An Investigation Into the Neural Basis of Convergence Eye Movements

An Investigation into the Neural Basis of Convergence Eye Movements

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Emmanuel Owusu

Graduate Program in Vision Science

The Ohio State University

2018

Dissertation Committee

Marjean T. Kulp, Advisor

Nicklaus F. Fogt, Co-Advisor

Andrew J. Toole

Bradley Dougherty

Michael J. Earley

Xiangrui Li

Copyrighted by

Emmanuel Owusu

2018

Abstract

Introduction: Different components of convergence are tonic convergence, disparity

convergence, accommodative convergence and proximal. However, it is not clear

whether these different components ultimately draw on similar innervational control.

Better understanding of the neurology of convergence eye movements could lead to

improvement in interventions for deficits in convergence eye movements. Therefore, the

purpose of this dissertation is to investigate the neural basis for convergence eye movements in binocularly normal young adults. Methods: Two approaches were used.

First, clinical measurements were used to determine the correlations among

accommodative, disparity and proximal convergence eye movements, as well as between

proximal convergence and vergence facility. These correlations were used as an index of

the extent of overlaps in their neurological control. Second, functional magnetic

resonance imaging (fMRI) was performed on a group of adults with normal binocular

function as they converged their eyes in response to stimuli for accommodative, disparity,

proximal and voluntary convergence eye movements. Results: In the clinical study,

stimulus gradient accommodative convergence was negatively correlated with far-near

proximal convergence (Spearman’s correlation = -0.6111, p < 0.0001). However, the

correlation may be at least in part attributable to the inclusion of gradient AC/A as a

component of the calculation of far-near proximal convergence. Disparity convergence ii did not correlate with measures of proximal convergence. Gradient accommodative convergence was not correlated with the amount of disparity convergence in operation

(Spearman’s correlation = -0.1512, p=0.3649). Finally, proximal convergence was not correlated with vergence facility (Spearman’s correlation = +0.1107, p=0.5082 and -

0.0149, p=0.9291 for far-near proximal and +2.50D proximal, respectively). In the fMRI study, cluster-based group analysis showed that disparity vergence activated regions in the cuneus, lingual gyrus, precuneus, and middle occipital gyrus of the occipital cortex.

Accommodative convergence stimulated regions in the occipital (cuneus and middle

occipital gyrus), parietal (precuneus, superior parietal and postcentral) and frontal lobes

(precentral and postcentral gyri). Proximal convergence activated regions in the occipital

(cuneus and middle occipital gyrus) and parietal lobes (precuneus, inferior parietal lobule

and superior parietal lobule). Voluntary convergence activated brain areas in the occipital

(cuneus, lingual gyrus and middle occipital gyrus), temporal (middle temporal gyrus),

and frontal lobes (precentral and middle central gyri) as well as culmen and declive in the

cerebellum. Each of the convergence stimuli activated the cuneus and middle occipital

gyrus in the occipital lobe. In addition, both accommodative and proximal convergence

eye movements activated the precuneus in the parietal lobe. Similarly, the

accommodative and voluntary convergence eye movements both activated the precentral

gyrus in the frontal lobe, and the disparity and voluntary convergence eye movements

activated the lingual gyrus in the occipital lobe. Conclusion: The imaging study suggests

that the convergence components share common neural control pathways even though

iii each also retains unique neural activations. The clinical study suggests that the open-loop vergence components are independent.

Dedication

This document is dedicated to Mrs. Patricia Asuama Owusu, the mum and dad to the

Owusu family while I was away on this pursuit. More is thy due than I can say… or pay.

Acknowledgments

I am immensely grateful to my dissertation mentors Dr. Marjean Kulp and Dr. Nicklaus

Fogt for their tireless and patient guidance during my graduate training. I also appreciate

Dr. Andrew Toole who wrote the program for displaying the stimuli in the functional

imaging study, and Dr. Nasser Kashou who helped in the design of the functional

imaging study. In addition, I am grateful to Drs. Bradley Dougherty and Andrew Toole

for serving on my Academic Progress Committee, and Drs. Michael Earley and Xiangrui

Li for serving on my dissertation committee. Further, I appreciate Dr. Xiangrui Li’s great

supervision of the functional imaging analysis. Also, I am grateful to the Ohio Lions Eye

Research Foundation for generously providing funds for the functional imaging study.

The Center for Behavioral Brain Imaging of the Department of Psychology, the Ohio

State University provided pilot scan hours and assistance with logistics to implement the functional imaging study, and the American Academy of Optometry provided a Student’s

Travel Fellowship to attend and present portions of the clinical study at Academy 2016 in

Anaheim, CA. In addition, I gratefully acknowledge the Graduate Research

Associateship from the College of Optometry, The Ohio State University which enabled me undertake my graduate training.

Finally, I am very grateful to all the participants who volunteered to be in both studies.

Vita

2014 to 2018 ……………………………….. Graduate Research Associate, The Ohio State University

2015 ………………………………………… Master of Optometry, University of KwaZulu-Natal, South Africa

2012 ………………………………………... Master of Science, University of London

2009 to 2014 ……………………………….. Technical Instructor, Kwame Nkrumah University of Science & Technology, Ghana

2001 to 2007 ………………………………… OD Optometry Kwame Nkrumah University of Science & Technology, Ghana

1997 to 1999 …………………………………. Sunyani Secondary School, Ghana

Publications

1. Koomson, N. Y., Amedo, A. O., Owusu, E., Ampeh, P. B., Kobia-Acquah, E., & Bonsu, K. (2015). Under-correction of myopia reduces lag of accommodation in school children in Kumasi, Ghana. International Journal of Health Sciences, 5: 137-150

2. Koomson, N. Y., Amedo, A. O., Owusu, E., Ampeh, P. B., Kobia-Acquah, E., & Bonsu, K. (2014). Under-correction Induces Peripheral Myopic Defocus in School

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Children in Kumasi, Ghana. International Journal of Innovation and Applied Studies, 9(4), 1598

3. Kumah, D. B., Owusu, E., & Kyeremaa, F. A. (2013). Prevalence of hyperopia among school children in the Kumasi metropolis, Ghana. Journal of the Ghana Science Association, 14(1), 63-68

Fields of Study

Major Field: Vision Science

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Table of Contents

Abstract ...... ii Dedication ...... v Acknowledgments...... vi Vita ...... vii List of Tables ...... xii List of Figures ...... xiii Chapter 1: Introduction ...... 1 Background ...... 1 The Maddox Components of Horizontal Vergence Response ...... 2 Open-Loop and Closed-Loop Vergence Errors and Vergence Control ...... 5 Classifying Vergence Components Based on Control of Response Error ...... 8 Feed-Forward and Feed-Back Control Systems in the Neural Control of Vergence ... 10 Conceptual Models on Control of Vergence Eye Movements ...... 13 Sequence for Vergence Eye Movement Response ...... 17 Organization of Neural Control for Vergence Eye Movements ...... 21 Methods for Investigating Vergence Eye Movements...... 23 Assessing Vergence Eye Movements ...... 27 Chapter 2: Correlations among Vergence Eye Movements ...... 37 Background ...... 37 Contribution of Vergence Components to Total Vergence Response ...... 38 Correlations among the Vergence Components ...... 40 Rationale for the Study ...... 42 Objectives ...... 43 Hypotheses ...... 43 Chapter 3: Investigating the Neural Basis of Vergence Eye Movements ...... 46 ix

Study Procedures ...... 46 Data Analysis ...... 51 Results ...... 52 Tests of Normality ...... 55 Correlation between Accommodative Vergences ...... 55 Correlation between Proximal Vergences ...... 56 Correlation between Accommodative Vergence and Proximal Vergence ...... 59 Correlation between Proximal Vergence and Disparity Vergence ...... 61 Correlation between Accommodative Vergence and Disparity Vergence ...... 63 Correlations of Accommodative Vergence with Vergence Facility ...... 65 Correlation between Disparity Vergence and Vergence Facility ...... 67 Correlation between Proximal Vergence and Vergence Facility ...... 68 Discussion ...... 72 Conclusion ...... 84 Limitations to the Study ...... 85 Chapter 4: Functional Imaging for Convergence Eye Movements ...... 87 Background ...... 87 Neural Control of Vergence Eye Movements ...... 87 The Two Streams of Visual Information Processing ...... 93 Techniques for Studying Neural Control of Vergence Eye Movements ...... 95 Functional Imaging for Studying Neural Control of Vergence Eye Movements ...... 101 The BOLD Response ...... 102 FMRI Study Designs...... 104 FMRI Data Analysis Processes ...... 105 FSL’s FEAT ...... 109 Objectives of the Study ...... 110 Hypothesis...... 110 Chapter 5: Functional Imaging of Convergence Sub-Types...... 112 Methodology for Functional Imaging of Convergence Sub-types ...... 112 Study Design ...... 112 Subjects ...... 112 Inclusion Criteria ...... 113

Exclusion Criteria ...... 114 Sample Size ...... 115 Functional Imaging ...... 115 Enrollment...... 115 Calibration of Eye Tracker and Autorefractor ...... 119 Eligibility Assessment ...... 121 Eligibility Testing Procedures...... 121 Eye Tracking ...... 126 FMRI Scanning ...... 127 Order of Scanning ...... 130 Details of Conditions for Functional Scanning ...... 132 Order of Conditions during Scanning ...... 140 Localizing & Structural Scans ...... 141 Scanning Parameters ...... 141 Duration of Time between Eligibility and Functional MRI Scan ...... 142 Pre-Processing and Image Preparation ...... 142 Data Analysis ...... 143 Identifying Areas of Significant Activation ...... 145 Determining Overlaps in Activated Areas among Different Stimulus Conditions ..... 150 Results ...... 151 Areas of Activation by Various Convergence Conditions ...... 153 Summary of Overlaps: ...... 218 Discussion ...... 220 Limitations of the Study...... 236 Conclusion ...... 237 Chapter 6: Overall Summary and Conclusion ...... 240 Comparing the Results between the Clinical and Imaging Studies ...... 240 Implications for Current Models of Neural Control of Accommodation and Convergence ...... 242 Bibliography ...... 247

List of Tables

Table 1: Summary of results for phoria, phoria through lenses and proximal vergence .. 53 Table 2: Summary of correlations among vergence measurements ...... 71 Table 3: Comparison of dorsal and ventral streams of visual information processing ..... 94 Table 4: Summary of techniques for studying the anatomy and physiology of the brain 97 Table 5: Normative values for normal binocular vision ...... 114 Table 6: The convergence insufficiency symptom survey ...... 116 Table 7: Summary of eligibility criteria for fMRI scanning Continued ...... 117 Table 8: Distribution of baseline eligibility testing results of subjects ...... 152 Table 9: Coordinates and names of brain areas activated by the accommodative vergence paradigm based on single point search using Talairach Client ...... 155 Table 10: Coordinates and names of brain structures within 1mm by 1mm by 1mm of the respective coordinates ...... 156 Table 11: Nearest gray matter structures and their respective distances in millimeters from coordinates from cluster-based group analysis of accommodative vergence paradigm ...... 157 Table 12: Name of structures that were activated by the disparity vergence paradigm and their coordinates ...... 165 Table 13: Structures showing significant activation by the proximal vergence paradigm and their coordinates ...... 173 Table 14: Nearest gray matter areas to the cluster-based coordinates without names in proximal vergence condition...... 174 T able 15: Names of the coordinates derived from the cluster-based group analysis of voluntary/gross vergence ...... 183 Table 16: Area within 1mm of coordinate derived from group analysis of voluntary vergence condition, but had no name in Talairach Client ...... 184 Table 17: Nearest gray matter to the coordinate derived from group analysis of voluntary/gross vergence condition, but had no name in Talairach Client ...... 184 Table 18: Comparison of the cortical lobes activated by the various vergence paradigms ...... 193 Table 19: Cross tabulation of areas of activation in various vergence paradigms ...... 193 Table 20: Summary of overlaps of vergence conditions ...... 219

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List of Figures

Figure 1: Simplified dual interactive model of accommodation and convergence ...... 15 Figure 2: The distribution of distance heterophoria among the participants ...... 54 Figure 3: Distribution of near heterophorias among the subjects ...... 55 Figure 4: Scatter plot showing the ranks of the far-near accommodative vergence and the accommodative vergence obtained from the gradient AC/A ...... 56 Figure 5: Box plot comparing the proximal vergence and the proximal vergence obtained from the gradient AC/A ...... 57 Figure 6: Bland-Altman plot comparing the two measures of proximal vergence ...... 58 Figure 7: Scatter plot showing the relationship between proximal vergence obtained from the difference between far-near and gradient AC/A ratios and the +2.50D method ...... 58 Figure 8: Scatterplot comparing the far-near proximal vergence to the far-near accommodative vergence ...... 59 Figure 9: Scatterplot showing the ranked correlation between the far-near proximal vergence to the gradient accommodative vergence ...... 60 Figure 10: Scatterplot showing the correlation between the +2.50D proximal vergence and gradient accommodative vergence ...... 61 Figure 11: Scatterplot showing the correlation between the far-near proximal and disparity vergence ...... 62 Figure 12: Scatterplot showing the correlation between +2.50 proximal and disparity vergence ...... 63 Figure 13: Scatterplot showing correlation between far-near accommodative vergence and disparity vergence ...... 64 Figure 14: Scatterplot showing correlation between gradient accommodative vergence and disparity vergence ...... 65 Figure 15: Scatterplot showing correlation between far-near accommodative vergence and vergence facility ...... 66 Figure 16: Scatterplot showing correlation between gradient accommodative vergence and vergence facility ...... 67 Figure 17: Scatterplot showing correlation between disparity vergence and vergence facility ...... 68 Figure 18: Scatterplot showing correlation between far-near proximal vergence and vergence facility ...... 69 Figure 19: Scatter plot showing correlation between +2.50D proximal vergence and vergence facility ...... 70 Figure 20: Correlation between far-near AC/A and gradient AC/A ...... 76

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Figure 21: A simplified summary of the organization of the neural control of vergence eye movements...... 89 Figure 22: Description of accommodative vergence stimulus and paradigm ...... 133 Figure 23: Description of proximal convergence stimulus and paradigm ...... 135 Figure 24: Stimulus and paradigm for disparity vergence ...... 136 Figure 25: Random dot target with zero prism diopters of vergence demand ...... 137 Figure 26: Random dot stereogram with vergence demand ...... 138 Figure 27: Description of the stimulus and paradigm for voluntary vergence ...... 139 Figure 28: Flow chart of process for identifying structures from coordinates ...... 148 Figure 29: Details of the clusters from group analysis of accommodative convergence 154 Figure 30: Threshold activation map for the group analysis of accommodative convergence-lightbox view ...... 159 Figure 31: Accommodative condition activation - cuneus of left occipital lobe ...... 160 Figure 32: Accommodative condition activation-postcentral gyrus of right parietal ..... 161 Figure 33: Accommodative vergence activation - postcentral gyrus in right parietal .... 161 Figure 34: Accommodative vergence activation - right middle occipital gyrus ...... 162 Figure 35: Accommodative vergence activation -right superior parietal lobule ...... 162 Figure 36: Gray matter in right occipital lobe - Brodmann Area 19 ...... 163 Figure 37: Disparity vergence group-based cluster activation ...... 164 Figure 38: Activation map for group analysis of disparity vergence ...... 166 Figure 39: Disparity vergence activation - lingual gyrus in right occipital lobe...... 167 Figure 40: Disparity vergence activation - another part of right lingual gyrus ...... 168 Figure 41: Disparity vergence activation - fusiform gyrus in left occipital lobe ...... 168 Figure 42: Disparity vergence activation - cuneus in left occipital lobe...... 169 Figure 43: Disparity vergence activation - right middle occipital gyrus ...... 169 Figure 44: Disparity vergence activation - declive in posterior lobe of left cerebellum 170 Figure 45: Proximal vergence group-based cluster activation ...... 172 Figure 46: Proximal vergence activation-group based activation maps ...... 175 Figure 47: Proximal vergence activation-cuneus of right occipital lobe ...... 176 Figure 48: Proximal vergence activation - cuneus of left occipital lobe (Brodmann Area 19) ...... 176 Figure 49: Proximal vergence activation - precuneus of right parietal lobe ...... 177 Figure 50: Proximal vergence activation - fusiform gyrus of left temporal lobe (Brodmann Area 37) ...... 177 Figure 51: Proximal vergence activation - right superior frontal gyrus (Brodmann Area 10) ...... 178 Figure 52: Proximal vergence activation - tuber of vermis in left posterior cerebellum 178 Figure 53: Proximal vergence activation - culmen in anterior lobe of left cerebellum .. 179 Figure 54: Proximal vergence activation - declive in vermis of posterior lobe of left cerebellum ...... 179 Figure 55: Proximal vergence activation - parahippocampal gyrus on left limbic lobe . 180 Figure 56: Proximal vergence activation - posterior cingulate in right limbic lobe ...... 180 Figure 57: Cluster-based group analysis of gross/voluntary vergence ...... 182 Figure 58: Gross/voluntary verence - group-based activation map ...... 186 xiv

Figure 59: Gross/voluntary activation - cuneus in left occipital lobe (Brodmann Area 18) ...... 187 Figure 60: Gross/voluntary activation - right middle occipital gyrus (Brodmann Area 19) ...... 187 Figure 61: Gross/voluntary vergence activation - precuneus of left parietal lobe (Brodmann Area 19) ...... 188 Figure 62: Gross/voluntary vergence activation - left inferior parietal lobule ...... 188 Figure 63: Gross/voluntary vergence activation- left superior parietal lobule ...... 189 Figure 64: Gross/voluntary vergence activation - precentral gyrus in right frontal lobe (Brodmann Area 4) ...... 189 Figure 65: Gross/voluntary vergence activation - precentral gyrus in right frontal lobe (Brodmann Area 6) ...... 190 Figure 66: Gross/voluntary vergence activation - middle temporal gyrus of left occipital lobe ...... 190 Figure 67: Gross/voluntary vergence activation - declive in posterior pole of left cerebellum ...... 191 Figure 68: Gross/voluntary vergence activation - anterior lobe of left cerebellum ...... 191 Figure 69: Gross/voluntary vergence activation - parahippocampal gyrus in left limbic lobe ...... 192 Figure 70: Comparison of accommodative vergence and disparity vergence activations ...... 195 Figure 71: Overlap between accommodative vergence and disparity vergence - cuneus in right occipital lobe ...... 196 Figure 72: Overlap between accommodative vergence and disparity vergence - right lingual gyrus...... 196 Figure 73: Overlap between accommodative vergence and disparity vergence - right middle occipital gyrus ...... 197 Figure 74: Overlap between accommodative vergence and disparity vergence - right sub- gyral white matter in the occipital cortex ...... 197 Figure 75: Comparison of cluster-based group activation maps between accommodative vergences...... 199 Figure 76: Overlap between accommodative vergence and proximal vergence - cuneus in the left occipital lobe (Brodmann Area 18)...... 200 Figure 77: Overlap between accommodative vergence and proximal vergence paradigms - precuneus of right occipital lobe (Brodmann Area 31)...... 200 Figure 78: Overlap between accommodative vergence and proximal vergence - precuneus in the left parietal lobe (Brodmann Area 19) ...... 201 Figure 79: Comparison of cluster-based group activation maps between accommodative and gross/voluntary vergences ...... 202 Figure 80: Overlap of accommodative and gross/voluntary vergences - cuneus of left occipital lobe (Brodmann Area 18)...... 203 Figure 81: Overlap between accommodative and gross/voluntary vergences - cuneus of right occipital lobe (Brodmann Area 7) ...... 203

Figure 82: Overlap between accommodative and gross/voluntary vergences - cuneus of left occipital lobe (Brodmann Area 19) ...... 204 Figure 83: Overlap between accommodative and gross/voluntary vergences - precuneus of right occipital lobe (Brodmann Area 31) ...... 204 Figure 84: Overlap between accommodative and gross/voluntary vergences - precuneus of right occipital lobe (Brodmann Area 19) ...... 205 Figure 85: Overlap between accommodative and gross/voluntary vergences - right superior parietal lobule ...... 205 Figure 86: Overlap between accommodative and gross/voluntary vergences - sub-gyral white matter in right frontal lobe ...... 206 Figure 87: Comparison of cluster-based group activation maps between disparity vergence ...... 207 Figure 88: Overlap between disparity and gross/voluntary vergences - cuneus of right occipital lobe ...... 208 Figure 89: Overlap between disparity and gross/voluntary vergences - lingual gyrus of right occipital lobe ...... 208 Figure 90: Overlap between disparity and gross/voluntary vergences - middle occipital gyrus of right occipital lobe ...... 209 Figure 91: Comparison of cluster-based group analysis of disparity and proximal vergence activations ...... 210 Figure 92: Overlap between disparity and proximal vergences - cuneus of left occipital lobe ...... 211 Figure 93: Overlap between disparity and proximal vergences - fusiform gyrus of left occipital lobe ...... 211 Figure 94: Overlap between disparity and proximal vergences - right inferior occipital gyrus ...... 212 Figure 95: Overlap between disparity and proximal vergences- left middle occipital gyrus ...... 212 Figure 96: Overlaps between disparity and proximal vergences - declive in posterior lobe of right cerebellum ...... 213 Figure 97: Overlap between proximal and gross/voluntary vergences activations ...... 215 Figure 98: Overlap between proximal and gross/voluntary vergences - lingual gyrus of left occipital lobe...... 215 Figure 99: Overlap between proximal and gross/voluntary vergences - posterior cingulate of left limbic lobe ...... 216 Figure 100: Overlap between proximal and gross/voluntary vergences - fusiform gyrus of left occipital lobe...... 216 Figure 101: Overlap of proximal and gross/voluntary vergences - precuneus of right parietal lobe ...... 217 Figure 102: Overlap between proximal and gross/voluntary vergences - declive in posterior lobe of right cerebellum ...... 217 Figure 103: Overlap between proximal and gross/voluntary vergences - culmen in anterior lobe of right cerebellum...... 218

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Figure 104: Model of the neural control of accommodation and vergence eye movements based on Schor’s studies ...... 244 Figure 105: Model of neural control of accommodation and vergence eye movements based on the results of this study ...... 245

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Chapter 1: Introduction

Background

Humans have two separate, frontally-positioned eyes. They also have retinas which have different levels of resolving ability across its area, with the foveae possessing the highest resolving ability. Consequently, it is necessary for humans to be able to move their eyes.

Eye movements help to ensure that images of objects in the visual environment are focused on the fovea. Besides that, eye movements also help to maintain the images of foveated objects on the retina despite the movement of the object or the observer. In addition, eye movements are necessary for observers to explore the visual environment by shifting their attention from objects at one distance or direction to another. Different types of eye movement systems exist to achieve the visual and perceptual functions described above.

For instance, to be able to optimize the perception of objects and positions that may be moving with respect to the observer, the observer must be able to track visual targets moving in depth at various points in the visual field. It is also necessary to change fixation from distant to near objects, or from near to distant objects. This requires a combination of conjugate (versional) eye movements and disjunctive (vergence) eye movements.

Conjugate eye movements refer to the movement of both eyes in the same direction, for

example when both eyes are moved to the right[1]. On the other hand, disjunctive eye movements refer to the movement of both eyes in opposite directions; for example when a person maintains fixation on an object that is slowly moving closer (i.e., moving in depth). When fixating an object that is slowly moving closer, the right eye rotates toward the left and the left eye rotates toward the right so as to maintain the image of the moving object on the fovea. Disjunctive eye movements are called vergence, and may be horizontal, vertical or torsional.

Vergence eye movement refers to the simultaneous movement of the eyes in opposite directions in order to obtain or maintain binocular single vision[2, 3]. Horizontal vergence eye movements can occur in one of two directions. The first is convergence in which the eyes move toward the nose, such as when one looks from a distant position to a near target. The second direction for horizontal vergence eye movements is divergence in which the eyes move outward towards the temples, such as when one looks from a near target to a distant one. Vergence eye movements allow the images of objects of regard to be bifoveally fixated or fall within corresponding retinal points. As such, inaccurate convergence or divergence causes images of objects to fall on non-corresponding retinal points, resulting in double vision.

The Maddox Components of Horizontal Vergence Response

Since the 19th century, different components of the horizontal vergence eye movement

response have been known. Maddox[4] was perhaps the first to describe convergence as

being composed of four subtypes. These four components of convergence eye

movements have been identified based on the stimulus that drives the particular sub- type[4-6]. The sub-types of steady-state convergence eye movements are disparity (or

fusional) convergence, accommodative convergence, proximal convergence (sometimes

referred to as psychic or voluntary convergence) and tonic convergence.

Fusional convergence (also called disparity convergence) refers to the convergence of the

eyes which is driven by absolute disparity[7] (the difference in vergence posture between the eyes and the vergence demand of the target being fixated on). Therefore, it is thought that disparity-driven fusional vergence works to reduce or eliminate the disparity, and is hence the only component of convergence that uses ocular misalignment as a cue. The visual system does not fuse significantly disparate retinal images well (disparities that are

greater than a few minutes of arc) [8, 9]. Thus, there is a limit to the magnitude of

disparity that can be adequately reduced or eliminated by the fusional convergence

component. Beyond a certain level of retinal disparity, fusional convergence may need to

be complemented by other convergence components. The fusional component of the

vergence response is also called disparity convergence. Disparity convergence is the

name that will be used in this dissertation when describing the fusional component of the

total convergence response.

Accommodative convergence is the sub-type of convergence that occurs reflexively when

a person’s accommodation changes[2, 7]. Accommodation may be driven by blur, and

this blur-driven accommodation in turn drives accommodative convergence.

Accommodative convergence is usually represented as the amount of convergence that

accompanies a unit change in accommodation (or the accommodative convergence to

accommodation ratio, AC/A ratio). The expected AC/A ratio is 4/1[2] which means that a

change in accommodation of one diopter is accompanied by four prism diopters of

accommodative convergence. Too high or too low accommodative convergence can

result in binocular vision anomalies[10, 11]. A High AC/A (i.e., AC/A ratio of 6/1 or

more), is often associated with convergence excess (inwardly turned eyes with greater

deviation at near than at distance)[10]. In convergence excess, the convergence output is more than what is required to bifoveally fixate a target at a near distance. On the other hand, a low AC/A ratio (3/1 or less) is often associated with convergence insufficiency.

Proximal convergence is convergence that is driven cognitively by the “awareness of nearness”[6, 12] of a target, or by the perceived distance of a target being fixated. It is clinically represented by the change in convergence that is brought about by the knowledge of target distance without any input from the other convergence components.

Proximal cues to convergence are not considered by some investigators to be as important as accommodative and disparity cues for the total convergence output [13].

Lastly, tonic convergence is regarded as the baseline level of convergence that is brought about by “neural bias” [5, 9] in the central control mechanisms and tonus of the extraocular muscles. Clinically, tonic convergence is not considered as alterable as the other types of convergence to the steady-state convergence response.

The term voluntary convergence is sometimes used to refer to the convergence of the eyes under the will power of a person in the absence of the other cues for vergence[14].

For instance, voluntary convergence occurs when a subject is able to converge when

asked to “cross the eyes’’[15]. Sometimes, voluntary convergence is used synonymously

with proximal convergence[9] because it has been argued that people who are able to

voluntarily converge their eyes do so using some cognitive cues to proximity. Indeed,

Heath[5] has pointed out that in his original classification of convergence eye

movements, Maddox classified proximal convergence as convergence “due to knowledge

of nearness, or in other words, voluntary convergence”[5].

The clinical importance of adequate and accurate convergence eye movements is

underscored by significant clinical symptoms experienced by patients with inadequate

convergence output or maintenance[16, 17]. Inability to accurately converge, or maintain

convergence on a target may disrupt the normal functioning of the eyes together. The

clinical condition of convergence insufficiency has significant public health implications

with attentional and cognitive manifestations[2, 18].

Open-Loop and Closed-Loop Vergence Errors and Vergence Control

The main purpose of vergence eye movement is the elimination or reduction of retinal disparity to ensure retinal correspondence for binocular single vision. Accurate bifoveal fixation due to accurate convergence and maintenance of convergence at a particular fixation point results in binocular single vision which enhances stereoscopic depth perception. On the other hand, errors in vergence response have the potential to disrupt the intended binocular alignment.

