial frontal and parietal regions (Christodoulou et al., 2001;
6
Newsome et al., 2007; Scheibel et al., 2007; Perlstein et al., 2004). The different WM
components are localised in different brain regions. The left temporoparietal region is
involved in the phonological loop (Vallar, DeBetta, & Silveri, 1997; Baddeley, 2003), while
the visuospatial sketchpad can be divided into a ventral (from occipital to temporal cortex)
and a dorsal stream (from occipital to parietal cortex; Müller & Knight, 2006). The
dorsolateral prefrontal cortex is involved in the central executive (D’Esposito et al, 1995).
Even several years post-injury, WM was still impaired in children who had sustained a
severe TBI compared to mild traumatic brain injury (Levin et al., 2002). The more time
elapsed since the occurrence of the injury and the older the individual is at the moment of
injury both have a negative impact on WM (Dunning, Westgate, & Adlam, 2016). Individuals
with TBI have impaired performance on dual task paradigms that involve the intervention of a
central executive, and this effect was more noticeable in individuals with severe TBI
(McDowell, Whyte, & D’Esposito, 1997; Leclerq et al., 2000; Perlstein et al., 2004; Phillips,
Parry, Mandalis, & Lah, 2015). A lot of studies have been focusing on the verbal component
of WM, but very few studies investigated the visuo-spatial component of WM (Dunning et al.,
2016).
Attention deficits in TBI. TBI is not only associated with memory disorders, another
cognitive function that is often impaired after TBI, is attention (Ponsford & Kinsella, 1992;
Mathias & Wheaton, 2007). Attentional networks are widespread throughout the whole brain,
including the parietal, frontal, temporal and cingulate cortices in addition to the midbrain
(Posner and Petersen, 1990; Petersen & Posner, 2012). Attention can be categorized into
different types. Sustained attention is the ability to direct and focus cognitive activity over
time without getting distracted. In one of the TBI studies that made use of the Continuous
Performance Test (Rosvold, Mirsky, Sarason, Bransome, & Beck, 1956), children with TBI
had a significant lower performance compared to children with mild to moderate TBI six
months post injury (Kaufman, Fletcher, & Levin, 1993). Children with moderate to severe
TBI also showed larger reaction times and less accurate responses compared to healthy
controls (Anderson & Pentland, 1998). Selective attention is the ability to focus on one task
and meanwhile filtering out unwanted information relevant to our goals, and this is often a
part of controlled processing (Desimone & Duncan, 1995). Studies have shown that children
with moderate to severe TBI performed significantly worse than a control group on a selective
attention task (Anderson & Pentland, 1998; Park, Allen, Barney, Ringdahl, & Mayfield,
2009), while other longitudinal studies show long-lasting impairments caused by the TBI
7
(Anderson & Pentland, 1998; Verger et al., 2000). When you shift your focus of attention
between several different tasks, one can speak of alternating attention. Children with severe
TBI show impaired performance on alternating attention tasks compared to children with mild
to moderate TBI (Catroppa, Anderson & Stargatt, 1999). A significant difference in
performance on a shifting attention task was also found between children with moderate to
severe TBI compared to a control group (Park et al., 2009). Divided attention is known as the
ability to simultaneously focus on more than one source of information at the same time. In
studies where children had to attend two different stimuli at the same time, performance was
significantly impaired according to the severity of the injury (Anderson, Fenwick, Manly, &
Robertson, 1998; Catroppa, Anderson, Morse, Haritou, & Rosenfeld, 2007). The two types of
attention that seem to be most sensitive to show impairment after the occurrence of TBI are
sustained and divided attention (Ginstfeldt & Emanuelson, 2010), but deficits in processing
speed, selective attention, attentional control and attention span are also frequently affected
following TBI (Mathias & Wheaton, 2007).
Cognitive impairment is one of the main consequences following DAI. To investigate
the relationship between white matter integrity and behavioural outcome into more detail, a
variety of neuroimaging techniques can be used to obtain more information about the severity
of the injury in specific white matter tracts, and subsequently link these results to measures on
the cognitive level.
Structural Imaging of TBI
Standard neuroimaging of TBI. In the acute stage following a head injury, standard
neuroimaging techniques are carried out to identify the location and size of possible brain
damage and to determine operability. A Computed Tomography (CT) scan is routinely the
first imaging method to be performed (Coles, 2007; Toyama et al. 2005), since it is highly
accurate to detect skull fractures and intercranial haemorrhage. A large advantage of this
technique is that it allows rapid assessment of brain pathology and it can even be used with
unstable and critically injured patients. However, CT scans are not very sensitive to detect
abnormalities and pathological changes in regions that are commonly injured following TBI
(Coles, 2007). Magnetic Resonance Imaging (MRI) is superior to CT in detecting diffuse
axonal injury (Coles, 2007), although it is not suited to use with severely injured patients due
to its sensitivity to motion artefacts. In MRI, a small number of tissue protons is able to
absorb and emit radio wave energy within a magnetic field, by aligning and displacing the
tissue protons from the main magnetic field with the use of radiofrequency gradients. It is a
8
non-invasive imaging method that holds different imaging modes to investigate the
neurological damage, including T1-weighted imaging (demonstrates differences in the
magnetization time of diverse tissues; useful for visualisation of normal anatomy) and T2-
weighted imaging (demonstrates different dephasing times of diverse tissues; useful for
visualization of pathology). Even though these MRI techniques are able to pick up white
matter damage, techniques such as Diffusion Weighted Imaging (DWI) are more suitable to
detect subtle markers of diffuse axonal injury.
