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Short-term Saccadic Adaptation in Patients with Amblyopia
by
Rana Arham Raashid
A thesis submitted in conformity with the requirements for the degree of Master of Science (with a collaborative program in Neuroscience)
Institute of Medical Science University of Toronto
© Copyright by Rana Arham Raashid 2013
ii
Short-term saccadic adaptation in patients with amblyopia
Rana Arham Raashid
Master of Science with a collaborative program in Neuroscience
Institute of Medical Science
University of Toronto
2013
Abstract
This thesis investigates sensorimotor adaptive mechanisms that maintain the accuracy of
goal-directed saccades in amblyopia, a developmental disorder characterized by impairment
of spatiotemporal visual processing. Saccadic adaptation was induced by displacing the
visual target toward initial fixation during the saccade. Eleven visually normal controls and
seven patients with amblyopia were tested binocularly and monocularly with the amblyopic
and fellow eye (non-dominant and dominant eye in controls) in three separate sessions.
Patients with amblyopia exhibited reduced adaptation of saccadic gain compared to controls
when viewing with the amblyopic eye and binocularly. Initiation of saccades was also
delayed in patients when viewing with the amblyopic eye. It is proposed that the adaptive
ability to modify the initial saccadic motor commands for maintaining short-term saccadic
accuracy is impaired in amblyopia due to imprecise error signals. Moreover, this thesis
reaffirms the notion that the error signals driving saccadic adaptation are visual in nature.
iii
Acknowledgments
This thesis is the result of the efforts of several individuals who in one way or another
brought something special to contribute to the timely completion of this study. It truly is a
pleasure to thank the many people that made it possible.
First and foremost, I offer my sincerest gratitude to my supervisor, Dr. Agnes Wong, for
giving me an excellent opportunity to work in her eye movement lab and to make an
important contribution to the field of neuroscience. Your unwavering supervision set me off
in the right direction and inspired me throughout the way. I deeply appreciate your
continuous guidance, intellectual input, tremendous leadership and prolific suggestions
during all my years. I could not have wished for a friendlier and an intellectually-superior
supervisor.
I had the fortune of working with Dr. Herbert Goltz, my co-supervisor, who treated me with
his patience and enlightened me with his knowledge through various scholarly discussions
while allowing me room to work in my own way. Thank you for always having encouraging
words, and bestowing me with your stellar language skills. This thesis would not have had
the consistent quality that it does if it were not for your excellent editorial skills.
I thank my program advisory committee members, Dr. Luc Tremblay and Dr. Susanne
Ferber, for keeping me in check and assuring that I reach my maximum potential in a timely
manner. My thesis would not have evolved if it were not for your valuable insights and
constructive feedbacks. Thank you for always keeping me focused on my research.
An extraordinary amount of technical work was involved in this study and it would not have
been achieved without the assistance of extraordinary laboratory technicians. I want to thank
my technical staff for helping me out in the numerous recordings and workshops for running
the lab equipments: Manokaraananthan Chandrakumar, for aiding me in all the recordings,
data processing and providing me with unmatched technical insight and support; Alan
Blakeman, for programming the data analysis software and the entire saccadic adaptation
paradigm, and most importantly for always providing me with an excuse to laugh when chips
were down; and Cindy Narinesingh for assisting me in the laborious and monotonous task of
iv
data processing and analysis. Thank you all for always saving the day whenever I ran into
technical problems and making me feel an integral part of the lab—I will always remember
our "intellectual" Friday afternoon discussions and data-parties. Also, I want to thank Dr.
Ewa Niechwiej-Szwedo, our research associate, for your invaluable support in statistics and
for always having the best advice for me. I am indebted to our orthoptist, Linda Colpa for
recruiting all the participants. It is a very challenging and demanding task to recruit
appropriate research participants, but you made it seem so easy with your exceptional social,
clinical and recruiting skills. I am truly grateful to all the participants for allowing me to
work with them and for contributing immensely to this research project. Without you, this
thesis would not have been published. No research is possible without having the immense
support from a generous funding program. I would like to thank the Vision Science Research
Program (VSRP), a joint University Health Network/University of Toronto program, for
providing the necessary funding for our research project.
Last but not least, I would like to thank all my family and friends for loving, supporting, and
believing in me unconditionally throughout my years. Sohaeb, Anum and Ghanwa, you guys
are the best siblings I could have had and thank you for always being there when I needed
you guys. The Sabi/Anum couple deserves a notable mention for always lightening up the
mood with their antics when things got intense. A special shout-out to my bus buddy, Ghano,
for always being the cuddly and fluffy friend that I needed to ease my mind—I always
enjoyed your tight hugs. To my best-est buddy..."Toothless"...thank you for always being
nearby and being a constant, inimitable pillar of support throughout these years. I would
especially like to thank my "dAWGs"—Aqeel, Yawer, Vikas, Qamber, Monyem, Adnan,
Reza—for sticking it out like true brothers during these 2 years. Our jamming and gaming
sessions were instrumental in taking my mind off stress and maintaining my focus when the
going got tough. Most importantly, I deeply thank my parents, Sameena and Raashid: you
have been there for me in every step of my life, have always loved me unconditionally, and
have supported me through all of my decisions. Thank you both for sacrificing so much for
me and always being there for me—I would not be the person that I am today and would
have never made this far in life without the two of you.
This thesis is dedicated to you, my folks!
v
Table of Contents
Abstract .....................................................................................................................ii
Acknowledgments ................................................................................................... iii
List of Tables ......................................................................................................... viii
List of Figures ..........................................................................................................ix
List of Abbreviations ...............................................................................................xi
List of Appendices ................................................................................................. xiv
Chapter 1 Introduction ............................................................................................ 1
Chapter 2 Amblyopia − a neuro-developmental visual disorder ......................... 3
2.1 Neural correlates of amblyopia ............................................................................ 5
2.2 Deficits in amblyopia ............................................................................................ 8
2.2.1 Differences between anisometropic and strabismic amblyopia patients ...... 10
Chapter 3 Neurophysiology of saccadic eye movements .................................. 12
3.1 Cerebral and cerebellar control of saccades ..................................................... 13
3.2 Brainstem generation of saccades ..................................................................... 15
Chapter 4 Adaptive control of saccadic eye movements ................................... 19
4.1 Pathologically-induced saccadic adaptation ...................................................... 21
4.2 Experimentally-induced saccadic adaptation ..................................................... 22
4.3 Error signal driving saccadic adaptation ............................................................ 25
4.4 Neural substrates of saccadic adaptation .......................................................... 27
4.4.1 Cerebellum .................................................................................................. 27
4.4.2 Other brain areas: NRTP, superior colliculus and higher-level cortical
structures .............................................................................................................. 29
Chapter 5 Short-term saccadic adaptation in patients with amblyopia ............ 31
5.1 Hypotheses ........................................................................................................ 32
vi
5.2 Materials and Methods ...................................................................................... 34
5.2.1 Participants .................................................................................................. 34
5.2.2 Experimental apparatus and stimuli............................................................. 36
5.2.3 Experimental procedure .............................................................................. 37
5.2.4 Data analysis and outcome measures......................................................... 43
5.3 Results ............................................................................................................... 47
5.3.1 Saccadic gain .............................................................................................. 48
5.3.2 Saccade latency .......................................................................................... 55
5.3.3 Saccade duration and peak velocity ............................................................ 57
5.3.4 Secondary saccade frequency .................................................................... 59
5.3.5 Secondary saccade amplitude .................................................................... 60
5.3.6 Secondary saccade latency ......................................................................... 63
5.3.7 Secondary saccade duration and peak velocity .......................................... 65
5.4 Discussion ......................................................................................................... 66
5.4.1 Choice of the experimental adaptation paradigm ........................................ 66
5.4.2 Decreased modulation of the saccadic gain during adaptation in patients .. 67
5.4.3 Incomplete adaptation versus slow adaptation of the saccadic gain ........... 72
5.4.4 Implications of reduced saccadic adaptation in patients with amblyopia ..... 74
5.4.5 Effect of visual acuity and different subtypes of amblyopia on adaptation ... 77
5.4.6 Insights on the mechanisms of short-term saccadic adaptation .................. 78
5.4.7 Saccade latency during adaptation ............................................................. 79
5.5 Conclusions ....................................................................................................... 81
5.6 Future Directions ............................................................................................... 82
5.6.1 Saccade dynamics during adaptation .......................................................... 82
5.6.2 Possible effects of using a gain-increase adaptation paradigm ................... 84
vii
5.6.3 Other paradigms and the real-world application of adaptation: scanning a
visual scene, head-unrestrained, long-term .......................................................... 86
5.6.4 Investigation of saccadic adaptation in the pediatric patient population ...... 88
Appendix I: Temporal course of saccadic adaptation ........................................ 90
Appendix II: Eye movement recordings: Video-Oculography ......................... 101
References ........................................................................................................... 104
viii
List of Tables
5.1: Clinical characteristics of visually normal control participants ...................................... 35
5.2: Clinical characteristics of patients with amblyopia ......................................................... 35
ix
List of Figures
3.1: Theoretical model of brain structures and pathways involved in the generation of
saccadic motor/pulse command .............................................................................................. 15
3.2: A schematic diagram of the brainstem neural network involved in the generation of
horizontal saccades ................................................................................................................. 17
4.1: Exponential time course of adaptation ............................................................................ 23
5.1: Double-step adaptation task............................................................................................. 42
5.2: Amplitude versus time tracing for target and eye positions at the beginning and near the
end of adaptation block ........................................................................................................... 42
5.3: Sample tracing of the analyzed data acquired from a single experimental session ......... 46
5.4: Raw data demonstrating changes in saccadic gain across all three experimental blocks
for non-dominant/amblyopic eye (A), both eyes (B) and dominant/fellow eye (C) .............. 50
5.5: Group mean saccadic gain for all three experimental blocks .......................................... 51
5.6: Mean percentage change in saccadic gain by group (A) and viewing condition (B) ...... 53
5.7: Mean percentage recovery in saccadic gain by group (A) and viewing condition (B) ... 54
5.8: Variability in saccadic gain by viewing condition .......................................................... 55
5.9: Mean saccade latency during all three experimental blocks ........................................... 56
5.10: Mean saccade latency (A) and variability in mean saccade latency (B) by viewing
condition ................................................................................................................................. 57
5.11: Mean saccade duration (A) and peak velocity (B) for all three experimental block ..... 58
5.12: Mean secondary saccade amplitude by viewing condition ........................................... 61
5.13: Proportion of post-saccadic error that was explained by the corrective movement
shown for all viewing conditions ............................................................................................ 62
5.14: Variability in mean secondary saccade amplitude by viewing condition ..................... 63
5.15: Mean secondary saccade latency by viewing condition (A) and experimental block (B)
................................................................................................................................................ 64
x
5.16: Variability in mean secondary saccade latency during all three experimental blocks .. 64
5.17: Mean secondary saccade peak velocity by group .......................................................... 65
5.18: Hypothetical forward model for the role of cerebellum in adaptive control of saccade
generation ............................................................................................................................... 70
5.19: Exponential-fitting functions for visually normal participants ......................... 91, 92, 93
5.20: Exponential-fitting functions for patients with amblyopia ...................................... 93, 94
5.21: Data-binning results for visually normal participants ................................................... 97
5.22: Data-binning results for patients with amblyopia.......................................................... 98
xi
List of Abbreviations
2D Two-dimensional
3D Three-dimensional
IIIn Oculomotor (or cranial nerve III) nucleus
VIn Abducens (or cranial nerve VI) nucleus
AE Amblyopic eye viewing
AI Abducens internuclear neurons
AM Abducens motor neurons
ANOVA Analysis of variance
BBG Brainstem burst generator
BE Binocular viewing
C-ETD Chronos eye-tracking device
CFN/FOR Caudal fastigial nucleus/fastigial oculomotor region
DE Dominant eye viewing
DLPC Dorsolateral prefrontal cortex
EBN Excitatory burst neurons
EOG Electro-oculogram
ERG Electroretinography
FE Fellow eye viewing
FEF Frontal eye fields
xii
fMRI Functional magnetic resonance imaging
IBN Inhibitory burst neurons
IML Internal medullary lamina of the thalamus
IRD Infra-red devices
LED Light emitting diodes
LGN Lateral geniculate nucleus
LIP Lateral intraparietal area
MLF Medial longitudinal fasciculus
NDE Non-dominant eye viewing
NRTP Nucleus reticularis tegmenti pontis
OMV Oculomotor vermis
PEF Parietal eye fields
PET Positron emission tomography
PPRF Paramedian pontine reticular formation
riMLF Rostral interstitial nucleus of the medial longitudinal fasciculus
RIP Raphe interpositus nucleus
SC Superior colliculus
SEF Supplementary eye fields
SNpr Substantia nigra pars reticulata
STN Subthalamic nucleus
xiii
V1 Striate cortex/primary visual cortex
V2, V3, V4, V5/MT Extrastriate cortex (visual areas 2, 3, 4 and 5/middle temporal)
VA Visual acuity
VOG Video-Oculography
xiv
List of Appendices
Appendix I: Temporal course of saccadic adaptation............................................................. 90
Appendix II: Eye movement recordings: Video-Oculography ............................................. 101
1
Chapter 1
Introduction
Amblyopia, also known as lazy eye, is a developmental disorder of the visual system
characterized by a reduction in the best-corrected visual acuity (unilateral, in most cases),
that cannot be directly attributed to any structural eye abnormality. Numerous studies have
documented the wide range of sensory and perceptual deficits in amblyopia. Recently, the
investigation of the performance of visuomotor tasks in amblyopia has received more
attention. One such basic visuomotor task affected by amblyopia is saccadic eye movement
that is used for exploring the visual environment by rapidly directing the gaze to a new
location. The performance of saccadic eye movements is under adaptive control by
sensorimotor mechanisms that process errors in movement and maintain the optimal
movement accuracy. This process is known as saccadic adaptation, and these sensorimotor
mechanisms require proper visual input to modulate the accuracy of saccades.
This thesis investigates the efficacy of adaptive mechanisms that maintain optimal saccadic
accuracy in patients with amblyopia. The content of this thesis is divided into five chapters.
Chapter 1 briefly defines the key terms and provides a general layout of the thesis. Chapter 2
describes the pathophysiology and neural correlates of amblyopia. The extent of sensory,
perceptual and motor deficits in amblyopia are summarized. Chapter 3 introduces the basic
neurophysiology of saccadic eye movements. The neural structures and pathways involved in
the generation of saccadic eye movements are discussed. Chapter 4 broadly covers the
underlying mechanisms of saccadic adaptation and how they work to maintain the accuracy
of saccades. The neural substrates of adaptation and the nature of error signals that drive this
adaptation are also discussed. Chapter 5 details the experimental study that investigated how
2
saccadic adaptation is affected in patients with amblyopia. The results indicate that patients
with amblyopia exhibit reduced and more variable short-term modulation of the adapted
saccadic gains as compared to visually normal observers when presented with persistent
saccadic endpoint errors.
3
Chapter 2
Amblyopia − a neuro-developmental visual disorder
Early maturation of the human visual pathways occurs during the first few post-natal months
of life. The neuronal connections within the brain regions involved in visual processing,
namely the lateral geniculate nucleus (LGN) and the striate cortex (area V1) (Garey & de
Courten, 1983), mature during this period leading to a rapid improvement in visual acuity
within the first few months following birth (Weinacht, Kind, Monting, & Gottlob, 1999).
This time period is critically important for the development of normal monocular and
binocular vision, and is widely known as the critical period of visual development—a term
that was popularized in the visual system literature by the groundbreaking work of David
Hubel and Torsten Wiesel who studied the impact of monocular visual deprivation on the
development of the LGN (Wiesel & Hubel, 1963a) and the striate cortex (Hubel & Wiesel,
1963; Wiesel & Hubel, 1963b) in cats. The striate cortex is mainly comprised of binocular
neurons (≈70%) due to anatomical convergence of projections from the LGN on the visual
cortex (Kandel, Jessell, & Sanes, 2000). The striate cortex requires proper binocular
stimulation during the early critical period for the maturation of the ocular dominance
columns (Hubel, Wiesel, & LeVay, 1977), and the development of optimal binocular vision
and stereopsis. As evidenced in the above studies, this is only possible when adequate and
equal retinal stimulation is provided to both eyes during development and when both eyes are
properly aligned. Thus, an abnormal visual experience due to inadequate/unequal retinal
stimulation or eye misalignment during the early critical period can result in abnormal
development of the visual system affecting normal binocular function (Wright, 2006).
4
Amblyopia is a neuro-developmental visual disorder that is characterized by spatiotemporal
visual deficits due to abnormal visual stimulation during early childhood in the absence of
any structural abnormalities of the eye itself (American Academy of Ophthalmology
Pediatric Ophthalmology/Strabismus Panel, 2007). Amblyopia is unilateral in most cases
(American Academy of Ophthalmology Pediatric Ophthalmology/Strabismus Panel, 2007),
as asymmetric input between the two eyes is more likely to cause amblyopia than
symmetrically degraded images due to competitive influences between the eyes. More
infrequently, amblyopia can also present bilaterally as a result of severe, symmetric bilateral
degradation (e.g., bilateral cataract, bilateral high refractive error) (American Academy of
Ophthalmology Pediatric Ophthalmology/Strabismus Panel, 2007). Because the structural
anatomy of the eye is intact, the resulting visual impairment cannot be immediately corrected
optically. Clinically, amblyopia is defined as an interocular difference in visual acuity of
greater than at least two lines on a Snellen eye chart, after any optical correction for
refractive error (Wright, 2006). Amblyopia is the most common cause of monocular
blindness globally, affecting about 3-5% of the population, and causes a substantial public
health burden as the visual impairments can be life-long and require millions of public health
dollars for treatment and prevention (American Academy of Ophthalmology Pediatric
Ophthalmology/Strabismus Panel, 2007).
Amblyopia is generally divided into four subtypes: anisometropic amblyopia, strabismic
amblyopia, mixed amblyopia, and deprivation amblyopia (Barrett, Bradley, & McGraw,
2004). Anisometropia refers to a significant difference in the refractive errors between the
two eyes, resulting in pattern distortion (i.e., retinal image blur) that causes amblyopia
(Wright, 2006). Strabismus refers to the misalignment of the visual axes such that the image
of the visual world formed by each eye cannot be fused. In this form of amblyopia, the
cortical activity from the deviated eye is constantly suppressed, mainly in the central portion
5
of the visual field (Sengpiel & Blakemore, 1996). In most cases, the deviation can either be
in the horizontal or vertical direction. Horizontal deviation in which the eye turns inward is
known as esotropia, and a deviation in which the eye turns outwards is called exotropia. Both
strabismus and anisometropia may be present simultaneously and cause mixed amblyopia.
More infrequently, visual deprivation due to conditions such as corneal opacity and
congenital cataracts can also lead to development of amblyopia known as deprivation
amblyopia (Barrett, Bradley, & McGraw, 2004; Wright, 2006).