Vergence response errors can exist in two situations. In the first situation, the vergence error exists when the two eyes are not allowed to work together, such as when one eye is

occluded. That situation is described as disparity vergence open-loop because there is no

disparity feed-back to guide corrective action from the vergence system. This open-loop

vergence error is called heterophoria (phoria)[19, 20]. Similar to the types of vergence,

open-loop vergence errors (heterophoria) can be horizontal, vertical or torsional. There

are two types of horizontal heterophoria. These are exophoria which refers to inadequate

open-loop convergence, and esophoria which results when open-loop over-convergence

results[2].

When the two eyes are allowed to work together without interruption, the individual

images from each eye are normally fused into a single percept. Under such binocular

conditions, vergences errors can still exist; this second type of vergence errors are

referred to as closed-loop. In closed-loop situations, disparity cues exist to help the

vergence system to identify and resolve the amount of disparity. Closed-loop vergence

errors utilize feed-back to attempt to correct open-loop convergence error[19].

It should be noted that accommodation can also be in either open-loop (where there is an absence of blur cues, such as focusing through a pinhole) or closed-loop where blur cues exist. When both accommodation and vergence are open-loop, this is described as dual

open-loop conditions. Conversely, a dual closed-loop accommodation and vergence system is said to exist when both accommodation and vergence are closed loop (i.e., both blur and disparity cues exist). A human visual system that is intact and functioning normally is in dual closed-loop.

It is important to note that despite the attempt by the closed-loop, feed-back control systems for accommodation and disparity vergence, errors of disparity and blur persist,

although normally in very small magnitude. The closed-loop vergence error is called

fixation disparity, and it represents the difference between the open-loop vergence error

(phoria) and the closed-loop vergence response. The magnitude of fixation disparity at the fovea is estimated to be at most about 6 minutes of arc (0.175 prism diopters) on average[20, 21].

The closed-loop accommodation error is called accommodative lag or lead, and is about

0.5D[2] on average.

The oculomotor response to the closed-loop retinal cues of disparity and accommodation has two distinct components[22]. There is an initial burst of phasic activity which is completed by about 200 milliseconds for disparity vergence and 300-400 milliseconds for accommodation[20]. The phasic components utilize an initial burst of innervation thought to be necessary to overcome inertial forces and tonicity of ocular muscles to begin the motor response, as compared to the tonic component which is more long lasting and keeps the motor system in a steady state[20]. Thus, the phasic fusion-initiating process in the vergence response is fast and of short latency[23] and rapidly changes the vergence posture following a change in the depth of a visual target. It is also easily dissipated. To maintain the vergence posture, an adaptable and slow fusion-sustaining component is needed to replace the fast fusional vergence component. This slow, fusion-sustaining component keeps the oculomotor system in a steady-state, maintaining the fixation on the visual target in depth.

It has been suggested that the phasic component of disparity vergence is more easily fatigued, and due to this, clinical symptoms are likely to result if one depends on it rather

than the slow, fusion-sustaining component [22]. This is because the phasic components have short latency, fast and not intended for sustained activity.

Although these eye movement response components may not appear separate and distinct, it is sometimes useful to know which component is in action or desired when analyzing vergence eye movements. It takes different stimuli to elicit each of them[23]:

ramped vergence stimuli are known to elicit the fusion-maintaining component of disparity vergence while stepped stimuli elicit the fusion-initiating component.

Classifying Vergence Components Based on Control of Response Error

Of the four components, disparity vergence is closed-loop while tonic, proximal and accommodative are open-loop [20] and have no feed-back correction for on-going eye movements.

When disparity, accommodative and proximal cues are absent, the vergence posture of the eye is controlled by tonic vergence[9]. Tonic vergence is an intrinsic neural bias in vergence posture which is slightly divergent, corresponding to the anatomical position of rest.

Proximal vergence is described as an open-loop vergence response to perception of proximity. Cues to proximal vergence can be either static, monocular (such as size, overlap, linear perspective and texture gradient), or dynamic (such as looming and motion parallax)[9]. There is a prevailing consensus that monocular proximal cues are responsible for the major proportion of proximal vergence response when changing vergence posture from very far to near (within arm’s length). This is because it is only

within arm’s length that binocular cues to proximity increase in importance. Furthermore,

within arm’s length the vergence response may be activated by both proximal and

disparity vergence[21].

Although the concept of proximal convergence is widely known and accepted [2, 7, 13,

24], a corollary to proximal convergence that is often less considered is proximal divergence[25]. However, it makes sense that if the eye’s position can be changed by

awareness of nearness, then awareness of lack of nearness (or awareness of distance)

must also exist to contribute to changing the visual posture from a near position to a

distant one.

Accommodative vergence is the open-loop reflex vergence that accompanies

accommodation. Accommodative vergence occurs because of the cross-link between

accommodation and convergence. Thus, accommodative convergence is part of the

synkinetic near response which also includes accommodation and pupillary constriction[21].

Generally, disparity vergence is considered to be the closed-loop vergence response to disparity. Thus, disparity vergence is only active under binocular conditions, and it occurs to reduce or eliminate binocular disparity[19, 21]. Disparity vergence has two components, a transient, fusion-initiating component and a more sustained component.

The first component of disparity vergence has been described as the fusion-obtaining component, with the second component being the fusion-maintaining part.

However, there is usually a small amount of residual disparity even in the presence of the

more sustained disparity vergence component. This amount of disparity is called fixation

disparity, and is regarded as a “purposeful error”[26], in the disparity vergence control system that keeps the disparity vergence system from slipping into a vergence dead space[21, 27]. This purposeful error for disparity is estimated to be about half the

magnitude of Panum’s fusional area at the fovea[21]. Fixation disparity makes it

necessary to have a slow disparity vergence adapting mechanism which is less fatiguing.

These components of vergence response are elaborated in vergence control system

models which will be described later.

The accommodation system has similar components. The corresponding purposeful error

for accommodation is accommodative lag or lead which occurs as a result of the depth of

focus of the eye. Accommodative “purposeful error” of about 0.5D keeps the slow- adapting part of the accommodative response in a steady state, without falling into its dead space[21, 28].

Feed-Forward and Feed-Back Control Systems in the Neural Control of Vergence

The main purpose of vergence eye movements is to keep the images of objects at different depths on the fovea (or corresponding retinal points) for single, clear binocular vision. In this attempt to foveate objects at different absolute and relative depths, there must be a system to determine whether errors have been made during and after the vergence eye movements. The vergence control system has a way of comparing the desired vergence eye movement to the response to identify the vergence error. Once this error is determined, it is thought that the system resolves this error in one or both of two

ways; by using either feed-forward or feed-back control strategies to reduce the vergence error[20].

In feed-back control, vergence response error is identified and corrected online. The

correction is fed back into the closed-loop control to affect the eye movement that is still

taking place. To be useful for the on-going eye movement, feed-back control systems are

found in the type of eye movement which are relatively slow enough to allow the

correction to be made to affect the eye movement, or those which are used to maintain

eye position[19].

For instance, the closed-looped disparity vergence utilizes a feed-back system for

improving or maintaining vergence posture in binocular, naturalistic conditions[21].

Besides disparity vergence, accommodation and accommodative vergence are also

thought to utilize closed-loop feed-back control. The information on vergence error is

usually derived from retinal sources.

On the other hand, fast eye movements with short latencies utilize a feed-forward control

system to monitor and make adjustments for errors. Because they are so fast, there is not

enough time to make corrections that affect the current eye movement. As such, in feed-

forward systems, information derived from differences in the desired eye movement and

the responses are fed forward into adjustments for subsequent eye movements[20]. They

do not affect the current eye movement. Feed-forward systems are usually open-loop.

They also are capable of utilizing extraretinal information[21]. An example of a feed-

forward system is the vestibular system which utilizes information derived from neck

proprioceptors, as well as accelerometers and angular rotation sensors in the ear to

stabilize retinal images during brief head movements through the vestibulo-ocular reflex

(VOR). The VOR is so fast that a feed-back control system would not be appropriate for it. Thus, proximal vergence which is relatively fast is speculated to utilize extraretinal feed-forward control similar to the VOR.

The interactions (termed cross-links) between accommodation and convergence

(accommodative convergence), and between convergence and accommodation

(convergence accommodation) are stimulated more by the phasic components than by their tonic components. Therefore, accommodative convergence and convergence accommodation are stimulated more during the dynamic period of vergence eye movements than they are during the tonic, steady-state period[19, 20].

Thus, the near response to accommodation and convergence demands may have an open- loop phase (voluntary, feed-forward, non-retinal based control like efference copy) and closed-loop (retinotopic, feed-back) phases. The open-loop phase of the response is phasic and is active during the dynamic state of vergence response while the closed-loop control is more tonic and active during the steady state.

Some neurons have been identified that appear to possess the properties that mirror the tonic and phasic properties of eye movements. These cells have burst, tonic or burst-tonic properties[29].

Various conceptual models have been developed to speculate on the neural underpinnings of vergence control. These models continue to be revised as the understanding of the neural control of vergence eye movements become available. Following is a brief description of the conceptual model of vergence control.

Conceptual Models on Control of Vergence Eye Movements

There are two main views on how the vergence system is organized neurologically. The first, and perhaps more predominant one, is the Maddox view which considers vergence eye movements as comprising distinct sub-components which are linearly additive to give the total vergence response.

The Maddox-sub-component view suggests distinct neural control areas or centers in the brain with possible overlaps or interactions in the neural or motor pathways. These interactions or overlaps are reflected in the cross-links between accommodation and convergence. A less obvious, but implied interaction based on the Maddox-sub- component view is the interaction between proximal vergence and accommodative and disparity vergences. It is largely agreed that proximal vergence initiates the vergence response in situations where the initial vergence demand is beyond the disparity detection range [21]. The disparity detection range has been estimated to be about 6 degrees (about

10.5 prism diopters)[19]. Bedell and Wick have provided evidence to show that the proximal vergence system is suited for initiating the vergence response[13]. Once the proximal vergence response reduces the vergence error to within the disparity detection range, the disparity system (and the cross-linked accommodation system) take over.

Further, it is suggested that proximal cues are cognitive. They depend on the perceptual awareness of nearness (or distance) to be engaged while the accommodative and disparity cues are reflexive. Schor[19, 20] has described these two ways of conceptualizing the proximal vergence response on one side, and the accommodative and disparity vergence systems on the other as spatiotopic and retinotopic respectively. The perceptual,

spatiotopic cue of proximity utilizes top-down processing while the reflexive, retinotopic

cues of blur and retinal disparity utilize bottom-up processing[19].

A less obvious implication is that the proximal vergence system somehow interacts with disparity vergence system as that is the only way the disparity system would know to take over from the proximal vergence system. The interaction suggests a negative correlation

between the two systems, with the proximal system operating at a cognitive level while

the disparity system is reflexive. According to Saladin[21], when the disparity (and

accommodative vergence) components are absent or ineffective in convergence

insufficiency, patients often make the conscious effort to converge. This suggests that patients with convergence insufficiency may attempt to compensate using proximal vergence. This is because proximal vergence is the component of vergence response which is under perceptual control. Therefore, proximal vergence is most likely to be affected by volitional efforts.

It is not clear at what level of control these distinctions or overlaps occur, from the higher control centers to the oculomotor plant (extraocular muscles). Clinically, this view also implies that if one system or combination of systems is not functioning normally, there is the potential to draw on other systems as Saladin’s view above seems to demonstrate.

However, there is another view of vergence eye movements which is the opposite of the

Maddox-sub-component view. This view considers vergence eye movement as a single system. For instance, Mitchell[8] argues that, the vergence response may not necessarily be made up of distinct components, but a single entity which manifests differently based on how it is measured. According to this view, the Maddox “components” of vergence

14 eye response is a reflection of how different cues are able to elicit response from the vergence system. Thus, the view that disparity and accommodative vergence components are the major components of the vergence response are explained by the vergence system having a higher affinity for disparity and accommodative cues.

These different views of vergence eye movements are important because they reflect the views on their neural control. Models have been developed to conceptualize the various views on the types and neural control of vergence eye movements.

Figure 1: Simplified dual interactive model of accommodation and convergence Accommodative control system (top): BLUR stimulates blur detectors (BLUR DET.) in the occipital cortex which transduce the blur into innervation that is sent to Continued

Figure 1 continued accommodative controllers. Two types of controllers- coarse (Coarse AC) and fine (Fine AC). Conscious control (VOLITION) feeds into the accommodative system at the controller; proximal innervation (PROXIMAL) enters later in the pathway. The signal is sent towards the ciliary muscles and lens (Ciliary M & Lens). A portion of this signal passes through the sustained accommodative elements (Sust. Acc, dotted). The output is accommodative response (Acc. Response) which loops back (shown here as looping above from right to left) in to feed back into the input. Blur (BLUR) is the cue to accommodation, and is related to the inverse of the distance from the eye in meters. Vergence control system (bottom): The vergence controller (VERG. CONT.) has two components; one for crossed (+ DET.) and one for uncrossed (- DET.). Each of the crossed and uncrossed detectors is itself subdivided into coarse (shaded) and fine (unshaded) portions. Proximal innervation (PROXIMAL) enters the pathway after the vergence controller. After the controller, innervational signals are sent to the extraocular muscles (EOMs) through positive (+ve SV) or negative (- SV) vergence adaptation mechanisms (dotted lines). A negative feedback loop compares the instantaneous vergence response (Verg. Response) to the desired response, and is shown as a loop that goes from right to left, at the bottom of the vergence pathway. The stimulus to the vergence is disparity (DISP.), which is related to the ratio of interpupillary distance in centimeters (PD (cm)) and distance in meters (D (m)). Modified from Saladin[21] and Wick[7].

There are modifications to this basic dual interactive model, particularly regarding the position of the cross-links. The main view regarding the position of the cross-links is that they occur before the adapting elements (as in figure 1 above). However, there are some researchers who believe that the cross-links should be positioned after the adapting elements. The position of the cross-links matters because these two different positions

have different neural implications. If the cross-links are positioned before the adapting

elements, it implies that vergence adaptation can take place without input from the

disparity system (or slow accommodation adaptation can be driven by disparity

alone)[21]. However, Hung has shown that sustained vergence adaptation cannot take

place when disparity vergence is open-loop[30, 31].

Whatever conceptual model is considered, there appears to be consensus that spatiotopic and retinotopic cues are effective over complementary operating ranges of depth or intervals[20]. Further, spatiotopic cues are perceptual, and operated through a top-down process to initiate coarse vergence. On the other hand, retinotopic cues are physical, operate through a bottom-up process to complete and fine-tune the processes of fine accommodative and vergence eye movement maintenance.

Sequence for Vergence Eye Movement Response

Based on the characteristics of the vergence components (i.e., fast, phasic open-loop with extraretinal feed-forward control for proximal vergence compared to relatively slow, tonic closed-loop control with retinal feed-back control for accommodation and disparity), the magnitude of the initial vergence demand determines which vergence component initiates the vergence response[21]. When the initial vergence demand requires a change from distance to near (a large vergence demand outside of disparity vergence range), proximal vergence initiates the vergence response. Proximal vergence acts to decrease the vergence angle to a level that is within the disparity and accommodative ranges. Therefore, following the initial perceptual and volitional input to

initiate the vergence response, accommodation and disparity vergence (and pupillary

constriction) continue the process through a retinal-based feed-back control process to fine-tune the vergence process to attain the steady state. Because the accommodative cues

(blur) of 2 or more diopters may be directionless (i.e., there is no perceptual difference between cues to positive and negative blur of the same magnitude), it is probably the cross-link between accommodation and convergence (disparity cues have direction) that helps to direct accommodation in the correct direction. However, it is believed that it is the phasic components of accommodation and convergence which drive these cross-

links[27]. This makes sense, as these are the components of accommodation and disparity

which drive the initial burst of stimulation, and where movement in the right direction is

most necessary. Once the disparity and accommodation cues are within their dead space

limits (0.5D for accommodation, 6 minutes of arc for disparity[20, 21]), their tonic

components are mostly active. A study by Wick and Bedell[13] showed that the velocity

and magnitude characteristics of the vergence components make proximal vergence

suited for initiating the vergence response.

When the initial vergence demand is small (within the accommodation and disparity

ranges), the perceptual input from proximal vergence to initiate the response is not

always necessary. Rather, the process is the same as described above, except that it

begins with accommodative and disparity vergences through the closed negative

feedback loop[21]. In situations of abnormal disparity vergence ability (such as in

convergence insufficiency), there is evidence that some patients use volitional efforts to

initiate the vergence response even when the magnitude of vergence should require

disparity vergence to do so[21].

However, it is also suggested that besides the monocular cues to proximity, binocular

cues to proximity also exist, and these are most effective within the arm’s length, close to

the disparity detection range[19, 21]. This suggests that proximal vergence may also have phasic (vergence-initiating) and tonic (vergence fine-tuning) components, with the phasic

mediated by static, monocular, perceptual or volitional efforts, and the more tonic

component is mediated by volitional, dynamic proximal cues of loom and motion parallax. However, there does not appear to be any evidence that proximal vergence also possesses any slow adapting components. If that is the case, it may help to explain why some patients who have convergence insufficiency may be able to obtain binocular vision with the necessary vergence output, but not sustain it. This ability to obtain binocular vision but inability to sustain it might be similar to those subjects with convergence insufficiency who have poor vergence adaptation as suggested by Schor[10].

In summary, the different horizontal vergence components are thought to employ

different control systems, depending on the magnitude, latency and velocity of eye

movement needed. At the initial stage of changing vergence posture from distance to

near, proximal cues engage voluntary or volitional inputs to rapidly decrease the

vergence demand in a feed-forward mode. The result of this is the reduction of blur and

disparity to within the threshold of accommodation and disparity vergence respectively.

Then disparity vergence and accommodation are activated to fine-tune the disparity using

feed-back retinal control. The cross-link between accommodation and convergence

ensures a reflex activation of accommodative convergence to further refine the vergence

eye movement.

Despite this, there is a lack of consensus on how important proximal vergence is clinically. On the other hand, the clinical importance of disparity vergence and

accommodative vergence are not in doubt, with many arguing that these two are the

sufficient and necessary components for vergence output[14, 20, 31]. In his original

classification vergence subtypes, Maddox[8] is reported to have assumed that tonic

vergence is used to move the eyes from the anatomical position of rest. After that,

disparity vergence is used to fuse the target of regard for a single, binocular percept. This

fusional vergence is accompanied by accommodative vergence. Thus, the main vergence

components were tonic and fusional and accommodative. It appears that proximal

vergence was acknowledged as a possibility, but not necessarily a component of every

vergence response.

Quite divergent from the usual view, Mitchell[8] argues that, these distinctions between the components of vergence are just artificial, reflecting the way they are clinically measured and the effectiveness on the available clinical measures to elicit response from the same vergence. Accordingly, the vergence sequence described above may just be drawing on the most effective cues available for the task. Consequently, the most effective cues that drive convergence when the initial eye position is at distance may be volitional (proximal cues), but at near, may be reflexive (disparity and accommodative vergence cues).

Therefore, it is important to investigate vergence eye movements at the level of the neural

control centers to determine the extent of overlaps of the vergence components if any.

However, the neural control centers are organized in a hierarchy, making the

investigation more challenging. Any overlaps can occur at any one of any of the neural

pathways, beginning from the cortical centers, supranuclear centers, nuclear and

internuclear pathways, or the oculomotor plants.

Organization of Neural Control for Vergence Eye Movements

One consequence of dividing vergence eye movements into distinct components is the

implication that each component draws on separate neural control. Following from this,

it is important to determine the level of distinctness or overlaps in the neural control for

vergence eye movements.

A common way of conceptualizing the neural control for vergence eye movements (and

eye movements in general), is to organize the neural paths and areas into a hierarchy

based on the anatomical locations in the brain and the neural pathway[20, 32].

At the top of this hierarchy is the supranuclear centers which are responsible for the

detection of the cues for vergence eye movements and for planning the motor activities.

The supranuclear centers are found in the cerebral cortex and cerebellum.

Below the supranuclear areas are the premotor centers in the brainstem which coordinate the control of the combined actions of several muscles to effect horizontal, vertical and torsional vergence eye movements. These premotor areas are often called gaze centers, and include the paramedian pontine reticular formation (the horizontal gaze center)[33]

and the rostral interstitial nucleus (the vertical gaze center)[34] of the mesencephalic reticular formation. The premotor areas are responsible for determining the direction, amplitude, velocity and duration of eye movements. The supranuclear centers are connected to the premotor areas by internuclear neurons.

Below the premotor areas, the next step in the hierarchy is occupied by the nuclei of cranial nerves III, IV and VI, and their cross-talking connections[20]. This level and the level below are often described as the final common pathway because from the motor neurons in the nuclei, their interneurons and the extraocular muscles below are involved in all classes of eye movements, ranging from fast, slow and fixational eye movements as well as horizontal, vertical and torsional eye movements. They are also utilized by the vestibulo-ocular and the optokinetic reflexes.

A common interneuron found at this level of oculomotor neural control is the medial longitudinal fasciculus which is responsible for the cross-talk between the VI and III cranial nuclei for horizontal conjugate eye movements[34]. This level contains the motor nuclei for the cranial nerves IV, VI, and the more complex III nerve (which is made up of sub-nuclei for inferior, medial and superior recti muscles and inferior oblique, levator palpebrae superioris muscles involved in elevating the upper eyelid). Together, the oculomotor nuclei control the six extraocular muscles which effect the eye movements.

The three pairs of extraocular muscles occupy the lowest level of the hierarchy[20]. The extraocular muscles exert forces that rotate the eyes horizontally, vertically and torsionally. They carry impulses for fast and slow eye movements, as well as reflex and

voluntary eye movements. They are also utilized by the vestibular and optokinetic

systems for gaze stabilizing eye movements.

It can be seen from this that many areas of the brain are involved in the generation and

control of vergence eye movements. Therefore, understanding how those brain areas

function may help to increase the underlying pathways involved in vergence eye

movements. Ultimately, that can help improve the clinical management of dysfunctions of vergence eye movements.

Methods for Investigating Vergence Eye Movements

The underlying pathways of convergence have been investigated through both clinical and non-clinical methods.

Clinical Methods for Investigating Vergence Eye Movements: Vergence eye movements

may be investigated using routine clinical methods[35]. Such methods may be used to isolate the components of convergence eye movements. Once that is done, statistical methods may be used to determine whether correlations exist among them. If two components are negatively or positively correlated, it can be interpreted to mean that the two components of convergence share at least some common pathways in the central control of convergence. On the other hand, if there is no correlation between two convergence components, it may be interpreted to mean that the two components do not share any pathways in the brain. Studies providing information regarding the presence or lack of common pathways between convergence subtypes may have clinical applications.

For example, if there is a lack of correlation between two components of convergence 23

eye movements, this suggests a lack of common neural pathways. If so, it may be

possible to enhance proximal or accommodative vergence to compensate for deficits in

disparity convergence.

However, existing clinical methods for understanding convergence eye movements have

been questioned[19, 27]. The methods for understanding convergence output by breaking

it down into its components have been criticized as artificial[31]. This is because under naturalistic conditions, the vergence system has all the cues (proximal, disparity, blur) available. When vergence eye movements are being studied, it is often necessary to isolate them by opening either the disparity vergence loop, the accommodative loop (to ensure accommodative vergence is prevented from influencing the vergence response) or both loops so that one vergence sub-type can be studied at a time. The problem with this approach is that such conditions are not real. It is possible that the behavior of the vergence system under such open-loop conditions is not the same as it is under naturalistic dual closed-loop conditions of disparity and accommodation. For instance, it is argued by some researchers that findings which indicate that proximal convergence may play more important roles in the total convergence output than previously thought are a result of the methods employed[19, 31]. Therefore, a better understanding about the interrelationships among the various components of convergence eye movements is needed.

Non-clinical Methods for Investigating Vergence Eye Movements: The second broad

classification for studying convergence eye movements refers to non-clinical methods.

These include electrophysiological techniques such as single-unit recordings (e.g., in 24

Rhesus monkeys)[3] and cats. Non-clinical methods also include histological studies and lesion studies. In addition, transcranial magnetic, electrical and chemical stimulations may be used to study the direct effects of chemicals and electromagnetic energies on particular brain areas. Each of these non-clinical methods for studying the neural basis of vergence eye movements is described briefly below.

First, many valuable insights have been gained into the neural basis of vergence by accidental or incidental lesions to particular brain areas. If such a lesion has effects on the motor responses, it is inferred that that particular part of the brain is involved in vergence control. Accidental lesions are not usually localized enough, or they affect other brain areas. This reduces the value of inferences. Therefore, in some cases, the lesions are created experimentally in animals. This affords the opportunity to carefully place the lesion to improve the value of inferences that can be drawn on the motor and behavioral effects of such lesions. Obviously, this strategy cannot be replicated in humans. Human lesion studies are thus limited to accidental cases whose values may be greatly reduced by individual unique features and lack of generalizability. Some lesions may be temporarily created with carefully placed electrical or magnetic energy. The short-term

durations of lesions that may be created this way makes it possible for use in humans.

Second, special visual stimuli are sometimes in combination with eye tracking to

investigate vergence eye movements. This may include special stimuli like targets of

specific spatial or temporal properties that are shown to research participants for certain

durations to investigate the properties of vergence eye movements, or to get at different

types of vergence eye movements. As an example, Jones and Kerr[36] used a stimulus

with either fusible or non-fusible visual targets which were then shown to subjects for

brief periods to study the temporal components of disparity vergence eye movements.

When the targets are fusible, and are left long enough for them to be fused by the

vergence system, the movement of the eyes can be tracked to determine the properties of

the resulting. Thus, the features of stimuli may be manipulated to achieve similar effects

as opening or closing vergence loops.

The third major approach is cell recordings. Different types of recordings have been used to localize, characterize and monitor the activities of single or a group of neurons during

convergence. In single-cell recordings, very thin wire or glass electrodes were inserted

into the brain in experimental rats, cats and monkeys[3, 29]. These electrodes were then

set up in such a way that action potentials from single cells could be recorded. When the

cell is excited, it signals increased activity by increasing its firing rate. However, because

single-cell recordings are only able to detect activity at the level of a single neuron, other

strategies are needed to detect cortical area activity as well as functional connectivities

among cortical areas. Single-cell recordings were used to detect neurons which encoded

for vergence velocity[29], type (convergence vs divergence), combination of

accommodation and vergence[3], etc. However, cell recordings could only be used on

non-human primates such as monkeys. Therefore, it is not clear whether the findings

from non-human primates are representative of humans.

Finally, improvement in imaging techniques such as functional magnetic resonance

imaging (fMRI)[37] and positron emission tomography have led to their increasing use to

study eye movements, including vergences. These two imaging modalities estimate

neuronal activity by measuring changes in regional cerebral blood flow as subjects

undertake visual tasks. The underlying assumptions for these techniques is that increased

blood flow signals increased neuronal metabolic activity, and thus increased utilization of

such regions of the brain. More details about fMRI are provided in chapter 4.

Assessing Vergence Eye Movements

Various strategies exist for assessing the sub-types of convergence eye movements in

practice. Usually, the sub-type being assessed is kept closed-loop, while the loops for the

other sub-types are open. Any change in the vergence posture is then attributed to the

sub-type whose loop is still closed. The following sections provide an overview of how

accommodative, disparity and proximal, as well as tonic convergence eye movements are

assessed.

Assessment of Accommodative Convergence: Accommodative convergence is measured

as a change in the posture of the eyes (usually phoria; disparity vergence is open-loop) that is accompanied by a unit change in accommodation[2, 38]. To assess the change in accommodation, clinicians typically use two main strategies[7], depending on the type of accommodative change that is measured. The first type of accommodative convergence depends on the change to accommodative stimulus, and is consequently called the stimulus AC/A.

The second approach to accommodative convergence is called the response AC/A which

measures the change in the vergence posture due to the actual change in accommodative

response. 27

Stimulus AC/A: Stimulus AC/A is the change in ocular alignment for a given

change in accommodative stimulus. In turn, stimulus AC/A is determined in one

of two ways. First, the far-near stimulus AC/A determines how much the vergence changes when the stimulus to accommodation is changed from a target at distance (which has zero accommodative demand) to near (40cm, where the stimulus to accommodation is 2.50 diopters), assuming that the accommodative response changed by 2.50 diopters. The far-near stimulus AC/A ratio is also called the calculated stimulus AC/A ratio.