Diffusion Weighted Imaging. DWI, as well as its extension into Diffusion Tensor
Imaging (DTI), is a neuroimaging technique based on MRI that measures the random
Brownian motion of water molecules within a voxel of tissue. Brownian motion or molecular
diffusion can be described as the random movement of microscopic particles suspended in a
liquid or gas, propelled by thermal energy. If there are few to no physical constraints, the
water molecules are able to spread out equally and randomly in all directions, the diffusion is
isotropic (e.g. cerebrospinal fluid regions). Diffusion is anisotropic if water molecules are
constrained by physical boundaries (e.g. axon walls and myelin sheaths enclosing the axons)
and have a dominant orientation (e.g. white matter fibre bundles such as the corpus callosum;
Deprez, Billiet, Sunaert, & Leemans, 2013; Rosenbloom, Sullivan, & Pfefferbaum, 2003, see
Figure 4). The relative amount of water in white matter is about 70 percent, while
cerebrospinal fluid consists of about 99 percent water (Rosenbloom et al., 2003). In DWI,
there is a diffusion gradient to measure the movement of the water molecules in the direction
of this gradient. This is quantified by the apparent diffusion coefficient (ADC), the magnitude
of diffusion derived from the DWI image and a reference image (also referred to as “b=0”
image).
Fig. 4. Example of isotropic (a) and anisotropic (b) diffusion (Deprez et al., 2013).
9
Although DWI is a useful technique to visualize diffusion contrast, it is unable to
measure anisotropy and primary fibre orientation in a direct way (Deprez et al., 2013). DTI
(Basser, Mattiello, & LeBihan, 1994) is a non-invasive neuroimaging technique derived from
DWI that is used to measure the diffusion of water molecules to make inferences concerning
white-matter tracts connecting different brain regions (Basser, Pajevic, Pierpaoli, Duda, &
Aldroubi, 2000; Alexander, Lee, Lazar, & Field, 2007). DTI needs at least six DWI images
(next to the “b = 0” image; the minimum number required for tensor calculation), each
obtained with a different orientation of the diffusion gradients. Diffusion Tensor Imaging is
more suited than other neuroimaging techniques to examine DAI that is caused by TBI, due to
its degree of sensitivity to changes in the microstructure of white matter (Neil, Miller,
Mukherjee, & Huppi, 2002). Whereas conventional magnetic resonance imaging (MRI) is
useful to reveal focal brain damage, it is less susceptible to detect diffuse axonal injury
(Azouvi, 2000; Goetz et al., 2004). The study of Huisman et al. (2004) revealed that changes
in white matter, as measured by DTI, show a significant correlation with acute scores on the
Glasgow Coma Scale and scores on the Rankin Scale at discharge, which is used to determine
the degree of disability in the daily activities (Rankin, 1957). The authors concluded that DTI
might be an important biomarker for the severity of tissue injury and a predictor for outcome.
In order to determine the magnitude and directionality of water diffusion along white
matter tracts as characterized by the diffusion tensor and its derived diffusion parameters. In
the current study, we made use of two DTI based measures, fractional anisotropy and mean
diffusivity (Le Bihan et al., 2001).
DTI based measures.
Fractional anisotropy. One way to detect TBI on neuronal level is through common
metrics such as Fractional Anisotropy (Nakayama et al., 2006; Xu, Rasmussen, Lagopoulos,
& Haberg, 2007). Fractional anisotropy (FA; Figure 5) is a diffusion anisotropy measure and
reflects the extent to which the diffusion process is anisotropic and the water molecules have a
dominant orientation (ratio of the diffusion in the principal direction of axons to diffusion in
the perpendicular directions; Wozniak et al., 2007). While FA is highly sensitive to
microstructural changes, it is less specific to the type of change (e.g. radial or axial;
Alexander et al., 2011), so multiple diffusion tensor measures should be used in order to
define the tissue microstructure. FA values vary between 0 (isotropic diffusion) and 1
(anisotropic diffusion) (Le Bihan et al., 2001). Water molecules located in fiber tracts are
more likely to be anisotropic (diffusion occurs only along a single axis), as they are restricted
10
in their movement, while water molecules located in the rest of the brain have less limited
motion and hence demonstrate more isotropy (unrestricted in all directions).
Fig. 5. Fractional anisotropy (Nagy et al., 2005)
Mean diffusivity. Mean Diffusivity (MD), also known as ADC, is a measure of the
average diffusivity (of water molecules) within tissue using DWI. MD represents the average
amount of water diffusion in a given region, and is considered to be influenced by the
integrity and size of the axon. Higher MD values are a possible indicator of increased
diffusion rates (Bennett, Madden, Vaidya, Howard, & Howard Jr., 2010; Soares, Marques,
Alves, & Sousa, 2013).