2.1 Neural correlates of amblyopia
The pioneering work to determine the neurological underpinnings of amblyopia was done by
Hubel and Wiesel (1965). In their initial 1963 study (see Hubel & Wiesel, 1963; Wiesel &
Hubel, 1963b), they deprived visual input to one eye in cats by either surgically suturing
their eye lids closed or rearing them in dark, and reported changes in the striate cortex (V1).
In the subsequent study, Hubel and Wiesel (1965) used the model of surgically-induced
strabismus to study the effect of monocular visual deterioration on the striate cortex of cats.
They proposed that the monocular visual deprivation during an early period of development
causes competitive interactions between the cortical afferents from the two eyes, resulting in
a disruption of normal binocular development. Since then, a large number of studies have
been conducted to determine the neural basis of amblyopia [see the following reviews:
(Barrett, Bradley, & McGraw, 2004), (Hess, 2001), (Kiorpes, 2006) and (Levi, 2006)]. The
earliest studies suggested that the abnormality was in the retina or LGN, or both. Ikeda and
Wright (1976) induced artificial strabismus in cats and recorded from the neurons within the
LGN, which receives visual inputs from the retina. They reported that the visual signals
received from the central retina (but not peripheral retina) of the strabismic eye had poor
6
spatial resolution, indicating deficits in the cells of either the central retina or the LGN.
Subsequently, Arden, Vaegan, Hogg, Powell and Carter (1980) assessed retinal function in
human patients with amblyopia using electroretinography (ERG). They demonstrated that
patients with amblyopia had reduced ERG responses, suggesting that the deficit possibly lies
within the retina. However, both of these studies were limited to some extent in terms of their
measuring techniques and stimuli, and further investigations were carried out using more
refined techniques [see Hess (2001) for a detailed review of further studies]. These
subsequent neurophysiological and electrophysiological studies showed that there was no
significant abnormality in the retina (Cleland, Mitchell, S. G. Crewther, & D. P. Crewther,
1980; Cleland, D. P. Crewther, S. G. Crewther, & Mitchell, 1982; D. P. Crewther, S. G.
Crewther, & Cleland, 1985) or the LGN (Blakemore & Vital-Durand, 1986; Derrington &
Hawken, 1981; Levitt, Schumer, Sherman, Spear, & Movshon, 2001) that could explain fully
the visual loss in amblyopia, although LGN may be involved indirectly through feedback
projections (Li, Mullen, Thompson, & Hess, 2011).
A growing body of current literature provides strong evidence of functional deficits within
the striate (V1) and extrastriate visual cortices (V2-V5, and well beyond) in amblyopia,
instead of deficiencies within the retina or LGN. A number of neurophysiological studies in
animals (Crawford & von Noorden, 1979; Hubel, Wiesel, & LeVay, 1977; Kiorpes, 2006;
Wiesel & Hubel, 1963b), demonstrated that early anomalous visual experience leads to
functional losses in neural processing within area V1. These deficits in neuronal "acuity" or
spatial resolution are most evident in the mid- to high-frequency range (which vary with the
subtype of amblyopia) (Kiorpes, Kiper, O'Keefe, Cavanaugh, & Movshon, 1998).
Additionally, amblyopia is associated with a substantial disruption of the binocular
organization of receptive fields, along with a significant reduction in the number of
7
binocularly driven V1 neurons and those that are driven by the amblyopic eye (Kiorpes,
Kiper, O'Keefe, Cavanaugh, & Movshon, 1998).
In addition to dysfunctional visual processing in area V1, the extrastriate cortex is also
implicated in amblyopia as the behavioural losses in amblyopia cannot be explained by
deficits in the striate cortex (V1) alone [see Kiorpes (2006) and Levi (2006) for a detailed
review of animal and human studies]. This is further supported by reports of numerous
deficits in higher-level visual processes that involve the extrastriate areas in amblyopia
(detailed in section 2.2 Deficits in amblyopia). Additional evidence of reduced cortical
function in amblyopia comes from neuroimaging studies in humans with amblyopia,
employing techniques including PET and fMRI. Collectively, these imaging studies show
that in addition to reduced activation in the striate cortex (V1) (Barnes, Hess, Dumoulin,
Achtman, & Pike, 2001), patients with amblyopia also demonstrate impaired extrastriate
cortex function as evidenced by reduced activity in higher-order cortical areas V2-V5
(Imamura et al., 1997).
In the light of these studies, the current consensus is that patients with amblyopia
demonstrate anomalous cortical function (in both striate and extrastriate cortices) without
any substantial impairment in the physiological functioning of the retina or the LGN [see
Hess (2001) for details]. These deficits arise from abnormal visual experience during the
critical period of early visual development in amblyopia that may be associated with
anisometropia, strabismus and/or visual deprivation. When the visual input to one eye is
anomalous during the critical period of visual development, the cortical cells shift their
preference to utilize the inputs from the eye that receives normal visual stimulation and
inhibit cortical activity from the eye that receives abnormal visual experience (due to
blurring or deviation). This inhibition is termed cortical suppression and it acts as a
8
mechanism for developing amblyopia (Wright, 2006). As a result, individuals with
amblyopia develop abnormal binocular vision and spatiotemporal visual deficits.
2.2 Deficits in amblyopia
In addition to losses in their spatial vision, i.e., reduced visual acuity (including
optotype/Snellen acuity and Vernier acuity), stereo-acuity and contrast sensitivity (McKee,
Levi, & Movshon, 2003), patients with amblyopia also exhibit deficits in tasks that involve
higher-order cortical processing in both the dorsal and ventral visual streams [pathways
involved in action and perception (Goodale & Milner, 1992)]. The perceptual deficits
involving the ventral pathway include abnormal global form perception (Hess, Wang,
Demanins, Wilkinson, & Wilson, 1999), spatial distortions (Sireteanu, Baumer, Sarbu, &
Iftime, 2007), temporal instability (Barrett, Pacey, Bradley, Thibos, & Morrill, 2003), spatial
and temporal crowding (Bonneh, Sagi, & Polat, 2007), disturbances in spatial localization
(Levi & Klein, 1983), and positional uncertainty (Levi, Klein, & Yap, 1987). Perceptual
deficits involving the dorsal pathway are also evident, including abnormalities in global
motion detection (Simmers, Ledgeway, Hess, & McGraw, 2003), complex motion detection
(Hess & Howell, 1977), and motion-defined form (Hayward, Truong, Partanen, & Giaschi,
2011). One possible explanation for the losses in spatial vision is the presence of more noise
in the visual system of an individual with amblyopia (Levi & Klein, 2003; Levi, Klein, &
Chen, 2005, 2007, 2008). Indeed, psychophysical studies have shown that the visual system
of individuals with amblyopia has poor position discrimination and an increased level of
stimulus-dependent internal noise (Levi & Klein, 2003; Levi, Klein, & Chen, 2008) which
can lead to deficits in spatial vision. These deficits are most apparent during amblyopic eye
viewing, but have also been detected (albeit less pronounced) during fellow eye (Levi &
9
Klein, 1985) and binocular viewing (Thompson et al., 2011). This observation suggests that
the sensory deficits in amblyopia are not purely driven by poor visual acuity alone, but rather
reflect impairments in elaborate visual processing mechanisms required for sensory and
perceptual tasks.
In addition to sensory deficits, there is increasing evidence that amblyopia also affects
visuomotor functions, an area that has lately received more attention. Recently, Niechwiej-
Szwedo, Goltz, Chandrakumar, Hirji and Wong (2010) investigated saccadic eye movements
in amblyopia and found increased saccade latencies and decreased spatial precision of
saccades during amblyopic eye viewing in patients as compared to visually normal
observers, indicating deficits in saccade initiation and execution. These impairments also
extend to visually-guided limb reaching movements in individuals with amblyopia,
impacting the planning and execution stages of movement (Grant, Melmoth, Morgan, &
Finlay, 2007; Niechwiej-Szwedo et al., 2011), temporal pattern of eye-hand coordination
(Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, & Wong, 2011), and online control of
three-dimensional reaching movements (Niechwiej-Szwedo, Goltz, Chandrakumar, & Wong,
2012). Other studies have shown that individuals with amblyopia also perform poorly on
tasks that require fine motor skills and speed-accuracy tradeoffs (Webber, Wood, Gole, &
Brown, 2008), and day-to-day motor tasks (Grant & Moseley, 2011) such as grasping,
walking, reading, further implicating motor deficits in an otherwise clinically-defined visual
disorder. Furthermore, these motor deficits are evident when individuals with amblyopia are
viewing binocularly, demonstrating that there are implications for optimal performance of
daily motor activities in amblyopia.
10
2.2.1 Differences between anisometropic and strabismic amblyopia
patients
Previous studies have demonstrated several differences in perceptual deficits among patients
with anisometropic vs. strabismic amblyopia. For example, patients with anisometropic
amblyopia exhibited deficits in contrast detection (Hess, Wang, Demanins, Wilkinson, &
Wilson, 1999) and spatial localization across the entire visual field (Hess & Holliday, 1992),
whereas patients with strabismic amblyopia exhibited more pronounced deficits in the central
visual field than the peripheral visual field. A study of 427 amblyopic patients has also
shown distinctive patterns of visual deficits among different amblyopia subtypes (McKee,
Levi, & Movshon, 2003). Patients with anisometropic amblyopia and moderate loss of acuity
had normal/subnormal contrast sensitivity and were more likely to have residual stereopsis,
whereas those with strabismic amblyopia and moderate loss of acuity had better than normal
contrast sensitivity at low spatial frequencies and were more likely to have reduced/absent
stereopsis (McKee, Levi, & Movshon, 2003). In addition, whereas the losses in Vernier
acuity and optotype acuity were scaled to losses in grating acuity (resolution) in
anisometropic amblyopia, in strabismic amblyopia, Vernier/optotype acuity losses were more
severe than losses in grating acuity (Levi & Klein, 1982, 1983). In other words, spatial losses
in strabismic amblyopia do not co-vary with grating resolution while similar spatial losses in
anisometropic amblyopia can be explained by losses in grating resolution. Similarly, losses
in spatial localization correlate with deficits in contrast sensitivity in anisometropic
amblyopia, but not in strabismic amblyopia (Hess & Holliday, 1992). Furthermore, there is
evidence that people with anisometropic amblyopia fail to develop normal spatial-frequency
channels, whereas the pattern of spatial frequency discrimination in strabismic amblyopia
shows similarities to that viewed by the peripheral retina of visually normal participants,
11
suggesting that strabismic vision might reflect non-foveal function (Mathews, Yager,
Ciuffreda, & Ettinger, 1987).
In summary, the extent of sensory and motor deficits suggests that amblyopia is more than
just an impairment of high contrast visual acuity. In addition to characteristic losses in spatial
vision due to abnormal visual experience during development, patients with amblyopia
exhibit impaired local and global visual processing mechanisms that involve integration of
spatiotemporal information. A greater level of internal noise in the amblyopic visual system
suggests that the ability to extract and segregate a meaningful signal from background noise
is impaired in amblyopia, which leads to functional losses in sensory and motor tasks evident
in amblyopia. Moreover, the pattern of these deficits is dependent upon the specific subtype
of amblyopia under study. The next chapter introduces a particular kind of eye movement
known as a saccade, and the basic neurophysiological mechanisms involved in their
generation and control.
12
Chapter 3
Neurophysiology of saccadic eye movements
Saccades are eye movements that shift the line of sight rapidly to bring the image of a visual
scene accurately onto the foveal region of the retina. Humans make 2-3 saccades per second
on average, each lasting less than 100 ms (depending on the amplitude) (Rayner, 1998).
Real-world saccades can be made to any sensory cues (except taste), and can also be
triggered to remembered targets. Saccades can be classified into several subtypes, depending
on the specific presentation of the stimuli which elicit them. Reflexive saccades are elicited
when a novel stimulus is suddenly introduced in the environment (A. M. F. Wong, 2008).
When saccades are executed as a part of voluntary behaviour they are termed volitional
saccades. Examples of volitional saccades include predictive saccades made in anticipation
of a target, memory-guided saccades made to the location of a previously presented target,
and antisaccades made in the opposite direction to a suddenly appearing target (A. M. F.
Wong, 2008). For a comprehensive review of neural substrates involved in the generation of
different saccadic subtypes, refer to Leigh and Zee (2006) and Scudder, Kaneko, and Fuchs
(2002). In this thesis, the adaptation of visually-guided reflexive saccades is investigated in
patients with amblyopia. This chapter summarizes the basic neural pathways involved in the
generation of saccadic eye movements. The adaptation of saccades will be discussed in more
details in Chapter 4.
13
3.1 Cerebral and cerebellar control of saccades
The visual image of any object is sharpest when it is placed in the center of the fovea, where
there is the highest density of cone photoreceptors and hence the highest spatial resolution.
As the image moves away from the center of the fovea to retinal periphery, visual acuity
declines progressively due to reducing density of cone photoreceptors (Jacobs, 1979). Thus,
in order to maintain the best possible acuity, accurate saccades are required to bring the
visual stimulus onto the fovea. When a novel stimulus appears in the periphery, it initiates
the firing of photoreceptor cells at a specific physical location on the retina. The firing of
photoreceptors provides a two-dimensional estimate of target position in space to which a
saccade should be executed. A neural signal carrying the spatial information about the target
position is then relayed to the lateral geniculate nucleus (LGN) in the thalamus through the
optic nerve, before being transmitted to the primary visual cortex/striate cortex (V1,
Brodmann area 17) in the occipital lobe (Wurtz & Kandel, 2000). Further visual processing
occurs in the extrastriate cortex (areas V2, V3, V4, V5/MT) before the visual signal reaches
different brain areas involved in generation of saccades (Wurtz & Kandel, 2000).
Several brain areas are involved in the programming of saccades, including cortical areas,
thalamus, basal ganglia and cerebellum, all of which project their inputs to the superior
colliculus (SC) in the midbrain either directly or indirectly. Prefrontal and frontal cortices,
including the frontal eye fields (FEF), supplementary eye fields (SEF) and dorsolateral
prefrontal cortex (DLPC), play a distinct role in the control of voluntary and visually-guided
reflexive saccades (Mort et al., 2003; Pierrot-Deseilligny et al., 2003; Russo & Bruce, 2000).
The parietal eye fields (PEF) and several regions along the intraparietal sulcus of the
posterior parietal cortex mainly mediate the programming of visually-guided reflexive
saccades (Mort et al., 2003; Pierrot-Deseilligny, Rivaud, Gaymard, & Agid, 1991).
14
Collectively, these fronto-parietal saccadic control centers project directly to the superior
colliculus. Additionally, cortical input (from FEF) is also relayed through thalamic and basal
ganglia structures to the superior colliculus (Petit et al., 1993). Together, these structures
control the memory, reward, and attentional aspects of voluntary saccades (Hikosaka,
Takikawa, & Kawagoe, 2000). The activation of the FEF and the SC in one hemisphere
generates contralateral horizontal saccades, whereas vertical and torsional saccades are
generated by simultaneous activation of the FEFs of both hemispheres or by simultaneous
activation of SC of both hemispheres (A. M. F. Wong, 2008).
The cerebellum is crucial for regulating the amplitude of saccades and maintaining optimal
saccadic accuracy. Particularly, the oculomotor vermis (OMV; lobules VI and VII of
posterior vermis) and caudal fastigial nucleus/fastigial oculomotor region (CFN/FOR) are
involved in the programming of saccadic amplitude and govern the metrics of ongoing
saccades (Selhorst, Stark, Ochs, & Hoyt, 1976). They receive neuronal projections from the
cortical eye fields via the nucleus reticularis tegmenti pontis (NRTP) located in the basal
pons. This ponto-cerebellar pathway bypasses the superior colliculus and projects directly to
the brainstem structures that generate the immediate saccadic motor command. Figure 3.1
[modified from Leigh and Zee (2006)] summarizes all the major brain structures involved in
the generation of saccadic motor commands and their pathways.
15
3.2 Brainstem generation of saccades
Together, the superior colliculus and cerebellum project to the brainstem burst generator
(BBG) that houses the pre-motor burst neurons and omnipause neurons. There are two types
of burst neurons: excitatory burst neurons (EBN) that augment the activity of ocular motor
neurons, and inhibitory burst neurons (IBN) that reduce the activity of ocular motor neurons.
For horizontal saccades, the EBNs and IBNs lie within the paramedian pontine reticular
16
formation (PPRF). For vertical and torsional saccades, the EBNs and IBNs are located in the
rostral interstitial nucleus of the medial longitudinal fasciculus (riMLF). Omnipause neurons
tonically inhibit the activity of both EBNs and IBNs, and are found in the pontine nucleus
raphe interpositus (RIP). The saccadic pre-motor commands generated from these burst
neurons control the activity of ocular motor neurons that innervate the six extraocular
muscles that move the eye. The muscles responsible for horizontal eye movements are the
medial rectus (for adduction of the eye) and lateral rectus (for abduction of the eye) muscles,
which are innervated by the oculomotor nerve (cranial nerve III) and abducens nerve (cranial
nerve VI), respectively.
In order to execute a saccade in a specific direction, omnipause neurons cease their tonic
inhibitory activity, which activates the particular set of EBNs and IBNs required for that
movement (see Figure 3.2). For instance, to perform a conjugate saccade in the rightward
direction, the ipsilateral (right) EBNs are activated which send excitatory signals to the
ipsilateral (right) abducens motor neurons (AM) in the abducens nucleus (VIn). These motor
neurons innervate the lateral rectus muscle of the ipsilateral (right) eye and their excitation
generates the saccadic pulse to move the right eye in the rightward direction. Additionally,
ipsilateral (right) EBNs also activate ipsilateral (right) abducens internuclear neurons (AI),
also present in the abducens nucleus. AI neurons synapse with the contralateral (left) motor
neurons housed in the oculomotor nucleus (IIIn) that control the medial rectus muscle of the
left eye, via the medial longitudinal fasciculus (MLF). Thus, their activation moves the left
eye in the rightward direction as well, resulting in a conjugate rightward eye movement of
both eyes. Concurrently, ipsilateral (right) IBNs send inhibitory inputs to the antagonist
lateral rectus muscle of the contralateral (left) eye and the antagonist medial rectus muscle of
the ipsilateral (right) eye, to prevent both eyes from moving in the leftward direction. Hence,
the ipsilateral EBN/IBN pair works in concert to trigger a conjugate saccade in the rightward
17
direction. The pathways that are involved in the generation of horizontal saccades are
schematized in Figure 3.2, replicated from Leigh and Zee (2006).
18
A simplified representation of the neural signal sent by the burst neurons to the extraocular
muscles is the pulse-step signal of innervation model (Leigh & Zee, 2006), with the pulse
encoding the velocity command and step encoding the position command of the saccade.