The second approach to stimulus AC/A determination involves measuring a

change in the posture of the eyes through lens powers (plus or minus lenses or

both) at a fixed distance. Here the power of the lens used constitutes the

accommodative stimulus. When a spherical lens is placed in front of an eye

viewing an accommodative target, it causes a change in the vergence of the light

entering the eye. This change causes blur at the fovea which stimulates

accommodation to attempt to nullify the blur. Plus (convex) lenses normally

increase the focusing of the light entering the eye, necessitating a relaxation of the

accommodation of the eye. On the other hand, minus (concave) lenses decrease

the vergence of light at the eye, inducing an increase in accommodation in order to clear the target. Thus, placing a minus or plus lens in front of the eye results in a change in accommodative stimulus, just as changing focus from distance to near constitutes a change in accommodative stimulus. Because the second approach

compares the posture of the eyes before and after a lens is introduced in front of

the eyes at a fixed distance, the technique is called gradient stimulus AC/A.

In measuring the stimulus AC/A, it is assumed that the accommodative system responds

perfectly to the change in accommodative stimulus caused by changing focus from far to

near, or to the lens power introduced at a fixed distance. The far-near AC/A is considered

to be a less direct measure of accommodative convergence than the gradient AC/A

because the change in focus from distance to near during AC/A measurement introduces

proximal cues that trigger proximal accommodation and proximal convergence. Thus, the difference between far-near stimulus AC/A and gradient stimulus AC/A ratios is one way by which the contribution of proximal cues to the near response output may be determined.

However, the assumption that the accommodation system responds perfectly to the accommodative stimulus is not correct. In reality, the accommodative system puts in the minimum effort necessary to achieve clarity from a blur stimulus. In normal healthy visual systems, the accommodative system can achieve the same level of clarity by under-accommodating by 0.50 diopters on average[21]. This is the accommodative lag described earlier. In one estimate, the accommodative response from far distance to 40cm was 1.25 diopters for a stimulus which required 2.50 diopters of accommodation[7].

Therefore, the accommodative response is usually less than the accommodative stimulus.

In some cases, the accommodative response output may be more than needed. In this unusual case, the accommodative output is said to lead the stimulus. Thus, the response

AC/A (change in vergence posture of the eyes brought about by a change in

accommodation) can be determined using the actual output of accommodative system,

rather than using the stimulus.

Response AC/A: The second strategy for assessing the amount of convergence

accompanied by change in accommodation is by determining the response AC/A.

This involves measuring the actual accommodative response of the eye to the

change in accommodative stimulus (e.g. change in distance or change in lens

power). The response AC/A is the change in posture of the eyes that accompanied

a diopter of change in actual accommodative response. The accommodative

response may be measured by a variety of methods, including dynamic

retinoscopy and autorefraction. Similar to the assessment of the stimulus AC/A

described above, response AC/A may be determined by using any method to

measure the response of the accommodative system to blur induced by changing

focus from distance to a near accommodative visual target. The AC/A determined

this way is the far-near response AC/A. The response AC/A determined from a

change in accommodative response to a lens at a fixed near distance is called

gradient response AC/A.

The response AC/A is usually higher than stimulus AC/A. This is because the accommodative response usually lower than the demand (the response lags the demand).

Thus, a smaller change in accommodation accompanying the change in phoria results in

an increase in the calculation of response AC/A. Due to accommodative lag, the response

AC/A is often considered a truer reflection of the AC/A.

Assessment of Disparity Convergence: Disparity convergence is usually assessed by two

related approaches. First, dissociative tests (such as the cover test and the Maddox rod

test) are used to open the disparity vergence loop to determine the effect on the amount of

heterophoria. Dissociation breaks the stimulus for fusional vergence. Thus, any

heterophoria that shows signifies the amount of fusional effort that was being used to

overcome the misalignment when fusional vergence was not broken. Second, prisms can

be introduced in front of one or both eyes as a target is fixated at either distance or near.

If fusion is maintained in the presence of the prism, the power of the prism implies the

amount of fusional effort that is used to overcome the additional fusional demand from

the prism. Base out prisms are used to assess positive relative fusional vergence

(convergence) (fusional vergence that takes place while accommodation is not changed),

and base in prism for negative relative fusional vergence (divergence). While the

heterophoria shows the magnitude of fusional vergence that is needed under steady state

conditions, the Base-In or Base-Out vergence ranges show how much fusional vergence

one has to overcome the heterophoria. The limit to positive or negative fusional vergence

can be determined from the highest prism power that causes a blur (or in the absence of a

blur, a break) in fusion.

When assessing fusional vergence by either of the two approaches briefly described

above, it is important to use an accommodative target, and for the subject to keep that

target in clear focus throughout the test. This is important so that all the images are

focused on the fovea, as this ensures that accommodation is actively engaged.

Assessment of Voluntary and Tonic Convergences: Voluntary and tonic convergences are

not routinely assessed in clinical practice. Voluntary convergence is typically assessed by

signaling the subject to converge, and determining the change in the convergence posture

of the eye due to this volitional effort. The signal may be verbal (such as converging the

eyes on instruction) or auditory (such as when a tone is heard). For instance, in a study by

McLin and Schor[14], subjects were asked to cross and relax their eyes by “thinking

near” and “thinking far” respectively.

Tonic convergence is assessed by removing all the cues to convergence (blur, disparity

proximity and voluntary influences). Often, the best and easiest way to achieve the

elimination of all these cues to vergence is to place the subject in total darkness where the

baseline convergence posture may be determined using objective measurement such as

infrared cameras and eye trackers. For this reason, tonic vergence is sometimes referred

to as dark vergence. When that is done, it is often found that the eye assumes an

intermediate convergent position which reflects a position within the range of total

absence of vergence innervation (such as deep sleep, anesthesia) and complete

convergence. That is, the dark vergence posture of the eye is somewhere in between the

anatomical position of rest on one hand, and the vergence posture where all the cues to

vergence are in place on the other hand[39]. The anatomical position of rest is somewhat divergent in blindness, death, anesthesia, etc. The fact that the eyes are not at that same level of divergence even in the absence of all the cues to vergence described above is taken as due to activity of tonic vergence[39]. Estimates of the dark vergence position can be obtained by asking subjects to “look into the distance” and being photographed

32 with an infrared system. Previous estimates for tonic found average positions that ranged from 53 cm to distance [39-41].

In the Maddox classification, it is indicated that fusional vergence is used to overcome this intermediate baseline vergence posture. Thus, if the fusional vergence loop is open at distance (at distance, the accommodative cues are absent with the optimal refractive correction, and proximal cues are absent), the vergence posture of the eye reflects the tonic vergence. This is because according to Maddox, fusional vergence is used to overcome the tonic posture of the eye. Thus, it is suggested that the most practical way to get at tonic vergence in clinic is to measure the distance heterophoria with the optimal refractive correction in place[7].

Assessment of Proximal Convergence: Unlike accommodative and disparity vergences, proximal vergence is not measured directly. This stems from the conception of proximal inputs to the near response as perceptual in nature. Being perceptual, it is challenging to measure directly or as easily as the other two. There are two main methods for assessing the contribution of proximal convergence to the overall steady-state vergence response[9]. First, the contribution of proximal convergence to the total steady-state convergence demand can be determined as a percentage of the total convergence demand.

By this method, the total vergence (vergence demand) is determined from the ratio of the interpupillary distance (PD) of the subject and the distance of the target using the relation

( ) = ( ) . 𝑃𝑃𝑃𝑃 𝑖𝑖𝑖𝑖 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚

From this total, the contribution of the various components of vergence is determined as follows.

Accommodative convergence is determined from a product of the AC/A ratio and accommodative demand of the target. Fusional convergence is determined from the heterophoria that occurs at fixation to near fixation. The assumption is that, when dissociative tests are used to determine the hetereophorias, fusion is broken (fusional convergence open-loop) so the misalignment that manifests as a heterophoria shows how much fusional convergence has been freed by the breaking of fusion.

From these determinations, it is assumed that the total vergence response is a linear sum of the four components (proximal, tonic, accommodative and disparity). Thus, proximal convergence is the remainder after accounting for accommodative, tonic and fusional vergence inputs. The proportion contribution approach thus uses a linearly additive model inferred from the Maddox classification of vergence components whereby the contribution from proximal convergence can be determined once the other components are determined.

The second method for assessing the contribution of proximal convergence is the proximal convergence to testing distance (PCT) ratio. The PCT ratio is the ratio of change in convergence resulting from awareness of nearness to change in distance (from far to near). To obtain this ratio, both accommodative and disparity loops are opened, and the change in vergence is determined. Under this dual open-loop condition, it is assumed that only proximal cues are available to drive convergence. Three main methods have been used to determine the PCT ratio

Using Phoria Values: The first approach of the PCT uses two heterophoria values:

one at far and the other at near measured through a plus lens that nullifies the

accommodative demand at near[7]. The dissociation that occurs during the

measurement of the heterophoria opens the fusional vergence loop. With the fusional

vergence loop thus fully opened, and the accommodative cues clamped, any change

in vergence posture is assumed to be due to perception of the change in distance to a

closer point. This relationship is represented by the following formula in which

esophoria is considered negative:

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐=𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐( + 2.50 )

+ 𝐶𝐶𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑑𝑑 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 − 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑝𝑝ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑡𝑡ℎ𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟ℎ 𝐷𝐷 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

Difference between𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 Far-Near𝑝𝑝ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 AC/A and Gradient AC/A: As described earlier, a

second approach to the PCT ratio utilizes a difference between the far-near AC/A

ratio and the gradient AC/A ratio. The AC/A ratios are obtained under fusional

vergence open-loop conditions. The far-near AC/A ratio is thought to be made up of

the combined effects of accommodative and proximal vergences. On the other hand,

the gradient AC/A ratio is obtained at one distance through an accommodative lens

under fusional convergence open-loop conditions. Therefore, the gradient AC/A ratio

is free from proximal and fusional convergence influences. A difference between

these two AC/A ratios thus represents the portion of the far-near AC/A ratio

contributed by proximal cues. This can be considered as the “Proximal AC/A”;

multiplying this by 2.50 (the accommodative demand of the near target distance)

yields the proximal convergence component to the overall convergence response.

Dual Closed-Loop AC/A Differences: A third approach for determining the PCT ratio

departs from the two approaches above, as dual closed-loop conditions (fusional vergence and accommodative vergence loops are both closed) are used in the calculation. The associated phorias used in calculating the AC/As are determined from fixation disparity curves. Otherwise, it uses an approach similar to the second approach described above; determining the difference between the far-near associated AC/A and gradient AC/A ratios.

Chapter 2: Correlations among Vergence Eye Movements

Background

It is thought that accommodative and disparity vergences are the most significant contributors to the steady-state vergence response[31]. However, there is evidence that proximal vergence not only contributes significantly to the vergence response, but it is suited for initiating it [24, 42].

In order to perceive in depth, the normal situation requires cognitively-driven perceptual cues of proximity to initiate the ocular motor response. Once the demand has been reduced to levels where they can be retinotopically sensed, reflexively-driven blur and disparity cues are used to further decrease the disparity, and to maintain the eye’s position. This sequence of events suggests that when any of the depth-initiating or depth- maintaining cues is abnormal, the vergence eye movements can be adversely affected.

The classical clinical definition of convergence insufficiency refers to poor ability for the disparity vergence to either obtain or maintain binocular single vision. Where there is some considerable debate is whether it is the fusion-initiating part of disparity vergence or whether it is the fusion-sustaining part that is abnormal in convergence insufficiency.

It has been observed that some patients who have convergence insufficiency attempt to volitionally move the eyes in depth even when the eye movement task is of magnitudes which should occur reflexively. This situation describes a type of convergence 37

insufficiency that results from low or poor ability of disparity vergence to function

normally. This disparity component is reflexively related to the accommodation through a cross-link. Therefore, when the disparity vergence system is low, the accommodative vergence system is also found to be low.

However, it is not clear whether convergence insufficiency may also be due to a

reduction in the cognitively-driven proximal vergence.

A better understanding of the normal relationships among the vergence components will

improve the understanding of the neural systems that underpins them. Ultimately, that

understanding may lead to improvements in interventions for resolving deficiencies in

vergence eye movements.

Contribution of Vergence Components to Total Vergence Response

There is a lack of agreement on the extent of contribution from each of the vergence

components to the total vergence response. At one end, results have been reported to

show that under “natural” dual closed-loop conditions, proximal vergence contributes

little to the overall vergence response[31, 43]. Other researchers have concluded that proximal vergence may not only contribute significantly to the overall steady-state

vergence response[42], but is actually suited for initiating vergence movements in

response to large disparities and accommodative cues.

For instance, Wick[7] used average AC/A ratios for calculations to demonstrate that the

proximal component of the total vergence response was 22%, compared to 25% and 31%

from disparity and accommodative cues respectively. In addition, Joubert and Bedell[24]

suggested that 35% to 64% of the vergence response is contributed by the proximal

component. Wick and Bedell[13] estimated that proximal vergence contributed about

70% of the total steady-state vergence response. They also showed that at peak velocity of 69 degrees per second (compared to 86.4 degrees per second when all three vergence cues are active), proximal vergence is suited for initiating the vergence response.

Some researchers[19, 31] have questioned the significant contribution from proximal cues reported under open-loop conditions, arguing that the results were an artifact of the

artificial open-loop conditions and that proximal contributions under naturalistic

conditions are actually quite small. They imply that proximal cues are only important

under experimental conditions with reduced accommodative and disparity cues. On the

other hand, North and Henson[42] used a cue disharmony paradigm which may be

considered to be closed-loop conditions to test the contributions to the total vergence in

three different situations. North, Henson and Smith[42] determined that proximal cues

provided the highest input to the vergence response in two of their three situations, and as

much as disparity in the other situation. They computed that proximal vergence

contributed 45% of the total vergence response, followed by 41% from disparity and only

14% from accommodation. The study by North, Henson and Smith[42] may be the

closest to natural conditions, and supports the argument of significant contribution of

proximal components. Joubert and Bedell[24] attributed almost half (49%) of vergence

output to proximal convergence from associated phoria findings (dual open-loop). Their results are in agreement with Wick’s[7] that proximal cues are significant. However, they

39 showed that accommodative cues contributed the least to the overall vergence posture while Wick speculated that it contributed the most.

On the other hand, Hung and colleagues[31] estimated that under naturalistic, dual-closed loop conditions, proximal convergence contributes only 0.04% of the vergence response.

They argue that accommodative and disparity cues contribute the rest of the steady-state vergence output under dual-open loop vergence. However, under dual open-loop conditions, they found that proximal convergence contributed as much as 88.5% of the total vergence output.

Correlations among the Vergence Components

There have been several studies that aimed to investigate the relationship between proximal and accommodative vergence. While Morgan[44] found an inverse relationship between proximal convergence and accommodative convergence, Hofstetter[12] found no such relationship. Mannen, Bannon and Septon[45] found subjects with low AC/A ratios had high proximal ratios, but this was among a few subjects.

Hofstetter found very large proximal vergence influences on steady-state disparity vergence determined from phoria values, but not on those determined from vergence ranges. He suggested that proximal and fusional vergences act to modify each other, even though they may have independent neural origins. However, Hofstetter[12] found no relationship between proximal convergence accommodative convergences.

Therefore, the relationship between proximal and accommodative vergences is unknown.

Functional imaging may provide more objective and clearer evidence of any relationships

that exist among the vergence sub-components.

Previously, Mannen et al[45] reported that disparity vergence was inversely correlated with proximal vergence on a limited number of subjects, but Hofstetter3 reported that

disparity vergence and proximal vergence were largely independent.

Accommodative vergence is commonly found to be highly correlated to fusional vergence clinically [46]. Patients with convergence insufficiency often have low accommodative-convergence to accommodation (AC/A) ratios[46, 47].

Thus, the relationship between proximal vergence and both disparity vergence and accommodative vergence is unresolved. If proximal vergence is correlated to either fusional or accommodative vergence, this may suggest common neural pathways between either accommodative or fusional convergence and proximal convergence.

Establishing common neural pathways is important in understanding how the neural systems controlling how vergence eye movements operate in health. Understanding the neurology of vergence eye movements in health could in turn help in improving the management of problems with vergence eye movements. For instance, if it turns out that proximal vergence is clinically important in the final vergence response, that may suggest that clinical and experimental approaches for resolving accommodative and vergence anomalies should be aimed at enhancing proximal convergence. Thus, new approaches that utilize or enhance proximal convergence may be a useful complement to existing treatment for anomalies of accommodation and convergence.

The limit of disparity for detecting changes in depth is about 6 degrees[19] (about 10.5

prism diopters) for central fixation. Clinically, vergence facility testing is frequently

performed with a combination of 12 Base-Out/3 Base-In prism flipper. This prism power combination has a combined vergence range of 15 prism diopters, which lies outside this putative disparity detection range of about 11 prism diopters. Therefore, clinical testing of vergence facility with a 12 Base-Out/3Base-In combination may require cues other than disparity. Therefore, proximal vergence may be primarily responsible for vergence changes in vergence facility testing with the 12 Base-Out/3 Base-In prism flipper. Thus, proximal vergence may be expected to correlate with vergence facility determined with the 12 ∆ Base-Out/3 ∆ Base-In prism flipper.

Rationale for the Study

It is important to establish the relationship between the sub-types of vergence, because these relationships reflect on the extent of the overlap in the neural underpinnings of each vergence type. While some of the neural substrates for vergence are known[29, 48], it is

important to more accurately elucidate these neural substrates as vergence issues are very

common clinically. An understanding of these neural substrates could impact how

patients with vergence dysfunction are managed.

If significant correlations exist among the vergence sub-types, it may imply common

neural pathways. If no correlation is found, this may be interpreted to mean that the

neural pathways are independent. These results will contribute to a clearer understanding

of the role the vergence sub-types play in steady-state vergence responses, and their potential to influence the clinical management of vergence anomalies.

Due to its dynamic properties, proximal vergence has been suggested to be suited for initiating vergence response [7, 24, 42]. Proximal vergence is thought to be (or may be) a meaningful component to the overall vergence response but there are no routine clinical tests that assess proximal vergence. A clinical test of proximal vergence is needed. One common clinical test that may provide a measure of proximal vergence is vergence facility testing because vergence facility tests the ability of the vergence system to rapidly change between convergence and divergence without changing accommodation.

Therefore, we investigated the relationship between performance on a test of vergence facility and established measures of proximal vergence in order to determine whether vergence facility may serve as a measure of or proxy for proximal vergence ability.

Objectives

The purpose of this study was to employ clinical testing to enhance our understanding of the neural underpinnings of convergence eye movements. The objective was to investigate the correlations among the sub-types of convergence eye movements in a normal population of adults without strabismus.

Hypotheses

The relationships among the various vergence sub-types are not clear. Conflicting results have been found between proximal vergence and accommodative, proximal and disparity,

43 and disparity and accommodative vergences. There is the notion that proximal vergence is only important in situations where disparity and accommodative cues are absent or minimal[19]. This suggests an inverse relationship between proximal and disparity vergences, as well as between proximal and accommodative vergences. However,

Hofstetter[12] argues that proximal vergence is not just a replacement for disparity under disparity open-loop conditions, but a separate entity that acts even in the presence of disparity activity. This may suggest either a lack of correlation between proximal vergence and the other vergence types, or the presence of correlations which have not yet been fully established. In addition, the clinical measurement of vergence facility is done with a 12 ∆ Base-Out/3 ∆ Base-In prism flipper (which amounts to a prism power range of 15 Δ). There is evidence that the limit to disparity vergence is about 6 degrees[49]

(approximately 10 prism diopters) at near. This means that the clinical measure of vergence facility requires more than disparity vergence. Because the test is performed at a fixed near distance of 40cm, accommodation, and by extension, accommodative vergence is expected to remain unchanged. Thus, clinical measures of vergence facility with a 12 ∆

Base-Out/3 ∆ Base-In prism combination may indirectly recruit the proximal vergence system. Consequently, it is hypothesized that clinical vergence facility measures would correlate well with established measures of proximal vergence, but not with disparity or accommodative vergence measures.

Therefore, the hypotheses that were examined in this study were:

1. There is correlation between proximal vergence and accommodative or disparity

vergence.

2. There is correlation between proximal vergence and vergence facility measured

with 12 ∆ Base-Out/3 ∆ Base-In (cycles per minute).

Chapter 3: Investigating the Neural Basis of Vergence Eye Movements

Study Procedures

Normal clinical procedures were used among a population of normal adults who had no

evidence of strabismus.

Subjects: This study involved non-strabismic, non-clinical subjects. This population was

chosen to enhance the generalizability of the findings of this study. All subjects signed a

consent form approved by the Ohio State University Biomedical Sciences Institutional

Review Board prior to participation in this study.

Inclusion Criteria: The inclusion criteria for the study included the following: The

participants were non-strabismic adults aged 18 years to 40 years. The upper age limit of

40 years was chosen to ensure that only non-presbyopic people participated. Presbyopes may not have the ability to accommodate for procedures such that required them to accommodate, such as accommodative facility, gradient AC/A, and near heterophoria through 2.50 diopter convex lenses. Also, they needed to have 20/20 visual acuity or better in each eye with spectacle or contact lens correction if needed, at both distance

(6m) and near (40cm). In addition, the participants needed to demonstrate good stereoacuity (at least 40 seconds of arc (local) tested with the Randot Stereotest; Bernell

Corporation, Mishawaka, IN).

Sample Size & Sample Selection: Morgan[44] found a correlation of 0.423 between proximal vergence and accommodative vergence. To obtain this correlation at 0.05 significance level and a power of 0.8, 42 subjects were required.

Clinical Measurements: All the instrumentation and testing procedures used in this study are part of standard, non-invasive, commercially available clinical instrumentation that is used in optometric examinations. The following procedures were performed:

1. Eligibility testing to rule out strabismus. This involved testing visual acuity in

each eye at 6m and 40 cm. Normal room lighting was used for the distance

testing, while extra illumination from overhead lights on a reduced Snellen chart

at 40cm was used for near visual acuity testing. Each eye was tested separately,

the right eye was tested before the left. The smallest line where the subject

correctly identified all 5 letters on the line was used as the threshold visual

acuity.

In addition, threshold local stereoacuity was tested at 40cm using the Randot

Stereotest (Bernell Corporation, Mishawaka, IN). Stereoacuity was recorded as

the smallest level of depth the subject correctly identifies on the Randot test.

2. Unilateral and alternate cover test at distance (6m) and near (40cm) with prism

neutralization. Focusing on an isolated 20/30 Snellen letter at 6m, a subject was

instructed to look at the letter at and keep it clear always. A cover paddle was

then used to cover each eye for 2 seconds while looking for fixation movement

in the other eye (for strabismus). Following this, each eye was alternately

covered and uncovered while looking for movement in the just-uncovered eye 47

(for heterophoria). After ruling out strabismus with unilateral cover test, any

movement seen in the alternating cover test was neutralized with a prism, and

recorded as the magnitude of the heterophoria. The lowest amount of prism that

caused reversal of movement was recorded as the magnitude of the deviation. If

no movement was seen, and 4 prism diopters Base-In caused the eye to move

out, and 4 prism diopters caused the eye to move in, it was recorded as

orthophoria.

3. Cover test at near (40cm) was repeated while the subject viewed a 20/30 text

through +1.00 and +2.50 lenses. The order of the near cover testing was

randomized, with five minutes of rest in between measures. Subjects held the

near card to ensure that their perception of near was enhanced through

proprioception.

4. Vergence ranges were measured with a horizontal bar prism at both distance

(isolated 20/30 Snellen letter) and near (The Ohio State University’s “Keep

these words clear” near card). The range of fusion prior to report of BI blur or

BO blur was recorded (or break if no blur occurred). Five minutes of rest was

allowed prior to, and subsequent to other near testing.

5. Vergence facility was measured with a 12 Δ Base-Out/3 Δ Base-In prism

flipper. With the subject holding the near “Keep these words clear” card at

40cm, the examiner presented a prism flipper in front of the subject’s eyes,

beginning with the 3 prism diopters Base-In. The subject was asked to indicate

when he/she saw the text single by saying “single”. At each report of “single”,

the prism was flipped to the other power. The number of cycles in one minute

was counted, with each complete pair of flips from 3 ∆ Base-In to 12 ∆ Base-

Out counted as one cycle. A flip was counted as one half cycle if clarity was

reported for one side of the flipper, but not the other.

Determination of Vergence Sub-types: The vergence sub-types were determined in the following way: First, disparity vergence was determined from the total heterophoria at near. When fusion is broken, the heterophoria that is measured reflects the amount of disparity vergence that was being utilized to maintain fusion. As indicated in chapter 1, when the open-loop vergence error (heterophoria) is exophoric, it implies that positive disparity (fusional) vergence effort was being used to overcome it under closed-loop vergence situations.

Second, accommodative vergence was determined in two ways using the stimulus calculated (far-near AC/A) and gradient AC/A ratios. The gradient stimulus AC/A ratio was determined by checking the phoria at near and then checking it again through a

+1.00D lens in front of both eyes. The stimulus gradient AC/A ratio was determined using the following equation[2, 50]:

[(heterophoria at 40 cm) - (heterophoria at 40 cm with +1.00D)]/1.

Multiplying the gradient stimulus AC/A by 2.50 (the accommodation demand at 40 cm) yields the amount of accommodative convergence that is active at 40cm.

The calculated (far-near) stimulus AC/A ratio was calculated using the equation:

IPD (cm) (m) + 0.4[near phoria-distance phoria][2, 50] 49

IPD refers to interpupillary distance in centimeters; D is near testing distance

(40cm) in meters (0.4m). In this formula, the magnitude of exophoria is assigned

a negative value.

Lastly, proximal convergence was assessed in two ways; the difference in far-near AC/A

and gradient AC/A (far-near proximal) and a second method described by Wick (+2.50D

method).

Far-Near Proximal[2]: The difference between the calculated and gradient

stimulus AC/As (which gives the “proximal AC/A”) multiplied by 2.50 (the

accommodative demand at 40 cm).

[(Calculated AC/A – Gradient AC/A)] x2.5)[18]

• +2.50 Method: The change in vergence posture from distance phoria to the near

phoria taken through the +2.50D lens[7].The phoria at distance and near are tested

by breaking fusion (i.e., with disparity vergence open-loop, disparity vergence is

eliminated). When the phoria is measured at 40cm through +2.50, accommodation

is assumed to be fully relaxed (the +2.50D lens is assumed to clamp

accommodation response by eliminating the 2.50D of accommodation demand at

40cm). With disparity and accommodative vergence thus eliminated, the change

in eye position from distance to near is assumed to be due to proximal vergence.

(IPD/D – Near phoria through +2.50) + Distance phoria [7]

In this calculation, exophoria is assigned a negative value.

In these calculations, the relation [IPD(cm)/D(m)] gives the total convergence demand for a subject at a distance measured from the center of rotation of the eye. When the 50

distance phoria is added to this convergence demand, and the near phoria is subtracted

from the sum (exophoria is positive, esophoria is negative), the result gives the amount of

proximal convergence in play under those conditions.

Data Analysis

Descriptive statistics and their distributions were used to assess the data on heterophoria,

base-in and base-out vergence ranges and AC/As (using means, standard deviation, 95% confidence interval, histograms).

Correlations among disparity, accommodative and proximal vergences were determined

using Spearman’s rank correlations because the data obtained were not normally

distributed. The correlations presented in this study are categorized as follows: 0 to 0.35

is classified low, 0.36 to 0.67 moderate, and 0.68 and above as strong, as discussed by

Taylor[51].

Bland-Altman’s method of determining agreement[52] were used to determine agreement

between proximal convergence obtained by the two approaches described above. The

correlation between the two measures of proximal convergence was used to establish

whether the two methods were highly correlated. Box plots were used to show

graphically how the two average proximal convergence values differed from each other.

To assess the correlations among the subtypes of convergence, the following correlations

were determined:

• Fusional convergence vs Proximal convergence

o Proximal convergence by each of the two approaches vs near heterophoria

o Proximal convergences by the two approaches vs Base-In and Base-Out vergence ranges

• Accommodative convergence vs proximal convergence

o Proximal convergence by the two approaches versus the two approaches to AC/A (far-near and gradient AC/As)

• Fusional convergence vs accommodative convergence

o Distance and near heterophorias vs the two approaches for AC/A

• Vergence facility vs each of the proximal vergence measures

Results

A total of 38 subjects, aged 22-40 years participated in the study. Of these, 21 were

females. Except for two female subjects who were 40 and 37 years old, all the other

subjects were below 30 years.