DTI studies in TBI. Studies involving DTI can reveal a spectrum of injuries
following TBI (Hulkower, Poliak, Rosenbaum, Zimmerman, & Lipton, 2013). A common
finding is that brain regions typically associated with TBI have reduced FA values, such as in
the genu of the corpus callosum in patients with chronic cognitive impairment after TBI
(Salmond et al., 2006; Kraus et al., 2007). This indicates that brain injury might likely be the
cause of cognitive deficits (Lo, Shifteh, Gold, Bello, & Lipton, 2009; Ewing-Cobbs et al.,
2008), even though sometimes no lesion has been found with conventional MRI (Nakayama
et al., 2006). Due to variability in study outcomes, the relationship between FA values
(indicator of white matter integrity) and cognitive functioning remains unclear (Nakayama et
al., 2006; Salmond et al., 2006). Corpus callosum, cingulum, inferior longitudinal fasciculus,
and superior longitudinal fasciculus are some of the neuroanatomical regions that show an
abnormal FA value after the occurrence of TBI (Hulkower et al., 2013; Caeyenberghs et al.,
11
2011; Treble et al., 2013). Kraus et al. (2007) found significantly reduced FA values in all the
regions of interest (analysis included corpus callosum, cingulum, cortico-spinal tract and
superior longitudinal fasciculus) in adults with moderate to severe TBI, and the larger the
white matter pathology, the larger the cognitive deficits were. However, in a study of
Bazarian et al. (2007), the researchers found significantly higher values of FA in TBI patients
in a more acute stage compared to controls. The most common locations of abnormal MD
values in TBI patients are corpus callosum, cingulum bundle, fronto-occipital fasciculus,
superior longitudinal fasciculus and inferior longitudinal fasciculus (Hulkower et al., 2013,
Wu et al., 2010).
One neuroanatomical region that is often affected by TBI, is the cingulum (Palacios et
al., 2013; Hulkower et al., 2013). The cingulum is a bundle of white matter tracts that
connects the cingulated gyrus to the entorhinal cortex, whereby components of the limbic
system can communicate with one another. In addition, the cingulum can also be described as
a white matter structure consisting of short and long association tracts, that runs within the
cingulate gyrus all surrounding the corpus callosum, and it connects the medial frontal and
parietal lobes as well (Catani, Howard, Pajevic, & Jones, 2002). A part of the cingulate cortex
called the anterior cingulate cortex is involved in cognitive control (Barch, Braver, Sabb, &
Noll, 2000), and disruption of this area (rostral cingulum) is correlated with mild cognitive
impairment (Metzler-Baddeley et al., 2012). Also the cingulum itself has been investigated on
the effects of TBI, and several lesion studies have shown that the cingulum is a part of an
important neural network that is responsible for cognitive abilities, and TBI has an impact on
the functioning of this network (Wilde et al., 2010, Kim et al., 2008; Levine et al., 2008).
Decreased FA and increased MD values of the cingulum have been correlated with impaired
performance on the flanker task (Wilde et al., 2010). Bonnelle and colleagues (2011) revealed
that smaller FA values reflected attention impairment in the right cingulum. Furthermore, FA
and MD values have also been linked to the left cingulum in TBI patients (Wu et al., 2010).
Another region that is often damaged after the occurrence of TBI (Hulkower et al.,
2013), is the Superior Longitudinal Fasciculus (SLF; Xiong et al., 2014). The SLF is a bundle
of white matter fibres underlying the dorsolateral prefrontal cortex (Kollias, 2009, Thiebaut
de Schotten et al, 2011). Vestergaard et al. (2011) reported that better spatial WM
performance is associated with increased FA values in left fronto-parietal connections, and
this significant effect was only found in regions of interest (ROI) that included SLF fibres.
Furthermore, Burzynska and colleagues (2011) revealed that the SLF is associated with WM,
and that white matter integrity of this fibre bundle reflects better performance on WM tasks.
12
A global decrease in FA is linked to performance on WM tasks, and positive correlations have
been found between the SLF and WM performance (Palacios et al., 2011). But not merely
memory processes are involved in the SLF, attention processes as well are influenced by this
region (Klarborg et al., 2013).
Current Study
Previous studies have revealed that attention and working memory are cognitive
functions that are often affected in TBI patients, as has been revealed by DWI measures. In
the past, studies have been focusing brain-behaviour relationships in healthy participants (e.g.
Burzynska et al., 2011), in adults (e.g. Bonnelle et al., 2011), and in adolescents with mild
TBI (e.g. Wu et al., 2010) or adolescents with moderate-severe TBI in a subacute stage (e.g.