When this pulse-step command is programmed correctly, the eyes move rapidly to the new
location and are held there steadily, rendering the saccade accurate. If the pulse size is too
small or too big (due to disease/fatigue) then saccades may undershoot or overshoot their
target. This is known as saccadic pulse dysmetria (Leigh & Zee, 2006). If the size of the
pulse and step are not matched properly, then the saccade is followed by a drifting movement
until the eye reaches the final position dictated by the step signal. This is known as pulse-step
mismatch (Leigh & Zee, 2006). Together, saccadic pulse dysmetria and pulse-step mismatch
lead to inaccurate saccadic eye movements [refer to Leigh and Zee (2006) for details]. The
key mechanisms involved in mediating saccadic accuracy are discussed in the following
chapter.
19
Chapter 4
Adaptive control of saccadic eye movements
As discussed in Chapter 3, saccades must bring the image of a target close to the fovea for
the brain to form a high resolution image of the desired visual target. Such saccades must be
programmed accurately to enable stable visual perception (Jacobs, 1979). The accuracy of
saccades is mainly assessed by their gain, defined as the ratio of the distance travelled by the
eyes (saccade amplitude) to the distance displaced by the target (target amplitude). If the
eyes land perfectly on the target using a single saccade, then the primary saccade has a gain
of 1.0 and no further movement is required. The gain of primary saccades is modulated as a
function of target eccentricity, with small target steps (<5º eccentricity) eliciting saccades
with the gain close to 1.0 (Kowler & Blaser, 1995). As the target distance increases (≥5º
eccentricity), primary saccades progressively become hypometric and land short of the
desired target location, necessitating a second corrective saccade (Ploner, Ostendorf, & Dick,
2004). The optimal gain of these normal hypometric (undershooting) primary saccades is
maintained within the range of 0.90-0.95 for healthy individuals (Becker & Fuchs, 1969;
Troost, Weber, & Daroff, 1974). The maintenance of this hypometric state is particularly
advantageous for the oculomotor system as the corrective saccades that follow hypometric
saccades have shorter latencies than those that follow hypermetric saccades (Cohen & Ross,
1978). Also, undershooting the target and then correcting with a saccade in the same
direction entails less motor costs than overshooting the target and executing the corrective
saccade in the opposite direction.
20
Non-pathological (due to natural aging and development) and pathological (due to
neurological or muscular disorders) processes affect the fidelity of the saccadic system,
possibly influencing the accuracy of executed saccades. In the face of changes that occur due
to aging (Warabi, Kase, & Kato, 1984) and/or disease (Abel, Schmidt, Dell'Osso, & Daroff,
1978; Choi, Kim, Cho, & Kim, 2008; Kommerell, Olivier, & Theopold, 1976; Optican, Zee,
& Chu, 1985), the oculomotor system attempts to maintain optimum saccadic accuracy.
Because saccadic eye movements are very brief (lasting <80 ms in most cases), the classic
view had been that visual feedback mechanisms do not play an instant role in their control
(D. A. Robinson, 1975; Westheimer, 1954). This, however, does not imply that saccades are
essentially open-loop or ballistic movements. Several models of saccade generation posit a
local feedback loop that monitors the efference copy of the ongoing saccadic motor
commands to ensure their accuracy [for a review, see Girard and Berthoz (2005)]. The
efference copy provides an estimate of the current eye position that can be compared to the
desired eye position and used to modify the saccade trajectory during execution (i.e., on-line)
(Leigh & Zee, 2006). More recent studies have provided evidence that visual feedback can
also be used to modify saccadic trajectory on-line, operating within less than 50 ms when
retinal input is available (Gaveau et al., 2003; West, Welsh, & Pratt, 2009). These putative
feedback loops attempt to correct any errors or variability in saccadic motor commands
during execution to maintain saccadic accuracy.
On the other hand, the oculomotor system also relies on accurate off-line programming
mechanisms and makes appropriate adaptive changes to maintain optimal movement control
over the long term. This accurate recalibration of the system when faced with persistent
saccadic dysmetria is achieved by using the error information available at the end of saccades
to repeatedly modify the motor command issued for similar subsequent saccades before
execution (i.e., off-line). This sensorimotor adaptive mechanism, which adjusts saccadic
21
movement amplitudes by iteratively modifying their motor commands, is known as saccadic
adaptation and maintains optimal saccadic accuracy [see reviews by Hopp and Fuchs (2004)
and Pelisson, Alahyane, Panouilleres and Tilikete (2010)].
4.1 Pathologically-induced saccadic adaptation
The earliest demonstration of the adaptive capability of the saccadic system was reported in
two patients with unilateral sixth nerve (abducens) palsy by Kommerell, Olivier and
Theopold (1976). They observed that even in the presence of muscle weakness, saccades
executed by the paretic eye were of normal amplitude because these patients preferred to
view with their paretic eye, which happened to have better visual acuity (not related to
paresis). At the same time, saccades made by the non-paretic eye were hypermetric
(overshooting), indicating that the oculomotor system had adaptively adjusted the pulse-step
innervation of both eyes to compensate for the muscle weakness in the viewing (paretic) eye.
Subsequently, when the investigators patched the paretic eye and forced the patients to use
their non-paretic eye for 3 days, the previously hypermetric saccades made by the non-
paretic eye became normal. Thus, the oculomotor system adaptively modified saccade
metrics to conform to the needs of the viewing eye. Similar observations were also reported
by Abel, Schmidt, Dell'Osso and Daroff in a patient with unilateral third nerve (oculomotor)
palsy (1978). When the fellow (non-paretic) eye was patched, saccades made by the paretic
eye increased their gain progressively to become accurate whereas the saccades made by the
fellow (non-paretic) eye became hypermetric. Afterwards, when the patch was switched to
the paretic eye, the fellow (non-paretic) eye adaptively decreased the gain of its saccades to
become normal again. These findings have also been observed in muscular lesion studies
22
within the monkeys (Optican & Robinson, 1980; Scudder, Batourina, & Tunder, 1998)
providing further evidence of the plastic nature of the oculomotor system.
4.2 Experimentally-induced saccadic adaptation
In laboratory settings, saccadic dysmetria can be induced artificially by using a double-step
target paradigm, originally designed by McLaughlin (1967). In this paradigm, a visual target
is shifted inconspicuously during the primary saccade to introduce an artificial post-saccadic
movement error that requires a secondary saccade for correction. After repetitive intra-
saccadic target jumps, the oculomotor system adaptively modifies the gain of the primary
saccades to minimize the post-saccadic movement error, eventually reducing the size and
frequency of corrective saccades (for details see section 5.2.3 Experimental Procedure and
Figure 5.2). The gain of primary saccades can either be increased or decreased depending on
the direction of the second target step relative to the initial step [gain-increase (Miller,
Anstis, & Templeton, 1981) and gain-decrease (Deubel, Wolf, & Hauske, 1986) paradigm,
respectively]. Typically, adaptation of the saccade gain occurs rapidly at first before
gradually reaching a steady asymptotic value, achieved in about 100 intra-saccadic target
steps in humans (for gain-decrease paradigm) (Deubel, Wolf, & Hauske, 1986; Frens & van
Opstal, 1994; Miller, Anstis, & Templeton, 1981; Semmlow, Gauthier, & Vercher, 1989).
The adapted steady state saccadic gain is also more variable, even within healthy controls,
due to the increased spatial imprecision about the final target location associated with the
intra-saccadic stepping targets. The course of such adaptation is best modelled by an
exponential function (see Figure 4.1).
23
Adaptation induced by this double-step paradigm is specific to a particular saccadic
amplitude and direction [i.e., vector-specific adaptation (Deubel, 1995; Frens & van Opstal,
1994; Semmlow, Gauthier, & Vercher, 1989)]. Various studies that investigated the transfer
of saccadic adaptation between different training paradigms have shown that adaptation to
target steps of a certain amplitude does not transfer to other amplitudes, and that adaptation
in one direction does not transfer to the opposite direction (Frens & van Opstal, 1994;
Semmlow, Gauthier, & Vercher, 1989). This vector-specific adaptation can be modulated by
initial orbital eye position, albeit only in certain contexts. When saccades are adapted at a
certain initial orbital eye position using a specific paradigm (gain-increase or gain-decrease
separately), adaptation does transfer fully to other eye positions. This observation has been
reported by several studies (Alahyane & Pelisson, 2004; Albano, 1996; Frens & van Opstal,
1994; Semmlow, Gauthier, & Vercher, 1989) and indicates that adaptation does not depend
on initial orbital eye position. However, when competing paradigms are involved in the same
24
experiment, initial eye position information can be considered as a contextual cue by the
adaptive mechanisms (Alahyane & Pelisson, 2004). A number of behavioural studies provide
evidence that gain-decrease and gain-increase saccadic adaptation may rely on different
neural mechanisms (Ethier, Zee, & Shadmehr, 2008a; Miller, Anstis, & Templeton, 1981;
Panouilleres et al., 2009; Schnier & Lappe, 2011; Semmlow, Gauthier, & Vercher, 1989;
Straube & Deubel, 1995). Generally, gain-decrease adaptation occurs rapidly and also
induces a greater degree of gain modulation for a given number of experimental trials
compared to gain-increase adaptation. Moreover, the two adaptation paradigms are
accompanied by different changes in the dynamics of adapted saccades (Ethier, Zee, &
Shadmehr, 2008a; Straube & Deubel, 1995), providing further evidence of different
adaptation mechanisms. Because gain-decrease adaptation can be achieved relatively quickly
and robustly, it is employed as the double-step paradigm of choice in many adaptation
studies.
Irrespective of the paradigm used, it is generally believed that experimentally-induced
saccadic adaptation reflects true neuronal plasticity rather than a cognitive strategy (Hopp &
Fuchs, 2004; Panouilleres et al., 2009). The intra-saccadic target perturbation is not
consciously perceived by the participant if it is kept to 40% or less of the initial target step,
due to the suppression of visual perception of displacement during saccades (Bridgeman,
Hendry, & Stark, 1975; Klingenhoefer & Bremmer, 2010). Moreover, the changes in saccade
metrics that occur due to adaptation are gradual and can be retained during and even beyond
the post-adaptation period (Deubel, Wolf, & Hauske, 1986). Short-term retention of the
adapted metrics in the post-adaptation period can be augmented by blanking the visual target
upon saccade initiation to prevent any visual feedback about final target position which could
result in de-adaptation/recovery of the adapted saccade gain (Seeberger, Noto, & Robinson,
2002; Semmlow, Gauthier, & Vercher, 1989). These short-term changes in gain persist up to
25
a few hours after the adaptation experiment, but show considerable recovery overnight
(Deubel, Wolf, & Hauske, 1986). Additionally, a recent study by Alahyane and Pelisson
(2005) provides novel evidence of long-term retention of saccadic adaptation in humans.
They reported that the induced changes in saccadic gain of their participants lasted up to 5
days, even in the presence of constant visual feedback. Thus, it is likely that the behavioural
changes induced by such adaptation reflect true oculomotor plasticity independent of any
conscious strategy.
4.3 Error signal driving saccadic adaptation
The intra-saccadic target step introduces a post-saccadic movement error that drives saccadic
adaptation. The nature of this post-saccadic error signal, however, is still not fully known.
Several hypotheses have been proposed with regard to the origin and nature of this error
signal. The most compelling evidence supports “retinal position error” as the driving signal
for saccadic adaptation, which is derived from the visual estimate of the spatial distance
between the fovea and target position at the end of primary saccades. Prior to the onset of eye
movement, the oculomotor system creates an internal representation of the terminal eye and
target position. This enables the system to derive an estimate of the anticipated visual error at
the end of the intended saccade. Because the intra-saccadic step modifies the target location
midflight, it creates discordance between the anticipated visual position error and the actual
post-saccadic visual position error. The “retinal position error” theory proposes that the
oculomotor system compares the two post-saccadic visual position errors (anticipated versus
actual) throughout the process of saccadic adaptation and modifies its parameters
accordingly to bring them in unison (Albano & King, 1989; Bahcall & Kowler, 2000; Collins
& Wallman, 2012; A. L. Wong & Shelhamer, 2011).
26
Alternatively, a different theory implicates the driving signal as being “motor” in nature,
derived from the direction and amplitude of executed corrective saccades after the primary
saccades. Under normal conditions, the oculomotor system generates an estimate of the
required corrective movement using the extra-retinal (Becker & Fuchs, 1969; Ohtsuka, Sawa,
& Takeda, 1989) (efference copy of the generated motor command) and/or retinal (Deubel,
Wolf, & Hauske, 1982; Henson, 1978; Prablanc & Jeannerod, 1975) information. However,
the introduction of an intra-saccadic target step modifies the direction and/or amplitude of
the required corrective saccades (depending on the adaptation paradigm). Consequently, this
conflict between the expected and actual motor error drives the oculomotor motor system to
recalibrate the required motor command throughout the course of adaptation (Albano &
King, 1989). A few studies have tested this “motor error” hypothesis by experimentally
controlling the metrics and occurrence of corrective saccades (Noto & Robinson, 2001;
Seeberger, Noto, & Robinson, 2002; Wallman & Fuchs, 1998). These studies found that the
execution of corrective saccades is not essential for adapting saccadic gains concluding that
the “motor error” does not provide the necessary signals for eliciting saccadic adaptation,
thus providing support for the “retinal position error” hypothesis.
Furthermore, because saccadic adaptation occurs normally even after deafferentation of the
extraocular muscles, sensory proprioceptive feedback information about the eye position
from the extraocular muscles cannot be a source of error signal driving saccadic adaptation
(R. F. Lewis, Zee, Hayman, & Tamargo, 2001). Thus, the current body of literature suggests
that the driving error signal for saccadic adaptation is most likely visual in nature, derived by
dynamically comparing the actual and anticipated retinal position errors. However, more
intensive studies are needed to further corroborate this visual hypothesis. Particularly, no
study to date has employed a model of reduced visual function (induced experimentally or by
a visual disorder) to provide support for the visual error hypothesis. The study of saccadic
27
adaptation using such a visual deprivation model may allow investigation of the visual nature
of the error signals directly.
In addition, it is necessary that the visual error signal is presented early during the execution
of primary saccades for robust adaptation. If the visual feedback of the final target position,
which provides the visual error signal for adaptation, is delayed by ≥100 ms after the
execution of the primary saccade, the efficacy of saccadic adaptation declines progressively
and reaches a non-significant value for delays ≥600 ms (Bahcall & Kowler, 2000; Fujita,
Amagai, Minakawa, & Aoki, 2002).
4.4 Neural substrates of saccadic adaptation
Several brain regions have been implicated for the adaptive control of saccades with the chief
structure being the cerebellum. The superior colliculus and nucleus reticularis tegmenti
pontis (NRTP) also contribute to some extent (Takeichi, Kaneko, & Fuchs, 2005, 2007).
4.4.1 Cerebellum
The cerebellum is crucial in maintaining normal saccade metrics. The earliest evidence of
cerebellum-mediated saccadic activity comes from patients with hereditary cerebellar ataxia.
These patients suffered from persistent saccadic dysmetria that did not resolve with time
[(Zee, Yee, Cogan, Robinson, & Engel, 1976); see also (Straube, Deubel, Ditterich, &
Eggert, 2001)], suggesting that the cerebellum plays a key role in the control of saccadic eye
movement amplitude. This observation was substantiated by another study in monkeys in
which total cerebellar ablation led to abnormal control of saccadic accuracy, and absence of
28
saccadic adaptation (Optican & Robinson, 1980). After surgical weakening of the extraocular
muscles (to induce saccadic dysmetria), these monkeys failed to adaptively repair the gain of
their dysmetric saccades when the entire cerebellum was removed. When the areas of the
oculomotor vermis and caudal fastigial nucleus were selectively ablated, monkeys could not
adapt the size of the saccadic pulse innervation and were thus unable to resolve the enduring
saccadic dysmetria. This study by Optican and Robinson was the first to pinpoint the
importance of the oculomotor vermis (OMV) and caudal fastigial nucleus/fastigial
oculomotor region (CFN/FOR) regions in facilitating saccadic adaptation.
Additional evidence of cerebellar involvement comes from several electrophysiological
single-unit studies that recorded the activity of neurons during adaptation. Purkinje cells in
the OMV that project saccade-related information to the CFN exhibit altered complex spike
activity during the course of saccadic adaptation (Kojima, Soetedjo, & Fuchs, 2010; Soetedjo
& Fuchs, 2006). Likewise, CFN neurons that generate the saccade-related cerebellar output
signals also change the magnitude and timing of their firing pattern throughout adaptation
(Inaba, Iwamoto, & Yoshida, 2003). Finally, when CFN neuronal activity is temporarily
inhibited (e.g., by administration of muscimol), saccades become dysmetric without any
evidence of adaptation until the neuronal activity is resumed (F. R. Robinson, Straube, &
Fuchs, 1993). In humans, selective metabolic changes were demonstrated in the region of
medio-posterior cerebellum (including OMV region) during the course of adaptation, using
neuroimaging techniques of both Positron Emission Tomography (PET) (Desmurget et al.,
1998) and functional Magnetic Resonance Imaging (fMRI) (van Broekhoven et al., 2009).
29
4.4.2 Other brain areas: NRTP, superior colliculus and higher-level
cortical structures
Other brain structures that lie upstream of the cerebellum may also participate in saccadic
adaptation. NRTP, located in the basal pons, relays information from the superior colliculus
to the neurons in the OMV and CFN of the cerebellum. To date only one group has
monitored the activity of NRTP neurons during saccadic adaptation (Takeichi, Kaneko, &
Fuchs, 2005). They found that more than half of the NRTP neurons they sampled modified
their burst patterns in response to gain-decrease adaptation in monkeys, suggesting that
NRTP activity may be involved in adaptation-related changes.
Another structure-of-interest is the superior colliculus, which receives converging sensory
projections from the cortical fields, thalamus and basal ganglia, and sends pre-motor
commands to the saccadic-burst generator. Additionally, it also projects to the oculomotor
part of the cerebellum (OMV and CFN) through the NRTP pathway. A few studies have
recorded single-unit activity from superior colliculus during saccadic adaptation, but they
report conflicting results. A classic study done by Frens and van Opstal (1997) reported no
difference in the discharge of superior colliculus burst neurons prior to and following
adaptation. In contrast, a more recent study by Takeichi, Kaneko and Fuchs (2007) showed
that superior colliculus neurons change their firing activity during the adaptation phase. They
suggest that the site of adaptation can be at the level of the superior colliculus or higher, or
that saccadic adaptation induces changes in the neuronal firing patterns observable at the
brainstem level.
A recent neuroimaging study used functional MRI to assess the level of activation in
different brain areas during saccadic adaptation, and reported that higher-level cortical
structures such as SEF and PEFs show some level of differential activation during adaptation
30
(Blurton, Raabe, & Greenlee, 2011). Thus, although the cerebellum (specifically regions of
OMV and CFN) has been classically viewed as the major structure involved in saccadic
adaptation, recent studies provide new evidence of involvement of other structures such as
NRTP, superior colliculus, and even higher-level cortical structures.