Also, the subjects had uncorrected or corrected-to-normal visual acuity of at least 20/20 and stereoacuity of 20 seconds of arc on the local Randot Stereotest.

The subjects had a mean of 1.61 ∆ of exophoria at distance (standard deviation=3.87), and the mean near heterophoria values 5.61 ∆ of exophoria (standard deviation =5.58).

Table 1 below presents details of the distance and near heterophoria, as well as those

taken through +1.00D and +2.50D lenses at near.

Factor Number of Mean Standard Subjects (n) Deviation Distance phoria* 38 -1.61 3.87 Near phoria* 38 -5.61 5.58 Near phoria+1.00D* 38 -9.66 5.65 Near phoria +2.50D* 38 -13.42 5.79 Vergence facility 38 16.01 4.91 Far-Near Proximal 38 1.87 4.86 +2.50D Proximal 38 4.18 3.91 *Negative refers to exophoria Table 1: Summary of results for phoria, phoria through lenses and proximal vergence

To test the correlations among the various vergence subtypes, the interpupillary (PD)

distances for 9 subjects were measured to investigate the relationships. Those 9 subjects

had an average of 62.44mm (range 56 to 65mm, standard deviation=2.6mm). With the

exception of one subject whose PD was 56mm, the rest of the PD were 62mm to 65mm.

30 20 Number of people 10 0 -20 -15 -10 -5 0 5 Magnitude of heterophoria: exophoria is negative

Figure 2: The distribution of distance heterophoria among the participants

15 10 Number of people 5 0 -30 -20 -10 0 10 Magnitude of heterophoria: exophoria is negative

Figure 3: Distribution of near heterophorias among the subjects

Tests of Normality

Shapiro-Wilk test of normality, only far-near AC/A was normally distributed (p=0.999), with gradient AC/A marginally normal (p=0.057). Therefore, Spearman’s correlations were run on the relevant factors, and these are presented in the sections below.

Correlation between Accommodative Vergences

The mean far-near AC/A was 4.80∆/D (SD = 1.64) compared to a mean of 4.05 ∆/D (SD

= 1.79) obtained from the gradient AC/A. The two AC/A measurements (far-near AC/A

and gradient AC/A) were positively correlated, but only weakly (Spearman’s correlation

= +0.34 (p = 0.037). Figure 4 below shows the correlation in a scatter plot of the ranks of the two measurements.

40 30 20 10 Ranks of Far-Near Accommodative Far-Near of Ranks Vergence 0

0 10 20 30 40 Ranks of Gradient Accommodative Vergence

Figure 4: Scatter plot showing the ranks of the far-near accommodative vergence and the accommodative vergence obtained from the gradient AC/A

Correlation between Proximal Vergences

The mean proximal vergence value obtained by the +2.50 method (4.18∆ ± 3.91) was higher than that obtained by the far-near proximal (1.87∆ ± 4.86).

The two methods were highly correlated (Spearman’s correlation: +0.70, p < 0.0001;

Bland-Altman 95% limits of agreement = 2.83∆); Bland-Altman bias = 2.3 ∆. These are shown in the two figures below.

The figure below shows the scatter plot of the two proximal vergence measures. The data were not normally distributed. Therefore, the Spearman’s correlations were calculated for all the relevant correlations, using the ranks of the data points. Consequently, all the scatterplots in this study were constructed using the ranks of the relevant variables.

p g 10 5 0 -5 -10 Proximal ConvergencePrism Diopters in -15

Far-Near Proximal 2.50D Proximal

Figure 5: Box plot comparing the proximal vergence and the proximal vergence obtained from the gradient AC/A

5 FarNearProximal - Proximal FarNearProximal

-8 -10.5 10 Average Figure 6: Bland-Altman plot comparing the two measures of proximal vergence

40 30 20 10 Ranks Ranks of Far-Near Proximal Vergence 0

0 10 20 30 40 Ranks of +2.50D Proximal Vergence

Figure 7: Scatter plot showing the relationship between proximal vergence obtained from the difference between far-near and gradient AC/A ratios and the +2.50D method 58

Correlation between Accommodative Vergence and Proximal Vergence

The Spearman’s correlation of far-near proximal vergence (the difference between the stimulus far-near AC/A and stimulus gradient AC/A) to the stimulus far-near accommodative convergence was +0.48 (p = 0.0024). Figure 8 shows a plot of correlation between the far-near proximal vergence and accommodative vergence derived by the far-near AC/A.

40 30 20 10 Ranks Ranks of Far-Near Proximal Vergence 0

0 10 20 30 40 Ranks of Far-Near Accommodative Vergence

Spearman’s r = +0.48; p = 0.0024 Figure 8: Scatterplot comparing the far-near proximal vergence to the far-near accommodative vergence

However, the correlation between the far-near proximal and the gradient AC/A was -0.61

(p < 0.0001).

40 30 20 10 Ranks Ranks of Far-Near Proximal Vergence 0

0 10 20 30 40 Ranks of Gradient Accommodative Vergence

Spearman’s r = -0.61; p < 0.0001 Figure 9: Scatterplot showing the ranked correlation between the far-near proximal vergence to the gradient accommodative vergence

40 30 20 10 Ranks +2.50D Proximal Vergence 0

0 10 20 30 40 Ranks of Gradient Accommodative Vergence

Spearman’s r = -0.13; p = 0.444 Figure 10: Scatterplot showing the correlation between the +2.50D proximal vergence and gradient accommodative vergence

Correlation between Proximal Vergence and Disparity Vergence

There was no statistically significant correlation between far-near proximal vergence and disparity vergence (Spearman’s r = -0.24; p=0.151). There was a statistically significant negative correlation between +2.50D proximal vergence and disparity vergence.

However, the correlation was also relatively weak (Spearman’s r = -0.28; p = 0.0030).

These correlations are shown in figures 11 and 12 below.

40 30 20 10 Ranks Ranks of Far-Near Proximal Vergence 0

0 10 20 30 40 Ranks of Disparity Vergence

Spearman’s r = -0.24; p = 0.151 Figure 11: Scatterplot showing the correlation between the far-near proximal and disparity vergence

40 30 20 10 Ranks +2.50D Proximal Vergence 0

0 10 20 30 40 Ranks of Disparity Vergence

Spearman’s r = -0.28; p = 0.0030 Figure 12: Scatterplot showing the correlation between +2.50 proximal and disparity vergence

Correlation between Accommodative Vergence and Disparity Vergence

There was a significant negative correlation between the far-near accommodative vergence and disparity vergence (Spearman’s r = -0.47; p = 0.0030). However, there was poor correlation between the gradient accommodative vergence and disparity vergence

(Spearman’s r = -0.15; p = 0.365). These are shown in figures 14 and 15 below.

40 30 20 10 Ranks of Far-Near Accommodative Far-Near of Ranks Vergence 0

0 10 20 30 40 Ranks of Disparity Vergence

Spearman’s r = -0.47; p = 0.0030 Figure 13: Scatterplot showing correlation between far-near accommodative vergence and disparity vergence

40 30 20 10 Ranks of Gradient Accommodative Gradient of Ranks Vergence 0

0 10 20 30 40 Ranks of Disparity Vergence

Spearman’s r = -0.15; p = 0.365 Figure 14: Scatterplot showing correlation between gradient accommodative vergence and disparity vergence

Correlations of Accommodative Vergence with Vergence Facility

Neither accommodative vergence measurements were significantly correlated with

vergence facility. These correlations are shown in the scatter plots shown in figures 15

and 16 below.

40 30 20 10 Ranks of Far-Near Accommodative Far-Near of Ranks Vergence 0

0 10 20 30 40 Ranks of Vergence Facility

Spearman’s r = +0.11; p = 0.508 Figure 15: Scatterplot showing correlation between far-near accommodative vergence and vergence facility

40 30 20 10 Ranks of Gradient Accommodative Gradient of Ranks Vergence 0

0 10 20 30 40 Ranks of Vergence Facility

Spearman’s r = -0.13; p = 0.446 Figure 16: Scatterplot showing correlation between gradient accommodative vergence and vergence facility

Correlation between Disparity Vergence and Vergence Facility

Disparity vergence was significantly positively correlated with vergence facility.

However, the correlation was low (Spearman’s r = +0.34; p = 0.035). This is shown in the scatterplot in figure 17 below.

40 30 20 10 Ranks of Disparity Vergence Disparity of Ranks 0

0 10 20 30 40 Ranks of Vergence Facility

Spearman’s r = +0.34; p = 0.035 Figure 17: Scatterplot showing correlation between disparity vergence and vergence facility

Correlation between Proximal Vergence and Vergence Facility

Contrary to what was expected, neither of the two proximal vergence measurements was correlated with vergence facility. The Spearman’s correlation between the far-near proximal vergence was +0.11 (p = 0.508). This is shown in the scatter plot below in figure 18.

40 30 20 10 Ranks Ranks of Far-Near Proximal Vergence 0

0 10 20 30 40 Ranks of Vergence Facility

Spearman’s r = +0.11; p = 0.508 Figure 18: Scatterplot showing correlation between far-near proximal vergence and vergence facility

Similarly, the Spearman’s correlation between +2.50D proximal vergence and vergence facility was -0.01 (p = 0.929). This is shown in the scatterplot below.

40 30 20 10 Ranks of +2.50D Proximal Vergence 0

0 10 20 30 40 Ranks of Vergence Facility

Spearman’s r = -0.01; p = 0.929 Figure 19: Scatter plot showing correlation between +2.50D proximal vergence and vergence facility

The correlations are summarized in table 2 below.

Vergence Types Spearman’s p-value Correlation Far-Near Accommodative vergence vs Gradient +0.34 0.037 Accommodative vergence Far-Near Proximal vergence vs +2.50D Proximal +0.70 <0.0001 vergence Far-Near Proximal vergence vs Far-Near +0.48 0.0024 Accommodative vergence Far-Near Proximal vergence vs Gradient -0.61 <0.0001 Accommodative vergence +2.50D Proximal vergence vs Gradient -0.13 0.444 Accommodative vergence Far-Near Proximal vergence vs Disparity Vergence -0.24 0.151

+2.50D Proximal vergence vs Disparity vergence -0.28 0.0030

Far-Near Accommodative vergence vs Disparity -0.47 0.0030 vergence Gradient Accommodative vergence vs Disparity -0.15 0.365 vergence Far-Near Accommodative vergence vs Vergence +0.11 0.508 Facility Gradient Accommodative vergence vs Vergence -0.13 0.445 Facility Disparity vergence vs Vergence Facility +0.34 0.035 Far-Near Proximal vergence vs Vergence Facility +0.11 0.508

+2.50D Proximal vergence vs Vergence Facility -0.01 0.929

Table 2: Summary of correlations among vergence measurements

Discussion

The results of the study are discussed in the sections below.

The average distance and near phorias were 1.6 Δ (± S.D. of 3.87) and 5.6 Δ (± 5.58) of exophoria respectively. The distance phoria is similar to norms reported by Scheiman and

Wick[2] and Stidwill[53]. These authors reported average normal distance phoria values of 1∆ (±2) of exophoria. However, the average near phoria is higher than the norm reported by Scheiman and Wick[53] (3 ±3 ∆), but similar to that reported and reported by

Stidwill and Fletcher[53] (5 ± 5∆). Wick reported 2.53∆[7] while Morgan reported

1.4∆[54].

The average far-near AC/As was 4.80 ∆/D (±1.64), while the average gradient AC/A was

4.05∆/D (±1.74). The difference in these two AC/A values was statistically significant

(Wilcoxon signed-rank test; z = -2.83, p=0.005)). The gradient AC/A is almost exactly what is reported as normal by Scheiman and Wick[2] and Stidwill and Fletcher[53] (4/1).

These values are said to have been derived from norms provided by Morgan[54]. These results show that the participants in this study had largely normal clinical results. The higher far-near AC/A is normal and expected since it is expected that the far-near AC/A also contains proximal convergence and accommodation inputs [2, 7, 12, 35].

In addition, the far-near proximal vergence value (1.87∆±4.86) was significantly lower than the +2.50D proximal vergence value (4.18∆±3.91). The Wilcoxon signed-rank test gave values of z=-4.03; p = 0.0001. It is not immediately clear what may account for this difference. To speculate, the results show the +2.50D lens that was used to clamp the accommodation response in the +2.50D approach for proximal vergence was somehow

72 more effective in affecting proximal vergence output than the near visual target in the far- near method. This may happen if possible magnification effects from the +2.50D lens induced awareness of nearness in the target by indirectly recruiting looming cues. When familiar objects appear larger (as can be the case when looking through plus lenses), they may be perceived as being closer or looming, thereby inducing proximal responses.

According to Wick[7], the “near effect” and small magnification could enhance proximal vergence more than may be predicted. It is not clear what he meant by the near effect, but he pointed out that its effects help to increase proximal vergence.

The +2.50D proximal is similar to the one reported by Hofstetter (2.6 degrees or 4.55∆) who used a similar strategy to determine proximal vergence by clamping the accommodative stimulus for distance (6m) and near (33.3cm), as described below.

Perhaps, Tait[55] was the earliest researcher to clamp the accommodative response this way. However, he used +3.00D to clamp accommodative response at 33.3cm and reported a modal value of 4∆.

Correlation between Proximal Vergence and Accommodative Vergence: Overall, both far-near proximal and the +2.50D proximal were positively correlated to far-near AC/A.

The +2.50D proximal had a higher correlation to the far-near AC/A than the far-near proximal. However, both of these proximal vergence measures were negatively correlated with gradient AC/A, although the correlation to +2.50D proximal was neither meaningful nor statistically insignificant.

First, the positive correlation between the proximal vergences obtained by both the far- near and +2.50D approaches and the far-near accommodation vergence is to be expected. 73

This is because for all these three measurements, there is an actual change in depth by a change in fixation from far to near. It has been established that the far-near AC/A is contaminated by proximal vergence [7, 13, 25, 44, 56]. Thus, it is not surprising that the far-near accommodative vergence (part of which is proximal vergence) will correlate with the two proximal vergence measures. In addition, the +2.50D approach for determining proximal vergence is the same as the approach for determining gradient accommodative convergence, but with a +2.50D lens, instead of +1.00D lens.

Therefore, it appears that between the two measures of accommodative vergence (far- near and gradient), gradient accommodative vergence is less susceptible to contamination. Likewise, the +2.50D proximal vergence is probably less susceptible to accommodative response deficits (e.g. lag of accommodation), as it obviates the need to accommodate altogether.

As a result, the correlation between proximal vergence and accommodative vergence in this study was based on the correlation obtained between +2.50D proximal vergence and gradient accommodative vergence. These two vergences were poorly correlated

(Spearman’s r = -0.13, p = 0.444). This lack of correlation between proximal vergence and gradient accommodative vergence has been interpreted to mean that the two vergence components have independent neural controls.

However, the correlation between the far-near proximal and gradient accommodative convergence was negative and significant (Spearman’s r = -0.61, p < 0.0001).

Morgan[44] also found a negative correlation between far-near proximal vergence and gradient accommodative convergence. He interpreted that to mean that when

accommodative vergence is high, not much proximal vergence is needed to meet the

necessary vergence demand output. However, he implied that accommodative vergence is

the preferred means of meeting the vergence demand, and that high proximal vergence is

only necessary when accommodative vergence is insufficient. In a subsequent study,

Hofstetter[12] did not find any correlation, and attributed Morgan’s correlation to a

statistical artifact.

While Hofstetter did not explain the nature of this statistical artifact that would explain

Morgan’s finding, a potential way that could occur is as follows.

If the gradient AC/A rises faster than the far-near AC/A, then the relationship reported by

Morgan (and observed in the current study) could potentially explain this artifact suggested by Hofstetter. The correlation between the two measures of AC/A is shown in figure 20 below.

10 8 6 Far-Near AC/A 4 2

2 4 6 8 Gradient AC/A

Pearson’s correlation: +0.36, p = 0.027; slope = +0.33, intercept 3.5 Figure 20: Correlation between far-near AC/A and gradient AC/A

A look at the figure above suggests that as the gradient AC/A rises, the far-near AC/A does not rise as fast (the slope is +0.33). It looks like for some subjects there is perhaps a

1:1 relationship between the far-near and gradient AC/As. But for the majority of the subjects, the far-near AC/A rises more slowly than the gradient AC/A, reducing the slope of the graph. This relationship between the two measures of accommodative convergence may in part account for the negative correlation between gradient AC/A and far-near proximal vergence. While this does not totally explain the correlation, the results suggest the possibility of an artifact. Second, the weak positive correlation observed between the two accommodative vergence approaches (Spearman’s r = +0.34; p = 0.037) shows that 76

although they are measuring the same thing, it is possible that there is something else

contributing to the results obtained. That additional entity is could be proximal vergence.

Therefore, the relationship between far-near proximal and gradient accommodative

convergence appears inconclusive.

In addition, the far-near proximal vergence could be more effective in driving vergence

change by virtue of the actual change in distance. Comparatively, the method for

determining gradient AC/A has no change in proximal cues, and thus is less likely to be

affected by change in proximal accommodation. Any proximal cues are held constant in

gradient AC/A determination, but they increase in far-near proximal vergence.

In a second study, Hofstetter[12] equalized accommodative demands at 6m and 33.3cm.

He compared positive and negative convergence amplitudes and lateral heterophoria

between these two distances on a plot of accommodative demand to vergence demand.

He observed a rightward shift in all the convergence measures for near, which were not

related to accommodation. This rightward shift was attributed to proximal vergence.

Apart from potentially being more effective in driving proximal vergence, the real or

perceptual difference in distance may also confer another advantage from proximal

inputs. Far-distance proximal cues can also drive proximal accommodation. This

proximal accommodative convergence has been acknowledged by Hofstetter[6, 12].

Thus, the approaches which are based on change in distance have vergence changes from

two sources: one is from direct distance-induced proximal vergence. The other is

potentially driven by indirect vergence change which accompanies distance-induced

proximal accommodation through a possible relationship similar to the AC/A ratio.

Altogether, the results show no evidence that proximal and accommodative vergences have overlaps in their neural control. Also, the results show that the cues that are based on actual change in distance from distance to near (far-near proximal, far-near AC/A and the +2.50D proximal) all tend to be positive while the one only one that is not based on distance change is negative. This may suggest that perhaps actual change in distance is more effective in driving proximal cues. In comparison, the fixed-distance cues hardly have any proximal input, and the difference is so much that the relationship is negative.

Another measure of proximal vergence that is based on actual change in distance may help to elucidate this further. One such measure is jump vergence (rapidly changing vergence posture from far to near without any intermediate eye positions) which is discussed in more detail below (on the section on correlation between proximal vergence and vergence facility).

Further, these tests of proximal and accommodative vergences suggest that proximal vergence is elusive with existing clinical tests. Gradient accommodative vergence is probably a better test than far-near accommodative vergence, even though it is susceptible to accommodative lag. Far-near proximal vergence is probably contaminated as suggested by Hofstetter. The +2.50 approach is similar to gradient AC/A (in both, accommodative demand is changed with a lens, and the effect on vergence determined) except that it is less susceptible to accommodative inaccuracies because it obviates the need for accommodative response.

Correlation between Proximal Vergence and Disparity Vergences: Both approaches for

measuring proximal vergence showed negative but insignificant correlations to the level

of disparity vergence in play in the steady-state vergence response at near. The correlation between far-near proximal vergence and fusional vergence was -0.2378 (p =

0.1505) and that between the +2.50D proximal and disparity vergence was -0.2781 (p =

0.0910). These results suggest that proximal vergence has independent, separate neural

pathways from disparity vergence. This may suggest that if there is any overlap, it is very

little. This will be in line with the thinking that volitional effort from awareness of

nearness is what drives initial vergence response from far to near (when the vergence

demand is higher than what the disparity vergence can cope with)[19, 20], and it does so

irrespective of whether normal disparity vergence exists or not. In other words, when the

initial vergence demand is beyond the disparity detection range, only volitional eye

movements can perform the task of changing focus from distance to near where disparity

vergence (and accommodation) takes over. Proximal vergence operates at complementary

ranges of depth demands with disparity vergence[20]. There is evidence to suggest that

when disparity is subnormal in convergence insufficiency, volitional efforts are often

utilized in an attempt to perform the role which is ordinarily performed by disparity[21].

However, proximal responses provide coarse, gross changes in disparity induced by

change of fixation from far to near. Comparatively, disparity responses provide fine

responses within the peripersonal/personal space. Thus, it is reasonable to suspect that

people with convergence insufficiency who try to draw on proximal responses to meet

the full vergence demand may fatigue. This is because there is no evidence that proximal

vergence also has slow adapting component which serves to reduce the stress on the

vergence system when binocular vision has to be sustained. This attempt to rely on

cognitively proximal vergence may help to explain fatigue with near work and loss of

attention during sustained near work in convergence insufficiency.

Correlation between Accommodative Vergence and Disparity Vergence: Generally, the

results indicate that far-near accommodative vergence is negatively correlated with the

level of disparity vergence in operation during the near task (Spearman’s r = -0.4683; p =

0.0030). However, the correlation between the gradient accommodative vergence and the

level of operating disparity vergence was not significantly correlated, although they were negatively correlated (Spearman’s r = -0.1512; p = 0.3649). Proximal influences in the far-near accommodative vergence may explain the differences in the correlations of the two measures of accommodative vergence. The gradient accommodative convergence, which is perhaps a better measure of accommodative vergence[12], was not correlated to

disparity vergence. This implies a lack of common neural pathways in the control of

these two vergence systems. This result is not in agreement with previous models of

accommodation and vergence. Based on the dual interactive model of vergence control,

accommodative vergence inputs into the vergence pathway to complement disparity

vergence under normal binocular conditions. Therefore, the normal expectation would be

an inverse correlation, as was the case in the correlation with the far-near accommodative

vergence. In monocular vision- it is even expected that this expected inverse relationship

would be stronger. In this disparity open-loop situation, accommodative vergence would

be expected to be very high to counteract the effect of the absence of the other

retinotopic, feedback-controlled accommodative vergence.

The unexpected lack of correlation from the gradient accommodative convergence may

be due to the use of a plus lens in the gradient measurement. Besides the possible

magnification effects, there is also the challenge of inducing the needed accommodative

response. It is known that when accommodative demand changes, the first 0.5 to 0.75

diopter of accommodative blur may not elicit any accommodative response due to the

depth of focus of the eye. Also, it has been reported that plus lenses and minus lenses of

the same power have different effects on stimulating accommodation[57]. It appears that

minus lenses are more effective in driving accommodation. Based on this, Majumder and

Mutusamy advocated the use of both plus and minus lenses for determining gradient

AC/A. However, the average stimulus gradient AC/A is similar to the average normal

AC/A. This shows that the accommodative responses of the subjects were probably

normal on average. To elucidate this further, it will be important to determine the

response gradient AC/As as that is less affected by accommodative lag. In addition, an

examination of table 1 shows that the average near phoria was 5.6Δ while the average

near phoria taken through +1.00D was 9.66Δ; a change in the right expected direction.

This suggests that it is not accommodative response per se (or even depth of focus, unless all the subjects had high AC/As) that accounts for the lack of expected correlation.

In clinical terms, the expected negative correlation between accommodative and disparity

vergences would suggest that people who have convergence insufficiency (from low

disparity vergence output) require increased output from accommodative vergence to

maintain clear, single binocular vision at near. Likewise, those with accommodative

insufficiency will be expected to have high CA/Cs if they are to be normal. This

81 conception appears to be the case because CI is often found associated with low AC/A in addition to reduced disparity vergence amplitudes, near point of convergence and symptoms. North and Henson[58] and Schor and Horner[10] have confirmed this low

AC/A, high CA/C in CI. Also, vision therapy does not appear to affect AC/A and

CA/C[11]. Rather, vision therapy for CI appears to improve symptoms by improvement in vergence amplitudes and vergence adaptation. Altogether, these may suggest that the cross-links may not be the main or sole mechanism by which these two eye movement systems interact. Therefore, the expectation of a negative correlation between accommodative vergence and disparity vergence (even though the correlation was insignificant) appears valid in the light of current conception of the how accommodation and vergence systems are controlled neurologically.

Consequently, it may mean that at least in cases of deficiency, it may not be the output per se which is important, but the accommodative effort, as suggested by several researchers[10, 20-22].

Correlation between Proximal Vergence and Vergence Facility: Neither of the two measures of proximal vergence was significantly correlated with vergence facility as measured with the 12 Δ Base-Out/3 Δ Base-In prism flipper. First, it is possible that the

15 prism diopter (equivalent to about 8.6 degrees) range from this combination of prism flipper is still within the disparity detection range. So far, no one seems to know exactly how much of the total vergence amplitude disparity vergence is responsible for.

Estimates for the disparity detection range put it at about 6 degrees[49]. In other words, it is possible that this combination of prism powers did not effectively tap into the 82

proximal vergence because the combined power still elicited response from the disparity

vergence system. Schor has argued that disparity is the sufficient and necessary cue that

drives the total vergence response[20]. To elucidate this further, it might be necessary to use different prism flipper combinations of varying power up to about 20 prism diopter

range to see if this trend with proximal vergence persists. In a study of disparity

detection, Westheimer and Tanzman[59] showed that a group of people had an average of

9 degrees (15.75Δ), with one subject achieving the highest disparity demand available in

the study (10 degrees; 17.5Δ). Although they described their method as qualitative, their

results show that perhaps the 15Δ range from the 12 Δ Base-Out/3 Δ Base-In prism flipper was not effective in tapping into the proximal vergence system.

It may also be that the flipper is a poor stimulus for eliciting the steady-state proximal vergence response. As explained above, the magnitude of the significant correlations of proximal vergence appear to come with proximal vergence responses that are based on actual change in distances. Thus, it may not be the magnitude of the vergence response per se that elicits proximal responses, but the magnitude of vergence responses which are due to perceptible differences in target distances. To investigate this further, methods that incorporate some modification of jump vergences may be a more effective stimulus as proximal cues appear more sensitive to cognitively-driven change in actual or perceived distance. There is no perceived or real distance change in prism flipper testing to activate the proximal vergence system.

To test this, it might be interesting to test how proximal vergence from the two approaches described in this study relate to another proxy for vergence proximal

vergence such as the facility of jump vergences. Jump vergences involve actual change in

distance and perception of distance, and may be a better measure of proximal vergence

than 12 Δ Base-Out/3Δ Base-In prism flipper.

Also, it is usually assumed that proximal vergence is mostly phasic while accommodative

and disparity vergences both have phasic and tonic components. However, it is possible

that proximal vergence also has a tonic component, and the prism flippers used as a proxy

measure of proximal vergences in this study were not very sensitive to steady state

proximal vergences. Possibly, only the phasic component of proximal vergence can be

measured with the prism flipper.

Conclusion

Accommodative vergence (gradient) was not correlated with proximal vergence. This has been interpreted to mean that there are no overlaps in the neural control of proximal and

accommodative vergence components. It is also speculated that the real or perceived

change in distance may be a more effective stimulus for driving proximal vergence than

disparity of similar magnitude but without distance change.

Also, proximal vergence was not well correlated with the level of steady-state disparity vergence. This is interpreted to mean that proximal and disparity vergence components are supported by largely different neural controls, even if they are known to operate at complementary operating ranges of eye movement in depth.

In addition, accommodative and disparity vergences were not statistically significantly correlated despite the expectation of significant correlation due to the cross-talk between

accommodation and disparity. Schor and Horner have postulated that when fast disparity vergence occurs with low slow disparity vergence, they drive convergence accommodation up[10]. This causes decreased blur accommodation and low accommodative vergence. From this, disparity vergence decreased slow disparity vergence is expected to be correlated with accommodative vergence. Thus, the absence of correlation between accommodative and disparity vergences in this study means that although the accommodative vergence and disparity vergence are known to be linked by retinal feedback, they probably maintain largely independent neural pathways.

Finally, proximal vergence was not well correlated with vergence facility. This has been explained as possibly due to the differences in the nature of the proximal vergence eye movements and how they are measured, or that the magnitude of the prism power was not enough to drive proximal cues. Besides, proximal vergence is known to be phasic under

open-loop disparity condition. However, but vergence facility was measured under

closed-loop disparity. Therefore, the lack of correlation may be due to proximal vergence

behaving differently under open-loop and closed-loop conditions: phasic under open-

loop, but tonic under closed-loop conditions. This is not surprising as accommodation

and disparity are known to exhibit different behaviors under open-loop and closed-loop

conditions when there are dual-interactive feed-back error signals [19].