Wilde et al., 2010). But to the best of our knowledge, no research has been done in
adolescents with moderate-to-severe TBI in a chronic stage (>1 year post-injury). In the
current study, we investigated the degree to which diffusion rates measures of the cingulum
and SLF can be linked to individual differences in attention and WM performance, within
adolescents suffering from moderate to severe chronic TBI. Since both the cingulum and the
SLF have been shown to be susceptible to DAI, we hypothesized that FA and MD values in
these white matter regions would account for variability in performance on attention and WM
tasks. We expected to find a positive correlation between FA values and attention and WM
tests, and a negative correlation between MD values and these neuropsychological tests.
Furthermore, we predicted to find stronger correlations between cingulum and attention, and
also between SLF and WM, since these regions have shown to have a stronger link with a
specific cognitive function (Wilde et al., 2010; Burzynska et al., 2011).
Method
Participants
Nine adolescents (seven boys and two girls) that were diagnosed with DAI as a result of TBI
(mean age = 16.23; SD = 0.95; range = 14.5-17.5) were selected to participate in the current
study, and they were recruited by contacting two rehabilitation centres, a specialized
rehabilitation centre for children and youth (Pulderbos), and the child rehabilitation centre of
Ghent University Hospital. All patients were earlier dismissed from these rehabilitation
centres before they were contacted to participate in the current study. At the time that the
current study was carried out, the children no longer participated in motor rehabilitation or
13
retraining programs. All adolescents had to be classified with moderate to severe TBI as
measured by the Mayo Classification system (Malec et al., 2007) in order to be allowed to
participate in the current study. In addition, all participants had to be assessed at least one and
maximum five years post-injury, when neurological recovery was stabilized. Exclusion
criteria were as follows: younger than 10 years old at the age of injury, contraindications to
take a MRI scan (e.g. braces), and large focal injuries as mentioned in the medical records.
This study was approved by the local Ethics Committee of Ghent University Hospital and was
performed in accordance with the ethical standards laid down in the 1964 Declaration of
Helsinki. All participants and their parents gave their written informed consent. Demographic
characteristics of participants are shown in Table 1.
Table 1. Demographic and clinical characteristics of the TBI patients.
TBI patient No. Gender Age Time since injury
(in years)
Age at time of injury IQ
TBI 1 M 16.38 3.83 12.55 124
TBI 2 M 16.7 3.15 13.55 90
TBI 3 M 17.54 2.24 15.3 117
TBI 4 M 15.57 3.15 12.42 104
TBI 5 F 16.67 4.55 12.13 113
TBI 6 F 14.47 1.29 13.18 98
TBI 7 M 15.54 1.67 13.87 95
TBI 8 M 17.26 1.35 15.91 122
TBI 9 M 15.99 2.79 13.2 76
Gender codes: M = male, F = female; IQ = Intelligence Quotient as measured by the WISC III
(Wechsler, 1991).
Tasks and procedure
First, all participants were asked to sign an informed consent, in which they stated that they
were informed about the content of the study. Prior to the scanning procedure, participants
had to take neuropsychological tests that measured both working memory and attention. After
completing these tests, participants were scanned at the University Hospital of Ghent (UZ
Gent) with a clinical Siemens Trio 3T scanner.
Neuropsychological Assessment. Subjects completed a series of tests that were
developed to measure executive function, attention and working memory. Tests included
14
some subtests of the CANTAB (Stockings of Cambridge, Intra-Extra Dimensional Set and
Spatial Span), Continuous Performance Test (Conners, Epstein, Angold, & Klaric, 2003),
some subtests of the WISC-III (Digit Span and Coding), and the Flanker task (Eriksen &
Eriksen, 1974). Participants and their parents were also asked to complete the Behavioural
Rating Inventory of Executive Function (Gioia, Isquith, Guy, & Kenworthy, 2000), a
questionnaire to assess the impairment of executive functioning. The Spatial Span and Flanker
Task were included in the current study (see Table 2).
Working memory task. The neuropsychological test that was used in this study to
measure visuo-spatial working memory, is the spatial span (SSP, see Figure 6). This is a
cognitive test that is included in the CAmbridge Neuropsychological Test Automated Battery,
rather known as CANTAB (Sahakian & Owen, 1992). The SSP was performed on a touch-
screen tablet supplied by the University of Cambridge, and had a duration of 5 minutes.
Several white squares were shown to the participants, and during each trial, some squares
briefly changed colour in a variable sequence. After a tone was presented, participants had to
recall the same sequence of squares as had been displayed before by touching the boxes which
changed colour in the same order that they were presented. The test started with two squares
that changed colour, and the length of the sequence increased as the task proceeded. It could
go up to a maximum sequence of nine squares at the end of the test, but the task finished
earlier when participants made errors for three sequences in a row of the same length.
Different sequences and colours were used throughout the whole test. In the current study, a
reversed version of the SSP was used as well. The same stimuli were shown to the
participants, but now they had to recall and tap the sequence of squares they had just seen
before in the reverse order. The dependent variables of the test that were measured were span
length (the longest sequence that has been recalled successfully), errors, number of attempts
and latency. For the analysis and correlation with white matter tracts, maximal span length of
the SSP (normal and reverse version) was used as variable of interest.
15
Fig. 6. Illustration of the Spatial Span (Sahakian & Owen, 1992).