To summarize, saccadic eye movements are constantly under adaptive control by the
oculomotor system, which monitors and attempts to correct any errors in motor performance
through a process called saccadic adaptation. This adaptation can be mimicked in the
laboratory setting using a double-step target paradigm that induces artificial movement
errors. The bulk of the current evidence suggests that this adaptation is most likely driven by
a retinal position error (visual) signal, and involves differential activity from several brain
regions including the cerebellum, superior colliculus and higher cortical regions. The
following chapter describes the experimental study that investigated how saccadic adaptation
is altered in patients with amblyopia.
31
Chapter 5
Short-term saccadic adaptation in patients with
amblyopia
In the previous chapters, I have discussed the basic neurophysiological mechanisms involved
in the generation of saccadic eye movements (visually-guided reflexive saccades in
particular) that bring the target of interest onto the fovea rapidly. Given the high frequency of
saccades executed by humans [2 or 3 saccades on average every second (Rayner, 1998)] to
scan their environment, it is imperative that these movements are accurate in order to enable
stable visual perception (Jacobs, 1979). The oculomotor system chiefly monitors and
maintains the optimal accuracy of goal-directed saccades using adaptive sensorimotor
mechanisms, known as saccadic adaptation. This accurate recalibration is achieved by taking
the visual error information at the end of inaccurate saccades and using it to modify
subsequent movements of similar amplitude and direction.
The central focus of research reported in this thesis is on a neuro-developmental visual
disorder known as amblyopia, which is characterized by spatiotemporal visual impairments
due to abnormal visual stimulation during early childhood in the absence of any structural
eye abnormalities. Although sensory and perceptual deficits have been well documented and
more recent studies have started to detail the visuomotor deficits in amblyopia, how
amblyopia impacts the adaptive control of saccadic eye movements remains unexplored. The
aim of the research presented in this thesis is to determine whether the spatiotemporal
deficits in amblyopia also affect saccadic adaptation, which is an important process that
maintains optimum saccadic movement accuracy during daily activities. My study attempts
32
to investigate the efficacy of adaptive error-correcting mechanisms in patients with
amblyopia, using the model of short-term saccadic adaptation. This chapter summarizes the
findings of my study which investigated the short-term adaptation of visually-guided
reflexive saccades specifically in patients with amblyopia.
5.1 Hypotheses
As discussed in the previous chapters, the adaptive control of saccadic gain/amplitude is
mediated mainly by the cerebellum. Such a control mechanism requires proper visual input at
the end of the saccade to compute and compensate for any error in motor performance
accurately. Even visually normal individuals exhibit increased variability in the gain of
adapted saccades (compared to pre-adaptation saccadic gains) due to the increased spatial
uncertainty associated with the intra-saccadically jumping target (Hopp & Fuchs, 2004). In
cases when the visual input to the oculomotor system is degraded (such as in amblyopia), the
post-saccadic visual error information (derived from comparing the estimated location of the
image on the retina with the actual location of image on the retina) may not be precise
enough for the cerebellum to implement accurate changes in the gain of subsequent saccades.
My first hypothesis is that when compared to visually normal control participants, the
adaptation of the saccadic gain to a given target amplitude and direction will be less robust
and more variable in patients during binocular and monocular amblyopic eye viewing, due
to reduced spatial precision in amblyopia (Levi & Klein, 1983; Levi, Klein, & Yap, 1987;
Levi, Waugh, & Beard, 1994; McKee, Levi, & Movshon, 2003; Niechwiej-Szwedo, Goltz,
Chandrakumar, Hirji, & Wong, 2010).
33
In addition, there is emerging evidence that the latency of adapted saccades increases in
response to the intra-saccadic target steps (in visually normal individuals) (Ethier, Zee, &
Shadmehr, 2008a), which may be related to the increased uncertainty associated with the
location of the jumping target. It is well-established that patients with amblyopia show longer
saccade latencies due to slower or delayed processing of visual information (Ciuffreda,
Kenyon, & Stark, 1978; Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, & Wong, 2010;
Schor & Hallmark, 1978). My second hypothesis is that patients will also exhibit longer
and more variable saccade latencies during adaptation compared to visually normal
participants, when viewing binocularly and with the amblyopic eye.
Finally, due to the temporal deficits in amblyopia, my third hypothesis is that patients will
manifest an altered time course of saccadic adaptation compared to visually normal
controls, during binocular and amblyopic eye viewing. Specifically, patients will exhibit a
slower temporal course of adaptation, which might not follow the typical exponential
course of adaptation. To test these three hypotheses, I implemented the following
experimental protocol.
34
5.2 Materials and Methods
5.2.1 Participants
Eleven control participants (7 females, age 26.0±7.0 years) who had normal or corrected-to-
normal vision (Snellen visual acuity 20/20 or better) in both eyes and seven adult patients
with amblyopia (6 females, age 28.3±8.4 years) were recruited (see Table 4.1 and Table 4.2
for clinical characteristics). All participants underwent a full visual and ocular motor
assessment prior to recruitment, including visual acuity testing using the Snellen eye chart, a
prism cover test to measure their eye alignment, assessment of refractive errors and
stereoacuity using the Titmus test. Amblyopia was defined as Snellen visual acuity of 20/30
or worse in the amblyopic eye, 20/20 or better in the fellow eye, and an inter-ocular visual
acuity difference of ≥2 Snellen lines. Anisometropic amblyopia was defined as amblyopia in
the presence of a difference in refractive error between the two eyes of ≥1 diopter (D) of
spherical or cylindrical power. Strabismic amblyopia was defined as amblyopia in the
presence of eye misalignment at distance and/or near fixation. Mixed amblyopia was defined
as amblyopia in the presence of a combination of anisometropia and strabismus. All seven
patients had visual acuity between 20/30 and 20/200 in the amblyopic eye; four patients had
mild amblyopia (visual acuity of 20/30 to 20/80) and three patients had severe amblyopia
(visual acuity of 20/100 to 20/200). Five patients had anisometropic amblyopia, one had
strabismic amblyopia and one had mixed amblyopia. Of the five anisometropic patients, one
was orthophoric and four had monofixation syndrome (Parks, 1969) (microtropia of ≤8 prism
diopters as a result of a foveal scotoma arising from the anisometropia; it is not the cause of
amblyopia). Exclusion criteria were any ocular cause for reduced visual acuity, prior
35
intraocular surgery or any neurologic disease. All participants provided written consent prior
to participating in the experiment. The study was approved by the Research Ethics Board at
The Hospital for Sick Children in Toronto and all experimental protocols conformed to the
guidelines of the Declaration of Helsinki.
Table 5.1: Clinical characteristics of visually normal control participants
Right Left Right Left
1 42 F 20/20 (0.00) 20/20 (0.00) -3.25 -3.25 40 Right
2 34 F 20/20 (0.00) 20/20 (0.00) -1.50+0.25x85 -1.50+0.25x75 40 Right
3 29 F 20/20 (0.00) 20/20 (0.00) -10.00+0.50x105 -11.00+1.00x70 40 Left
4 23 M 20/20 (0.00) 20/20 (0.00) -4.50+2.50x98 -5.00+3.50x78 40 Right
5 29 M 20/15 (-0.10) 20/15 (-0.10) -2.00 -1.25 40 Right
6 23 F 20/20 (0.00) 20/15 (-0.10) -2.50 -2.50+0.50x100 40 Right
7 20 M 20/20 (0.00) 20/15 (-0.10) plano plano 40 Right
8 21 F 20/20 (0.00) 20/15 (-0.10) plano plano 40 Right
9 23 F 20/20 (0.00) 20/15 (-0.10) -1.50+cyl -1.00+cyl 40 Right
10 24 M 20/20 (0.00) 20/20 (0.00) plano plano 40 Right
11 18 F 20/20 (0.00) 20/20 (0.00) plano plano 40 Right
Dominant
eyeI.D. Age Sex
Snellen Visual Acuity (LogMAR) Refractive error Stereoacuity
(seconds of
arc)
Table 5.2: Clinical characteristics of patients with amblyopia
Right Left Right Left
12 25 F 20/20 (0.00) 20/50 (0.40) -1.50+1.50x80 -3.00+2.50x80 120 Left Anisometropic Mild
13 18 F 20/20 (0.00) 20/60 (0.48) -1.50+0.50x80 +1.00+1.25x95 200 Left Anisometropic Mild
14 31 F 20/15 (-0.10) 20/100 (0.70) +2.25 +4.00 3000 Left Mixed Severe
15 29 F 20/15 (-0.10) 20/30 (0.18) plano plano 120 Left Strabismic Mild
16 30 M 20/15 (-0.10) 20/200 (1.00) +4.00 +6.00+1.75x90 negative Left Anisometropic Severe
17 44 F 20/200 (1.00) 20/15 (-0.10) +4.50 plano 3000 Right Anisometropic Severe
18 21 F 20/60 (0.48) 20/15 (-0.10) -11.25 -3.00+0.75x15 negative Right Anisometropic Mild
Amblyopic
eye
Type of
amblyopia
Severity of
amblyopiaI.D. Age Sex
Snellen Visual Acuity (LogMAR) Refractive error Stereoacuity
(seconds of
arc)
36
5.2.2 Experimental apparatus and stimuli
Eye movements were recorded binocularly using the video-oculography technique [see
details of the technique in Appendix II: Eye Movement Recordings: Video-Oculography], by
a head-mounted pupil tracking system (Chronos Eye Tracking Device or C-ETD, Chronos
Vision©, Germany). The basic C-ETD head-unit consisted of two panels of infra-red LEDs
positioned in front of each eye, below the nasal level, that provided illumination for tracking
the pupils. The light emitted from both eyes was reflected off the two dichroic glass mirrors
placed directly in front of the eyes, positioned at an angle of 45º from the line of sight, and
captured by two adjustable digital eye-tracking video cameras on each side of the C-ETD
head-mounted unit. The digital video cameras could achieve high image sampling rates (up
to 400 Hz for 2D eye movements) but in this study, all eye movement signals were sampled
at 200 Hz. The C-ETD system also included a separate 6 degree-of-freedom head movement
sensor that recorded raw pitch, yaw and roll velocities to ascertain any head movement
during recordings. The C-ETD system had a measurement resolution of 0.1º and linearity of
<2.5% for horizontal and vertical movements within the range of ±20º.
The visual target was a red dot subtending an angle of ≈0.2º rear-projected onto a translucent
screen using a laser-beam galvanometer (GSI Group©, USA), with a bandwidth of 5000 Hz.
The central target was always the fixation point and eccentric targets were displayed by
appropriately re-positioning the mirrors placed in the path of the laser. The presentation of
eccentric target steps took <1 ms which guaranteed that a motion streak was not perceivable
by the participants. The experiments were conducted in an otherwise dimly-lit room with the
participant seated 80 cm from the projector screen. To minimize measurement errors, the
participant’s head movements were restrained using a chin rest. The target position feedback
signal from the galvanometer was low-passed using a hardware Butterworth anti-aliasing
37
filter (MAX295, Maxim©, USA) at 90 Hz and digitized concurrently with the eye position
signal from C-ETD at 200 Hz. Before each experimental session, the outputs from the eye
tracker were calibrated by having participants repeatedly fixate a set of five visual targets (at
0º, ±10º horizontally and ±10º vertically) to generate a linear function for converting raw eye
tracker values to horizontal and vertical eye positions calibrated in degrees of eye rotation.
Real-time video output from C-ETD was displayed on a dedicated computer and monitored
by one of the experimenters to ensure that the digital video cameras were tracking the pupils
throughout the experiment. Another experimenter monitored real-time feedback about the
participant's computed eye position and the target position information using a separate
computer (that was also controlling the laser galvanometer). A recalibration was initiated if
any slippage of the head-mounted C-ETD unit or a significant movement of the participant's
head was detected. The real-time eye position data was differentiated using a five-point
quadratic polynomial Savitzky-Golay smoothing filter (Savitzky, 1964) to yield on-line eye
velocity profile. The real-time eye velocity signal was then used to trigger the intra-saccadic
target steps, as soon as the eye exceeded the threshold velocity of 50º/s. The acquired eye
movement image files were recorded using C-ETD's pre-installed on-line recording software
(ETD) and stored for subsequent off-line analysis and/or visual evaluation. The target
information data from the galvanometer were written onto a separate file. The detailed
experimental protocol that was followed for each participant is given below.
5.2.3 Experimental procedure
Saccadic adaptation was induced experimentally using a double-step target paradigm devised
by McLaughlin (1967). In this paradigm, participants are required to make a saccade to a
visual target that is briefly presented at one location (the first target step) which is then
38
shifted to a new location (the second target step) during execution of the primary (first)
saccade. This double-step target introduces a retinal position error during the saccadic
movement that drives the gain adaptation of primary saccades after repeated movements (see
Figure 5.1). Initially, the primary saccade lands closer to the first target step and is followed
by a secondary corrective saccade in order to fixate the shifted target. However, after several
iterations of this intra-saccadic target step over successive trials, the gain of the primary
saccade is progressively modified such that it lands closer to the shifted target, with a
corresponding decrease in the size and frequency of secondary corrective saccades (see
Figure 5.2). If the second step is toward the initial fixation point, then the gain of the primary
saccade is reduced (gain-decrease adaptation), whereas if the second step is away from initial
fixation, then the gain of the primary saccade is increased (gain-increase adaptation).
In this study, only gain-decrease adaptation was tested as it is easier to elicit and is more
robust than gain-increase adaptation (Miller, Anstis, & Templeton, 1981). Participants were
instructed to follow the visual target as accurately as possible. A single experimental session
(450 trials) lasted ~45 minutes and consisted of four test blocks: main sequence, pre-
adaptation, adaptation, and post-adaptation blocks performed sequentially. Participants were
given a one minute break between individual blocks when they were instructed to close and
rest their eyes to minimize fatigue. The experimental details of each block are described
below:
Main Sequence block:
The ‘main sequence’ for normal saccades refers to the relationship between the peak velocity
and amplitude over a wide range of saccades. The peak velocity of saccades increases
linearly as the amplitude increases for small saccades (<30 degrees), but approaches a
saturated asymptotic value for saccades larger than 30 degrees (A. T. Bahill, M. R. Clark, &
39
L Stark, 1975). All experiments began with a main sequence block to quantify this relation
between saccade peak velocity and amplitude for each participant. Trials began with
participants fixating a central red dot. After a randomized delay of 750-1250 ms, the central
target jumped to a different target eccentricity along the horizontal meridian and the
participants executed a saccade to re-fixate the target. The target stayed at the new position
for a constant period of 800 ms before returning to the central position again and the next
trial began. Target eccentricities tested were ±3º, ±5º, ±8º, ±10º, ±13º, ±15º, ±18º, ±20º, ±23º,
and ±25º, repeated 5 times in each direction (right and left) in a random order for a total of
100 trials.
Adaptation block:
During the double-step adaptation block, the target only stepped to ±19º eccentricity along
the horizontal meridian followed by a second backward step of 4.18º (Figure 5.1).
Specifically, after fixating a central target for a random period of 750-1250 ms (F, Figure
5.1), the target stepped to 19º in the rightward or leftward direction (T1, Figure 5.1). As the
eyes started moving, the target was shifted back toward central fixation by 4.18º. This second
target step (T2, Figure 5.1) was triggered once the initial eye velocity of the primary
saccades exceeded the threshold value of 50º/s, corresponding to ~15 ms after saccade onset.
The target stayed at the new location for 800 ms before returning to central position. The
second step was triggered for saccades in one direction only (the adapting direction -
rightward or leftward), randomized between subjects before each experiment. Thus, if
saccades were to be adapted in the rightward direction, then the second backward target step
(T2: 4.18º) was presented after initial target step (T1: 19º) in the rightward (i.e., adapting)
direction only. All the target steps (T1: 19º) in the leftward (i.e., non-adapting) direction did
not receive the second backward step in this case. Catch trials involving target jumps to
eccentricities of ±19º in the vertical meridian were also included to minimize any
40
anticipatory eye movements and any possible effect of boredom or inattention on saccade
dynamics (Kowler, Anderson, Dosher, & Blaser, 1995). The entire adaptation session
consisted of 200 trials, with 120 trials in the adapting direction, 60 trials in the non-adapting
direction, and 20 vertical catch trials. The decision to use 120 adaptation trials was based on
the well-documented evidence that a steady-state for gain-decrease adaptation is reached
within 100 saccades in humans (Albano, 1996; Deubel, Wolf, & Hauske, 1986; Frens & van
Opstal, 1994; Semmlow, Gauthier, & Vercher, 1989). The sequence of individual trials was
pre-determined using a custom written script that presented adapting trials interleaved with
the catch trials in a pseudo-random order.
Pre- and post-adaptation blocks:
A pre-adaptation and a post-adaptation test were performed to assess saccade
metrics/dynamics before and after adaptation. Target steps were presented randomly only at
±19º horizontal positions after an initial fixation period of 750-1250 ms, and the visual target
stayed there for 800 ms. The pre-adaptation block consisted of 50 trials (30 in the adapting
and 20 in the non-adapting direction) and the post-adaptation block consisted of 100 trials
(65 in the adapting and 35 in the non-adapting direction). A greater number of trials in the
adapting direction were used in the post-adaptation block compared to the pre-adaptation
block to assess the recovery of the adapted saccadic gain to the baseline level. Previous
studies have shown that this recovery of the adapted gain in humans may require a large
number of trials (Deubel, Wolf, & Hauske, 1986; Semmlow, Gauthier, & Vercher, 1989).
Hence, the decision to use 65 post-adaptation trials provided a sufficient number of eye
movements for testing the post-adaptation recovery of the saccadic gain without causing
much eye muscle fatigue that could impact saccade dynamics (Schmidt, Abel, Dell'Osso, &
Daroff, 1979).
41
Individual experimental sessions (comprised of all four of the above test blocks) were
performed under three different viewing conditions for each participant in the following
order: amblyopic eye (patient)/non-dominant eye (control) viewing (AE/NDE), binocular
viewing (BE), and fellow eye (patient)/dominant eye (control) viewing (FE/DE). There has
been some evidence that previous training on a saccadic adaptation paradigm may have some
effect on subsequent trainings (Kojima, Iwamoto, & Yoshida, 2004, 2005). Therefore, in
order to maintain a consistent training effect in all the participants (for both patient and
control groups), the order of viewing conditions was not fully randomized between subjects.
Moreover, each viewing condition was tested on a separate day at least one week apart to
prevent any possible long term retention of the adapted saccadic gain from previous
recordings interfering with subsequent recordings (Alahyane & Pelisson, 2005; F. R.