Limitations to the Study

The main limitation of this study is the use of accommodative stimulus for calculations of

accommodative convergence values. Inherent in this calculation is the assumption that

85 accommodative response is equal to accommodative stimulus. This is however not the case. The normal eye has an average depth of focus of 0.50D[2]. In addition, Saladin[21] and Wick has pointed out that for the first 0.75D of accommodative stimulus, the accommodative system hardly responds to the stimulus. In fact, Wick[7] has estimated that for an accommodative stimulus of 2.50D, the accommodative response is just half of the stimulus. However, the accommodative vergence results are similar to the results provided by Wick[7] and other researchers[54]. Therefore, it would be interesting to know how the correlations observed, and the strengths of the correlations would look like when compared with those observed with accommodative response.

Chapter 4: Functional Imaging for Convergence Eye Movements

Background

While unique stimuli are associated with each of the vergence components, it is not clear

whether most of the vergence innervation is ultimately drawn from the same cortical

areas[20]. It may or may not be the case that the neural correlates for each of these

vergence components are the same.

It is important to establish the neural relationship between the sub-types of vergence, because these relationships reflect the extent of the overlap in the neural underpinnings of each vergence sub-type. While some of the neural substrates for vergence are known, it is important to more accurately elucidate these neural substrates as vergence issues are very common clinically. An understanding of these neural substrates in health will lead to improved understanding of how vergence eye movements are controlled. That understanding will guide efforts to improve management to impact how patients with vergence dysfunction are managed.

Neural Control of Vergence Eye Movements

Several brain areas are involved in the planning, coordination and execution of vergence eye movements[32]. These brain areas can be functionally organized into a hierarchy ranging from the supranuclear control centers down to extraocular muscles in peripheral

areas[20], as shown in the figure below. The supranuclear regions include cortical areas

in the occipital cortex, temporal, parietal and frontal lobes, as well as cortical areas in the

cerebellum.

The cortical areas involved in vergence eye movements include the striate visual cortex

and the extrastriate visual areas and the extrastriate areas including middle temporal

(MT)[60-62] and middle superior temporal (MST)[62] areas. In addition to these, the

frontal eye fields (FEF)[63, 64] and supplementary eye fields (SEF)[29, 63, 64] [32]are

also involved in vergence at the supranuclear level. Areas in the cerebellum can also be considered supranuclear as they exert control over the oculomotor nuclei.

Also, the lateral interposed nucleus and the posterior parietal areas have been implicated in vergence eye movements.

The primary visual cortex (also called the striate cortex), located in the posterior part of occipital lobe, receives sensory afferent signals (blur and disparity) from the retina through the lateral geniculate nucleus in the thalamus[65]. These signals are coded in the primary visual cortex. Some cells are sensitive to depth, and may be tuned-zero (sensitive to objects in the same plane as the point of regard), tuned-far (sensitive to objects located behind the plane of regard) or tuned-near (sensitive to objects located in front of plane of regard)[66, 67]. Some cells in the primary visual cortex also incorporate vergence signals to code egocentric depth (for proximal information)[20, 68].

Figure 21: A simplified summary of the organization of the neural control of vergence eye movements Supranuclear regions are shaded. PEF: parietal eye field, FEF: frontal eye field, SEF: supplementary eye fields, MT: medial temporal area, MST: middle superior temporal area, SOA: supra-oculomotor area, NRTP: nucleus reticularis tegmenti pontis [20, 34, 68].

Sensory information on disparity, blur and proximity are then passed on to frontal lobe

after they are relayed through the extrastriate cortex[67]. There are cells in the extrastriate areas of MT and MST[62] which respond to retinal disparity and changing size while cells in the parietal cortex respond to motion in depth. Binocular disparity and blur cues are coded in in the primary visual cortex (V1); some cells in V1 incorporate vergence to code egocentric (head-referenced) distance. The blur and disparity cues are projected to cells in MT and MST which respond to retinal disparity and looming cues.

Cells in parietal cortex respond to motion in depth[68].

Areas MT and MST lie in the extrastriate cortex in superior temporal sulcus[68]. Area

MT receives inputs from primary visual cortex, and encodes velocity and direction of visual stimuli in three-dimensions relative to the eye. It projects impulses to the area

MST and frontal eye fields. Cells in MST respond to head-centric target movement, and respond to both retinal and efference copy signals. Each hemisphere of MST codes motion to the ipsilateral side, and may respond to motion parallax stimuli[65]. Therefore,

MST may be involved in perceptual/voluntary proximal vergence.

The sensory information is further passed on to higher control centers in the frontal lobes

(FEF and SEF) for motor planning[34, 63]. In the FEF, neurons involved in vergence planning and control are located anteriorly, as opposed to those involved in saccadic eye movements which occupy a more posterior location. The SEF is perhaps involved in predictive vergence eye movements[34, 68].

Efferent commands for vergence from the FEFs are thought to be sent to the midbrain premotor area known as nucleus reticularis tegmenti pontis (NRTP). The NRTP is

located ventral to the rostral portion of the paramedian pontine reticular formation

(PPRF). Besides the frontal eye fields, the NRTP also receives projections from the

supplementary eye fields. The NRTP appears to be related to accommodation and

vergence, and projects to the cerebellum[34].

The cerebellum is thought to be important for sorting out eye and head rotations, and for sorting out ocular pursuit signal from visual and eye-head motor inputs. Three areas in the cerebellum are known to be involved in vergence[34, 69]. First, the dorsal vermis converts three-dimensional signals to control signals for vergence eye movements.

Secondly, the posterior interposed and the fastigial nuclei receive projections from the dorsal vermis, which they project to the supraoculomotor area of the midbrain mesencephalic reticular formation (MRF)[34, 64]. The fastigial nucleus contains neurons related to convergence while the posterior interposed nucleus contains those related to divergence. Finally, the cerebellar flocculus contains neurons related to vergence, but probably for changing the gain of the vestibulo-ocular reflex with vergence angle[64].

The midbrain’s MRF contains both burst and tonic cells which code velocity signals for vergence, and position signals for maintenance of vergence angle, respectively[64]. From the MRF, excitatory signals project to the third nerve nucleus to drive the medial rectus, and inhibitory signals to the abducens nucleus to inhibit the lateral rectus. The midbrain also houses the supraoculomotor area which is located close to the oculomotor nucleus at its dorsolateral aspect. Through monosynaptic connections, the supraoculomotor area relays control for both accommodation and disparity vergence to the Edinger-Westphal nucleus in the superior colliculus to control accommodation, and to the oculomotor 91

nucleus for vergence [34, 70, 71]. The superior colliculus is also implicated in the generation of both saccadic and vergence eye movements. It is the area where saccadic- vergence interactions are thought to occur.

Below the supranuclear control centers are premotor nuclei (or the gaze centers) located in the brainstem. These areas coordinate the actions of several muscles to execute horizontal, vertical and torsional eye movements including vergences. Premotor areas involved in vergence eye movements orchestrate the direction, amplitude, velocity and duration of the movements. The premotor areas send out interneurons which all converge on motor nuclei in the final common pathway.

Based on electrophysiological studies in alert non-human primates, Mays et al[3, 29],

Judge and Cumming[48], and Gamlin et al[34, 64], have shown that motor commands for vergence appear in cells in the frontal eye fields (FEF)[63]. Hence the FEF, in addition to being involved in saccades and smooth pursuits, also participates in vergence. Thus, it appears that the prearcuate regions of the frontal cortex contain areas specialized for all classes of voluntary eye movements including saccades, smooth pursuit and vergence[34].

In the midbrain, the premotor nucleus reticularis tegmenti pontis (NRTP)[72] is located just ventral to the rostral portion of the paramedian pontine reticular formation, and receives projections from the FEF and the superior colliculus. The NRTP, which projects to the cerebellum, is thought to be associated with both accommodation and convergence[34, 64].

The posterior interposed nucleus (PIN)[70, 73] of the cerebellum projects to the

supraoculomotor regions which also contain near response cells in the mesecencephalic

reticular formation (MRF)[64, 73]. The supraoculomotor nucleus thus relays control for burst and tonic neurons for both accommodation and vergence.

Excitatory connections of the supraoculomotor nucleus project to the oculomotor nucleus

to drive the medial recti [64, 71, 73]. The supraoculomotor nucleus also projects inhibitory connections to the abducens to inhibit the lateral rectus[74].

There are still several brain areas and neural pathways for vergence that are yet to be identified. It is also not clear whether all the neural pathways identified in non-human primates are the same in humans. The development of functional magnetic resonance imaging offers the opportunity to confirm the involvement of these brain areas in convergence in humans, identify other areas and the neural pathways involved.

The Two Streams of Visual Information Processing

Visual information processing occurs early in the visual system, beginning in the primary visual cortex[67]. This process is usually conceptualized as being composed of a dual stream mode; the ventral (“what”) stream which passes through the temporal lobe, and the dorsal (“where or how”) stream which passes through the posterior parietal lobe[61].

The ventral stream is thought to be perceptual because its main purpose is vision for perception. This occipito-temporal pathway mostly helps in the recognition and discrimination of shapes and objects by encoding spatial properties of visual objects[75].

Information about the spatial properties such as size and location relative to other visual

targets in the visual field help the observer to perceive those objects. All areas in the

ventral stream are also thought be influenced by extraretinal factors (factors which are

not associated with retinal input, such as efference copy, neck proprioception, etc).

On the other hand, the dorsal stream is regarded as vision for action because it is used for visual action planning and coordination. The occipito-parietal dorsal stream computes the actual properties of objects relative to the observer[75]. Thus, by determining “where” an object is, dorsal stream processing provides information about the proximity of the target

to the observer. Table 3 below details more differences between the two streams:

Property Ventral Stream Dorsal Stream Function Recognition & identification Visually-guided behavior Sensitivity High spatial frequencies; details High temporal frequencies; motion Memory Long-term stored Only short-term storage representations Speed Relatively slow Relatively fast Consciousness Typically high Typically low Frame of Allocentric or object-centered Egocentric or viewer-centered reference Visual input Mainly foveal or parafoveal Across retina Monocular Generally reasonable small Often large effects, e.g. motion vision effects parallax Based on the review by Norman[76] Table 3: Comparison of dorsal and ventral streams of visual information processing

The dorsal stream contains detailed maps of the visual field, and is good for detecting and analyzing movements. The posterior parietal cortex is an important brain center for the dorsal stream. It is essential for perceiving and interpreting spatial relationships, accurate body image and for learning tasks involving coordination of the body in space.

Despite this dichotomy, it is also recognized that these two streams interact at several levels[77]. The ventral stream has strong connections to object locations and motion. This interconnectivity makes sense because functionally, vision for perception contributes towards vision for action. For instance, one must be able to determine the location of an object with the dorsal stream before the ventral stream is used to act on it.

Techniques for Studying Neural Control of Vergence Eye Movements

The methods for studying the neural control of vergence eye movements are the same as the methods for studying other neural activities. Several methods have been used to understand how vergence eye movements and other motor and cognitive activities are controlled in the brain. Broadly, these methods can be classified as those which are based on observation of the effects of diseases or lesions, those which record the electrical activities in the brain and imaging studies. The methods have various levels of invasiveness, spatial and temporal resolutions, and the ability to detect functional activities and functional connectivities. Spatial resolution refers to the smallest unit that the strategy can study, while temporal resolution refers to how quickly the information about the brain area can be obtained. For instance, differences in spatial resolution can be exemplified by comparing electrophysiological techniques such as single unit[3, 71]

recordings to electroencephalography (EEG)[78]. While single unit recording can detect activities down to the level of dendrites in neurons, EEG may not be able to differentiate

between activations in the precentral gyrus in the frontal lobe and the postcentral gyrus in

the parietal lobe.

Examples of the methods, how they work, and their advantages and disadvantages are

summarized in table 4 below.

Method Brief Description of Advantages Disadvantages Example Method Lesion or disease Observing the effects of a Inexpensive, Tendency to be Lesion in the studies disease or lesion in an ethical, possibility unique, challenging to cerebellar vermis area of the brain of long-term study generalize impairs slow vergence eye movements[79]

Directly recording Electrodes and detectors High spatial and Cannot be used on Recording of electrical activity in are inserted into the brain temporal humans-neural vergence activities the brain near the point of interest resolutions correlates in animals in rhesus monkey to record electrical may not be replicated brains[70, 71, 73]

97 activities of neurons in humans

Electroencephalograp A network of electrodes Non-invasive, can Low spatial resolution EEG in sleep studies hy (EEG) is placed on the skull to be used in awake, [80] record electric al sleep or stimulation anesthetized subjects, subjects can move around during recording (useful in young children) Chemical stimulation Active medications and Helps to Low spatial resolution Studies of

Table 4: Summary of techniques for studying the anatomy and97 physiology of the brain Continued

Table 4 continued

other chemicals are determine effects recreational drugs injected into particular of anesthetics, and nicotine[81] brain areas to determine recreational drugs, effects on function etc. on the brain and their mode of action Computer-based brain 3D images of the brain Non-invasive (can Relatively expensive, Computer Aided imaging are obtained by rotating be used in cannot show Tomography (CAT) • Structural x-rays (CAT/CT) or humans), high functional or Computed imaging (eg, strong magnetic field contrasts between connectivities. Tomography (CT) CAT/CT scan, (MRI) through the intact certain tissues, scans or Magnetic MRI) skull good spatial Resonance Imaging

98 resolutions (MRI) possible.

• Functional 3D images of the brain Shows areas of the Relatively expensive, fMRI of imaging are obtained as subjects brain moderating magnetic field can be convergence (fMRI or perform activities (fMRI) the functional disturbing for some insufficiency PET) or with radioactive task, good spatial people, not all treatment effects[37] isotopes injected into the resolutions subjects can be safely veins possible (up to scanned, poor 1mm) temporal resolution

Continued 98

Table 4 continued

Transcranial magnetic Magnetic pulses are Allows inferences More invasive than Study of effects of stimulation (TMS) applied to brain areas in to be drawn on fMRI; antiepileptic drugs living humans to observe brain structures on on human brain effects of “temporary cognition, function[82] lesions” from the perception and stimulation behaviors. Good spatial and temporal resolutions

Much of the understanding of the neurology underpinning convergence eye movements

have been derived from studies on non-humans. Studies in rats, cats and monkeys[3, 83]

using anatomical, behavioral and electrophysiological techniques have shown that several

areas of the visual cortex[83], frontal lobe[63], midbrain, pons and the cerebellum are

involved in convergence[64]. However, because these studies are done on animals, it is

not clear that findings can be replicated in humans. For instance, Quinlan and Culham

[84] have shown that although both humans and macaques have an area in the dorsal

stream for perceiving objects in the peripersonal space (the space around the body that

can be acted on by the arms, hands, legs and other body effectors[85]), this area is in different locations in the two species of primates. They show that this area resides in the dorsal parieto-occipital sulcus in humans compared to its location in the ventral

intraparietal area in macaques[84].

Besides, some of the techniques used (e.g., single-unit recordings) are only able to be

used at microcellular level. As such, it is difficult to map out a functional location of the

brain, or detect functionally connected areas among several areas of the brain. Also, compared to humans, complex behaviors cannot easily be elicited in animals under experimental conditions[60]. Therefore, it is challenging to identify brain areas involved in complex processes such as vergence eye movements, and to identify the functional connectivities of such areas. Functional imaging provides the opportunity to study the neural basis of vergence eye movements in humans.

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Functional Imaging for Studying Neural Control of Vergence Eye Movements

Functional imaging modalities provide opportunities to perform studies in living humans as they perform various functional activities. Imaging modalities such as positron emission tomography (PET)[86], transcranial magnetic stimulation (TMS)[87] and magnetic resonance imaging (MRI) offer the opportunities to study the neural processes involved in healthy, awake humans as they engage in experimental functional activities.

In PET, a radioactive substance such as radioactive glucose is injected into the blood of an individual as they perform an experimental functional activity[86]. The radioactive substance is carried in blood to all parts of the body, including the brain. The presence of the radioactive tracer in parts of the brain as the subject performs the experimental activity is determined, and this is used as an index of blood flow and metabolic activity in that part of the brain. On the other hand, transcranial magnetic stimulation utilizes carefully placed magnetic pulses to parts of the brain[87], and the observation of the effects of the temporary upset in the participation of that part of the brain on the performance of the experimental activity. Functional magnetic resonance imaging (fMRI) uses changing magnetic fields to detect structures of the brain, and to note changes in the electrical activities in these structures as a subject performs an experimental activity.

In fMRI, changes in cerebral blood flow are detected in the three-dimensional brain, and these changes are synchronized to the performance of an activity to determine variations in regional brain metabolism[88, 89]. If the changes in brain metabolism follow the pattern of changes in the performance of the experimental activity, such changes in metabolism are attributed to the functional activity. Thus, the areas of the brain which

101 increase or decrease their metabolic activities in line with the performance of the function are considered to be involved in the functional activity.

FMRI is an important tool that has been gaining popularity in the study of neurology of vergence eye movements. Previously, studies in neurology of vergence eye movements were performed with electrophysiological and anatomical techniques. These could not be performed in humans because of their invasiveness. Now, fMRI offers the opportunity for those studies to be performed in humans. Thus, fMRI has been used in humans to try to confirm the involvement of certain brain areas in convergence eye movements. Also, there have been fMRI studies to determine which brain areas are involved in convergence deficits[37] and how brain areas are modified after treatment for such deficits in convergence.

The commonest mechanism by which fMRI techniques are used to track metabolic changes in neurological studies is briefly described below.

The BOLD Response

Metabolically-active brain neurons areas require oxygen to generate energy. This oxygen is carried to these active neurons through blood in hemoglobin. fMRI is sensitive to changes in the oxygenation level of hemoglobin. As such, changes in rate and volume of blood flow and changes in concentrations of oxygenated and deoxygenated hemoglobin to an area of the brain indirectly signal neural activity in that area or areas downstream.

Therefore, blood oxygen level is used to signal the involvement of certain brain structures and areas by comparing the blood oxygen level of the area during an activity to the

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oxygen level at baseline[88]. It is then assumed that that area must be at least involved in

the functional activity if blood oxygen consumption increases above baseline. Also, the

level of activity can be correlated with the blood oxygen level to estimate the relative

levels of involvement in that activity. Thus, blood oxygen-level dependent (BOLD) fMRI

has been used to study various behavioral and psychological processes in humans,

including eye movements[23, 90]. BOLD functional imaging is safe, relatively quick with good temporal and spatial resolutions, and has consequently been approved for research involving human subjects.

The BOLD signal is convolved with the hemodynamic response function (HRF[89]) in

order to predict the shape of the BOLD response. FMRI primarily uses information on

the observable BOLD response to draw conclusions on neuronal activations which cannot

be directly observed[88]. For the indirect conclusions about unobservable neuronal

activation to be valid, it is important to understand the general pattern of blood inflow

and outflow in response to increased metabolic activity[88, 90]. If this pattern of blood

flow is significantly similar to the pattern of presentation of the experimental stimulus, it

can be assumed that the pattern of blood flow is due to neuronal response to the stimulus.

Thus, the HRF is an experimental model used to predict the BOLD response.

The rate of hemodynamic response to neural metabolic demands follows a known,

predictable shape, the canonical HRF. The shape of the HRF is known from

physiological studies. As such, although the time course is unknown in functional studies,

the BOLD response can reasonably be predicted to follow this shape once it starts. Thus,

the convolution of stimulus pattern to the canonical HRF gives a more realistic shape to

103 predict the BOLD signal. Following this, a model of the functional activity is created using the time-course of the functional activity. If the HRF-convolved experimental model of the functional activity significantly follows the BOLD response, the BOLD response is assumed to be explained by the functional activity. The convolution is usually done during most standard image analysis tasks, and is part of image analysis packages.

FMRI Study Designs

There are two main ways of designing functional studies based on the BOLD signal. The first one is the block design which basically compares the BOLD signal at baseline to the

BOLD signal due to a stimulus condition. It follows an “on” condition which corresponds to the task being assessed and an “off” condition which corresponds to the null task or baseline. For instance, if one desires to see the effect of seeing an image, the two conditions could be the “on” task condition where the image is shown and the “off” task where a black screen is shown to the subject. The BOLD signals generated by these two conditions can then be compared, assuming that the only difference between the two tasks was the appearance of the image. These two tasks can be repeated a number of times, and the respective average BOLD signals for the two conditions compared. This basic design may be extended to a number of conditions.

The block design is relatively simple, and has very high ability to detect changes in the

BOLD response. It is therefore suited for BOLD fMRI. The main criticism against block design is the fact that it is susceptible to prediction and habituation[91]. Due to the repetition in the same number of conditions in a predictable manner, there is a tendency

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for subjects to predict the onset of particular functional tasks. This can have the effect of

affecting the performance of the tasks. In addition, the constant repetitions can lead to a

situation where the blood flow that signals neural activity in BOLD fMRI may not

change significantly from one functional task to another[89]. This habituation of blood

flow can significantly affect the ability to elicit differences in signals due to differences in

functional tasks.

The second type of design for functional BOLD studies is the event-related design[91]. In the event related type, the number of functional tasks may be the same as in the block design. However, the time between different tasks (called inter-trial interval) is not constant, unlike in the block design. The timing of the tasks and the order of presentation is randomized, and the task durations can be within a certain range of time. This eliminates the prediction and habituation issues inherent in block designs. However, the event-related design has lower power for detecting BOLD signal changes.

Often, it becomes necessary to use a hybrid approach that comprises a mix of the two basic design types described above. This approach can maximize the benefits inherent from each design approach while minimizing their drawbacks. Therefore, a mixed design can comprised of a block design with randomized inter-trial intervals and task presentations, or block at one point and event at another.

FMRI Data Analysis Processes

The process for analyzing fMRI research results can broadly be grouped into three sequential steps. These are preprocessing, statistical analysis and post-processing. Several

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software packages exist for performing these analysis steps. Examples of these software

packages include Brainvoyager [92, 93], AFNI[94], SPM[95, 96] and FSL. The software

packages have the similar basic functions, even though the approaches for analysis may

be different for each software, and they run on different platforms (operating systems).

For instance, FSL is Linux-based. The choice of which package to use is usually based on

cost, speed of analysis or user preference.

The Oxford University’s Center for Functional MRI of the Brain’s (FMRIB) Software

Library (FSL)[97] version 5.10 was used for functional and structural image analysis in

this study. The steps involved in imaging analysis are briefly described below. The

description begins with a general description of the process and its purpose. Then the

description focuses on the steps taken in the analysis in this study with FSL.

Preprocessing: In preprocessing, the images undergo motion correction and filtering.

During scanning, the images are collected at different time points[97, 98]. For instance, along the vertical Z axis, the most superior slice may be collected first, and the most inferior last. Also, movement of the subject, even from breathing, can affect the

intensities of the various voxels which are not due to neuronal activity. Therefore, part of

the analysis may involve a correction for the timing of the slices. In addition, motion

correction may be applied prior to analysis to realign all the slices collected to correct for

differences in intensity of the various voxels due to motion of the subject.

Preprocessing may also involve slice timing correction which synchronizes the timing of

the slices to account for differences in time points at which they were imaged. In addition, adjustments may be made to account for magnetic field inhomogeneities. The 106

strength of the electromagnetic field is different when the machine is first switched on

compared to when scanning has been going on for some time. The first few images may

appear brighter than subsequent ones. As such, preprocessing steps can involve

discarding the first few images to ensure that all the images are taken at the time when the electromagnetic field strength is in a more stable state.

Statistical Analysis: After preprocessing, statistical analyses are carried out to determine

significant activations in different regions of the brain for different stimulus conditions.

The main approach used is general linear modelling[88, 89]. The time course for the

BOLD response is plotted for every voxel (voxel refers to volumetric pixel). A general

linear model (GLM) is used to model the various conditions as explanatory variables for

the BOLD response. Thus, the explanatory variables (stimulus conditions) are assumed to

sum up to give the trend observed in the BOLD response. Therefore, if the predicted

model from the combination of stimulus conditions closely follows the model of the

BOLD response using an a priori level of significance, the predicted model is assumed to

explain the BOLD response. A t test statistic can be performed to determine the fit of the

GLM.

Because the statistical analysis of the fMRI data is based on the GLM, the BOLD

response is assumed to be the result of a stimulus condition or group of conditions.

Statistical testing is used to test how the model fits the data using an a priori level of

significance. It is also used to test how each condition or group of conditions fit the

overall model. Using the linear model, the conditions can be compared by subtracting one

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from another, or even by averaging conditions across subjects and comparing those

averages by analyses of variances.

When one stimulus condition is arithmetically subtracted from another during statistical

analysis of functional MRI data, it is referred to as contrasting, and the result is a

contrast. Consider a study on disparity detection in the visual system. Two conditions are

created with a random dot stereogram, one with zero disparity (disparity-off condition), and another also with random dot stereograms, but with some magnitude of disparity

(disparity-on condition). Assuming these two stimuli are similar in everything, except the

magnitude of disparity, a contrast is obtained when the disparity-off condition (baseline in this example) is subtracted from the disparity-on condition (active condition). The resulting contrast shows which activations in the visual system are due to disparity alone.

In fMRI statistical analysis, the contrast shows, on average, which areas in the visual system are involved in disparity detection[97]. Similarly, the contrast obtained by subtracting the disparity-active condition from the disparity-inactive condition shows where in the visual system the non-disparity stimulus has higher activity. Also, the baseline (disparity-off condition) may be viewed as areas in the brain which are involved in perception of random-dot stereograms.

In addition to contrasts, average BOLD activations among a number of conditions can be

determined, and such averages compared to baseline in the form of contrast.

Part of the statistical analysis in fMRI involves determining which voxels are

significantly activated using an a priori threshold Z. This is equivalent to a t test to

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answer the question such as: on average, which voxels are significantly activated by the

task compared to baseline?

FMRI statistical analyses may be considered lower or higher level. Lower-level analyses

refer to analyses at the individual level where activations for a single condition and its

various contrasts may be obtained. For instance, an active condition may be contrasted

against its baseline in an individual subject in lower level analysis.

In higher-level analyses, different runs of a condition for a subject may be combined and

averaged[97, 98]. In addition, data from a condition may be averaged across multiple

subjects in higher-level analyses, or even different conditions for a single subject or

multiple subjects compared. Higher-level analyses may be performed with any of the

outputs from the lower-level analysis, including contrasts from different conditions.

Post-Processing[98]: After processing the functional and structural images, activities

such as registration of the functional images onto structural images (which are usually of

higher spatial resolution) can be performed. The registered image may then be registered

onto a standardized image through the process of spatial normalization. In spatial normalization, inter-subject anatomical variability is reduced by using brain landmarks and other transformations to adjust position, orientation, and size of an individual brain to match a reference standardized brain. This is done so to permit group analysis.

FSL’s FEAT

Oxford University’s Center for Functional MRI of the Brain’s (FMRIB) Expert Analysis

Tool (FEAT) is the main tool for most of the imaging analysis steps. Besides brain 109

extraction (which is performed with FSL’s BET), functional imaging analysis steps

ranging from preprocessing to higher-level analyses can all be performed in FEAT.

Objectives of the Study

The goal of this study was to compare the blood oxygenation-level dependent (BOLD) signals of the brain from fMRI scans of adult subjects with normal binocular vision as they perform tasks involving the various types of convergence eye movements (proximal, accommodative, disparity, voluntary). Brain areas activated in the performance of these eye movements were compared to the functional tasks to determine the areas involved in the neural control of the various vergence eye movements and the extent of overlaps among them.

Hypothesis

Several studies have found conflicting results regarding the correlations among accommodative, disparity and proximal vergences. The hypothesis that was explored in this study concerns whether the areas of the brain that are activated by proximal, disparity, accommodative and voluntary vergence eye movements among adults with normal binocular vision are the same. It was assumed that if one brain area was activated by the vergence components enumerated above, it would imply that there are overlaps in their neural control pathways. Thus, it was expected that area activated by accommodative convergence would overlap with those activated by disparity vergence due to the cross-link between accommodation and convergence. Likewise, it was

110 expected that proximal vergence and voluntary (gross) vergence would overlap due to both vergence types being driven volitionally.

Generally, it was expected that any significant correlations observed in the clinical correlations study in chapter 3 would translate into overlaps in the functional study.

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Chapter 5: Functional Imaging of Convergence Sub-Types

Methodology for Functional Imaging of Convergence Sub-types

The following sections describe the methods used in the study, including the study design, participant selection and testing procedures.