Attention task. To test selective mechanisms of attention, the current study made use
of an Eriksen Flanker task (Eriksen & Eriksen, 1974) created with PEBL software (Mueller &
Piper, 2014). In this test, a row of five arrows is presented on each trial, and participants get
the instruction to respond to the central arrow by pressing the right or left shift key on the
keyboard (See Figure 7). Each trial started with a central fixation cross that stayed on the
screen for 500 ms. Subsequently, the stimulus (row of arrows) would appear on the screen.
Once the arrows were presented, participants had to determine the direction of the central
arrow and they were given a limited timeframe of 800 ms to respond. A response was given
by pressing one of the response buttons (right and left control key on the keyboard) as quickly
and accurate as possible. After a response was given (or no response was given within 800
ms), a blank screen appeared with a fixed inter trial interval of 1000 ms. Participants
performed a short practice of 12 trials before the actual experiment started. Feedback was
given after each trial (accuracy and reaction time in milliseconds). The experiment consisted
of 120 trials, and feedback was no longer given after the practice block. There are three
conditions in the experiment: a congruent, an incongruent and a neutral condition. In the
congruent condition, the middle arrow points in the same direction as the other arrows, while
the middle arrow points in the opposite direction as the other arrows in the incongruent
condition. In the neutral condition, only one arrow is presented, surrounded by dashes. Within
congruent trials, the distracter items are the same as the target stimulus, whereas in
incongruent trials, the distracter items are different from the target stimulus, so that two
possible response tendencies become active and conflict occurs (Botvinick, Barch, Carter, &
16
Cohen, 2001). Because of the occurrence of conflict, larger reaction times and more errors are
usually measured on incongruent trials. Conflict cost (reaction time difference between
congruent and incongruent trials) was taken into account as variable of interest.
Fig. 7. Illustration of an incongruent trial of
the Eriksen Flanker Task ((Eriksen & Eriksen, 1974).
MRI data acquisition. MR examination was performed on a Siemens 3T Magnetom
Trio MRI scanner (Siemens, Erlangen, Germany) with a 32-channel head coil. A DTI SE-EPI
(diffusion weighted single shot spin-echo echoplanar imaging) sequence ([TR] = 10800 ms,
[TE] = 83 ms, voxel size = 2.5 × 2.5 × 2.5 mm , slice thickness = 2.5 mm, [FOV] = 240 × 240
mm , 60 contiguous sagittal slices covering the entire brain and brainstem) was acquired. A
diffusion gradient was applied along 64 noncollinear directions with a b-value of 1200 s/mm²
. Additionally, one set of images with no diffusion weighting (b = 0 s/mm² ) was acquired.
Moreover, a high resolution T1-weighted image was acquired for anatomical detail using a
3D magnetization prepared rapid acquisition gradient echo (MPRAGE; [TR] = 2250 ms, [TE]
= 4.18 ms, voxel size = 1 × 1 × 1 mm , slice thickness = 1 mm, [FOV] = 256 × 240 mm , 176
contiguous sagittal slices).
DTI preprocessing. The DTI data were analyzed and processed in ExploreDTI
(Leemans, Jeurissen, Sijbers, & Jones, 2009). First, the raw data quality was visually
inspected (investigation of a loop through the separate raw diffusion-weighted images to
17
identify and exclude gross image distortions, inspection of orthogonal, axial and saggital
views, inspection of residuals and outliers). Subsequently geometrical distortions, induced by
subject motion and eddy currents (originated from rapid switching of magnetic gradients), and
Echo Planar Imaging (EPI) and susceptibility distortions were corrected (Leemans, and Jones,
2009). Next, the diffusion tensors and the diffusion parameters were calculated using a non-
linear regression procedure (Mori et al., 2008), and finally the DTI data were transformed to
standardised anatomical space (Montreal Neurological Institute space). FA and MD were
selected for further analysis as proxy markers of white matter microstructural organization
(Table 2). A region of interest (ROI) analysis is useful when a research question wants to
investigate specific white matter fibre bundles, and since the current study is interested in the
relationship between cognitive functions and specific white matter tracts, a ROI analysis was
carried out. ROIs can be drawn around a particular white matter structure, either by manual
delineation or by automatic segmentation or parcellation (Van Hecke, Emsell, & Sunaert,
2016). The SLF and cingulum were chosen as the white matter tracts of interest, and were
delineated in an automated fashion by transforming atlas labels from the ICBM Mori Atlas
(Mori et al., 2008) to each subject, and subsequently the DTI measures were calculated.
Statistical analysis. To investigate whether there is a relationship between
neuropsychological measures on the one hand and white matter integrity on the other hand, a
Spearman correlation analysis was performed to examine the relationship between mean FA
and MD of right and left cingulum/SLF bundles and results on the behavioural tests.
Statistical analyses were carried out using SPSS version 23 (IBM SPSS Statistics, IBM Corp,
New York, NY). Bonferroni corrections for multiple comparisons were made (hence p <
0.00625 was considered significant following correction for DTI metrics regarding the four
ROIs).