Robinson, Soetedjo, & Noto, 2006). Additionally, participants had to commit to three visits
in order to complete all their experimental sessions and there was a potential of high attrition
rate due to the level of commitment. I anticipated this potential problem and chose the order
of the viewing conditions accordingly. The amblyopic eye was the most important viewing
condition for my experiment because of the known visual deficits in that eye, thus it was
chosen to be the naïve viewing condition (non-dominant eye for controls). The binocular
viewing condition was chosen next in order as losses in binocular vision are central to
amblyopia and have real-life implications. Finally, the fellow eye (dominant eye for controls)
was tested as the last viewing condition. All participants, however, successfully completed
all three of their viewing conditions for my study. The three viewing conditions will be
referred using their abbreviations (i.e., AE/NDE, BE, FE/DE) for the rest of the document.
42
43
5.2.4 Data analysis and outcome measures
Recorded eye movement video files were post-processed off-line using C-ETD's analysis
software (Iris). This was achieved by using a circle-fitting algorithm to track the pupils off-
line and convert raw eye position data from video frame pixels to degrees of eye rotation. A
custom-written C++
program was used to display and analyze the processed eye movement
data. All trials were inspected for the presence of primary saccades and first corrective (i.e.,
secondary) saccades, which were then marked by the program and adjusted manually if
required. The horizontal eye velocity trace was derived from the position signal using a five-
point differentiation method by a second-order polynomial Savitzky-Golay smoothing filter
(Savitzky, 1964). Saccades were detected on the basis of a velocity threshold − 20º/s for
primary saccades and 15º/s for corrective saccades. Saccade amplitudes were calculated by
measuring the difference in mean eye positions across 40 ms windows before and after the
saccade. The difference between the eye positions at the onset and offset of saccades was not
used to compute amplitudes as it does not account for any dynamic overshoot of saccades (A.
T. Bahill, M. R. Clark, & L. Stark, 1975) or post-saccadic drifts (Kapoula, Robinson, &
Hain, 1986). Saccades were omitted from analysis if they did not reach a threshold peak
velocity (>100º/s for primary and >30º/s for corrective saccades), if they had a latency of
<100 ms and/or exceeded a latency of 500 ms for primary and 300 ms for corrective
saccades, if they were contaminated by eye blinks or exhibited an atypical saccade profile
(e.g., staircase saccades, glissades), or if the amplitude of the primary saccade was less than
half of the final target displacement.
The outcome measures for primary saccades were saccadic gain, percentage change in
saccadic gain, percentage recovery, variability in adapted saccadic gain, saccade latency and
variability in saccade latency, saccade duration and peak velocity. The gain of primary
44
saccades was defined as the ratio of saccade amplitude to target amplitude. Mean saccadic
gain value for the pre-adaptation block was calculated using the last 25 trials (of 30 trials in
the adapting direction). For the adaptation block, last 30 trials (of 120 trials in the adapting
direction) were used to calculate the mean adapted gain value—when the saccadic gain had
reached a steady adapted state. Percentage change in saccadic gain following adaptation was
then calculated using the following formulas:
Actual change in saccadic gain = Mean pre-adapted gain − Mean adapted gain
Mean pre-adapted gain
Desired change in saccadic gain = Size of the target back-step (i.e., ≈4.18º) ≈ 0.22
Size of the initial target step (i.e., ≈19º)
Percentage change in saccadic gain = Actual change in saccadic gain x 100
Desired change in saccadic gain
This measure is more sensitive than simply taking the difference in saccadic gain after
adaptation (without normalizing it to the pre-adaptation gain) as it accounts for any normal
under-shooting of primary saccades (Becker & Fuchs, 1969; Troost, Weber, & Daroff, 1974)
during the pre-adaptation block. The calculated percentage value is interpreted as a change in
the saccadic gain in response to the 4.18º target back-step adaptation. A value of 0 indicates
no adaptation whereas a value of 100 reflects total adaptation. A sample calculation of
percentage change in saccadic gain is illustrated in Figure 5.3. Since only a gain-decrease
paradigm was used, the percentage change in saccadic gain was always positive.
Additionally, percentage recovery of the saccadic gain at the end of the post-adaptation block
was also measured. Mean gain value for the post-adaptation block was calculated using the
45
last 20 trials (of 65 trials in the adapting direction), when the saccadic gain had almost
reached the pre-adaptation level. Percentage recovery in saccadic gain was then calculated
using the following formula:
Percentage recovery in saccadic gain = Mean post-adaptation gain x 100
Mean pre-adaptation gain
For the rest of the primary saccade measures (including latency, variability in latency,
duration and peak velocity), mean values for the pre-adaptation, adaptation, and post-
adaptation blocks were calculated using the last 25, 30 and 20 trials in the adapting direction
of those blocks, respectively.
With respect to corrective saccades, only the first saccades executed after the primary
saccades were marked and analysed. The outcome measures for secondary saccades were the
frequency of secondary saccades executed, saccade amplitude and variability in amplitude,
saccade latency and variability in latency, saccade duration, peak velocity, and the proportion
of post-saccadic error explained by the corrective movement. To calculate the proportion of
post-saccadic error explained by the corrective saccade, the amplitude of the corrective
saccade was plotted against post-primary saccade error, and a linear regression was
performed. The slope of the equation yielded the proportion of post-saccadic error explained
by the corrective movement.
Statistical analyses were performed using the SAS 9.2 software package. A two-way mixed
ANOVA was used to assess the effect of amblyopia on the percentage change in saccadic
gain, variability in the gain of adapted primary saccades, amplitude of secondary saccades
and variability in the amplitude of secondary saccades using Group (two levels: controls and
patients) as the between-subjects factor and Viewing Condition (three levels: AE/NDE, BE,
and FE/DE) as the within-subjects repeated factor. All other measures (except frequency)
46
were submitted to a three-way mixed ANOVA with Group as the between-subjects factor,
and two within-subjects repeated factors: Viewing Condition and Experimental Block (three
levels: pre-adaptation, adaptation and post-adaptation). All assumptions of ANOVA were
tested statistically—the normality assumption was tested using the Shapiro-Wilk's W statistic
and by visual inspection of the Q-Q probability plots for each cell, and the assumption of
compound sphericity was tested using the Mauchly test. The significance level was set at
p<0.05. All significant main effects and interactions were tested using post-hoc pair-wise
comparison Student t-tests. The frequency of corrective saccades executed in each viewing
condition for controls and patients was compared using Pearson's chi-squared test.
47
5.3 Results
A preliminary analysis was performed to assess the effect of adapted direction on saccadic
adaptation i.e., to determine if the adaptation of saccadic gain was different in the rightward
or leftward direction. The data acquired from all participants were submitted to a two-way
Repeated Measures ANOVA using Direction (two levels: rightward and leftward) as the
between-subjects factor and Viewing Condition (three levels: BE, FE/DE, and AE/NDE) as
the within-subjects factor. This analysis was done separately for control participants and
patients with amblyopia. Within the controls, the percentage change in saccadic gain did not
differ significantly when saccades were adapted in the rightward direction (77.75%; n=5)
compared to the leftward direction (67.65%; n=6) (F(1, 9)=4.5; p=0.06). Furthermore, the
interaction between the adapted direction and viewing condition was also non-significant
(F(2, 18)=1.24; p=0.31), indicating that the saccadic gain adapted similarly for both horizontal
directions across all viewing conditions. Similarly, within patients with amblyopia, there was
no difference between the percentage change in gain of saccades adapted in the rightward
direction (54.25%; n=4) when compared to those adapted in the leftward direction (66.49%;
n=3) (F(1, 5)=4.5; p=0.07), and no significant interaction between the adapted direction and
viewing condition (F(2, 10)=0.8; p=0.49). Therefore, data from both horizontal directions were
pooled together (within each group) for all subsequent analyses.
Each eye was analyzed separately for the binocular (BE) viewing condition in order to
ascertain whether the right eye and the left eye adapted to a similar extent. A simple t-test
was carried out on data acquired from the right eye and left eye separately for both control
participants and patients. No significant difference was observed between the percentage
change in saccadic gain values obtained from the right eye (78.4%) compared to the left eye
for controls (75.3%; t(18)=0.6; p=0.56) and for patients (right eye = 58.7% vs left eye =
48
56.1% respectively; t(10)=0.3; p=0.81). Thus, all results reported henceforth represent eye
movement data acquired from only one eye for the binocular viewing condition. Generally,
for my data, right eye recordings had less noise than left eye recordings. This could be due
the fact that most of the patients had amblyopia in the left eye (5 out of 7 patients) which
could have led to noisy data acquisition from that eye. Because, data from both the eyes were
comparable for all participants, the decision was made to report only right eye data for all
binocular recordings. An exception to this rule were participants #9 and #17 whose right-eye
binocular data were too noisy to be used, and as a result only left-eye binocular data for these
two participants were reported. Mean and standard deviation values are presented for all the
outcome measures.
Primary saccades analysis
5.3.1 Saccadic gain
Figure 5.4 depicts changes in saccadic gain during the three experimental blocks for a typical
control participant and a typical patient with amblyopia. Generally, control participants
responded to the experimental paradigm by rapidly reducing the gain of primary saccades
during the adaptation block until they reached a steady value, which was followed by
recovery to the previous baseline gain level during the post-adaptation block. Patients with
amblyopia responded similarly by reducing their saccadic gain, albeit to a lesser magnitude
(see the results for the "percentage change in saccadic gain" measure below) which depended
on the specific viewing condition, before restoring their saccadic gain back to pre-adaptation
level. Figure 5.5 illustrates changes in the mean saccadic gain over individual experimental
49
blocks (pre-adaptation, adaptation, and post-adaptation) for both patients and control
participants, averaged across all three viewing conditions. A significant main effect was
found for Experimental Block (F(2, 32)=293.5; p<0.0001). As expected, in control participants,
the mean saccadic gain was significantly reduced from 0.95±0.05 during the pre-adaptation
block to 0.80±0.03 by the end of adaptation, which increased back to 0.89±0.05 during the
post-adaptation block. Similarly, patients with amblyopia exhibited a significant reduction in
their mean saccadic gain from 0.95±0.04 during pre-adaptation to 0.83±0.04 by the end of
adaptation, which increased back to 0.91±0.04 during post-adaptation. No other effects were
significant.
50
51
To assess directly the difference in the extent of saccadic adaptation between the control and
patient groups, the percentage change in saccadic gain was analyzed. Figure 5.6 (A & B)
summarizes the result of this analysis for both control participants and patients across all
three viewing conditions (BE, FE/DE, and AE/NDE). An overall main effect was found for
Group (F(1, 16)=8.3, p=0.011; see Figure 5.6A), indicating that patients (59.5±16.4%)
exhibited a reduction in their saccadic gain to lesser degree compared to control participants
(72.2±12.5%). Additionally, a significant interaction between Group and Viewing Condition
was also observed (F(2, 32)=3.8; p=0.032). Post-hoc between-group analysis revealed that the
percentage change in saccadic gain was significantly lower in patients during amblyopic eye
viewing (48.9±11.5%) when compared to control participants during non-dominant eye
viewing (72.3±9.7%). Similarly, during binocular viewing, patients exhibited significantly
lower percentage change in saccadic gain (63.5±19.3%) when compared to control
participants (77.5±11.6%). However, when viewing with the fellow eye, the saccadic gain
52
(66.0±14.0%) in patients was similar to that in the control participants during dominant eye
viewing (66.9±14.5%, see Figure 5.6B). The within-group analysis indicated that control
participants achieved the highest percentage change in saccadic gain when viewing
binocularly (77.5±11.6%), which was significantly higher than that obtained during the
dominant eye viewing only (66.9±14.5%). In contrast, patients exhibited a significantly
lower percentage change in saccadic gain when viewing with the amblyopic eye
(48.9±11.5%) when compared to viewing binocularly (63.5±19.3%) or with the fellow eye
(66.0±14.0%). It is interesting to note that even in the control group, adaptation of saccadic
gain did not reach 100% as required by the adaptation paradigm; their gain decreased by
approximately 70% at the end of the adaptation block.
To assess the effect of visual acuity deficits on the extent of saccadic adaptation in patients, a
Pearson’s product-moment correlation was performed between the “visual acuity of the
amblyopic eye” and the “percentage change in saccadic gain observed during the amblyopic
eye”. A correlation coefficient (r) of +0.21 was obtained with the p-value of 0.66 which
indicated no significant relationship between visual acuity and percentage change in saccadic
gain during amblyopic eye viewing.
53
A sub-group analysis was also carried out to isolate the deficits in saccadic gain adaptation
specifically for patients with anisometropic amblyopia (n=5). Figure 5.6 (A & B) also
illustrates percentage change in saccadic gain data from five patients with anisometropic
amblyopia across the three viewing conditions. Similar to all patients as a group, patients
with anisometropic amblyopia also exhibited a lower percentage change in their saccadic
gain after adaptation when compared to control participants (56.9±17.3%, F(1, 14)=10.6,
p=0.0058; see Figure 5.6A), but the effect was much stronger. A significant decrease in the
percentage change in saccadic gain was also observed during amblyopic eye (43.2±6.84%)
and binocular (58.3±19.67%) viewing for patients with anisometropic amblyopia when
compared to the non-dominant (72.3±9.7%) and binocular viewing (77.5±11.6%) for
controls, respectively (F(2, 28)=6.8; p=0.004; see Figure 5.6B).
The post-adaptation recovery of the mean saccadic gain was also examined using the
percentage recovery measure. Figure 5.7 displays the percentage recovery results for the two
groups across all viewing conditions. The post-adaptation recovery of the mean saccadic gain
was comparable between control participants (94.1±3.33%) and patients (95.0±3.98%), with
54
no significant main effect (F(1, 16)=0.81, p=0.38; see Figure 5.7A) or interaction (F(2, 32)=1.1,
p=0.34; see Figure 5.7B). The recovery of the mean saccadic gain at the end of the post-
adaptation block almost reached completion (around 95%) in both groups.
The measure of variability in saccadic gain was defined by the standard deviation of the
mean saccadic gain calculated over the last 25, 30 and 20 trials for the pre-adaptation,
adaptation and post-adaptation blocks respectively. No main effect for Experimental Block
was found, indicating that the variability in saccadic gain was comparable between the pre-
adaptation, adaptation and post-adaptation blocks for all participants over all viewing
conditions (F(2, 32)=0.8; p=0.50). An overall main effect for Group was observed (F(1, 16)=7.0;
p=0.018), indicating that the saccadic gains were more variable for patients (0.053±0.01)
when compared to controls (0.043±0.01). In addition, a significant main effect for Viewing
Condition was also present (F(2, 32)=8.8; p=0.0009), with higher variability in saccadic gain
when the participants viewed with the amblyopic/non-dominant eye (0.051±0.02) compared
to binocular (0.043±0.01) and fellow eye/dominant eye viewings (0.047±0.01). The
interaction between Group and Viewing Condition was not significant (F(2, 32)=3.0; p=0.067).
Nonetheless, the interaction plot suggested that a higher variability in saccadic gain observed
during all viewing conditions (i.e., the main effect for the Viewing Condition) was mainly
55
driven by the patients (see the interaction plot in Figure 5.8). This was especially the case
during amblyopic/non-dominant eye viewing condition, when patients exhibited increased
variability in saccade gain (0.061±0.02) as compared to controls (0.045±0.01).
5.3.2 Saccade latency
Saccade latency, defined as the time elapsed between target presentation and saccade onset,
was affected by adaptation. Figure 5.9 illustrates the significant main effect for Experimental
Block when mean saccade latency was analysed (F(2, 32)=3.7; p=0.036). The mean saccade
latencies were increased for all participants during the adaptation block (207±28 ms)
compared to pre-adaptation block only (198±28 ms). The mean saccade latencies during the
56
post-adaptation block (204±29 ms) did not differ significantly from those during the pre-
adaptation or adaptation block.
Furthermore, no overall main effect was found for Group for mean saccade latency (F(1,
16)=2.1; p=0.17), but a significant main effect for Viewing Condition (F(2, 32)=10.5; p=0.0003)
and a significant interaction between Group and Viewing Condition was observed (F(2,
32)=3.3; p=0.050; see Figure 5.10A). Post-hoc tests revealed that the mean saccade latencies
were significantly increased when patients viewed with the amblyopic eye (232±39 ms) as
compared to when the control participants viewed with the non-dominant eye (202±28 ms).
Moreover, within the patient group, mean saccade latencies were significantly higher when
they viewed with the amblyopic eye (232±39 ms) as compared to viewing with the fellow
eye (206±19 ms) or binocularly (196±27 ms). No other effects were significant for mean
saccade latency.
57
For the measure of variability in saccade latencies (i.e., the standard deviations of mean
saccade latencies), a significant interaction was observed between Group and Viewing
Condition (F(2, 32)=7.7; p=0.002; see Figure 5.10B). Post-hoc testing indicated that patients
exhibited higher variability in saccade latencies during amblyopic eye viewing (53±20 ms),
when compared to control participants viewing with their non-dominant eye (35±9 ms). In
addition, for the patient group, increased variability in saccade latencies was also observed
during the amblyopic eye viewing (53±20 ms), as compared to fellow eye (36±13 ms) or
binocular viewing (37±15 ms). No other significant effects were present for variability in
saccade latency.
5.3.3 Saccade duration and peak velocity
Mean saccade duration and peak velocity were analyzed to determine whether adaptation
altered the kinematic properties of saccades beyond just their amplitude. As expected, a
decrease in mean saccade amplitude induced by the adaptation paradigm was accompanied
by a corresponding decrease in the mean saccade duration and peak velocity, consistent with
58
the main-sequence relationship (A. T. Bahill, M. R. Clark, & L Stark, 1975). Similarly,
during the post-adaptation block when mean saccade amplitude increased, an increase in the
mean saccade duration and peak velocity was also observed. This main effect detected for
Experimental Block was significant for mean saccade duration (F(2, 32)=35.6; p<0.0001; see
Figure 5.11A), indicating that the mean saccade duration was significantly longer during the
pre-adaptation block (68±8 ms) when compared to the adaptation (63±6 ms) and post-
adaptation (66±7 ms) blocks. Similarly, the main effect for Experimental Block was also
significant for mean peak velocity (F(2, 32)=44.1; p<0.0001; see Figure 5.11B), with peak
velocity significantly higher during the pre-adaptation block (411±59º/s) when compared to
the adaptation (386±56º/s) and post-adaptation (392±58º/s) blocks. However, both mean
saccade duration and peak velocity were comparable between controls and patients, and no
other significant effect was observed.
59
Secondary saccades analysis
In the previous section, I reported the effects on primary saccades before, during and after
adaptation. In this section, I report the analyses of secondary saccades. Only the first
corrective saccades executed within the 300 ms time frame following primary saccade
termination were inspected for all analyses. It is worthy to note that participant #16 (a patient
with anisometropia) failed to execute any corrective saccades during the amblyopic eye
viewing condition and thus his data were excluded from all the secondary saccades analyses.