Study Design

A mixed-block fMRI design was used to investigate the brain areas that are involved in the various convergence eye movements. The block design is optimal for identifying

BOLD responses in functional imaging, and has been used in similar studies [23, 37, 99].

Subjects

Adult subjects (aged 18 years to 30 years inclusive) with normal binocular vision and visual acuity of 20/25 or better in each eye at distance and near, were involved in the study. Also, they had to be safe to scan in the 3-Tesla magnetic field scanner. Safety for

MRI scanning was done in accordance to the criteria determined by following the standard screening form given by the Center for Cognitive Behavioral Brain Imaging

(CCBBI) of the Ohio State University’s Department of Psychology. Each subject was pre-screened for MRI safety prior to eligibility testing for normal binocular vision. If they were eligible, each subject was screened again by the MRI Technologist of the CCBBI.

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Inclusion Criteria

To be included in the study, each potential subject had to meet all of the following criteria:

• Age 18 to 30 years

• Best corrected visual acuity of 20/25 or better in each eye tested with Snellen

chart at distance, and reduced Snellen chart at near.

• Heterophoria at near between 2∆ esophoria and 6∆ exophoria without more

than 6∆ difference between distance and near phorias;

• vergence range must satisfy Sheard’s criterion[100] and be at greater than 7∆

Base-In Break/5∆ Base-In recovery as well as 10∆ Base-Out Break/7∆ Base-

Out recovery

• Presence of normal binocular vision as determined during eligibility testing.

The criteria for normal binocular vision are listed in table 5 below.

• Willingness to wear glasses or contact lenses to correct refractive error, if

necessary

• No previous near addition lens or prism use, or more than two weeks of

therapy for any accommodation and/or vergence disorder

• Informed consent and willingness to participate in the study

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• Normal Values for Tests of Normal Binocular Vision Negative fusional vergence at near greater than 7∆ BI-break/5∆ BI-recovery[100] Positive fusional vergence at near greater than 10∆BO-break/7∆ BO-recovery[100] Near Point of Convergence closer than 6 cm break[2] Monocular amplitude of accommodation greater than [15 - 0.25 x (Age)][101] ≥500 seconds of arc on the global Randot Stereotest and 20 seconds of local stereopsis [100] Table 5: Normative values for normal binocular vision

Exclusion Criteria

The exclusion criteria for potential participants in this study include any one of the following:

• Presence of any systemic disease that is known to affect accommodation or

convergence eye movements

• Current use of any ocular or systemic medication that is known to affect

accommodation or convergence eye movements

• History of traumatic brain injury or neurological disease that affects eye

movements.

Subjects were also excluded if they met any of these criteria which make them

incompatible for fMRI scanning:

• Having any surgical or medical metallic implant, metallic debris or other

metal foreign bodies in or near the head, heart or spinal cord, or of uncertain

location

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• Presence of tattoos on the head or neck, tattoos not obtained from a licensed

artist

• Being pregnant or not being certain of pregnancy status.

• Being left handed.

• Failing to meet any of the requirements set out by the CCBBI for scan

compatibility.

Sample Size

A total of ten subjects participated in this study. Based on unpublished data from Kashou

and Fogt[102], this sample size was determined to be sufficient to identify any brain

areas activated during vergence eye movements using fMRI analysis. This is a typical

sample size for fMRI research due to the strength of statistical analysis from repeated

presentation of the stimulus during scanning.

Functional Imaging

The study used the 3 Tesla Siemens Magnetom Prisma magnetic scanner (Siemens

Medical Solutions, USA) which is available at the Center for Cognitive and Behavioral

Brain Imaging (CCBBI), The Ohio State University.

Enrollment

Subjects were recruited from staff and students at The Ohio State University. Emails and approved flyers were posted at various locations on the university campus and at the

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College of Optometry. Subjects who responded for the advertisements were pre-screened for safety for MRI by email. If they appeared MRI-safe, they were invited for eligibility testing. At the eligibility testing visit, they were screened with the full MRI safety screening form after signing the informed consent form. Following this, the eligibility testing was performed. Table 6 shows the Convergence Insufficiency Symptom

Survey[103] which was used as part of the screening for the presence of normal binocular vision.

Nev Infreque Som Fairly Alw Sign/Symptom er ntly etim often ays es 1. Do your eyes feel tired when reading or doing close work? 2. Do your eyes feel uncomfortable when reading or doing close work? 3. Do you have headaches when reading or doing close work? 4. Do you feel sleepy when reading or doing close work? 5. Do you lose concentration when reading or doing close work? 6. Do you have trouble remembering what you have read? 7. Do you have double vision when reading or doing close work? 8. Do you see the words move, jump, swim or appear to float on the page when reading or doing close work? 9. Do you feel like you read slowly?

10 Do your eyes ever hurt when reading or . doing close work? Table 6: The convergence insufficiency symptom survey Continued 116

Table 6 continued

11 Do your eyes ever feel sore when reading or . doing close work? 12 Do you feel a "pulling" feeling around your . eyes when reading or doing close work? 13 Do you notice the words blurring or coming . in and out of focus when reading or doing close work? 14 Do you lose your place while reading or . doing close work? 15 Do you have to re-read the same line of . words when reading? To obtain score, total the number of “X”s in each column Multiply by the column value x0 x1 x2 x3 x4 Sum 5 values

Table 7 below summarizes all the eligibility criteria subjects had to meet prior to undergoing functional imaging.

Yes No Age 18 to 30 years? Subject’s age Informed consent for study obtained? Best corrected VA of 20/25 or better in each eye at distance and near? Subject has worn appropriate refractive correction for at least 2 weeks? Subject is willing to continue to wear eyeglasses for reading and other near work as required? Subject is right-handed? Phoria at distance & near between 2Δ EP – 6Δ XP (no more than 6Δ difference in measures)? Negative fusional vergence at near (greater than 7Δ BI-break/ 5Δ BI- recovery)? Table 7: Summary of eligibility criteria for fMRI scanning Continued

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Table 7 continued Positive fusional vergence at near (greater than 10Δ BO-break/ 7Δ BO- recovery)? Adequate NPC of < 6cm break? Passed stereo acuity on Eligibility Exam Form – Part 1? Monocular amplitude of accommodation (greater than 15-0.25*age)? CI Symptom Survey score (mean) < 21? Subject has not used plus add or base-in prism at near for at least the past 2 weeks? Subject willing to discontinue plus add or base-in prism for duration of study? Subject had cycloplegic refraction within the past 3 months? Previous CI treatment (Any office- or home-based VT or more than 2 weeks of pencil push-ups)? Amblyopia (≥ two line difference in best correct VA between the two eyes)? Constant strabismus of history of strabismus surgery? Vertical heterophoria greater than 1∆? Anisometropia ≥ 2.0 D spherical equivalent? High refractive error? Prior refractive surgery? CI secondary to acquired brain injury? Diagnosed with multiple sclerosis, Graves thyroid disease, myasthenia gravis, diabetes, or Parkinson’s disease? Current use of any ocular/systemic medication known to affect accommodation or vergence? Any affirmative response on the Checklist for Neurological Symptoms? Manifest or latent nystagmus? Developmental disability, mental retardation, ADHD or learning disability that, in the investigators opinion, would interfere with treatment? Family or household member or sibling currently/previously enrolled in a VT program/study, an eye care professional, ophthalmic technician, ophthalmology/optometry student/resident? No access to a computer with internet to perform the computerized home therapy (HTS)? CLINICIAN: Difficult to gather data from subject? Based on above information, is subject eligible for study? Subject agrees to fMRI testing and passes screening questionnaire? Successfully completes eye tracking stimulus activity?

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Those who were eligible were next taken through eye tracking with Visagraph II eye

tracker (Compevo AB, Sweden).

This eye tracking served two purposes. First, it was used to obtain objective eye tracking

while subjects performed the same visual tasks they would consequently perform inside

the scanner. Of the five visual tasks performed (accommodative convergence, fusional

convergence, proximal convergence, voluntary vergence and vertical and horizontal

saccades), only vertical saccades were not recorded because the Visagraph II does not

have the capability to measure vertical eye movements. Thus, the eye tracking was used

to confirm that the subjects could perform the eye movements required in the study.

Secondly, the eye tracking exercise served to familiarize the subjects with the instructions

and the visual tasks to be performed during the fMRI scanning. Therefore, during the

scanning, all the subjects were familiar with the instructions and the visual tasks because

they had undergone the eye tracking. Also, the accommodative responses for each subject

for each visual task were recorded using the Grand Seiko WAM-5500 Autorefractor

(Japan). This also served to confirm that they could accommodate correctly to the

accommodative task.

Calibration of Eye Tracker and Autorefractor

Both the eye tracking and assessment of accommodative response were preceded by

calibration of the equipment. The Grand Seiko autorefractor was calibrated using the method described by the manufacturer. Using the special calibration eye that is provided

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with the equipment, the power of the eye is read and compared to the expected reading

provided (-4.50D for the calibration eye at a vertex distance of 12mm).

The Visagraph II eye tracker was calibrated using a Visagraph target modified for use on a monitor. The target consisted of a series of 5 dots which subtended a total of 20 degrees at the center of a 22-inch Dell monitor (screen resolution: 1920 (horizontal) x 1080

(vertical); screen refresh rate: 60 Hertz) at 74cm. The central dot of the calibration target subtended 0 degrees (straight ahead), with two dots to the right at 5 degrees and 10 degrees respectively from the central dot. Similarly, two dots subtending 5 and 10 degrees respectively existed at the left of central dot. Thus, the calibration target for the

Visagraph consisted of a series of five numbered dots at 10 degrees left, 5 degrees left, 0 degrees (center) and 5 degrees right and 10 degrees right from the center. These dots were respectively numbered 1, 2, 3, 4 and 5. The size of each dot subtended less than 1 minute.

The Visagraph II eye tracker was calibrated for each subject by first adjusting the interpupillary distance on the goggles. After that, the subject was told sequentially to fixate each dot in the order called out by the examiner. The examiner then called out the numbers 1, 2, 3, 4, 5, (forward sweep), then 4, 3, 2, 1. Three forward and backward sweeps were performed for each subject, and the traces examined to confirm that they were well calibrated for saccadic eye movements. The duration of fixation on a calibration dot was approximately the same. The subject could blink as needed during the whole calibration and eye tracking exercise.

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This baseline eye movement (saccade) recording was used as the baseline for quantifying

subsequent convergence eye movements. Therefore, knowing how much deflection a

five-degree saccadic eye movement shows on the calibration tracking, the magnitude of

other eye movements may be compared to this baseline to determine the magnitude of

eye movement.

Eligibility Assessment:

Assessment for eligibility based on binocular vision status was done by one examiner, using standard clinical procedures. The alignment, convergence and focusing

(accommodation) abilities of each participant were assessed to confirm their normal

binocular vision status.

Eligibility Testing Procedures

Ambient and overhead lighting was used to provide good illumination for all test procedures. Also, all testing was performed while the subjects wore their optical correction. Distance visual acuity was performed using a distance Snellen chart. The procedures used in the near tests are described below.

NPC (break and recovery): Using the Astron International Accommodative Rule and

printed Gulden fixation target (single column of letters of 20/30 equivalent at 40 cm), the

subject was instructed “to look at letters and report when they become double or break

into two but try to keep the target one/single as long as possible.” The target was moved

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at approximately 1 to 2 cm/s until the subject reported double vision or the examiner

observed a loss of fusion (one eye turned out). If diplopia was reported, the movement of the target was stopped, and the subject was asked, “Does it stay two or does it come back

into one?” If it came back into one within 1-2 seconds, the movement of the target

towards the patient was continued until the subject was unable to regain fusion. The fixation target was not held in place for longer than 2 seconds. If it stayed double, this endpoint (distance from this point to the center of the forehead) was recorded as the NPC break. If the examiner observed a loss of fusion (without a report of double), the point at which the examiner observed a loss of fusion was considered the NPC break. If the patient continued to converge until the target was against the nose/brow (i.e. break did

not occur), how closely the subject converged was measured and considered the NPC

break. The center of the forehead just above the level of the brow was used as the

starting point for measurement. After determining the NPC break, the patient was asked

to tell “when it comes back together into one.” The target was then slowly moved away

(also at approximately 1 to 2 cm/s) from the subject until the subject reported single

vision or a recovery of fusion was observed. This was considered the NPC recovery.

The NPC break and recovery were each measured 3 times, waiting at least 10 seconds between measurements. The responses are rounded to the nearest 0.5cm, and the average of these three values was recorded as the NPC break and recovery points.

Accommodative Amplitude: The subject’s left eye was occluded. The Astron

Accommodative Rule (with the printed Gulden fixation target consisting of a column of

20/30 letters at 40 cm) was held gently with edge of rule placed above the subject’s right 122

eye just above the level of the brow. Measurement was begun with the target placed at

the 40cm mark on the rule. The subject was instructed to, “Tell me when the letters first

start to blur, but try to keep the letters clear as long as possible.” The target was then

slowly moved toward the subject at approximately 1 to 2 cm/sec until subject reported

first blur. When first blur was reported, the subject was asked if the letters stayed blurry

or became clear. If target became clear, the examiner continued moving the target closer

until it was blurred, stopping at the “first sustained blur.” The test ended when “first

sustained blur” is reported. The measurement was then taken to the nearest one-half

centimeter (using forehead just above the level of the subject’s brow as the zero point for

measurement).

Accommodative Facility: After occluding the left eye, the subject was asked to view the

Gulden fixation target (single column of letters of 20/30 equivalent at 40 cm). The plus

side of +/- 2.00 lens flipper was placed before subject’s right eye, and the subject was

asked to try to make letters clear as quickly as possible. The instruction to the subject was

to say “clear” as soon as the letters were clear. When letters were reported to be clear, the

examiner quickly flipped the flipper to the minus side, again instructing subject to read

letters and report when clear. The examiner started timing as the plus side of the flipper lens was placed in front of the subject’s eye. The examiner continued to alternate sides of flipper lenses for 1 minute, while counting the number of “cycles” of the lens. A cycle

was counted when the subject reported “clear” for the both the plus and minus sides of

the flipper.

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Cover Test: After selecting an isolated 20/30 letter at 6m, the subject was instructed to

fixate the letter and to "keep it clear" throughout testing. First, the examiner quickly

covered the right eye, looking for movement in the left eye as soon as the right eye was

covered. This was repeated 5 times. Any movement in the left eye when the right eye was

covered indicated the presence of heterotropia in the left eye. Then the left eye was

quickly covered while looking for movement in the right eye as soon as the left eye was

covered to look for heterotropia in the right eye. If there was heterotropia in either eye,

the subject would be ineligible for the study. After the presence of heterotropia was ruled

out, the amount of heterophoria present was determined. The right eye was covered for at

least 2 seconds, and the cover quickly moved to the left eye, at the same time looking for

movement in the just-uncovered eye (in this case, the right eye). The left eye was also

covered for at least 2 seconds, and the occluder quickly transferred to the right eye, while

looking for movement in the just-uncovered left eye. This alternate cover and uncover

was repeated at least 5 times with instruction to the subject to keep the fixation target

clear at all times. Any movement in the just-uncovered eye indicated the presence of a heterophoria. If any movement was observed, a prism was used to neutralize it. For instance, if the just-uncovered eye was observed to move towards the nose, a Base-In

prism was used to neutralize the movement. The highest prism power that resulted in

neutrality before reversal of movement was recorded as the high neutral value on

alternate cover testing. This was repeated with an isolated 20/30 letter at 40 cm for near

heterophoria. If no movement was apparent on the alternate cover test, a 4 prism diopter

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prism was placed Base-In and Base-Out before an eye. If these two positions gave equal opposite movements, orthophoria was recorded.

Vergence Range Testing: A horizontal prism bar (Gulden B-16 horizontal prism bar

levels from 1 to 45) and a hand-held fixation target (Gulden Fixation Stick #15302)

with a single column of letters of 20/30 equivalent held at 40cm were used. The subject was instructed to “tell me when the letters become blurred or become double (split into

2), but try to keep the target single as long as possible.” The examiner increased the

prism at ~ 2 per second, pausing at each prism to confirm that the target was “single

and clear.” If the subject reported blur, the examiner paused and noted the prism amount then continued to increase prism pausing at each prism power to confirm that the target was “single.” (If no blur was reported, “X” was recorded.) When the subject reported

double or break, he was asked subject, “Does it stay two or does it come back into one?”

More prism was introduced if the subject recovered single vision. When subject could no

longer maintain single vision and had diplopia, the prism amount was noted and record as

the “break.” After the subject reported diplopia, the prism was increased by 5∆, and then

reduced at a rate of about 2∆/second, until the subject reported single vision. This second

value was considered the “recovery” finding. If recovery finding was higher than the

break, the examiner repeated the entire measurement (blur, break and recovery).

Negative Fusional Vergence at Near (Base-In Vergence): The prism bar was held in in a

base-in direction, and the process described above for vergence range testing was

followed to measure the blur, break and recovery points for each subject.

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Positive Fusional Vergence at near (base-out vergence): Hold the prism bar in a base-out

direction, and follow the process described above for vergence range testing.

The test began with the Base-In vergence testing followed by 3 measures of Base-Out

vergence testing with at least 30 sec. between tests.

Eye Tracking

Following calibration of the eye tracker and autorefractor, each subject performed the eye

movements using the same program that would be subsequently used during imaging.

Only the vertical saccadic condition was not tracked because the Visagraph II is not

capable of vertical eye movement tracking. The eye tracking data could allow for future quantitative comparisons between the eye movement parameters and the amplitude/extent of activated areas measured with fMRI. However, its current use was to familiarize subjects with the stimuli and ensure that the task could be carried out before they were scanned. In addition, accommodative responses were measured to confirm that they accommodated appropriately for each task.

The duration of each task was shorter than its duration during imaging. Where necessary, a sequence was performed as many times as needed to ensure that enough eye tracking and autorefraction data had been obtained. Each eye tracking and autorefraction session lasted about half an hour. The eye tracking and autorefraction sessions were performed only after the brain imaging had been scheduled and confirmed. This ensured that only a few days elapsed after eye tracking so subjects would not forget the tasks and instructions.

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FMRI Scanning

Eligible subjects underwent fMRI scan while performing the following four types of convergence:

• Disparity vergence condition with a random dot stereogram target presenting stepped

convergence demands of 0, 4, 8, 12, 16, and 20 prism diopters.

• Accommodative convergence with disparity eliminated by alternately viewing red or

blue 20/30 text monocularly on a screen through red/blue filters. The text was

displayed at distance with a minus lens placed in front of one eye. Proximal cues were

left unchanged by keeping the distance text target at the same distance in the

accommodation-active and accommodative-inactive conditions.

• Proximal vergence with blur cues eliminated using a DOG target on the Wesson

Fixation Disparity Card[104]. The DOG target works by eliminating the ability to

detect blur[105] using reflex accommodation, thereby opening the accommodation

loop. Therefore, with one eye covered, fixating the DOG target is assumed to utilize

proximal cues to vergence.

One DOG target was fixated at distance and another at near. The subject was cued to

fixate the far or near DOG target with one eye covered to eliminate disparity cues.

• Voluntary vergence was stimulated with visual and auditory cues for the subjects to

voluntarily “cross the eyes”.

In addition, there were two saccadic conditions (horizontal and vertical) conditions.

These were designed to determine the activation due to any small saccadic eye

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movements that may have occurred with the accommodative convergence and proximal

convergence conditions respectively. For the accommodative convergence condition,

minus 2.00 diopter lens placed in front of one eye to stimulate accommodation in that

eye, hence accommodative convergence (the Müller’s paradigm). However, because the

minus lens was in front of one eye, there appeared to be a perceptual shift in the position

of the letters on the monitor as they were switched from one eye to the other. The amount

of this perceived shift in letter position was estimated to be about 4 to 5 degrees. As such,

a horizontal saccadic eye movement condition was designed to simulate about 4 to 5

degrees of saccadic eye movement. This was to be compared with the accommodative convergence condition to determine if some of the activation observed was due saccadic eye movement. Subsequent analysis of the pilot data showed that these perceived saccades did not result in any meaningful activation for the accommodative convergence condition.

Similarly, the proximal convergence condition had a vertical saccadic component; the distance DOG target on the monitor was in a central location from the subject’s viewpoint. The near DOG target was placed in such a way that it was reflected in the upper part of the viewing mirror on the head coil. This superior placement was to prevent it from obscuring the view of the distance DOG target on the monitor which was also viewed through the mirror. As such, a subject in the scanner needed to raise the eyes to view the near target from the monitor. Conversely, the subject had to lower the eyes to look on the distance DOG target from the near. The level of vertical movement was

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estimated and simulated in the vertical saccade paradigm to determine the level of

contamination of the proximal BOLD signals from the vertical saccadic eye movement.

Therefore, the saccadic conditions were of the same aspects (duration, presence of auditory tone, and sequences) as the respective accommodative and saccadic conditions.

The same scan sequence was used on every subject. The sequence used and the parameters of the scan were standardized in the scanner. A standardized field of view was used, but when necessary, it was oriented to be able to image the whole cerebellum.

Therefore, the CCBBI’s technologist controlled the scanner while the researcher administered the stimulus on a separate stimulus computer. These two computers are synchronized such that, the scanner computer takes input from, and controls the stimulus computer. As such, when the stimulus is started on the stimulus computer, the stimulus can only run after the scanner computer is ready and starts it. This makes it easy and efficient for the researcher and technologist to work together for every scan.

Each functional scan was preceded by a high resolution (1mm x 1mm) T1-weighted anatomical scan. This anatomical scan was used to acquire structural images of the brain of each subject that was subsequently co-registered to the functional scan to locate areas of BOLD activation. As a standard, these structural images were also read by certified radiologist for any abnormality.

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Order of Scanning

The order for scanning sequence was as follows:

Each functional scan began with the accommodative convergence run. This is because it

was necessary to have a lens taped in front of one eye, and this lens had to be removed

after the accommodative functional scan. The lens was taped on the red-blue filter glasses

in front of the left eye before the subject entered the scanner.

After the accommodative convergence scan, the subject was quickly brought out of the

scanner, and while still lying still without any shift in position, the following processes

took place:

• The near DOG target which had been previously taped to the back of the

mirror in the head coil was flipped to position in front of the eyes. This placed

it in a position where it was reflected in the upper part of the mirror so that the

subject could look back and forth between the distance DOG target on the

monitor (74cm away) and near DOG target reflected in the upper part of the

mirror (10cm from the center of the forehead).

• The minus lens which was taped in front of the left eye for the accommodative

vergence paradigm had to be removed. This was followed by taping a piece of

opaque paper over the left eye by the experimenter.

These preparations were necessary to prepare for the proximal convergence scan.

The technologist removed the head coil and flipped the near DOG in place, and the

researcher removed the taped lens and covered the left eye. After these changes, the

subject was put back in the scanner to begin the next functional scan. This had been

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rehearsed several times to ensure it would be the same. It took about one and half minutes

in total to do this.

The second functional scan to be performed was always the proximal convergence scan.

This scan was performed with the lights inside the scanner switched on to make the near

DOG target visible to the subject. The proximal convergence scan was the only run

which had lights on in the scanner.

After the proximal convergence scan, the subject would again be taken out. The technologist would remove the head coil and flip the near DOG target out from in front of

the mirror. The researcher removed the cover from the left eye. Again, the subject was

reminded to remain completely still without changing the position of the head.

After these, the subject was never taken out of the scanner again. All these activities had

been recorded in the scan set up to serve as a reminder so that they would be performed

the same way for each subject. After each time the subject was taken out and put back in

the scanner, the scanner readjusts (it runs the localizer and shims the magnetic field)

itself.

These were followed by horizontal saccades run, disparity convergence run, then vertical

saccades run, voluntary convergence run and a modified rest (where the subject just lay

still and awake without performing any activity).

The functional scan took 24 minutes in total for all the visual tasks. The localizer run,

structural scans and readjustments after taking the subjects out all added to this during to

make a total of about 50 minutes of scan time for each subject.

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Details of Conditions for Functional Scanning

A description of the stimulus used for each condition and its duration are given below:

Accommodative Vergence Condition: Wearing Red/Blue glasses, subject alternately read

red letters displayed on the monitor (seen with right eye, 0.00D accommodative demand) and blue letters displayed on the monitor through -2D lens with left eye (2.00D accommodative demand). Viewing the blue letters through the -2.00D lens was the accommodation-active condition which required 3.35D of accommodation (2.00 diopters from the -2D lens, 1.35D from viewing 20/30 text at 74cm). On the other hand, viewing the red letters was the accommodation-inactive condition (required 1.35D of accommodation due to the 74cm distance).

The accommodation run began with the accommodation-inactive condition of 7.5

seconds duration, followed by 5 seconds of accommodation-active condition. This

alternation between accommodation-inactive and accommodation-active conditions was repeated 14 times for a total of 197 seconds. The accommodative vergence paradigm used in the study is shown in figure 22 below.

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Figure 22: Description of accommodative vergence stimulus and paradigm The stimuli are shown in a to d. In a), the subject wears red-blue paper glasses, with red over right eye, and a -2.00D spherical lens taped over the left eye (over blue glass). b) Visual stimulus comprises red and blue letters of approximately 20/30 acuity size. c). Appearance of visual stimulus in b) through the red-blue glasses when only red letters appear. d) Appearance of the visual stimulus in b) through blue glasses with lens taped. Only the left eye sees targets, and the target is viewed through a -2.00D lens. Therefore, being able to see the blue letters clearly constitutes the Accommodative-on condition. Viewing the red letters is the accommodative-off vergence condition. e) The paradigm begins and ends with a 10-second black screen. The visual targets (20/30 letter size) are alternatively seen through either red or blue, each color lasts for 5 seconds.

Proximal Vergence Condition: The proximal vergence condition was comprised of

alternatively fixating two Difference of Gaussian (DOG) targets, one on the monitor

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screen (distance from eye: 74cm, screen resolution: 1280x1024 resolution, image height:

24cm), and a near one fixed on the mirror (size 8cmx 6cm, distance from eye: 8cm). The

left eye of the subject was covered to open the disparity vergence loop. When prompted

by an auditory tone (the tone signaled the time to change fixation from distance DOG

target to near, or vice versa), the subject looked from a DOG target on a monitor at

distance to a DOG target at near (the near DOG target was taped so that it is reflected in

the upper part of the mirror on the head coil). The signal to look from distance to near

was accompanied by the disappearance of the DOG target on the monitor. As such, the

only visible target would be the near DOG target. Another auditory tone signaled the

subject to look back at the distance DOG target which reappeared on the monitor.

Fixation on the DOG target on the monitor was the proximal-inactive condition, while fixating on the near DOG target on the head coil was the proximal-active condition. This was repeated 14 times for each subject, for a total of 197 seconds. The proximal vergence

paradigm is shown in figure 23 below.

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Figure 23: Description of proximal convergence stimulus and paradigm With one eye covered, a subject views Difference of Gaussian (DOG) targets either on the monitor at distance (proximal-OFF condition), or fixed to the scanner’s head coil at near (10cm from spectacle plane). The subject views the distance DOG target for 5 seconds. It is replaced by a black screen on the monitor, and also accompanied by an auditory tone (beep, shown here by dashed, vertical lines on the top of the figure). The black screen on the monitor and beep cue the subject to look at the near DOG target, also for 5 seconds. Another beep, and the reappearance of the DOG target on the distance monitor signal the subject to look on the distance DOG target. Each trial begins and ends with a 10-second black screen.

Disparity Vergence Condition (Random Dot Stereogram): The disparity vergence

stimulus was comprised of a random dot stereogram (RDS) of 500 seconds of arc

disparity on a black background created with visual basic. When viewed with the MRI-

safe red-blue glasses, the RDS stimulus presented stepped convergence demands of 0 to

20 prism diopters in 4 prism diopter steps on the monitor (at 74cm). The 0 prism diopter 135 condition presented the RDS with 0 vergence demand on the monitor (at 74cm), and acted as the disparity-off condition. The subject was instructed to try and fuse the two random dot squares so that a smaller square in the center was seen in depth (the small square appeared closer when converged). The disparities were such that, they could only be fused with convergence (i.e., they were crossed). This ensured that subjects did not diverge their eyes to fuse them.

Each convergence demand lasted 5 seconds, after which a black screen was presented for

7.5 seconds (rest). This was repeated 7 times for a total of 282.5 seconds. The disparity vergence paradigm is shown in figure 24 below. Examples of the RDS stimuli are shown in figures 25 and 26.