Results
On average, participants could recall a sequence of 7.22 items on the SSP forward and 7 items
on the reverse version. On the flanker task, there was an average difference of 59.68 ms
between congruent and incongruent trials (conflict cost). The results from the diffusion
parameters can be found in Table 2. The calculated correlations between behavioural
performance and DTI based measures are described below (for a clear overview, see Table 3).
18
Table 2. Group results for DTI metrics (Fractional Anisotropy and Mean Diffusivity), mean
± standard error for each ROI.
Anatomical Region FA MD (×10−3
mm²/s)
Right SLF 0.26 ± 0.05 0.85 ± 0.10
Left SLF 0.22 ± 0.04 0.89 ± 0.11
Right Cingulum 0.23 ± 0.07 0.88 ± 0.11
Left Cingulum 0.2 ± 0.07 0.89 ± 0.11
Relationship Between DTI Measures And Behavioural Outcome
Fractional Anisotropy. If we look at the correlations between SSP (forward), there are no
significant findings for both right SLF (r = -0.165, p = 0.671) and left SLF (r = -0.294, p =
0.443), as well as for right (r = 0.101, p = 0.796) and left (r = -0.009, p = 0.981) cingulum.
For the reverse version of the SSP, no significant relationship was found for right (r = 0.173, p
= 0.657) and left (r = 0.000, p = 1) SLF, and the correlation with right (r = 0.035, p = 0.930)
and left (r = -0.173, p = 0.657) cingulum turned out to be non-significant as well. Finally, if
we take a look at the correlations of the white matter tracts with the flanker task, we found no
significant correlations for right (r =0.25, p = 0.516) and left (r = 0.2, p = 0.606) SLF, as for
right (r = 0.233, p = 0.546) and left (r = 0.033, p = 0.932) cingulum. Overall, there seems to
be no stronger correlation between SLF-WM and cingulum-attention compared to other
correlations.
Mean Diffusivity. There is no significant relationship between SSP (forward), and both right
SLF (r = 0.376, p = 0.318) and left SLF (r = 0.376, p = 0.318), and the same holds for right (r
= 0.000, p = 1) and left (r = -0.11, p = 0.778) cingulum. For the reverse version of the SSP, no
significant correlation was found for right (r = -0.138, p = 0.723) and left (r = -0.276, p =
0.472) SLF, nor with right (r = -0.38, p = 0.314) and left (r = 0.035, p = 0.93) cingulum turned
out to be non-significant as well. Subsequently, when we examine the correlations of the
white matter tracts with the flanker task, no significant relationship was found for conflict cost
and right (r =-0.283, p = 0.46) and left (r = -0.417, p = 0.265) SLF, as for right (r = -0.517, p =
0.154) and left (r = -0.083, p = 0.831) cingulum. All correlations between cognitive outcome
and MD values taken into account, correlations between SLF-WM and cingulum-attention did
not seem larger on average compared to other correlations.
19
Table 3. Results of the correlation analyses between DTI metrics (Fractional Anisotropy and Mean Diffusivity) and
behavioural tests (SSP, SSP reverse and Flanker task).
Fractional Anisotropy Mean Diffusivity
ROI SSP SSP reverse Flanker SSP SSP reverse Flanker
Right SLF
-0.17
p = 0.671
0.17
p = 0.657
0.25
p = 0.516
0.38
p = 0.318
-0.14
p = 0.723
-0.2
p = 0.46
Left SLF
-0.30
p = 0.443
0
p = 1
0.20
p = 0.606
0.38
p = 0.318
-0.28
p = 0.472
-0.42
p = 0.265
Right Cingulum
0.10
p = 0.796
0.035
p = 0.93
0.23
p = 0.546
0
p = 1
-0.38
p = 0.314
-0.52
p = 0.154
Left Cingulum
-0.01
p = 0.981
-0.17
p = 0.657
0.03
p = 0.932
-0.11
p = 0.778
0.04
p = 0.93
-0.08
p = 0.831
Discussion
The aim of the current study was to investigate the link between cognitive functions (attention
and memory) and two specific white matter tracts, namely the cingulum and SLF, in
adolescent individuals who suffer from moderate to severe TBI. In order to do so, participants
completed neuropsychological tests that measured both WM and (sustained and selective)
attention, and DWI images were acquired. Subsequently, diffusion parameters FA and MD
were correlated with outcome on the neuropsychological tests to investigate whether there is a
link between performance on the behavioural and neuronal level. We expected to find a
positive correlation with FA and task performance, and a negative relation with MD outcome
on both cognitive tasks. In addition, we hypothesized larger correlations between SLF-WM
and cingulum-attention.
If we take a look at the correlations of both cingulum and SLF with the flanker task, it
turned out that none of the compared brain-behaviour relationships are significant. Also, the
link between attention and cingulum did not seem larger than other obtained correlations.
These results are not in line with findings of previous studies. For example, Wilde et al.