5.3.4 Secondary saccade frequency
The frequency of secondary saccades executed increased significantly during the adaptation
(64 saccades) and post-adaptation (52 saccades) blocks as compared to the pre-adaptation
block (17 saccades; χ2
(df=1)=26.2, p<0.0001) for all participants, averaged across all viewing
conditions. During the pre-adaptation block, the number of secondary saccades executed by
the control participants and patients were comparable across all viewing conditions (17
saccades and 17 saccades; χ2
(df=1)=0.001, p=0.98). Also, the average number of secondary
saccades executed were comparable during individual viewing conditions for both control
participants (binocular: 16 saccades, dominant eye: 20 saccades, non-dominant eye: 16
saccades; χ2
(df=2)=0.55, p=0.66) and patients (binocular: 20 saccades, fellow eye: 19 saccades,
amblyopic eye: 13 saccades; χ2
(df=2)=1.83, p=0.40). Similarly, during the adaptation block,
the frequency of secondary saccades was comparable in controls (63 saccades) and patients
(66 saccades; χ2
(df=1)=0.066, p=0.80) across all viewing conditions, and also during the
individual viewing conditions for both control participants (binocular: 64 saccades, dominant
eye: 60 saccades, non-dominant eye: 66 saccades; χ2
(df=2)=0.35, p=0.84) and patients
60
(binocular: 74 saccades, fellow eye: 67 saccades, amblyopic eye: 58 saccades; χ2
(df=2)=1.98,
p=0.37). Finally, during the post-adaptation block, comparable frequencies of executed
secondary saccades were observed across all viewing conditions (controls: 50 saccades,
patients: 53 saccades; χ2
(df=1)=0.076, p=0.78), and during individual viewing conditions for
both controls (binocular: 54 saccades, dominant eye: 53 saccades, non-dominant eye: 45
saccades; χ2
(df=2)=0.89, p=0.40) and patients (binocular: 58 saccades, fellow eye: 57 saccades,
amblyopic eye: 45 saccades; χ2
(df=2)=2.1, p=0.35).
5.3.5 Secondary saccade amplitude
Statistical analysis of the mean amplitude of secondary saccades executed at the end of the
adaptation block revealed a significant difference between the control participants (1.1±0.2º)
and patients (1.4±0.4º; F(1, 16)=4.7; p=0.048). A significant main effect was also observed for
Viewing Condition (F(2, 32)=8.3; p=0.001), with participants executing secondary saccades of
a larger amplitude when viewing with the amblyopic/non-dominant eye (1.4±0.4º), as
compared to viewing with both eyes (1.1±0.3º) or with the fellow/dominant eye (1.1±0.2º).
The interaction between Group and Viewing Condition was found to be non-significant (F(2,
32)=2.4; p=0.11; see Figure 5.12). Despite the non-significant result, the data suggested that
when viewing with the amblyopic eye, patients executed secondary saccades that were larger
in amplitude as compared to control participants viewing with their non-dominant eye.
Hence, the main effect of large secondary saccade amplitudes observed during
amblyopic/non-dominant eye viewing condition seemed to be driven mainly by the patients
(see the interaction plot in Figure 5.12).
61
Additionally, a linear regression was performed (for the adaptation block only) between the
post-saccadic error remaining after primary saccade execution (independent variable) and the
amplitude of the subsequent secondary saccade (dependent variable). The slope parameter
obtained from the linear regression provided a quantitative measure of the proportion of post-
saccadic error that was compensated by the subsequent corrective movement. A slope of 1
indicated perfect compensation while any value lower than 1 reflected incomplete
compensation. A significant interaction was observed between Group and Viewing
Condition for the slope of the linear regression (F(2, 32)=3.6; p=0.041; see Figure 5.13). Post-
hoc tests indicated that patients did not correct the post-saccadic error by subsequent
secondary saccades as well when viewing with the amblyopic eye (slope = 0.54±0.16), as
compared to control participants viewing with the non-dominant eye (slope = 0.74±0.13).
However, when viewing binocularly (slope = 0.66±0.08) and with the fellow eye (slope =
62
0.60±0.14), the extent of correction shown by patients was comparable to control participants
viewing binocularly (slope = 0.70±0.11) and with their dominant eye (slope = 0.66±0.12).
For variability in secondary saccade amplitude at the end of the adaptation block, all
participants exhibited increased variability in their saccade amplitude when viewing with the
amblyopic eye/non-dominant eye (0.51±0.16º), as compared to viewing with both eyes
(0.41±0.14º) or with the fellow/dominant eye (0.40±0.11º; F(2, 32)=4.6; p=0.019; see Figure
5.14). No other significant effects were obtained.
63
5.3.6 Secondary saccade latency
A significant main effect was observed for Experimental Block (F(2, 32)=10.6; p=0.0004),
similar to the effect found for mean latency of primary saccades. The mean latency of
secondary saccades during the adaptation block (194±33 ms) was increased in all participants
(patients and controls), when compared to pre-adaptation (177±33 ms) and post-adaptation
block (172±34 ms; see Figure 5.15B). Furthermore, a significant interaction was also found
between Group and Viewing Condition (F(2, 32)=8.7; p=0.001; see Figure 5.15A). Patients
exhibited significantly longer mean latencies for secondary saccades when viewing with the
amblyopic eye (205±28 ms) compared to control participants viewing with the non-dominant
eye (178±32 ms), averaged across all experimental blocks.
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When variability in the latency of secondary saccades was inspected, only a significant main
effect for Experimental Block was evident (F(2, 32)=10.6; p=0.0004; see Figure 5.16). All
participants exhibited less variability in the mean latency of their secondary saccades during
the adaptation block (33±8 ms) as compared to the pre-adaptation block (39±16 ms), across
both groups and all viewing conditions. No other factors were significant.
65
5.3.7 Secondary saccade duration and peak velocity
Mean duration and peak velocity of the secondary saccades executed at the end of the
adaptation block were examined for kinematic analysis. The mean duration of secondary
saccades was comparable between control participants (26±1 ms) and patients (27±4 ms) at
the end of the adaptation block (F(1, 16)=3.2; p=0.094). However, patients executed secondary
saccades of higher peak velocity near the end of the adaptation block (75±17º/s) compared to
control participants (59±12º/s; F(1, 16)=10.1; p=0.006; see Figure 5.17). The latter observation
implied that the amplitude of the secondary saccades executed was larger for patients
compared to control participants (due to the main-sequence relationship between saccade
amplitude and peak velocity (A. T. Bahill, M. R. Clark, & L Stark, 1975)), which was in
accordance with the results reported for secondary saccade amplitude earlier. Together, these
results suggest that patients adapted their primary saccades to a lesser degree than control
participants, hence they needed to make secondary saccades of larger amplitude and peak
velocity.
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5.4 Discussion
To my knowledge, this study is the first to investigate the effects of amblyopia on short-term
saccadic adaptation mechanisms in human patients. The results show that: (1) When viewing
with the amblyopic eye and with both eyes, patients with amblyopia exhibited a lower
percentage change in their saccadic gain as compared to visually normal controls.
Importantly, the adapted saccadic gain was also more variable in patients during amblyopic
eye viewing when compared with visually normal controls; (2) this reduced adaptation of
saccadic gain was statistically more pronounced in the 5 (out of 7) patients who had
anisometropic amblyopia when compared to visually normal observers; (3) patients showed
longer and more variable primary saccade latencies when viewing with the amblyopic eye;
and (4) patients executed secondary corrective saccades of higher amplitude and peak
velocity as a result of the reduced adaptation of their primary saccades; however, these
corrective secondary saccades explained a lower percentage of the post-saccadic error, as
compared to visually normal control participants.
5.4.1 Choice of the experimental adaptation paradigm
In order to achieve a robust saccadic adaptation response in both control and patient groups,
target steps to one eccentricity (±19º) only in the horizontal plane were tested, in a single
direction (gain-decrease step of 4.18º) to maximize adaptation (Frens & van Opstal, 1994;
Semmlow, Gauthier, & Vercher, 1989). A 19º target jump was used to ensure that the initial
target step did not fall within the physiological blind spot when participants viewed
monocularly. The blind spot is a region in the visual field where no light (i.e., a visual
stimulus) can be detected due to the absence of photoreceptors, and is located at a distance of
67
about 12º-15º nasally from the fovea (Carpenter, 1977). In addition, a gain-decrease second
step was used instead of a gain-increase step because several studies have shown that a more
robust adaptation response could be elicited with fewer trials using a gain-decrease
adaptation paradigm (Bahcall & Kowler, 2000; Deubel, Wolf, & Hauske, 1986; Ethier, Zee,
& Shadmehr, 2008a; Miller, Anstis, & Templeton, 1981; Panouilleres et al., 2009).
Moreover, the back-step of 4.18º (amounting to ≈22% of the initial step) was big enough to
elicit a substantial decrease of saccadic gain in patients with amblyopia. As seen in the
section 5.3 Results, my experimental paradigm successfully induced a reduction in saccadic
gain in both control participants and patients, with adaptation of the saccadic gain reaching
up to 70% of desired change in control participants.
5.4.2 Decreased modulation of the saccadic gain during adaptation
in patients
Typically, adaptation of the saccadic gain is driven by the positional error signal at the end of
the saccade, which is believed to be visual in nature (Noto & Robinson, 2001; Seeberger,
Noto, & Robinson, 2002; Wallman & Fuchs, 1998). One possible way to achieve a change in
gain is by executing a saccade with a modified initial motor command. In this "off-line"
modification, saccades are executed toward an updated target position, which is derived from
the post-saccadic visual error information obtained from previous inaccurate movements.
Alternatively, saccadic gain change can also occur through the internal feedback processes
that predict the sensory consequences of ongoing motor commands using forward models
(Wolpert, Ghahramani, & Jordan, 1995; Wolpert, Miall, & Kawato, 1998). In this model, any
variability in the initial motor command can be reduced through commands that arrive later
during the same movement, thereby correcting the saccade during the movement itself.
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Indeed, it has been shown recently that saccades can be modified "on-line" (i.e., during
execution), for both non-adapted (Gaveau et al., 2003; West, Welsh, & Pratt, 2009) and
adapted (Chen-Harris, Joiner, Ethier, Zee, & Shadmehr, 2008) saccades. For example, using
a cross-axis adaptation paradigm, Chen-Harris et al. (2008) displaced the intra-saccadic step
vertically after an initial horizontal target step. In addition to the initial oblique component of
the resulting saccade, they also observed a vertical curvature of saccades later into the same
movement. This observation indicated that there was some component of the initial saccadic
motor command that was modified on-line later in movement. A more recent study by Ethier
Zee and Shadmehr (2008a) suggested that gain-decrease adaptation particularly seems to be
driven by changes in the same internal feedback processes that act as a forward model to
modify the gain of saccade on-line. Thus, in response to the intra-saccadic target step, the
gain of the adapting saccades can be modulated in two ways: 1) through the off-line
modification of the subsequent saccadic motor command based on the post-saccadic visual
error information—accounting for a majority of the adaptation response (Chen-Harris,
Joiner, Ethier, Zee, & Shadmehr, 2008), and 2) through on-line modifications that correct for
any errors in the initial saccadic motor command (i.e., the saccade trajectory) during
execution.
The most important finding of my study is that the patients with amblyopia exhibit reduced
and more variable percentage change in saccadic gain compared to visually normal control
participants, when viewing with the amblyopic eye i.e., they show a diminished modulation
of the saccadic gain in response to the adaptation paradigm. Does a deficit in the error-
processing mechanisms (off-line, on-line or both) cause such a reduced adaptation response
in patients with amblyopia? To answer this question, we need to consider a neural model of
saccadic adaptation and the effect of amblyopia on it. The cerebellum is a key brain structure
that maintains the optimal gain and direction of saccades, and is also involved in most forms
69
of learning and adaptation (Huok, Buckingham, & Barto, 1996). Not surprisingly, numerous
studies have implicated the cerebellum in a forward model of saccadic adaptation [see Girard
and Berthoz (2005) for a comprehensive review]. A simplified schematic of one such model
[adapted from Dean's models (Dean, 1995; Dean, Mayhew, & Langdon, 1994)] detailing
how adaptation of saccadic gain can be controlled is shown in Figure 5.18. According to this
model, the superior colliculus receives converging visual signals from the retinal and cortical
areas to arrive at the visual coordinates of the target location. The superior colliculus then
sends out a neural signal representing desired change in eye position to the brainstem burst
generator, which creates the required saccadic motor command. A copy of the signal is also
relayed to the cerebellum via the nucleus reticularis tegmenti pontis (NRTP). The
cerebellum, in turn, projects to the brainstem burst generator and provides inputs to refine the
generation of an accurate motor command. During adaptation, the presentation of intra-
saccadic target steps results in post-saccadic visual errors. This visual error signal [possibly
generated in the superior colliculus (Takeichi, Kaneko, & Fuchs, 2007)] is transmitted to the
cerebellum through the climbing fibres via the inferior olive. The visual error signal then
causes the cerebellum to modify the initial saccadic motor command before the next iteration
such that the subsequent saccade minimizes the magnitude of these post-saccadic errors (i.e.,
off-line). Additionally, an efference copy of this initial saccadic motor command is sent to
the cerebellum where the estimated current eye position is compared with the desired eye
position. Any discrepancy between the current and desired eye position generates a modified
motor command that is used to correct any bias and variability in the initial saccadic motor
command during the same movement (i.e., on-line).
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The findings in the patients with amblyopia could be explained using this model, via two
mechanisms: off-line and on-line control. First, increased sensory (visual) noise (Levi &
Klein, 2003) and spatial uncertainty (Levi & Klein, 1983; Levi, Klein, & Yap, 1987) in
amblyopia may lead to an imprecise post-saccadic visual error signal. Consequently, the
error information generated is not reliable enough to incur proper adaptation through off-line
modification of the initial saccadic motor commands. Thus, the resultant saccadic motor
commands, even after several iterations, may not lead to a robust change in the saccadic gain
in response to the adaptation paradigm. Second, increased sensory noise and spatial
uncertainty in amblyopia may also lead to a less reliable estimation of the current eye
position using the efference copy of a highly imprecise initial saccadic motor command
(Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, & Wong, 2010). This is supported by the
71
finding that the gain of primary saccades was highly variable in patients during all
experimental blocks (refer to Figure 5.8 in section 5.3 Results), indicating that the initial
saccadic motor command is more variable in amblyopia. When the efference copy of this
imprecise initial motor command is compared with the desired eye position, it may generate
an imprecise estimate for on-line correction. It may be possible that this estimate is not
precise enough to account for the variability in the initial motor command and/or that the
ongoing saccade completes before the necessary on-line corrections can be implemented.
The design of my study allows investigation of whether off-line control is affected during
saccadic adaptation in amblyopia as the precision of the error signal provided for saccadic
adaptation is more variable in patients due to deficits in spatiotemporal vision (given the
type/degree of amblyopia) (Levi & Klein, 1983, 2003; Levi, Klein, & Yap, 1987; Levi,
Waugh, & Beard, 1994). The results showed that there is a decreased extent of gain
adaptation during amblyopic eye and binocular viewing in patients, indicating that whenever
the amblyopic eye is involved in viewing, the resulting saccadic motor commands do not
benefit from accurate off-line modifications that ensure the optimal gain of adapted saccades,
as evident from the reduced and more variable percentage change in saccadic gain (Figure
5.6B and 5.8). My study, however, was not designed to directly test whether the on-line
modulation of saccadic motor commands is affected in amblyopia. To systematically test this
mechanism, a more thorough assessment of saccade dynamics before, during and after
adaptation is required (see section 5.6 Future Directions for details on the preliminary
analysis of saccade dynamics), which is currently ongoing and will be reported in future
studies.
The decreased gain modulation of primary saccades in response to the adaptation paradigm
was also evident in the metrics of secondary saccades executed by the patients with
amblyopia. When viewing with the amblyopic eye, patients executed secondary saccades of a
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higher amplitude (Figure 5.12) and peak velocity (Figure 5.17) in response to the larger
visual error remaining after the termination of the primary saccades. This observation
provides additional evidence that even after several hundred adaptation trials, the primary
saccades of patients land far from the final target position, necessitating secondary saccades
of a larger amplitude and peak velocity. However, these larger secondary saccades failed to
completely compensate for the post-saccadic errors when patients viewed with the amblyopic
eye, indicating that some portion of the movement error was still unexplained (see Figure
5.13). Along with the decreased modulation of primary saccades, this observation can be
attributed to reduced spatial precision in amblyopia (Levi & Klein, 1983, 2003; Levi, Klein,
& Yap, 1987; Levi, Waugh, & Beard, 1994) that leads to an incomplete compensation for the
post-saccadic movement errors in patients using more variable corrective saccades.
5.4.3 Incomplete adaptation versus slow adaptation of the saccadic
gain
Generally, saccadic adaptation in normal participants follows a characteristic temporal
course—the adapting gain undergoes an initial rapid change (decrease or increase depending
on the paradigm), followed by a more gradual change which asymptotes at a new steady-gain
value that can be characterized by an exponential function (Albano, 1996; Deubel, Wolf, &
Hauske, 1986; Frens & van Opstal, 1994). It is well-established that for gain-decrease
adaptation, this steady-state gain value is reached within a hundred saccades in visually
normal participants (Albano, 1996; Deubel, Wolf, & Hauske, 1986; Frens & van Opstal,
1994; Miller, Anstis, & Templeton, 1981; Semmlow, Gauthier, & Vercher, 1989). Is the
reduced adaptation of saccadic gain in the patients due to a “real” impairment in the ability to
adapt their saccades (at least in the short-term), or is it due to a difference in the temporal
73
course such that they require more trials to adapt to a similar extent as visually normal
people?
I attempted to measure the time course of adaptation quantitatively using two methods (see
section Appendix I Temporal course of saccadic adaptation for details). The first method is
to fit an exponential function to the data and obtain a rate constant. Several studies have
reported that the adaptation of saccadic gain typically follows an exponential time course and
have implemented this method of temporal analysis (Fuchs, Reiner, & Pong, 1996; Fujita,
Amagai, Minakawa, & Aoki, 2002; Miller, Anstis, & Templeton, 1981; F. R. Robinson,
Noto, & Bevans, 2003; Rolfs, Knapen, & Cavanagh, 2010; Srimal, Diedrichsen, Ryklin, &
Curtis, 2008; Straube, Deubel, Ditterich, & Eggert, 2001; Straube, Fuchs, Usher, &
Robinson, 1997). A major limitation of using this method in my analysis is that it can be
quite difficult to achieve "good" exponential fits (i.e., robust r2 values) for the patient group,
especially when viewing with the amblyopic eye. My analysis showed that only 2 out of 7
patients exhibited a robust exponential course of adaptation when viewing with the
amblyopic eye (data shown in Figure 5.20 in section Appendix I Temporal course of saccadic
adaptation). The rest of the patients' data were either too variable to attain a robust
exponential fit or failed to demonstrate an exponential course of adaptation. This may partly
be due to the unreliable visual error signal available to patients with amblyopia that prevents
the saccadic gain from decreasing rapidly during the initial period of adaptation (i.e.,
following a stereotypical exponential course of adaptation). Alternatively, it might reflect a
certain strategy employed by the patients with amblyopia that enables them to modulate
short-term changes in their saccadic gain following a different temporal course of adaptation
in the presence of an uncertain error signal.