Figure 24: Stimulus and paradigm for disparity vergence

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Wearing red-blue glasses (red over right eye), a subject views red-blue random dot stereograms from 0 to 20 prism diopters in 4 prism diopter steps. Each disparity level (0, 4, 8, 12, 16 and 20 prism diopters) is viewed and kept single for 5 seconds. Each step is immediately followed by a black computer screen of 5 seconds duration. The trial begins and ends with 10 seconds of black screen. The 0 prism diopters is the disparity-off condition and the rest are disparity-on.

Figure 25: Random dot target with zero prism diopters of vergence demand

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Figure 26: Random dot stereogram with vergence demand When fused through the red-blue glasses, they appear like the one in the figure 25 above.

Voluntary/Gross Vergence Condition: The voluntary convergence stimulus consisted of a purple vertical line (500 pixels long, 50 pixels wide) on a black screen. The color ensured that both eyes could see it at the same time. This line was the voluntary-off condition.

The voluntary vergence condition required subjects to voluntarily converge their eyes and keep them converged for 5 seconds at an auditory tone. The subjects began by looking at the large vertical line on the monitor (dimensions 500 pixels high, 50 pixels wide;

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voluntary-inactive condition) for 7.5 seconds. This was followed by the auditory tone and

the disappearance of the line from the monitor to be replaced by a black screen. This

auditory tone and disappearance of the line signaled the subject to cross their eyes and

keep them crossed until he/she heard the auditory tone (at which time the line would reappear on the monitor). Looking at the line on the monitor was the voluntary-off

condition while voluntarily converging and holding the convergence was the voluntary-

on condition. This was repeated 14 times for a total of 197.5 seconds. The

gross/voluntary vergence paradigm is shown in figure 27 below.

Figure 27: Description of the stimulus and paradigm for voluntary vergence The trial begins and ends with a black screen of 10 seconds duration. A purple line (seen by both eyes through the red-blue glasses) on the monitor serves as the voluntary/proximal-OFF condition. Voluntary-ON condition is the disappearance of the purple line and a beep (beep is shown as dashed, vertical lines); it is the subject’s cue to voluntarily cross their eyes. Each (voluntary-OFF or voluntary-ON) condition lasts 5 seconds.

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Vertical Saccades Condition: The voluntary saccade condition was similar to the

proximal convergence condition in terms of presentation duration, sequence and auditory

tone. Non-accommodative, single dots appeared either at the top or bottom of the

monitor. Looking from one to the other required one to a vertical saccade of 46 degrees,

similar to how much the subjects had to raise or lower their eyes to look between the distance and near DOG targets in the proximal run. Also similar to the proximal run, the appearance of the dot on the lower part of the monitor (and simultaneous disappearance of the dot from the upper part of the monitor) was accompanied by an auditory tone. This was repeated 14 times for a total of 197.5 seconds.

Horizontal Saccades Condition: In terms of duration and sequence, the horizontal

saccadic run was similar to the accommodative convergence condition. Subjects looked

at non-accommodative dots either at the center of the monitor (saccade-off condition), or

5 degrees to the left or right (saccade-on condition) of the central dot. To decrease the

chance of predictive saccades from knowing where the next dot would appear, the order

of appearance of the left or right dot from the center was randomized. Subjects therefore

just had to follow the dots with their eyes. The central dot appeared for 7.5 seconds while

each of the left or right dots appeared for 5 seconds. This was repeated 14 times for a

total of 197.5 seconds duration.

Order of Conditions during Scanning

The accommodative and proximal conditions require that the subjects are brought out of

the scanner; for the minus lens to be removed after the accommodative condition and 140

prior to the proximal condition, and for the near DOG target to be positioned and

removed. As such, the accommodative vergence condition was scanned first, followed by

the proximal vergence condition.

Localizing & Structural Scans

Prior to experimental scanning, a localizing scan and structural magnetization-prepared rapid gradient-echo (MPRAGE) scan were performed on each subject.

Each of the conditions began with the text of the instructions displayed on the monitor for the subject to read. This was verbally repeated by the researcher through the two-channel

audio interface of the scanner. Once the subject indicated that he/she understood what

was next required, the stimulus was started on the stimulus computer. A black screen

would follow, awaiting a trigger from the scanner to begin scanning.

Each run began with 5 seconds of black screen. It also ended with 7.5 seconds of black

screen.

Scanning Parameters

The scanner used in this study was a 3 Tesla Siemens Magnetom Prisma.

1. Head coil: 20 channel head-neck matrix coil

2. Repetition time (TR): 2500 milliseconds

3. Echo time (TE): 28 milliseconds

4. Voxel resolution: 3 mm isotropic

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5. Image display: rear-projected screen with resolution 1280 (horizontal) x 1024

(vertical) at 74cm, display height: 24cm.

Duration of Time between Eligibility and Functional MRI Scan

The time between eligibility testing and functional scanning ranged from 1 day to 3

weeks (eligibility testing was repeated and confirmed for 2 subjects due to exceeding this time period). Both subjects were still eligible after eligibility retesting. The eye tracking

was always done either one or two days prior to scanning.

Outcome Measures

The primary outcome measure is the BOLD response in the regions of interest for the

four conditions enumerated above.

Pre-Processing and Image Preparation

The fMRI software library (FSL), version 5.0.10 was used to analyze the structural and

functional data. The FSL package has a suite of tools, including FMRIB’s (Oxford

University’s Center for Functional MRI of the Brain) An Expert Analysis Tool (FEAT),

which is used for the functional data analyses, and FSLView[106] for viewing the images

and analysis results. Prior to analyzing the data in FEAT, it was necessary to remove the

skulls from the structural images using the FSL tool called Brain Extraction Tool (BET).

This forms part of the process of converting individual images into standardized shapes,

sizes and coordinates.

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Also, the output from the Siemens Magnetom Prisma magnetic scanner is in the Digital

Imaging and Communication (DICOM)[107] format. However, the FSL analysis tools

use the Neuroimaging Informatics Technology Institute (NIfTI)[108] format. Unlike the

DICOM format, NIfTI is compatible to most fMRI analysis software packages, including

FSL. Thus, the NIfTI format facilitates the inter-operation of functional MRI data software packages. Therefore, prior to viewing and analyzing the data in FSL, the software DICM2NII[109] (Mathworks, Natick, MA, USA) was used to convert the native

DICOM format to NIFTI in Matlab.

Data Analysis

An α-level of 0.05 corresponding to Z score of 2.3 was used to indicate statistical significance. These thresholds were specified to find clusters of voxels with significant intensities.

The following sections present the details of the analysis pipeline in FSL’s FEAT:

First Level Analysis

• Miscellaneous: All were left at default

o Brain/background threshold: 10%

o Noise level: 0.66%

o Temporal smoothness: 0.34

o Z threshold: 5.3

• Pre-Stats:

o Motion correction: MCFLIRT

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o Slice time correction: None (default)

o Spatial smoothing FWHM (mm): 5 (default)

o Temporal filtering: High pass

• Post-stats

o Thresholding: cluster

o Z threshold: 2.3

o Cluster P threshold: 0.05

o Contrast masking: default

o Rendering: use actual Z min/max

• Registration:

o Select main structural images: Linear (normal search, BBR)

o Standard space: MNI152_T1_2mm_brain

o Linear: Normal search, 12 degrees of freedom

o Non-linear, warp resolution -10mm

• Data:

o Delete 2 volumes

o High pass filter cutoff(s): 100

• Full model

o EVs: . Basic shape: Custom (3 column format)

. Convolution: Double-Gamma HRG

. Add temporal derivative; Apply temporal filtering

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o Contrasts & F-tests: Condition-On –Condition-Off; Condition-Off- Condition-On

• Group (Higher Level) Analyses: Mixed effects modelling

• Miscellaneous: Brain/background threshold: 10%

• Data:

o Inputs are lower-level FEAT directories/ lower level COPES

• EVs: Contrasts – 1;(group mean)

• Model setup wizard: single group average

• General linear model:

o Number of EVs: 1

o Number of additional, voxel-dependent EVs:0

o Input 1-10; Group:1; EV1: each has a weight of 1.0

Identifying Areas of Significant Activation

Two different search strategies were used to identify the names of the areas of significant activation for the various conditions following the group-level analysis with FSL. These two strategies are described below:

Talairach Client: The first strategy used Talairach Client[106] (version 2.4.3; Research

Imaging Institute, University of Texas Health Science Center San Antonio, TX, USA).

The Talairach Client is used to identify labels for user-defined coordinates in X, Y, Z

format. The X, Y, Z coordinates used entered in Talairach Client are based on the

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Talairach-Tournoux stereotaxic coordinate system[110] which defines each brain location in terms of three orthogonal axes (X, Y, Z).

In this strategy, the coordinates obtained from the analyses (group-level, mean activations

from all 10 subjects) were entered into Talairach Client. Three possible search strategies

can be used in Talairach Client:

• Single point search: For the first search strategy, Talairach Client produces the

name of the brain area that has the specific coordinates entered. If no specific

name exists, it produces a blank.

• Structures within a user-defined distance: In this approach, Talairach Client can

search for structures within a distance in millimeters from a particular user-

defined coordinate. In this study, when necessary, structures within

1mmx1mmx1mm cube distance from the coordinate were searched if the single

point search described above returned no results. If multiple areas exist within a

1mm cubed distance from the coordinate, they are all produced with the number

of times each produces a hit. Such multiple hits were all reported in this study

• Nearest gray matter search: In addition to these, a search can be performed to

request the nearest gray matter to the coordinates entered. In this situation, the

nearest gray matter is returned with the dimensions in mm from the coordinates.

In this case, the distance from the coordinate is not user-defined. Talairach Client

begins by searching for the gray matter within the shortest distance in millimeters

from the coordinate and expanding outwards if nothing is found. The maximum

searchable area is within 5mmx5mmx5mm from the coordinate entered.

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The sequence of steps used when the Talairach Client was used in this analysis followed this process described below:

First the single point search strategy was conducted. If that resulted in a hit (i.e., the

Talairach client returned a name), that would be the end of the search for that particular co-ordinate. However, if nothing was returned by Talairach Client using a single point search, the next step was followed.

Next, if the single point search strategy described above did not return any name for the particular coordinate, the search was expanded to identify areas within a

1mmx1mmx1mm cube of the coordinate. If the search for a structure within 1mm of the coordinate returned a name, that would be the end of the search. If multiple areas were returned, they were all listed.

However, if this second search strategy also returned no names, the third process was used, which searched for the nearest grey matter within 5mmx5mmx5mm of the coordinate entered. Any results were returned with the list of all identified areas with the number of times they were identified, as well as the distance in millimeters any particular structure identified was from the coordinate entered. All these are listed. This process is summarized in figure 28 below.

There was only one instance among all the vergence conditions that no results were returned from all three search strategies described above. The details of that coordinate are provided later in this report.

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Figure 28: Flow chart of process for identifying structures from coordinates

Using Activation Maps in FSLView: The second approach used FSLView[111] 4.0.1, an image-visualization tool in the FSL analysis package. FSL has some in-built atlases of the standardized, normalized human brain which can be used to determine the names of areas. When an activated area in an image is clicked, the coordinates of that area are

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shown, and based on selected atlases, the possible names are shown based on

probabilities. That is, the probability for the highlighted voxel being a number of brain

areas is presented. The most probable name is presented first, followed by the next most

probable. In other words, FSLView can be used to identify the X, Y, Z coordinates for an

area of the brain. Once those coordinates are found, an atlas that accompanies the FSL

package (such as the Talairach-Tournoux used in Talairach Client) is used to identify the

name of that coordinate.

A number of atlases are included in FSL, including the Harvard-Oxford Cortical

Structural Atlas, Harvard-Oxford Cortical Structural Atlas (Lateralized), MNI’s

stereotaxic atlas, etc. Each atlas has a certain level of detail. For instance, the Harvard-

Oxford atlas is only able to tell which cortical lobe a particular coordinate is located; it

does not tell what structure in the lobe the coordinate is. On the other hand, the lateralized

Harvard-Oxford Cortical Structural Atlas (Lateralized) is be able to determine whether

that coordinate is located in the right or left hemisphere in addition to determining in

which lobe the coordinate is located. In this approach, the Talairach-Tournoux stereotaxic

atlas of an MNI152 2mm standardized brain [111] was used. This is because the MNI152

2mm atlas provided the most comprehensive details of the areas of the brain. Among the atlases in FSL, it was the only one that could tell whether an area is part of gray or white

matter, in which cortical area it is located, whether it is in the right or left hemisphere,

and its Brodmann Area name, if there is an associated Brodmann area. That atlas is what is used in Talairach Client described above.

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Using this second approach, additional areas of activation were determined in addition to those identified from the coordinates of the clusters derived from the level two analyses.

Thus, this second search approach was used as a supplement to the cluster coordinate approach described in the search with the Talairach Client above. In addition to that, the feasibility of the second approach made it suitable for determining more overlaps among the activated areas in the various stimulus conditions.

Determining Overlaps in Activated Areas among Different Stimulus Conditions

Two complementary strategies were used to identify overlaps between pairs of paradigms. The first approach was based on the names of the areas identified from

Talairach Client using the coordinates from the respective group analyses of the paradigms. The names were compared to see which structures appeared in more than one vergence paradigm. This approach used only the coordinates that had named structures in

Talairach Client. However, as indicated earlier, not all the coordinates had names. Those coordinates which did not have names were first used to identify structures within 1mm of the coordinates. If no structure was identified within 1mm, then the nearest gray matter within a 5mm distance, and the distance from the indicated coordinate was searched in

Talairach Client.

The second search approach described above was also modified to determine the names of the brain areas which were activated by pairs of stimulus conditions. To do this, the images for the two conditions being compared were first overlaid on top of the other.

Each condition had a different color maps, making it easier to identify the overlaps in

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activated areas. For instance, if the group activation image for the accommodative

convergence condition has activated brain areas colored red, and the disparity

convergence image is colored blue, the overlap between the two images comes can be

easily identified by color, when the transparency of the image on top is increased to make

the one below visible. The names of color-rendered overlaps were then determined using the Talairach-Tournoux stereotaxic atlas in FSLView.

The names of areas of overlaps identified this way are shown in FSLView in MNI152 atlas and coordinates.

Results

Eighteen subjects were screened for the study; 10 subjects, aged 21 to 29 years (mean age=24.4 years, standard deviation=2.41), were eligible and participated in the study. Six were female. The findings from binocular vision testing for the 10 subjects is presented in

table 6 below. The study took place from April 2017 to January 2018. Table 8 below

presents the distribution of baseline scores for the participants.

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Factor Mean Standard (n=10) Deviation Age in years 24.4 2.41 CISS Score 4.90 2.23 Distance Phoria (prism diopters) -0.8* 1.69 Near Phoria (prism diopters) -4.0* 2.31 Vertical Phoria (prism diopters) 0.1** 0.21 Global Randot Test (seconds of arc) 250 0 Local Randot Test (seconds of arc) 20 0 Base-In to Blur (prism diopters) 9.6 5.48 Base-In to Break (prism diopters) 16.9 4.33 Base-In to Recovery (prism diopters) 14 3.27 Base-Out to Blur (prism diopters) 16.6 12.79 Base-Out to Break (prism diopters) 41.6 8.96 Base-Out to Recovery (prism diopters) 35.9 8.72 NPC Break (cm) 3.32 0.98 NPC Recovery (cm) 3.95 1.19 Amplitude of Accommodation (OD, cm) 9.95 0.83 Accommodative Facility (OD, in cycles per minute) 13.65 3.90 Vergence Facility (in cycles per minute) 18 1.75 Right Eye Correction (diopters) -3.58 1.96 Left Eye Correction (diopters) -3.54 1.54 * Negative value indicates exophoria ** Positive value indicates right hypophoria Table 8: Distribution of baseline eligibility testing results of subjects

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Areas of Activation by Various Convergence Conditions

The areas of the brain that were significantly activated (p<0.05) in each of the convergence conditions are described below. For each condition, the number of clusters that had significant activation based on the threshold of Z=2.3 (p<0.05) is identified. In addition to these, the coordinates for the individual voxels within each of these clusters, as well as the names of the brain areas for those coordinates is indicated. Furthermore, the main lobes of the cerebral cortex within which the identified coordinates are indicated.

Accommodative Convergence: The accommodative convergence paradigm activated brain areas mainly in the occipital, parietal and frontal lobes in four clusters. The first cluster was made up of 235 voxels in the parieto-frontal cortical area. The second cluster was up of 466 voxels in the middle occipital and cuneal area of the occipital lobe. The third cluster comprised 473 voxels in the occipito-parietal area of the cerebral cortex.

Finally, the fourth cluster of activation by the accommodative vergence paradigm was made up of 878 voxels in the parieto-frontal cerebral area.

Figure 29 below shows details of these clusters, Talairach-Tournoux coordinates and the levels of significance of activation.

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Figure 29: Details of the clusters from group analysis of accommodative convergence

Therefore, the accommodative vergence paradigm appears to have activated an occipito- parieto-frontal network of neurons.

In addition, the activated areas in the accommodative convergence condition were all in

the superior posterior pole with equal activation of the right and left cerebral cortices.

In summary, the accommodative vergence paradigm activated the middle occipital gyrus

and cuneus (Brodmann Areas 18 and 19) in the right and left occipital lobes. It also

154 activated the postcentral gyrus, the precuneus (Brodmann Area 19) and superior parietal lobule in the parietal lobes. Finally, the accommodative vergence paradigm activated the precentral gyrus (Brodmann Area 4) in the frontal lobes.

Names of Activated Coordinates in Talairach Client: Table 9 below shows the coordinates of the areas of activation obtained from the group analysis of the accommodative vergence paradigm and their respective names based on a single point search with Talairach Client.

Name of Area Coordinate X Y Z -62 -18 40 Precentral gyrus in left frontal lobe -60 -16 34 Postcentral gyrus in left frontal lobe 52 -12 38 Grey matter in precentral gyrus of right frontal lobe (BA 4) 58 -16 34 Grey matter in precentral gyrus of right frontal lobe (BA 4) 58 -18 42 Grey matter in precentral gyrus of right frontal lobe (BA 4) -58 -22 20 White matter in postcentral gyrus in left parietal lobe -60 -52 30 Grey matter in postcentral gyrus in left parietal lobe -6 -82 40 Grey matter in precuneus of left parietal lobe (BA 19)

30 -50 62 White matter in superior parietal lobule of right parietal lobe 52 -28 48 White matter in postcentral gyrus in right parietal lobe Table 9: Coordinates and names of brain areas activated by the accommodative vergence paradigm based on single point search using Talairach Client Continued

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Table 9 continued

24 -94 10 Grey matter in middle occipital gyrus in right occipital lobe

16 -88 34 Grey matter in cuneus of right occipital lobe (BA 19)

12 -92 28 Grey matter in cuneus of right occipital lobe (BA 19)

26 -86 18 White matter middle occipital gyrus in right occipital lobe

-16 -76 22 Grey matter in cuneus of left occipital lobe (BA 18)

-16 -78 26 Grey matter in cuneus of left occipital lobe (BA 18)

Coordinates without Names in Talairach Client: However, not all the coordinates from the cluster-based group analysis of the accommodative vergence showed names. For those coordinates, Talairach Client was used to search for brain areas within 1mm by

1mm by 1mm area of such coordinates. The names of the brain structures identified using this search approach are shown in table 10 below.

Within 1mm off: Coordinate X Y Z 16 -86 38 Cuneus of right occipital lobe (BA 19); White matter in cuneus of right occipital lobe -4 -80 46 Precuneus of left parietal lobe 58 -16 34 Grey matter in precentral gyrus of right frontal lobe (BA 4) Table 10: Coordinates and names of brain structures within 1mm by 1mm by 1mm of the respective coordinates

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Besides these, there were also a number of coordinates from the cluster-based group analysis of the accommodative vergence paradigm that neither had names of structures in

Talairach Client nor were within 1mm of any structures. For such coordinates, the nearest gray matter structure was searched in Talairach Client.

The nearest grey matter areas and their distances from the respective coordinates for the accommodative vergence condition are shown in table 11 below.

Nearest Grey Matter Distance Coordinate From X Y Z Coordinate (mm) -62 -20 44 Grey matter in precentral gyrus of left 2 frontal lobe (BA 4) -68 -20 38 Grey matter in postcentral gyrus of left 4 parietal lobe (BA 4) 14 -84 46 Grey matter in precuneus of right parietal 4 lobe (BA 19) Grey matter in cuneus of left occipital 3 0 -80 40 lobe (BA 19) Grey matter in precuneus of left parietal 4 -16 -82 50 lobe (BA 7) Grey matter in precuneus of left parietal 4 30 -54 66 lobe (BA 7) Table 11: Nearest gray matter structures and their respective distances in millimeters from coordinates from cluster-based group analysis of accommodative vergence paradigm

Using the coordinates of the clusters returned from the group analyses, the activated brain

areas for the accommodative vergence condition based on the midpoint of the three

stereotaxic axes are summarized below:

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Mid-sagittal: Right side of the brain (positive X): 12; left side of brain (negative X): 11; interhemispheric (mid-point of the brain): 1

Mid-coronal: front of the brain (positive Y): 0; back of the brain (negative Y): 24

Axial view: top of the brain (positive Z): 24; bottom of the brain (negative Z): 0

Accommodative Convergence Areas by Activation Maps: The cluster-based group

analysis of the accommodative vergence paradigm was viewed with FSLView, a tool for

viewing images analyzed in FSL. Several examples of activated areas are provided. Each

example is presented in sagittal, coronal and axial view respectively. The area of interest

is indicated by the cross-hair.

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Figure 30: Threshold activation map for the group analysis of accommodative convergence-lightbox view These images show axial (horizontal) sections through the brain. The upper rows are images beginning from the inferior (ventral) parts of the brain (such as the brainstem and medulla oblongata) to the middle parts (such as midbrain), then to the lower rows which are images of the superior parts of the brain (such as the cerebral cortex). It can be seen that the activations are near the precentral gyrus of the frontal lobe, postcentral gyrus of the parietal lobe, and the middle to superior part of the occipital lobe.

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Figure 31: Accommodative condition activation - cuneus of left occipital lobe For this and subsequent figures with three different views, the views are respectively in sagittal (left), coronal (middle) and axial (right) views. Cross-hairs at gray matter in the cuneus of left occipital lobe (Brodmann Area 18). The lower part of the images contains the X, Y, Z Talairach-Tournoux stereotaxic coordinates of the cross-hairs. To the right of that is the name of the area shown by the cross-hairs obtained from Talairach Client.

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Cross-hairs at Gray matter in postcentral gyrus in right parietal lobe (Brodmann Area 2) Figure 32: Accommodative condition activation-postcentral gyrus of right parietal

Cross-hairs at white matter in postcentral gyrus of the right parietal lobe. Figure 33: Accommodative vergence activation - postcentral gyrus in right parietal

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Cross-hairs at middle occipital gyrus of right occipital lobe Figure 34: Accommodative vergence activation - right middle occipital gyrus

Cross-hairs at the gray matter in the superior parietal lobule in the right parietal lobe (Brodmann Area 7) Figure 35: Accommodative vergence activation -right superior parietal lobule

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Figure 36: Gray matter in right occipital lobe - Brodmann Area 19

Disparity Convergence: The disparity vergence paradigm activated only brain areas in

the occipital lobe in one cluster based on the cluster-based group analysis. The cluster was made up of 4339 voxels.

Figure 37 below shows details of the cluster, the Talairach-Tournoux coordinates of the

voxels in the cluster and their levels of significance of activation. This cluster had 6

coordinates. The names of brain areas for the 6 coordinates were found on single point

search using Talairach.

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Figure 37: Disparity vergence group-based cluster activation

The coordinates obtained in the disparity vergence paradigm were for areas in the cuneus

(BA 17), lingual gyrus and middle occipital gyrus.

Names of Activated Coordinates in Talairach Client

All these occipital areas were found mostly inferiorly (below the mid-axial axis, negative

Z), and equal left and right hemispheres. Table 12 below details the coordinates and the structures found in those coordinates using a single point search strategy on Talairach

Client.

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Coordinate Name of Area X Y Z White matter in cuneus of left occipital -16 -96 0 lobe White matter in lingual gyrus of left -10 -88 -8 occipital lobe Grey matter in cuneus of right 16 -94 0 occipital lobe (BA 17) White matter in cuneus of right 22 -92 -2 occipital lobe Grey matter in middle occipital gyrus -20 -86 10 of left occipital lobe (BA 18) White matter in lingual gyrus of right 16 -92 -4 occipital lobe Table 12: Name of structures that were activated by the disparity vergence paradigm and their coordinates

Unlike the accommodative, proximal and gross/voluntary paradigms, all the activated coordinates in the disparity vergence paradigm had names using single point search strategy in Talairach Client.

Using the coordinates of the clusters returned from the group analyses, the activated brain areas for the disparity convergence condition based on the middle of the coordinates of the three axes or views are summarized below:

Mid-sagittal: Right side of the brain (positive X): 3; left side of brain (negative X): 3

Mid-coronal: front of the brain (positive Y): 0; back of the brain (negative Y): 6

Axial view: top of the brain (positive Z): 0; bottom of the brain (negative Z): 4; midline

(Z=0): 2

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Disparity Convergence Areas by Activation Maps: Examples of the images obtained

from the cluster-based group analysis of disparity vergence paradigm are shown in

figures below.

Figure 38: Activation map for group analysis of disparity vergence

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Compared to the accommodative vergence condition (figure 30), the disparity vergence activation appears to be lower in the axial direction. Also, the disparity activation is more occipital.

Cross-hairs show the gray matter in the lingual gyrus of the right occipital lobe (Brodmann Area 17). Figure 39: Disparity vergence activation - lingual gyrus in right occipital lobe.

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Cross-hairs show the gray matter in another part of the lingual gyrus of the right occipital lobe (Brodmann Area 18) Figure 40: Disparity vergence activation - another part of right lingual gyrus

Cross-hairs show the gray matter in the fusiform gyrus in the left occipital lobe (Brodmann Area 19) Figure 41: Disparity vergence activation - fusiform gyrus in left occipital lobe

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Cross-hairs show the white matter in the cuneus of the left occipital lobe. Figure 42: Disparity vergence activation - cuneus in left occipital lobe.

Cross-hairs show the gray matter in the middle occipital gyrus in the right occipital lobe Figure 43: Disparity vergence activation - right middle occipital gyrus

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Cross-hairs show gray matter in the declive of the posterior lobe of the left cerebellum Figure 44: Disparity vergence activation - declive in posterior lobe of left cerebellum

Proximal Convergence: The proximal convergence condition mainly activated brain

areas in the occipital and parietal lobes. In addition, the proximal vergence paradigm also

activated areas in the cerebellar cortex and the limbic system. The areas of activation

showed up in 4 clusters in the parietal and occipital lobes, with 24 coordinate areas. The

first cluster was made up of 271 voxels in the precuneus region of the right parietal lobe

(Brodmann Area 7). The second cluster was made up of 278 voxels in the right parietal

(Brodmann Area 7) and inferior and superior parietal lobules of the parietal lobe. The third cluster comprised 298, but did not have any single point names in the Talairach

Client. They were within 1 to 4 mm of the middle and superior frontal gyri in the right frontal lobe (Brodmann Area 10). Finally, the fourth cluster of activation by the proximal

170 was made up of 18370 voxels in the cuneal region and the middle occipital gyrus in the occipital lobe (Brodmann Area 19).

There were 24 coordinates in these clusters, 14 of which had structures with names in

Talairach Client using a single point search strategy. The identified brain areas were mostly found in the right hemisphere, and mostly at the superior half of the posterior part of the cortex. Figure 45 below shows details of these clusters, Talairach-Tournoux coordinates and the levels of significance of activation from the cluster-based group analysis.

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Figure 45: Proximal vergence group-based cluster activation

Names of Activated Coordinates in Talairach Client: The structures identified with single point search in Talairach Client and their respective coordinates are detailed in table 13 below.