(2010) found a significant relationship between the FA and MD values of the left cingulum
bundle and reaction time on the flanker task in children who sustained moderate-to-severe
TBI. In another study, it was shown that performance on working memory tasks is associated
20
with the cingulum (Wilde et al., 2011). Furthermore, Wu and colleagues (2010) revealed
significant correlations between immediate recall and left cingulum bundles in adolescents
with mild TBI. Also the right cingulum bundle is associated with attention (Takahashi et al.,
2010), and reduced FA values are correlated with impairments of (sustained) attention in TBI
patients (Bonnelle et al., 2011).
Correlations between performance on the forward and reverse version of the SSP, a
measure of visuo-spatial WM, and diffusion parameters of two selected white matter bundles
were calculated as well, and the outcome revealed no significant relationship between
working memory and each ROI. On average, there did not seem to be a larger effect between
SLF and WM. However, previous research has shown that these white matter tracts are
related to higher cognitive functioning. In a study of Burzynska and colleagues (2011), higher
FA values of the SLF were related to better WM performance in young controls. Palacios and
colleagues (2011) found that FA values of the SLF correlated with WM measures.
Furthermore, white matter microstructure of the right cingulum has been linked to outcome on
a task that measures WM capacity (Takahashi et al., 2010).
Although most of the abovementioned studies investigated the relationship between
cognitive outcome and microstructure of white matter bundles in TBI patients in similar ways
to our study, there are still some differences with the current study which makes it not that
easy to make straightforward comparisons. For example, while some studies examined TBI in
adults (e.g. Palacios et al., 2011; Bonelle et al., 2011), our study focused on young
adolescents (the youngest participant was 14 years old and all adolescents were younger than
18 years old). One should be careful to generalize these results to children/adolescents
because of two reasons. First, the brain of adolescents is less mature and might be more
vulnerable to the effects of DAI, and in addition injury at a young age can have an impact on
the development of cognitive abilities and this could lead to cumulative impairment
(Anderson & Moore, 1995). Second, an opposing argument might be that the paediatric brain
is more plastic, and this plasticity might enable the brain to recover in a dramatic recovery of
function after the occurrence of TBI (Ruijs et al., 1994). Another difference with other studies
that investigate cognitive processes in TBI patients, is the time since injury. Some studies
investigated subacute patients (up to three months post-injury, e.g. Wilde et al., 2010; Wu et
al., 2010), while the population of the current study exists of adolescents in a chronic stage
(>1 year post-injury). Participants tend to report more improvement as the time since injury
increases (Brown et al., 2011), and higher FA values have been detected over time, indicating
improvement of white matter integrity (Farbota et al., 2012).
21
Limitations and Future Directions
Despite its widespread use and its sensitivity to detect microstructural abnormalities in white
matter, DTI also holds some disadvantages. One of the major confounds of DTI, is that it
cannot measure more than one dominant fibre orientation in complex white matter
configurations (Van Hecke et al., 2016), also known as ‘crossing fibres’ (different orientations
relate to different fibre populations; Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007).
The amount of white matter voxels that contain crossing fibres is about 90% (Jeurissen,
Leemans, Tournier, Jones, & Sijbers, 2013), and this has an impact on the DTI derived
measures (Alexander, Hasan, Lazar, Tsuruda, & Parker, 2001). FA is very sensitive to the
presence of crossing fibres in complex fibre configurations (Pierpaoli & Basser, 1996;
Alexander et al., 2001). If there is no single dominant diffusion direction, they might average
out and this will result in lower FA values (Van Hecke et al., 2016). This could be an
explanation for the fact that we did not obtain any significant findings, especially because the
SLF and cingulum are both complex white matter configurations (Behrens et al., 2007;
Douaud et al., 2011). However, the opposite could also be the case in white matter structures
that have been damaged: because of the degeneration of a specific fibre bundle, another
neighbouring fibre bundle might become more dominant and this leads to a paradoxical
increase in FA (Van Hecke et al., 2016). In a study that investigated mild cognitive
impairment, patients showed increased FA values compared to controls (Douaud et al., 2011).
Next to FA, MD can also be affected by the occurrence of crossing fibres (Van Hecke et al.,
2016). Lower MD values have been found in complex white matter tissues compared to
structures with a single dominant fibre orientation (Vos, Jones, Jeurissen, Viergever, &
Leemans, 2012). These reduced values depend on a variety of factors such as relative
contributions of different fibre bundles, microstructural properties, and acquisition settings
(Vos et al., 2012). An increased variance in MD values in certain regions can be a result of a
combination of single dominant fibre and crossing fibre configurations, which will lead to less
statistical power to reveal differences in MD (Van Hecke et al., 2016).
Another possible explanation for the fact that the current study was not able to find
any significant correlations, is that the ROIs were delineated in an automatic fashion.
Registration tools are used to transform atlas labels of certain anatomical regions to each
individual subject, and subsequently DTI measures are calculated for each ROI (Mori et al.,
2008). Compared to manual delineation, automatic segmentation has some major advantages
(less observer dependent, more reproducible) but it is not always the best method to use, since
22
it holds a risk of not exactly delineating the boundaries of white matter structures so the co-
registration accuracy is less reliable (Verhoeven et al. 2010; Oishi et al. 2011).