The second method I attempted was to measure the time course of adaptation by dividing the
entire adaptation block into small bins of several trials and calculate the mean saccadic gain
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of each bin (in other words, by using a low-pass representation of the trial-by-trial data). The
difference between the mean gain of adjacent bins of trials can then be compared, and the bin
in which the saccadic gain stops showing a significant difference marks the asymptote or the
steady-gain state. This method of data binning has also been used by numerous studies (Abel,
Schmidt, Dell'Osso, & Daroff, 1978; Deubel, Wolf, & Hauske, 1986; Ethier, Zee, &
Shadmehr, 2008b; Fuchs, Reiner, & Pong, 1996; Griffiths, Whittle, & Buckley, 2011;
Klingenhoefer & Bremmer, 2010; Panouilleres, Urquizar, Salemme, & Pelisson, 2011;
Scudder, Batourina, & Tunder, 1998). Indeed, this method yields a more reliable and
comparable measure of "number of trials" or "time" taken to reach a steady adaptation state
for my data compared to the rate constant measure obtained after fitting exponential
functions (i.e., the first method). However, owing to the high inter-subject variability in
adaptation, a larger sample size of participants (both controls and patients) would be required
to yield a precise measurement of the temporal characteristics using this method. Despite the
limitation of these two methods, a preliminary qualitative analysis of the raw data revealed
no sign of any further decrease in saccadic gain near the end of adaptation period of both
patients and controls, suggesting that a steady-gain state had been reached after 120
adaptation trials. Nonetheless, a study with a larger number of patients and adaptation trials
is required to determine the time course of adaptation in amblyopia.
5.4.4 Implications of reduced saccadic adaptation in patients with
amblyopia
Sensory-motor adaptive mechanisms are essential for monitoring and correcting any changes
in the saccade metrics that occur due to normal aging (Warabi, Kase, & Kato, 1984) and/or
diseases (Choi, Kim, Cho, & Kim, 2008; Optican & Robinson, 1980), in order to maintain
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optimal saccade accuracy and precision. Accurate saccades are important as they minimize
the time taken by the eyes to reach the desired target location, and aid in creating a stable
perceptual representation of our environment (Rayner, 1998). Moreover, saccades are
required for other motor functions including coordinated limb movements, reaching,
grasping, and locomotion, making it important that these eye movements remain accurate.
My experimental paradigm specifically tested short-term adaptation of saccades which is
elicited within minutes to hours (and typically shows considerable recovery overnight which
was not investigated) (Semmlow, Gauthier, & Vercher, 1989). The results showed that when
faced with consistent movement errors, patients with amblyopia exhibit a reduced ability to
modulate short-term changes in saccadic gains. Does this imply that saccades in patients with
amblyopia are generally inaccurate due to an impaired short-term adaptation mechanism? If
this were true, patients with amblyopia should exhibit hypometric or hypermetric saccades
when compared to visually normal participants. However, saccade dysmetria was not
observed in any patients in this study or those in other studies (Ciuffreda, Kenyon, & Stark,
1978, 1979; Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, & Wong, 2010; Schor, 1975),
suggesting that mechanisms that mediate long-lasting changes in saccade metrics may
remain intact in amblyopia.
Recent studies have provided evidence of a distinct long-term saccadic adaptation
mechanism that is different from its short-term counterpart in both monkeys (F. R. Robinson,
Soetedjo, & Noto, 2006) and humans (Alahyane & Pelisson, 2005). The long-term saccadic
adaptation study by F. R. Robinson, et al. (2006) employed a large number of trials to induce
gain-decrease adaptation in monkeys over a span of 19 days. Moreover, they also kept the
monkeys in the dark overnight to minimize any extinction of adaptation. After 19 days,
monkeys exhibited an enduring adapted-state of their saccadic gain that showed no recovery
overnight. Similarly, Alahyane and Pelisson (2005) tested five adult humans using a rigorous
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paradigm with a large number of adaptation trials, and reported that the adapted saccadic
gain was retained for up to 5 days. Both studies suggested the presence of a long-term
adaptation mechanism, which in contrast to short-term adaptation develops over several days
and shows no considerable recovery overnight. Thus, a distinct mechanism seems to be
involved in long-term retention and maintenance of the adapted gain state. Strong evidence
of this long-term adaptation/retention of the saccadic gain also comes from clinical literature
pertaining to extraocular muscle paresis. A number of clinical studies (Abel, Schmidt,
Dell'Osso, & Daroff, 1978; Kommerell, Olivier, & Theopold, 1976; Optican, Zee, & Chu,
1985; Snow, Hore, & Vilis, 1985) showed that patients who develop saccadic hypometria
(undershooting) due to weakening of the eye muscles eventually increase their saccadic gain
to a normal value through adaptive mechanisms, and retain these normal saccade metrics
over a period of time.
In this study, the patient group comprised adult participants with a childhood onset of
amblyopia. These patients might have adapted well to their surroundings in the presence of
the amblyopic deficit that has been present all their life. This could explain why the average
saccade accuracy in patients was comparable to normals during the pre-adaptation block;
however the gain of saccades was still more variable in patients during the pre-adaptation
block. In this case, long-term adaptation processes in patients might have had enough
exposure (through regular day-to-day viewing), resulting in robust retention of a preferred
gain state of saccades that is close to the normal value (as observed during the pre-adaptation
block). However, when artificial movement errors were introduced (during the adaptation
block), patients had difficulty quickly adapting their gain state to a new value due to the
spatiotemporal visual deficits imposed by amblyopia. Further studies are required to
investigate long-term adaptation of the saccadic gain in patients with amblyopia.
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5.4.5 Effect of visual acuity and different subtypes of amblyopia on
adaptation
Is it possible that the reduced adaptation of saccadic gain is simply an effect of visual acuity
deficit alone, rather than a reduced ability that is specific to amblyopia? Two lines of
evidence suggest that the diminished adaptation observed in patients is most likely
attributable to the complex spatiotemporal deficits that arise from amblyopia rather than
simply a visual acuity deficit. First, a recent study by our lab investigated the effect of
induced monocular blur on saccadic eye movements on visually normal individuals. We
found no change in kinematics of primary saccades in subjects with blurred vision, even after
5 hours of blur exposure, compared to subjects without blurred vision (Niechwiej-Szwedo et
al., 2012). In contrast, patients with amblyopia, who had similar losses in visual acuity,
exhibited longer saccade latencies and increased variability of their saccade amplitude
compared to non-blurred subjects. These data indicate that spatial uncertainty evident in
amblyopia is not simply due a loss of visual acuity alone, but rather due to abnormal
development of visuomotor pathways in amblyopia. Second, in the current study, no
significant correlation (Pearson’s correlation coefficient r = +0.21; p=0.66) between visual
acuity and the percentage change in saccadic gain was observed during amblyopic eye
viewing. Specifically, patients with mild acuity deficits in the amblyopic eye (i.e., visual
acuity better than 20/100, n=4) exhibited comparable impairment in adaptation to those with
severe visual acuity deficits in the amblyopic eye (visual acuity of 20/100 and worse, n=3).
The patient group of this study comprises five anisometropic, one strabismic and one mixed
amblyopia patients. There are known differences in sensory and/or perceptual deficits seen in
strabismic amblyopia vs anisometropic amblyopia, notably distinct patterns of contrast
sensitivity (Campos, Prampolini, & Gulli, 1984; Hess & Bradley, 1980), motion perception
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(Ho & Giaschi, 2009), Vernier acuity (Birch & Swanson, 2000), spatial localization and
positional uncertainty (Hess & Holliday, 1992; Levi, Klein, & Yap, 1987) between the two
subtypes. The visual deficits in anisometropic amblyopia usually involve the whole visual
field, whereas those in strabismic amblyopia mainly involve central/foveal visual field due to
the misaligned visual axes. Therefore, it is possible that different amblyopia subtypes may
exhibit different patterns of saccadic adaptation. In the current data, 5 out of 7 patients who
had anisometropic amblyopia showed a pronounced effect of reduced saccadic adaptation,
both binocularly and when viewing with the amblyopic eye (see Figure 5.6B in section 5.3
Results). However, due to the small number of strabismic (n=1) and mixed (n=1) amblyopia
patients, a similar sub-group analysis for these amblyopia subtypes could not be carried out.
Further studies that include more patients with different subtypes of amblyopia are needed to
investigate if the pattern of saccadic adaptation differs across subtypes.
5.4.6 Insights on the mechanisms of short-term saccadic
adaptation
Short-term saccadic adaptation has been studied extensively over the past decade in order to
probe plasticity mechanisms that maintain optimal saccade accuracy. Despite the volume of
research on this topic, the neurophysiological mechanism of saccadic adaptation has not yet
been identified definitively, including the exact anatomical location(s) of adaptation,
pathways involved in carrying the error signal, and unique mechanisms for different
adaptation paradigms. One such mechanism that is still actively investigated is the nature of
the error signal(s) that drive saccadic adaptation. Currently, there is an ongoing debate as to
whether this error signal is visual or motor in nature (see section 4.3 Error signal driving
saccadic adaptation for details). Most studies that investigated the nature of the error signal
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reported that execution of corrective saccades is not necessary to induce adaptation, thereby
concluding that the error is not motor in nature (Noto & Robinson, 2001; Seeberger, Noto, &
Robinson, 2002; Wallman & Fuchs, 1998). More recent studies suggested that the error
signal is visual in nature which is derived from comparing the actual post-saccadic retinal
position with the predicted post-saccadic retinal position (Bahcall & Kowler, 2000;
Havermann & Lappe, 2010; A. L. Wong & Shelhamer, 2011).
My experiment tested saccadic adaptation in a visual disorder. Amblyopia is an excellent
visual deprivation model to study the basic neurophysiological mechanisms of saccadic
adaptation, because the well-documented spatiotemporal visual deficits most likely affect the
visual error information, which in turn has been hypothesized to drive the adaptation process.
The impairments in saccadic adaptation in patients when viewing with the amblyopic eye
and both eyes strongly suggested that visual error signal is important for adaptation. Hence,
using a disease model, my study provides novel evidence and additional support for the
existing hypothesis that the error signals that drive saccadic adaptation are visual in nature.
5.4.7 Saccade latency during adaptation
The adaptation paradigm significantly increased the latency of primary (Figure 5.9) and
secondary saccades (Figure 5.15B) compared to the pre-adaptation latency values, for all
participants. A similar observation was reported by Ethier, Zee and Shadmehr (2008a) who
studied the dynamics of adapted saccades compared to the non-adapted saccades of similar
amplitude. They postulated that the high latency values during adaptation may arise from the
increased uncertainty associated with location of the intra-saccadic stepping target [see the
discussion in Ethier et al. (2008a) for details]. If this were true, then one would expect
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patients to exhibit longer saccadic latencies than visually normal participants, due to
increased spatial and positional uncertainty in amblyopia (Levi & Klein, 1983, 2003; Levi,
Klein, & Yap, 1987; Levi, Waugh, & Beard, 1994). Indeed, my data shows that when
viewing with the amblyopic eye, patients had longer latencies of both primary and secondary
saccades during adaptation compared to visually normal participants (see Figure 5.10A and
5.15A in section 5.3 Results). Moreover, this effect of increased latency of primary saccades
in patients during amblyopic eye viewing was also evident during the pre-adaptation and
post-adaptation blocks. This general increase in saccade latency in patients with amblyopia
has been previously reported by several studies from other research groups and from our lab,
and is believed to be caused by slower processing of the visual information in amblyopia
(Ciuffreda, Kenyon, & Stark, 1978; Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, &
Wong, 2010; Schor & Hallmark, 1978). Hence, my data provides additional support for the
idea that the initiation of saccades is delayed in amblyopia—a phenomenon that was also
present during saccade adaptation.
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5.5 Conclusions
In conclusion, this study is the first to investigate short-term sensorimotor adaptation of
saccadic eye movements in patients with amblyopia. The results demonstrated that patients
with amblyopia exhibit decreased and more variable adaptation of their saccadic gain when
viewing with the amblyopic eye and binocularly. This reflects an impaired ability of patients
with amblyopia to implement short-term changes in saccadic gain required for maintenance
of optimal movement accuracy, whenever the amblyopic eye is involved in viewing. I
propose that this impaired adaptation results from a reduced capability of the initial saccadic
motor commands to be accurately modified in the presence of imprecise error signals
generated in amblyopia. Moreover, the temporal course of such adaptation appears to vary in
amblyopia and needs to be investigated in future studies. Finally, by studying saccadic
adaptation in a visual disorder i.e., amblyopia, this study provides additional evidence
supporting the current hypothesis that the error signals that drive adaptation of saccadic gain
are visual in nature.
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5.6 Future Directions
There are several important research questions that emerge from the work presented in my
thesis, some of which I am currently investigating and others that should be addressed in
future studies to gain a better understanding of how saccadic adaptation mechanisms are
altered by amblyopia.
5.6.1 Saccade dynamics during adaptation
The primary focus of my research project was to investigate the short-term adaptive control
mechanisms that maintain saccadic accuracy in patients with amblyopia. I found that
amblyopia results in less accurate pre-programming of the adapted saccades as evident from
a significantly less reduction in the saccadic gain of patients compared to visually normal
controls in response to the intra-saccadic adaptation steps. It is well known that in addition to
being pre-programmed, saccades are capable of receiving on-line corrections that can modify
their trajectory during execution. Therefore, it is also important to determine whether the
ability of saccades to receive corrective on-line modifications during adaptation is impacted
by amblyopia.
One method of achieving this is by analysing saccade dynamics during adaptation. The two
variables of interest in this case are the "time elapsed from the onset of saccade to the peak
velocity (i.e., the duration of the acceleration phase)" and the "time elapsed from the point of
peak velocity to the offset of saccade (i.e., the duration of the deceleration phase)".
Typically, saccade velocity traces show a bell-shaped profile, where the duration of the
acceleration phase is almost equal to the duration of the deceleration phase. If saccades
receive corrective commands during execution then the saccade velocity profiles will be
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skewed as the saccades might extend or reduce the duration of the deceleration phase to
make corrective changes in-flight. This skewness in the velocity profile can be quantified as
the ratio of “duration of the acceleration phase” to the “duration of the deceleration phase”,
which is typically close to 1 for pre-programmed saccades (Collins, Semroud, Orriols, &
Dore-Mazars, 2008). In cases where saccades undergo on-line changes in their trajectory,
this ratio might be different depending on the time spent in the deceleration phase compared
to that spent in the acceleration phase. The skewness ratio can be assessed for saccades of
specific amplitudes before and during adaptation in both controls and patients to determine
whether they receive any on-line corrections during adaptation, and if whether there are any
differences between the controls and patients. The main sequence data collected for each
participant (see section 5.2.3 Experimental Procedure) provides a baseline measure of the
saccade dynamics before adaptation for a range of amplitudes (3-25º saccades). The
skewness ratios before adaptation can be computed for the amplitudes ranges of 13-14º, 14-
15º, 15-16º, 17-18º and 18-19º for each participant, and compared with the ratios of
corresponding amplitude ranges during adaptation. A difference between the two will
suggest that saccades are receiving different on-line modifications during adaptation when
compared to during the baseline condition. This in-depth analysis of saccade dynamics will
be reported in later studies from our lab.
Another method of analysing the extent of on-line correction during adaptation is by
implementing a modified experimental paradigm [as used by Ethier, Zee and Shadmehr
(2008a)] that entails a more accurate and rigorous measurement of saccade dynamics. This
paradigm should include an additional set of control "non-adapted" saccades of similar
amplitude as the "adapted" saccades, such that the “peak velocity” and the “time to peak
velocity” variables for both sets of saccades can be measured and compared directly. If the
dynamics of the “adapted” saccades differ significantly from the control “non-adapted”
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saccades of equivalent amplitude, then it will imply that the saccade trajectory is receiving
on-line modifications during adaptation. This analysis is currently beyond the scope of my
study, due to the absence of a sufficient sample of control “non-adapted” saccades with
amplitudes similar to the “adapted” saccades in the current paradigm, but will be carried out
in future studies.
5.6.2 Possible effects of using a gain-increase adaptation paradigm
This experiment only tested gain-decrease adaptation, in which the intra-saccadic target steps
systematically reduce the gain of adapted saccades. An alternative adaptation paradigm is the
gain-increase paradigm which induces larger amplitude saccades by employing intra-
saccadic target steps that move further away from central fixation. However, a vast body of
literature indicates that it is easier to elicit gain-decrease adaptation than gain-increase
adaptation in humans (Bahcall & Kowler, 2000; Deubel, Wolf, & Hauske, 1986; Ethier, Zee,
& Shadmehr, 2008a; Miller, Anstis, & Templeton, 1981; Panouilleres et al., 2009). These
studies reported that gain-increase adaptation exhibits a slower temporal course and a lower
degree of gain modulation in humans compared to the gain-decrease adaptation. Thus, the
gain-decrease paradigm provided a more convenient method of testing saccadic adaptation in
patients with amblyopia.
More recently, several studies have provided strong evidence that the two adaptation
paradigms may actually rely on partially separate neural mechanisms (Ethier, Zee, &
Shadmehr, 2008a; Panouilleres et al., 2009; Schnier & Lappe, 2011; Semmlow, Gauthier, &
Vercher, 1989). These studies adapted a particular type of saccade using one paradigm and
then tested the transfer of adaptation across different types of saccades (reactive, volitional,
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scanning, memory-based, anti-saccades). In addition to testing this adaptation transfer, they
also compared the dynamics of adapted saccades between both gain-decrease and gain-
increase paradigms. Collectively, these studies (Ethier, Zee, & Shadmehr, 2008a;
Panouilleres et al., 2009; Schnier & Lappe, 2011; Semmlow, Gauthier, & Vercher, 1989)
reported that gain-decrease adaptation most likely relies on the internal feedback processes
that modify the saccades during movement (i.e., the on-line mechanism) whereas gain-
increase adaptation is mainly driven by initiating saccades towards an updated goal, akin to
the visual target being remapped to a different location on the retina [i.e., the off-line
mechanism; see Pelisson, Alahyane, Panouilleres and Tilikete (2010) for a detailed review].
The two adaptation paradigms might induce different levels of adaptation in patients with
amblyopia, both spatially and temporally. The current results show that for gain-decrease
adaptation, patients with amblyopia do not learn as effectively from imprecise saccadic
endpoint errors that drive the gain modulation of saccades to maintain accuracy, and thereby
exhibit modest changes in their saccadic gain when compared to visually normal individuals.
