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

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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!

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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.

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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

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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.

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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.

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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

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(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.

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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.

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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

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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

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(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).

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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 =

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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.

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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.

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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

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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

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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

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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

74

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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