A ROI analysis was used in this study, but this is not the only possible technique that
can be used to analyse DTI datasets. Other analysis techniques include whole-brain analysis
and more region-specific analyses such as tractography and voxel-based analysis (Van Hecke
et al., 2016). ROI analysis might not always draw exactly around the white matter tract of
interest and this could exclude some potential valuable information. Tractography (also
known as fibre tracking) refers to the reconstruction of white matter pathways, based on local
diffusion orientations from every voxel (Lazar, 2010). The tractography process starts at a
seed point (a specific voxel) and continues by following the maximal amount of diffusion in a
certain direction, and keeps tracking until a new voxel is reached (Van Hecke et al., 2016).
Termination criteria depend on selection and exclusion ROIs, FA values, fibre length and
curvature thresholds (Van Hecke et al., 2016). Compared to the conventional ROI based
analysis, tractography is able to obtain more anatomically specific diffusion estimates (Deprez
et al., 2013). However, this analysis technique also holds some disadvantages. The
reconstructed tracts cannot be directly linked to the actual underlying microstructure, so
tractography cannot prove the existence of an actual tract (Jbabdi & Johansen-Berg, 2011).
Furthermore, high signal-to-noise ratio is required to be able to reduce the error accumulation
that occurs during tract propagation (Lazar & Alexander, 2003), and tractography results are
also more prone to be affected by the crossing fibres problem. Still, it is a valuable technique
to reconstruct 3D virtual representations of white matter in vivo (Van Hecke et al., 2016) and
can be more sensitive to changes than conventional ROI analysis (Kanaan et al., 2006).
Another limitation of the present study is its rather small sample size (n = 9), so an
underlying effect might not have been detected. Although many studies face this problem, it is
worth mentioning this shortcoming since these results are part of a larger study (in which the
number of participants is at least twice as large as in the current study), therefore the current
study should be regarded as preliminary. Another disadvantage is that this study did not
include a control group, in contrast to most of the studies that investigate white matter
abnormalities in DTI (Hulkower et al., 2013). Adding a control group might reveal some
insights on the brain-behaviour relationships of healthy young controls. For example, if there
would be a significant relationship between performance on the behavioural level and FA and
MD values in the control group, than this might be an indication that the white matter
structures of interest are more related to attention and working memory than the current study
suggests, and this would also confirm previous findings. It would also be an opportunity to
23
gain more knowledge about the differences between healthy controls and TBI patients.
Future studies might consider to add an age-, education-, and gender-matched
control group to the design to draw more solid conclusions on brain-behaviour relationships in
young adolescents who sustained a moderate-to-severe TBI. Although rather time-consuming,
using manual delineation of the white matter ROIs instead of automatic segmentation could
also improve the methodological quality, so differences in DAI may reflect the amount of
cognitive disability in a more conclusive way.
Conclusion
The present study investigated the link between cognitive outcome (working memory
and attention) as measured by neuropsychological tests and the integrity of two white matter
tracts (SLF and cingulum) as measured by DTI based parameters. More specifically, we
expected to find a positive relationship between FA and task performance, and a negative
relationship between MD values and task performance. We did not find any significant
relationships, although previous research has shown that the ROIs of the current study are
related to higher cognitive functioning. This might indicate that the current study lacked to
find significant results due to biological confounds and methodological issues such as
crossing fibres and automatic segmentation of the ROIs.
24
Nederlandstalige Samenvatting
Traumatisch hersenletsel is een belangrijke oorzaak van mentale en fysieke beperkingen bij
jongeren, en gaat vaak samen met diffuse axonale schade. Deze schade kan leiden tot
neurologische stoornissen, en dit beïnvloedt hogere cognitieve functies zoals cognitieve
controle, aandacht en geheugen. Eerdere studies hebben al een verband aangetoond tussen
specifieke witte stof structuren en prestaties op cognitieve tests. Het huidige onderzoek heeft
als doel te onderzoeken of er een verband bestaat tussen aandacht en werkgeheugen enerzijds,
en de Superior Longitudinal Fasciculus (SLF) en cingulum als witte stof bundels anderzijds
bij jongeren met chronische TBI. In vergelijking met andere medische
beeldvormingstechnieken, is Diffusion Tensor Imaging (DTI) meer geschikt om diffuse
axonale schade te detecteren en gevolgen op cognitief vlak te voorspellen. DTI parameters
(fractional anisotropy en mean diffusivity) werden berekend en vervolgens gecorreleerd met
de prestaties op neuropsychologische tests (spatial span en flanker taak). De data verworven
in negen adolescenten met matige tot ernstige TBI toonde geen significante relatie tussen het
werkgeheugen en witte stof bundels, en ook de correlatie met aandacht bleek niet significant
te zijn. Deze resultaten zijn niet in overeenstemming met eerdere bevindingen, die ontdekten
dat de verminderde structurele connectiviteit en de integriteit van het SLF en cingulum in TBI
patiënten wel een significante invloed heeft op bepaalde aspecten van cognitie. Onze
bevindingen zouden onder meer kunnen verklaard worden door methodologische keuzes en
biologische confounds.
25
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