How will patients with amblyopia respond to gain-increase adaptation that presumably relies
on a hypothetical target-remapping mechanism? It is possible that due to spatial uncertainty
in amblyopia (Hess & Holliday, 1992; Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, &
Wong, 2010), the remapped target position on the retina will also be imprecise and
inaccurate, thus patients with amblyopia will also exhibit diminished saccadic gain
adaptation (i.e., a lower percentage change in saccadic gain) when viewing with the
amblyopic eye or both eyes. It would be interesting to investigate the effects of amblyopia on
gain-increase adaptation in future studies.
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5.6.3 Other paradigms and the real-world application of adaptation:
scanning a visual scene, head-unrestrained, long-term
Given the basic properties and high intra- and inter-subject variability of saccadic adaptation,
the present study was confined to examining a specific saccade type (reflexive saccades),
gaze vector (±19º amplitudes) and saccadic adaptation paradigm (gain-decrease paradigm)
for achieving maximal adaptation in both groups of participants. This limits the extent to
which the results of the current study can be generalized across different types of saccades,
paradigms of saccadic adaptation and subtypes of amblyopia. As mentioned above, testing
patients using a gain-increase paradigm will shed some light as to how different neural
mechanisms of saccadic adaptation are affected in amblyopia. Additionally, for a more
thorough analysis, patients should be adapted on a range of amplitudes and directions for
several different types of saccades, e.g., volitional saccades—that include memory-guided
saccades and saccades that scan the visual scene or environment. Firstly, testing several
amplitudes and directions will elucidate how the deficits in saccadic adaptation scale across
the entire visual field of patients with amblyopia. Secondly, adaptation of scanning saccades
might have a greater implication in day-to-day viewing as a majority of real-world saccades
are executed to re-direct the gaze to objects already present in one's environment. Therefore,
studying adaptive mechanisms that maintain the accuracy of scanning saccades in amblyopia
might provide a deeper understanding of the "real-world" deficits in saccadic adaptation
within these patients.
The scope of the current study can also be extended to examine sensorimotor adaptation of
other body movements in amblyopia. This can include the adaptation of limb movements
(chiefly hand movements), head movements and/or the combined eye-hand and head-eye
movements. For instance, variant adaptation paradigms can be employed to investigate the
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following potential research questions in amblyopia: prism adaptation of both eye and hand
movements, adaptation of combined eye-hand movements to forced target perturbations,
adaptation of eye and hand movements to magnifying and minifying lenses, and adaptation
of the vestibulo-ocular reflex (VOR). These studies will enable us to gain a better
understanding of how overall adaptation mechanisms involving a variety of body movements
are affected in amblyopia. An important sensorimotor adaptation paradigm that is most
pertinent to this study is the head-unrestrained saccadic adaptation paradigm. The current
study tests saccadic adaptation in head-restrained conditions, i.e., movements defined only in
the eye coordinates with the head stabilized. However, in real life head movements are
seldom restrained and any visual object is localized by making a coordinated head and eye
movement. The investigation of adaptation of these combined head-eye gaze shifts has
recently received much attention, but most of the research has been carried out in primate
models. To date only two studies have investigated the adaptation of combined head-eye
movements in visually normal humans. Firstly, Kroller, Pelisson and Prablanc (1996)
reported no transfer of eye coordinate-specific saccadic adaptation to head movements.
Secondly, Cecala and Freedman (2008) found comparable adaptation of saccades in the
head-restrained and head-free viewing conditions. The latter study also reported that the level
of adaptation did not depend on whether the gaze shifts were initiated with different eye
positions relative to the head indicating that head-eye gaze adaptation possibly occurs at the
level of the gaze shift command and not the at the level of eye or head signals separately.
These results apply to visually normal individuals but it will be interesting to investigate
whether the combined head-eye saccadic adaptation occurs at the level of gaze command in
patients with amblyopia as well.
Additionally, the current study focuses on the short-term mechanisms of saccadic adaptation
specifically, which are purported to be involved in maintenance of saccadic accuracy in the
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short-term, e.g., in response to transient extraocular muscle weakness due to fatigue. A
different adaptation mechanism is in place that maintains saccadic accuracy in the long-term
e.g., in response to permanent extraocular muscle weakness due to diseases. In visually
normal humans, long-term changes in saccadic gain can be endured up to a period of 5 days
in response to the intra-saccadic double step adaptation paradigm (Alahyane & Pelisson,
2005). It will be interesting to study whether patients with amblyopia will exhibit any long-
term/overnight retention of their adapted saccadic gain state in response to a rigorous
adaptation paradigm involving a large number of double step trials. Since the average
accuracy of saccades in patients with amblyopia is comparable to that in visually normal
individuals (only the precision of saccades is reduced in amblyopia) (Niechwiej-Szwedo,
Goltz, Chandrakumar, Hirji, & Wong, 2010), this would imply that the adaptive mechanisms
that maintain the long-term accuracy of saccades might be preserved even in the presence of
spatiotemporal visual deficits in amblyopia. It is an important question that will clarify how
the long-term adaptive mechanisms that maintain saccadic accuracy develop or progress in
amblyopia (i.e., in the presence of a visual deficit) and should be addressed in future research
work.
5.6.4 Investigation of saccadic adaptation in the pediatric patient
population
The target population of my study comprised of adult patients (mean age of 28.3±8.4 years)
who developed amblyopia in their childhood. It will be interesting to study whether saccadic
adaptation mechanisms are equally hampered in a much younger pediatric amblyopia patient
population. The visual system continues to mature during the early 6-8 years of life [i.e., the
critical period of visual development; see a detailed review by T. L. Lewis & Maurer (2005)]
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and may be more susceptible to developing new neural projections during childhood. A
couple of studies have investigated saccadic adaptation in visually normal children (Dore-
Mazars, Vergilino-Perez, Lemoine, & Bucci, 2011; Salman et al., 2006), and reported that
there is no difference in the level of gain-decrease adaptation achieved by children as
compared to adults. It will be worthy to investigate how saccadic adaptation might be
affected in children with amblyopia. Will they exhibit an effect of decreased gain adaptation
similar to the adult patients or will they manifest a differential level of saccadic adaptation
when compared to adult patients with amblyopia and/or age-matched visually normal
children? These research questions will provide more insight into the development and extent
of visuomotor deficits in ambyopia, and should be addressed in future studies.
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Appendix I: Temporal course of saccadic adaptation
As mentioned in section 5.4 Discussion, the reduced adaptation exhibited by patients with
amblyopia can possibly be due to a slower time course of adaptation. To explore whether the
spatiotemporal visual impairments in amblyopia alter the time course of adaptation, I
attempted two methods for quantifying the temporal course of adaptation in my participants:
first, by fitting an exponential function to the raw data and second, by binning data into sets
of 5 trials. The preliminary results of both methods are reported below (only for the AE/NDE
viewing). Due to high between-subject variability in saccadic adaptation, a larger sample size
of participants is required for a more thorough analysis. Our group is currently pursuing this
research question by actively recruiting more participants.
Exponential-fitting technique:
All data were fit with the following mathematical function, widely used in modelling
exponential decays:
G(t) = G0 + ΔGe−λt
where G(t) is the saccadic gain at a given trial t, G0 is the steady-state (asymptotic) saccadic
gain value (reached at the end of adaptation), ΔG is the change in saccadic gain (i.e., from
the baseline gain to steady-state gain), and λ is the decay constant of adaptation. By
definition, the reciprocal of the decay constant (i.e., 1/λ value) yields the exponential time
constant of adaptation, i.e., the number of trials taken to reach "1−(1/e)" or "≈63.2%" of the
asymptotic saccadic gain value. This exponential time constant can be used as a measure of
quantifying the temporal characteristics of saccadic adaptation in my participants. The results
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of exponential fitting are shown below (Figure 5.19 shows control data and Figure 5.20
shows patient data).
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #1
Participant #3
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
ccad
ic G
ain
0.6
0.7
0.8
0.9
1.0
1.1
Subject #2
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #2
Participant #4
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Saccadic
Gain
0.6
0.7
0.8
0.9
1.0
1.1
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Participant #7
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #8
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #9
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Saccadic
Gain
0.6
0.7
0.8
0.9
1.0
1.1
Participant #5
Trial Number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
ccad
ic G
ain
0.6
0.7
0.8
0.9
1.0
1.1
Subject #10
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
ccad
ic G
ain
0.6
0.7
0.8
0.9
1.0
1.1
Participant #10
Participant #6
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Saccadic
Gain
0.6
0.7
0.8
0.9
1.0
1.1
93
Participant #11
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
ccad
ic G
ain
0.6
0.7
0.8
0.9
1.0
1.1
Figure 5.19: Exponential-fitting functions for visually normal participants. Data are shown for
all visually normal participants (#1-#11) during non-dominant eye viewing condition. 2 out of 11
control participants (#5 and #10) did not show an exponential course of adaptation.
Participant #12
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
ccad
ic G
ain
0.6
0.7
0.8
0.9
1.0
1.1
Participant #13
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #14
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Saccadic
Gain
0.6
0.7
0.8
0.9
1.0
1.1
Subject #15
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #15
94
Figure 5.19 shows the results of fitting an exponential function to the data of visually normal
participants. 2 out of 11 visually normal participants (participants #5 and #10) did not exhibit
an exponential time course of adaptation. Moreover, the exponential time constant calculated
for participant #7 was not significant (p > 0.05). All the exponential fits had an r2 value of
<0.50 due to a large spread of data points around the fitted curve. The exponential time
constants for each participant are given below:
Participant #18
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
1.1
Participant #17
Trail number
0 10 20 30 40 50 60 70 80 90 100 110 120
Saccad
ic G
ain
0.6
0.7
0.8
0.9
1.0
1.1
Figure 5.20: Exponential-fitting functions for patients with amblyopia. Data are shown for all patients (#12-
#18) during amblyopic eye viewing condition. 2 out of 7 patients (#12 and #18) did not show an exponential
course of adaptation.
Subject #16
Trial number
0 10 20 30 40 50 60 70 80 90 100 110 120
Sa
cca
dic
Ga
in
0.6
0.7
0.8
0.9
1.0
Participant #16
95
Participant #1: 8 trials Participant #2: 7 trials Participant #3: 39 trials
Participant #4: 32 trials Participant #5: N/A Participant #6: 20 trials
Participant #7: 56 trials* Participant #8: 19 trials Participant #9: 8 trials
Participant #10: N/A Participant #11: 55 trials
* marks non-significant exponential time constants (p > 0.05).
N/A indicates that the data could not be fit with the exponential function.
Figure 5.20 depicts the results of fitting an exponential function to the data of patients with
amblyopia. Similar to visually normal participants, 2 out of 7 patients (participants #12 and
#18) did not exhibit an exponential time course of adaptation. Moreover, the exponential
time constants calculated for participants #13, #14 and #16 were not significant (p>0.05).
The exponential fits also had an r2 value of <0.50 similar to visually normal participants. The
exponential time constants for each patient are given below:
Participant #12: N/A Participant #13: 18 trials* Participant #14: 55 trials*
Participant #15: 34 trials Participant #16: 9 trials* Participant #17: 15 trials
Participant #18: N/A
* marks non-significant exponential time constants (p > 0.05).
N/A indicates that the data could not be fit with the exponential function.
It can be seen that most of the patients' data could not be fit well with the exponential
functions. 5 out of 7 patients either show no exponential course of adaptation or yield a non-
96
significant time constant. Hence, time course of adaptation in patients could not be directly
compared to that in visually normal participants, using this method.
Data-binning technique:
The second method I attempted was to measure the time course of adaptation by dividing the
entire adaptation block into small bins of 5 trials and calculating the mean saccadic gain of
each bin. The gain values of 5 trials of each bin were then compared with gain values of the
last 30 trials of the adaptation block (when the saccadic gain had reached an asymptotic
value) using an unpaired t-test, and the first bin in which the saccadic gain stopped showing a
significant difference was marked as the instance when the saccadic gain had reached a
steady-state (or an asymptotic value that was calculated over the last 30 adaptation trials).
This data-binning technique was also attempted for AE/NDE viewing condition only, and the
results are shown below in Figures III and IV. Data from only those participants that failed to
give a statistically significant exponential time constant using the first method (i.e.,
participants #5, #7, #10, #12, #13, #14, #16, #18) are shown. A sample binned data from a
typical control participant (#1 in this case) is also shown in Figure 5.21.
97
Figure 5.21: Data-binning results for visually normal participants, shown for participants #1, #5, #7
and #10.
Participant #1 Participant #5
Participant #7 Participant #10
98
Figure 5.22: Data-binning results for patients
with amblyopia, shown for participants #12,
#13, #14, #16 and #18.
Participant #12 Participant #13
Participant #14 Participant #16
Participant #18
99
Figure 5.21 shows the data-binning results for four visually normal participants; a typical
visually normal participant (depicted by participant #1 in this case), and three other
participants whose exponential time constants obtained from the first method were
statistically non-significant (#5, #7 and #10). Similarly, Figure 5.22 illustrates the data-
binning results for those participants whose exponential time constants calculated from the
first method were not statistically significant. It can be seen that using this method of data-
binning, a consistent measure of the number of trials taken to reach an asymptotic gain value
can be achieved for participants that do not necessarily follow an exponential time course of
adaptation, as evidenced by the results for participants #5, #7, #10, #13, #14 and #16. The
number of trials taken to reach an asymptotic gain value for each participant (#1-#11:
visually normal participants, #12-#18: patients with amblyopia) calculated using the data-
binning method are given below.
Participant #1: 20 trials Participant #2: 20 trials Participant #3: 45 trials
Participant #4: 30 trials Participant #5: 40 trials* Participant #6: 50 trials
Participant #7: 25 trials* Participant #8: 20 trials Participant #9: 20 trials
Participant #10: 10 trials* Participant #11: 45 trials Participant #12: 5 trials*
Participant #13: 30 trials* Participant #14: 30 trials* Participant #15: 25 trials
Participant #16: 20 trials* Participant #17: 15 trials Participant #18: 5 trials*
* marks those participants whose exponential time constants were non-significant using the
first measure. It can be seen that a more consistent quantitative measure for the time course
of adaptation can be achieved using the second method. However, participants #12 and #18
(both patients with anisometropic amblyopia) did not exhibit a substantial decrease in their
100
saccadic gain during adaptation (see Figure 5.22), and hence their temporal values were very
low (i.e., 5 trials). Nonetheless, our group is currently recruiting more participants in order to
increase the power of the temporal analysis (using both methods) and a more thorough
analysis of the temporal characteristics of saccadic adaptation in patients with amblyopia will
be reported in future studies from our lab.
101
Appendix II: Eye movement recordings: Video-
Oculography
The eyes are capable of moving with six degrees of freedom (horizontal, vertical and
torsional movements) allowing us to fix our gaze to new positions in order to scan our visual
environment. A precise and accurate measurement of eye movements is critical to the field of
oculomotor research. The development of modern-day eye tracking techniques has enabled
us to quantify eye movements that are not directly perceived such as the saccadic eye
movements, vestibulo-ocular reflex and the optokinetic reflex, and eye movements that are
not visible to the naked eye such as the microsaccades of fixational eye movements. The eye
tracking devices measure the amount of rotation in the eyes, most commonly using
techniques of electro-oculogram (EOG), infra-red reflection (IR photodiodes), scleral search
coils and video-oculography (VOG). In this study, all eye movements were recorded using
the Chronos Eye Tracking Device (C-ETD, Chronos Vision©, Germany) which employed
the technique of video-oculography. This section details the basic concepts of video-
oculography and some of the advantages and drawbacks of the technique.
Video-oculography is a technique that uses photographic and video-based methods for
dynamic measurement of eye movements (both 2D and 3D movements). It typically involves
recording the video image of the eye during the required tasks, and then processing the
recorded image using algorithms that detect and localize certain eye-fixed markers (such as
iris, pupil, limbus and episcleral blood vessels). The video cameras can be mounted on a
head-gear which can be worn firmly by the participant. If the head remains relatively stable
(using a bite-bar or a chin-rest), then the eye position with respect to the head can be
computed accurately from the recorded 2D image coordinates. If the cameras move relative
102
to the head (for instance due to slippage of the head-mounted gear), then head-fixed markers
can be used to compensate for movement transitions. However, it can be difficult to detect
and localize the head-fixed markers with high precision as compared to eye-fixed markers.
Alternatively, combined head-eye gaze movements can be tracked using remote eye trackers
that are not head-mounted and do not require head stabilization. But, the spatial and temporal
resolution of these remote systems is not as good as the head-mounted systems. Therefore, as
long as the head movements are stabilized, the head-mounted eye tracking systems are the
most accurate of all VOG systems.
Video-oculography has increasingly become more popular due to recent progress in the field
of electronic image processing. With the development of better detection algorithms, most
VOG systems are now capable of recording both 2D and 3D eye movements with a sampling
rate of 400 Hz or better—which covers the temporal bandwidth of all physiological eye
movements—and a spatial resolution of about five hundredth of a degree of eye rotation
(0.05º). Given the non-invasive nature of VOG systems, they serve as an equally-accurate
alternative to other more invasive eye-tracking techniques, such as the scleral search coils
which are placed on the eyes. Moreover, the setup time of VOG systems is quicker than other
eye movement techniques (including EOG, IR and coils).
As mentioned above, for my experiments we used a head-mounted C-ETD VOG system
(developed by Chronos Vision©) that used the position of pupils to track eye movements.
The main advantage of using C-ETD over other video-based VOG system is that all the
recorded experimental data can be processed offline by defining the pupil detection threshold
levels. This means that if the eye tracker loses a few frames during actual recording, e.g., due
to irregular illumination of the eyes, frequently changing pupil size and/or eyelash artifacts,
the lost frames can still be retrieved by properly defining pupil detection thresholds when
processing the data offline. This minimizes the amount of data that might be lost due to the
103
unsuccessful tracking of the pupil marker during live recordings. However, there are some
limitations of using a VOG based system when compared to other techniques. Firstly, as
mentioned earlier VOG systems typically consist of a head-mounted recording camera which
is prone to slippage if it is not firmly attached to the participant's head. In our case, the C-
ETD's head band can slide on participants head if they move their head on the chin-rest
during recordings which can change the relative position of the pupil marker on the head.
This potential problem was circumvented by initiating a re-calibration of the eye tracker as
soon as any slippage of the head-mounted gear was detected. Secondly, C-ETD calibration is
based on fixating a set of five visual targets (at 0º, ±10º horizontally and ±10º vertically) and
the variability of fixations for each target can range from 1−2º (Imai et al., 2005; van der
Geest & Frens, 2002). This means that the accuracy of the C-ETD is no better than the
standard error of the fixations. Therefore, if the initial calibration is noisy, then the accuracy
of recorded data is reduced as well. In spite of these few limitations, the C-ETD provided a
quick, accurate and non-invasive method of tracking eye movements with a spatial resolution
of about one tenth of a degree (0.1º) and a temporal resolution of 200 Hz in all our
participants [see Eggert (2007) for a detailed review of all eye movement recording
techniques].
104
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