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Coordinate Name of Area X Y Z 12 -42 48 Grey matter in precuneus in right parietal lobe (BA 7) 6 -50 46 Grey matter in precuneus in right parietal lobe (BA 7) Precuneus in the right parietal lobe 2 -56 54 6 -48 44 Grey matter in precuneus in right parietal lobe (BA 7) White matter in precuneus in right parietal lobe 32 -46 52 22 -54 58 Grey matter in precuneus in right parietal lobe (BA 7) 24 -48 52 White matter in precuneus in right parietal lobe 36 -56 46 White matter in inferior parietal lobule Grey matter in superior parietal lobule in right parietal lobe (BA 30 -56 50 7) 30 -44 44 Sub-gyral white matter in right parietal lobe -8 -92 30 Cuneus in left occipital lobe Grey matter in left middle occipital gyrus in left occipital lobe -42 -86 10 (BA 18) 8 -84 36 Grey matter in cuneus of right occipital lobe (BA 19) 6 -80 32 Grey matter in cuneus of right occipital lobe (BA 19) Table 13: Structures showing significant activation by the proximal vergence paradigm and their coordinates

Coordinate without Names in Talairach Client: For the coordinates that did return any names in Talairach client, one was for an area found within 1mm of gray matter in the right superior frontal gyrus. This stereotaxic coordinate was 26, 66, -4 for X, Y, Z respectively.

The nearest grey matter areas to the coordinates that returned no named brain areas nor were within 1mm of known brain areas, and how far they are from the respective coordinates for the proximal vergence condition are shown in table 14 below:

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Coordinate Distance X Y Z Nearest Grey Matter From Coordinate (mm) Grey matter in the right middle frontal gyrus 3 40 64 -12 (BA 10) Grey matter in right superior frontal lobe (BA 4 34 66 -12 10) Grey matter in right middle frontal gyrus (BA 3 44 64 4 10) Grey matter in right middle frontal gyrus (BA 3 32 70 12 10) Grey matter in right superior frontal gyrus (BA 2 36 70 4 10) Grey matter in precuneus of left parietal lobe 2 -18 -32 44 (BA 19) Grey matter in precuneus of left parietal lobe 2 -8 -86 40 (BA 19) Grey matter in precuneus of right parietal lobe 4 12 -60 66 (BA 7) Grey matter in precuneus of right parietal lobe 4 4 -58 66 (BA 7) Table 14: Nearest gray matter areas to the cluster-based coordinates without names in proximal vergence condition

Using the coordinates of the clusters from the group analyses, the activated brain areas for the proximal convergence condition based on the middle of the coordinates of the three axes or views are summarized below:

Mid-sagittal: Right side of the brain (positive X): 20; left side of brain (negative X): 4

Mid-coronal: front of the brain (positive Y): 6; back of the brain (negative Y): 18

Axial view: top of the brain (positive Z): 21; bottom of the brain (negative Z): 3

Thus, the main activated areas identified from the cluster-based thresholds were occipito- parietal, with a few areas in the frontal, cerebellar cortex and the limbic system.

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Proximal Convergence Areas by Activation Maps: Examples of the images obtained from the cluster-based group analysis of the proximal vergence paradigm are shown in figures below. These images were obtained with FSLView.

Figure 46: Proximal vergence activation-group based activation maps This shows relatively more extensive activations that the accommodative and disparity vergence conditions. It can be seen that the activations begin low in the cerebellar cortex all the way up to superior regions in the parietal, frontal and occipital lobes in the cerebral cortex.

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Figure 47: Proximal vergence activation-cuneus of right occipital lobe

Figure 48: Proximal vergence activation - cuneus of left occipital lobe (Brodmann Area 19)

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Figure 49: Proximal vergence activation - precuneus of right parietal lobe

Figure 50: Proximal vergence activation - fusiform gyrus of left temporal lobe (Brodmann Area 37)

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Figure 51: Proximal vergence activation - right superior frontal gyrus (Brodmann Area 10)

Figure 52: Proximal vergence activation - tuber of vermis in left posterior cerebellum

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Figure 53: Proximal vergence activation - culmen in anterior lobe of left cerebellum

Figure 54: Proximal vergence activation - declive in vermis of posterior lobe of left cerebellum

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Figure 55: Proximal vergence activation - parahippocampal gyrus on left limbic lobe

Figure 56: Proximal vergence activation - posterior cingulate in right limbic lobe

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Gross/Voluntary Vergence: Similar to the proximal vergence condition, there were more extensive activations in the voluntary/gross vergence condition than in accommodative and disparity vergences. Also, the areas of activations spanned the bottom to the top of the brain. There were three clusters of activation in the group analysis of the voluntary/gross convergence condition. The first cluster of activation contained 416 voxels located in the precentral and middle frontal gyri in the frontal lobe. The second cluster contained 543 voxels in occipito-temporal cortical areas. Finally, the third cluster of activation contained 15,748 voxels located in the lingual gyrus and cuneal regions in the occipital cortex, as well as structures in the cerebellar cortex.

These activated areas were found mostly in the right hemisphere, mostly superior and all at the posterior cortex. These clusters had 18 coordinate areas. The names of brain areas for 14 coordinates were found on single point search using Talairach Client. The identified brain areas were mostly found in the right hemisphere, and mostly at the superior half of the posterior part of the cortex. The figure below shows the cluster parameters from the cluster-based group analysis.

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Figure 57: Cluster-based group analysis of gross/voluntary vergence

Names of Activated Coordinates for Gross/Voluntary Vergence in Talairach Client: The table 13 below shows the names coordinates of the areas activated by the gross/voluntary vergence paradigm based on the cluster-based group analysis. Table 15 also shows the names of each coordinate as obtained from Talairach Client.

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Coordinate Name of Area X Y Z Sub-gyral white matter in right frontal lobe 32 -10 42 White matter in precentral gyrus in right frontal lobe 38 -12 40 Middle frontal gyrus in right frontal lobe 38 -4 54

50 -6 48 Grey matter in precentral gyrus in right frontal lobe (BA 4) Sub-gyral white matter in right temporal lobe 40 -66 12 White matter in middle temporal gyrus in right temporal 38 -68 18 lobe Grey matter in middle temporal gyrus in right temporal 48 -68 12 lobe (BA 39) White matter in middle temporal gyrus in right occipital 40 -62 16 lobe White matter in middle occipital gyrus in right occipital 44 -76 14 lobe White matter in middle occipital gyrus in right occipital 40 -72 14 lobe Grey matter in cuneus in right occipital lobe (BA 19) 20 -84 34 Grey matter in lingual gyrus in right occipital lobe (BA 19) 12 -48 0 Grey matter in declive in posterior pole of left cerebellum -6 -70 -10 Grey matter in culmen in the anterior lobe of the left -12 -66 -10 cerebellum T able 15: Names of the coordinates derived from the cluster-based group analysis of voluntary/gross vergence

Names of Activated Coordinates for Gross/Voluntary Vergence Names in Talairach Client: For the coordinates that did not return any names in Talairach client, the search

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for brain areas within 1mm of the coordinates showed one brain structure shown in table 16 below:

Coordinate Within 1mm of: X Y Z Grey matter in right parahippocampal gyrus in right limbic lobe (BA 19); Grey matter in sub-gyral of right limbic lobe (BA 19) 16 -44 -4 Table 16: Area within 1mm of coordinate derived from group analysis of voluntary vergence condition, but had no name in Talairach Client

The nearest grey matter areas to the coordinates that returned no named brain areas nor

were within 1mm of known brain areas and their distance from the respective coo- ordinates for the proximal vergence condition are shown in table 15 below:

Coordinate Nearest Grey Matter Distance X Y Z From Coordinate (mm) Gray matter in precentral gyrus in right frontal 4 52 -2 58 lobe (BA 6) Grey matter in precuneus of left parietal lobe 2 -10 -86 42 (BA 19) Table 17: Nearest gray matter to the coordinate derived from group analysis of voluntary/gross vergence condition, but had no name in Talairach Client

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The brain area in the coordinate (X, Y, Z = 46, -2, 64) returned no label, and was not

within 5mm of any gray matter area. It can only be inferred that that area is in the right

hemisphere, in the posterior cortex 2mm behind the midline and in the superior region.

Using the coordinates of the clusters from the group analyses, the activated brain areas

for the proximal convergence condition based on the middle of the coordinates of the

three axes or views are summarized below:

Mid-sagittal: Right side of the brain (positive X): 15; left side of brain (negative X): 3

Mid-coronal: front of the brain (positive Y): 0; back of the brain (negative Y): 18

Axial view: top of the brain (positive Z): 14; bottom of the brain (negative Z): 3; middle

(Z=0): 1

Voluntary/Gross Convergence Areas by Activation Maps

Examples of activated brain areas obtained from group-level analysis of threshold-based cluster analysis of the voluntary/gross vergence paradigm are shown in the figures below.

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Figure 58: Gross/voluntary verence - group-based activation map Similar to the proximal vergence condition, extensive areas of activation can be noted, beginning from the bottom (ventral) areas in the cerebellar cortex, up to the superior portions involving areas in the parietal, temporal and occipital cortices.

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Figure 59: Gross/voluntary activation - cuneus in left occipital lobe (Brodmann Area 18)

Figure 60: Gross/voluntary activation - right middle occipital gyrus (Brodmann Area 19)

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Figure 61: Gross/voluntary vergence activation - precuneus of left parietal lobe (Brodmann Area 19)

Figure 62: Gross/voluntary vergence activation - left inferior parietal lobule

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Figure 63: Gross/voluntary vergence activation- left superior parietal lobule

Figure 64: Gross/voluntary vergence activation - precentral gyrus in right frontal lobe (Brodmann Area 4)

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Figure 65: Gross/voluntary vergence activation - precentral gyrus in right frontal lobe (Brodmann Area 6)

Figure 66: Gross/voluntary vergence activation - middle temporal gyrus of left occipital lobe

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Figure 67: Gross/voluntary vergence activation - declive in posterior pole of left cerebellum

Figure 68: Gross/voluntary vergence activation - anterior lobe of left cerebellum

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Figure 69: Gross/voluntary vergence activation - parahippocampal gyrus in left limbic lobe

Overlaps among Accommodative, Disparity, Proximal and Vergence Activations

The following sections provide details of the areas of overlap among the various

vergence paradigms based on the cluster-based group analysis. These overlaps were

determined in two ways.

Using this approach, tables 18 and 19 below details the overlaps between the respective

vergence paradigms. The following two tables detail the extent of overlaps among the

various vergence paradigms, first at the general region of the lobes, followed by the

names of the structures within those lobes.

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Vergence Cortical Lobe Paradigm Occipital Parietal Temporal Frontal Cerebellar Accommodative √ √ √ Disparity √ Proximal √ √ Gross/Voluntary √ √ √ √ √ A checkmark (√) shows that a particular cortical lobe was activated on the cluster-based group analysis Table 18: Comparison of the cortical lobes activated by the various vergence paradigms

The only temporal lobe activation obtained from the cluster-based threshold group analysis occurred in the voluntary/gross paradigm: middle temporal gyrus (BA 39)

Vergence Disparity Proximal Gross/Voluntary Paradigm Accomm. . Cuneus (BA 19) in . Precuneus in . Cuneus in occipital occipital lobe parietal lobe lobe (BA 19) . Middle occipital . Cuneus in occipital . Middle occipital gyrus in occipital lobe (BA 19) gyrus lobe . Middle occipital . Precentral gyrus in gyrus in occipital right frontal lobe lobe (BA 18) Disparity . Cuneus in occipital . Cuneus in occipital lobe lobe . Middle occipital . Middle occipital gyrus gyrus . Lingual gyrus Proximal . Cuneus in occipital lobe . Middle occipital gyrus Table 19: Cross tabulation of areas of activation in various vergence paradigms 193

The areas of overlap among pairs of vergence paradigms are shown as intersection

between the cross-table (table 19 above). The areas were based on names of structures identified from coordinates from group-level analyses. These names were identified using

Talairach Client.

Determination of Overlaps Using Threshold Image in FSLView: The second approach

for determining overlaps utilized the FSLView. To compare two paradigms, the color- coded images from the two paradigms were overlapped so that the colors could be visually inspected in FSLView to identify overlaps. For instance, to determine overlaps between accommodative and disparity vergence paradigms, the areas of activation from the disparity vergence were colored blue, and those from the accommodative vergence were colored yellow. These two rendered images were then placed one on top of the other. The image on top image can be adjusted so that the underlying image becomes visible. That way, two images can be viewed simultaneously so that the blue and red colored areas can be compared.

Overlaps between Accommodative Vergence and Disparity Vergence Paradigms: Based

on the cluster-based threshold group analyses, the overlaps in activations for the

accommodative vergence and the disparity vergence paradigms occurred at the right

cuneus, right lingual gyrus, right middle occipital gyrus and sub-gyral white matter, all in

the right occipital cortex.

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Accommodative vergence (upper row, blue color); disparity vergence (lower row, red- yellow color) Figure 70: Comparison of accommodative vergence and disparity vergence activations

Examples of these overlaps are shown in the figures below. In the following figures, activations by the accommodative vergence paradigm are colored blue while those by the disparity vergence paradigm are colored orange-yellow.

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Figure 71: Overlap between accommodative vergence and disparity vergence - cuneus in right occipital lobe This figure also shows the laterality of the overlaps; the occipital overlaps occurred only in the right hemisphere because the accommodative paradigm (blue) was lateralized to the right hemisphere in its occipital activations.

Figure 72: Overlap between accommodative vergence and disparity vergence - right lingual gyrus

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Figure 73: Overlap between accommodative vergence and disparity vergence - right middle occipital gyrus Here, it can be seen that unlike the occipital activations for the accommodative vergence paradigm (indicated by blue), the parietal activations are not lateralized.

Figure 74: Overlap between accommodative vergence and disparity vergence - right sub- gyral white matter in the occipital cortex

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Overlaps between Accommodative Vergence and Proximal Vergence Paradigms: The cluster-based group analysis showed that the accommodative vergence paradigm activated occipito-parieto-frontal networks. On the other hand, the proximal vergence paradigm activated occipito-parietal areas. Therefore, the overlaps in activations for the two conditions occurred in the occipital and parietal lobes. These overlaps were in the cuneus and precuneus in the occipital lobe (Brodmann Areas 18 and 31), and precuneus in the parietal lobe (Brodmann Area 19).

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Accommodative vergence – upper row, red-yellow color, proximal –lower row, green color Figure 75: Comparison of cluster-based group activation maps between accommodative vergences

In the examples of images showing these overlaps in the figures below, proximal activations are colored green, accommodative activations are red-white.

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Figure 76: Overlap between accommodative vergence and proximal vergence - cuneus in the left occipital lobe (Brodmann Area 18).

Figure 77: Overlap between accommodative vergence and proximal vergence paradigms - precuneus of right occipital lobe (Brodmann Area 31).

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Figure 78: Overlap between accommodative vergence and proximal vergence - precuneus in the left parietal lobe (Brodmann Area 19)

Overlaps between Accommodative Vergence and Gross/Voluntary Vergence Paradigms:

The cluster-based group analysis showed that the accommodative vergence paradigm activated occipito-parieto-frontal networks. On the other hand, the gross/voluntary vergence paradigm activated occipito-temporo-frontal networks as well as cerebellar areas. Therefore, the overlaps in activations of the two paradigms occurred in the cuneus

(Brodmann Areas 7, 18, 19) and precuneus (Brodmann Areas 19, 31) in the occipital lobe, precuneus in the parietal lobe (Brodmann Area 19), and also in the frontal lobe.

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Accommodative vergence - upper row, blue color; proximal vergence - lower row, red- yellow color. Figure 79: Comparison of cluster-based group activation maps between accommodative and gross/voluntary vergences

The figures below show examples of overlaps in the activations between the two vergence paradigms. In these images, accommodation is colored blue, gross/voluntary is colored orange-yellow.

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Accommodative vergence activations are colored blue Figure 80: Overlap of accommodative and gross/voluntary vergences - cuneus of left occipital lobe (Brodmann Area 18)

Accommodative vergence activations are colored blue Figure 81: Overlap between accommodative and gross/voluntary vergences - cuneus of right occipital lobe (Brodmann Area 7)

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Accommodative vergence activations are colored blue Figure 82: Overlap between accommodative and gross/voluntary vergences - cuneus of left occipital lobe (Brodmann Area 19)

Accommodative vergence activations are colored blue Figure 83: Overlap between accommodative and gross/voluntary vergences - precuneus of right occipital lobe (Brodmann Area 31)

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Accommodative vergence activations are colored blue Figure 84: Overlap between accommodative and gross/voluntary vergences - precuneus of right occipital lobe (Brodmann Area 19)

Accommodative vergence activations are colored blue Figure 85: Overlap between accommodative and gross/voluntary vergences - right superior parietal lobule

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Accommodative vergence activations are colored blue Figure 86: Overlap between accommodative and gross/voluntary vergences - sub-gyral white matter in right frontal lobe

Overlaps between Disparity Vergence and Gross/Voluntary Vergence Paradigms: The

disparity vergence activations were in the occipital lobe while the gross/voluntary

vergence activations were in the occipital, parietal, temporal, frontal areas, etc. Therefore,

the overlaps between these two activations occurred in the occipital lobe. However, there

was relatively little area of overlap, even at the occipital lobe.

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Disparity- upper row, blue color; gross/voluntary-lower row, red-orange color Figure 87: Comparison of cluster-based group activation maps between disparity vergence

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Disparity vergence: blue; gross/voluntary vergence: yellow-orange Figure 88: Overlap between disparity and gross/voluntary vergences - cuneus of right occipital lobe

Figure 89: Overlap between disparity and gross/voluntary vergences - lingual gyrus of right occipital lobe

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Figure 90: Overlap between disparity and gross/voluntary vergences - middle occipital gyrus of right occipital lobe

Overlaps between Disparity Vergence and Proximal Vergence Paradigms: With the

disparity vergence paradigm mainly activating occipital areas and the proximal activating

mainly occipito-parietal areas, overlaps were noticed at the occipital areas. Overlaps

occurred in the cuneus, as well as inferior and middle occipital gyri and the fusiform

gyrus in the occipital lobe. In addition to these, there were also overlapping activations in

declive in the posterior cerebellum.

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Upper row: disparity vergence activations (orange-yellow); lower row: proximal vergence activations (green) Figure 91: Comparison of cluster-based group analysis of disparity and proximal vergence activations

The following images show examples of these overlapping activations. In the images, proximal activations are colored green, disparity orange-yellow.

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Figure 92: Overlap between disparity and proximal vergences - cuneus of left occipital lobe

Figure 93: Overlap between disparity and proximal vergences - fusiform gyrus of left occipital lobe

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Figure 94: Overlap between disparity and proximal vergences - right inferior occipital gyrus

Figure 95: Overlap between disparity and proximal vergences- left middle occipital gyrus

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Figure 96: Overlaps between disparity and proximal vergences - declive in posterior lobe of right cerebellum

Overlaps between Proximal Vergence and Gross/Voluntary Vergence Paradigms: The proximal vergence and gross/voluntary vergence paradigms activated the most brain areas based on the cluster-based group analyses. The proximal vergence paradigm activated mainly occipito-parietal and other areas including the frontal lobe, while the gross/voluntary paradigm activated occipital, temporal, frontal and parietal lobes and other areas. Based on this, the greatest extent of overlaps between any pairs of paradigms was observed between these two paradigms.

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Disparity vergence activations: upper row, green color; Gross/voluntary vergence activations: lower row, orange-yellow color

There were overlaps at the occipital, parietal and cerebellar areas. The images below show examples of the overlapping activations. Proximal vergence activations are in green.

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A lot of overlaps are seen at the occipital cortex and in the cerebellum. Figure 97: Overlap between proximal and gross/voluntary vergences activations

Figure 98: Overlap between proximal and gross/voluntary vergences - lingual gyrus of left occipital lobe

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Figure 99: Overlap between proximal and gross/voluntary vergences - posterior cingulate of left limbic lobe

Figure 100: Overlap between proximal and gross/voluntary vergences - fusiform gyrus of left occipital lobe

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Figure 101: Overlap of proximal and gross/voluntary vergences - precuneus of right parietal lobe

Figure 102: Overlap between proximal and gross/voluntary vergences - declive in posterior lobe of right cerebellum

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Figure 103: Overlap between proximal and gross/voluntary vergences - culmen in anterior lobe of right cerebellum

Summary of Overlaps:

The list of overlaps in table 20 below is a combination of the names of the coordinates obtained from the cluster-based group analysis and the overlaps of color-rendered thresholds. (BA= Brodmann Area). If two Brodmann Areas in the same cerebral lobe overlap, both are indicated in this table, but the coordinate is presented for only the first.

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Pairs of Name of Brain Area Coordinate Vergence X Y Z Accommo- 1. Cuneus in right occipital lobe 32 20 44 dative vs 2. Lingual gyrus in right occipital lobe 32 19 39 Disparity 3. Middle occipital gyrus in right occipital lobe 33 20 45 4. Sub-gyral white matter in right occipital lobe Accommo- 1. Cuneus in left occipital lobe (BA 18) 52 26 47 dative vs 2. Middle occipital gyrus 31 22 39 Proximal 3. Precuneus in right occipital lobe (BA 31) 41 26 53 4. Precuneus in left parietal lobe (BA 19) 53 21 60 Accommo- 1. Cuneus in left occipital lobe (BA 18; BA 19) 52 26 47 dative vs 2. Cuneus in right occipital lobe (BA 17) 41 26 54 Gross/Vol- 3. Precuneus in right parietal lobe (BA 31; BA 19) 37 22 60 untary 4. Sub-gyral white matter in right frontal lobe 27 56 55 5. Superior parietal lobule in right parietal lobe (BA 27 38 68 7) 31 20 38 Disparity 1. Cuneus in left occipital lobe 57 18 43 vs 2. Fusiform gyrus in left occipital lobe 65 25 31 Proximal 3. Inferior occipital gyrus in left occipital lobe 63 18 36 4. Middle occipital gyrus in left occipital lobe 57 17 45 5. Declive in posterior lobe of right cerebellum 30 25 27 Disparity 1. Lingual gyrus in right occipital lobe (BA 18) 38 22 35 vs 2. Cuneus in right occipital lobe 33 20 47 Gross/Vol- 3. Middle occipital gyrus in left occipital lobe 57 21 46 untary Proximal 1. Fusiform gyrus in right occipital lobe 56 28 31 vs 2. Precuneus in right parietal lobe (BA 7) 41 41 63 Gross/Vol- 3. Culmen in anterior lobe of right cerebellum 40 39 36 untary 4. Declive in posterior lobe of right cerebellum 40 30 25 5. Lingual gyrus in left occipital lobe 54 34 36 6. Middle occipital gyrus in left occipital lobe 64 24 41 7. Middle temporal gyrus in right occipital lobe 24 31 45 8. Precuneus in left occipital lobe 54 26 52 9. Precuneus in right parietal lobe 40 42 61 10. Sub-gyral white matter in right parietal lobe 30 42 59 11. Middle temporal gyrus in left temporal lobe (BA 65 24 44 39) 40 28 44 12. Posterior cingulate in right limbic lobe 54 42 33 13. Parahippocampal gyrus in right limbic lobe Table 20: Summary of overlaps of vergence conditions

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Discussion

The sections below discuss the results of the functional imaging study of accommodative,

disparity, proximal and gross/voluntary vergence eye movements.

Overview of Vergence Stimuli: This study looked at which parts of the brain were

activated by various visual stimuli which possessed either accommodative, disparity,

proximal or gross/voluntary cues in binocularly normal adults.

The accommodative stimulus consisted of red or blue colored 20/30 letters projected on a

screen 74cm from the eyes. These letters were viewed anaglyphically through red or blue

MRI-safe glasses. This monocular display (20/30 red letters seen by right eyes, 20/30

blue letters seen through -2.00D lens placed over the left eye) opened the disparity

vergence loop. Thus, at any point in time, only one eye was seeing the letters on the

screen. When the right eye saw the red letters, they would be expected to accommodate

by 1.35D (accommodative-inactive condition). Blue letters of the same size seen through

-2.00D was the accommodative-active condition (with total accommodative demand of

3.35D). In the analysis, the accommodative-inactive condition is contrasted with (i.e. compared to) the accommodative-active condition, the only difference being -2.00D accommodative lens and color of letters. This contrast would show areas which were activated by the effort to accommodate through the -2.00D lens. Individually, each accommodative condition would activate brain areas including the primary visual cortex.

Therefore, the contrast would show only areas which are due to the additional 2.00D of accommodation.

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The disparity condition comprised random dot stereograms (RDS) which had disparity of

500 seconds of arc. These were presented on a computer monitor at 74cm, with additional vergence demands of either 0 Δ (disparity-inactive condition) or 4, 8, 12, 16 and 20 prism diopters when viewed through red blue glasses. These were viewed by both eyes. The disparity contrast consisted of comparing activations between the accommodative- inactive condition (both eyes looking at RDS with 0Δ demand) and the average of the activations for the 5 levels of vergence demand (disparity-active condition). Base on this contrast, any activation from the disparity vergence stimulus arose from the additional disparity vergence demand.

The proximal vergence stimulus was a DOG target viewed at distance (a monitor located

74cm away) and near DOG target (10cm) with one eye occluded. The participant was audibly cued to look between the two DOG targets. The DOG target opened the accommodation loop, and the occlusion of one eye opened the disparity loop. Viewing the distance DOG target constituted the proximal-inactive condition, while viewing the near DOG target involved proximal cues (near DOG target is proximal-active condition)

Therefore, it is assumed that the change in vergence position from distance to near DOG target is mediated by proximal vergence responses.

Finally, the voluntary vergence stimulus was an auditory prompt to voluntarily cross the eyes (gross/voluntary-active). The inactive condition was a vertical line on the monitor, while the active condition was when the vertical line was replaced by a black screen and the audio signal to cross the eyes. The contrast between the gross/voluntary-active and inactive was thus assumed to be due to activations due to voluntary effort alone.

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Two Approaches to Identifying Activations: The cluster-based analysis shows activations

in clusters and how many voxels are activated in each cluster (see figures 22, 30, 37 and

49). The second, complementary approach identified activations at the individual voxel

level. Some activated voxels were not included in voxel clusters, but were shown as

activated on color rendered activation maps. Such voxels were more likely to be

identified in the voxel-level identification. Therefore, FSLView was able to identify

smaller activations, and was complementary to the main, cluster-based activations.

Activations by the Different Vergence Paradigms: The activations by each of the

vergence paradigms are discussed below.

Activations in the Disparity Vergence Paradigm: The cluster-based group analysis of the

vergence eye movements showed that the disparity vergence activated regions were in the

occipital lobe. These regions were the cuneus, lingual gyrus, precuneus and middle

occipital gyrus. The cuneus (Brodmann Area 17 or primary visual cortex) is known for

processing basic visual features including disparity. The middle occipital gyrus is

important in spatial depth perception, whether visual or non-visual. In a recent fMRI study in humans[112], sighted individuals showed significant activations in the middle

occipital gyri, while non-sighted individuals showed activations when using either

auditory or tactile senses to determine the spatial location of objects. The authors

concluded that the middle occipital gyrus has a functional specialization for spatial depth

perception. The left lingual gyrus has been reported to be involved in active

memorization of faces while the right is involved in the passive geometric figures[113].

While, there were no faces in the RDS target in the disparity stimulus, the disparity 222 stimuli were perceived as a small, central square perceived in depth. This may explain the lingual gyrus activation.

Among the four vergence eye movement types, disparity is the only type that was largely restricted to the occipital cortex. Thus, the processing of disparity vergence eye movements appear to begin very early in the visual system. Previous studies in monkeys have shown that some cells in the monkey primary visual cortex are tuned for near, far, or no disparities[66]. Disparity-processing cells in that study were either general or specific in their detection. The general disparity-sensitive cells were sensitive to disparities which were either farther, at, or closer than the fixation plane. On the other hand, the more specific disparity detectors in the monkey visual cortex could be excitatory for near but inhibitory for far, or vice versa. An earlier component of this study compared activations between adults with convergence insufficiency (CI) and normal controls also found activations for disparity in both controls and subjects with CI[114], although the subjects who had CI had more diffuse activations. However, Alvarez et al[37], who performed a similar study among adults with CI and normal controls, did not report occipital activations specifically for disparity vergence. Three reasons may account for this. First, they used a stimulus which differed from the ones reported in this study. They used light emitting diodes 5cm by 2mm positioned at different distances to stimulate the disparities they needed. Thus, the stimulus probably elicited other vergence types (e.g., proximal vergence, and even accommodative vergence). That is, their stimulus probably contained more gross depth cues while the RDS targets used in this study would be expected to specifically activate the disparity component of the vergence response. Second, Alvarez

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et al used a region-of-interest analysis. They focused specifically on the dorsolateral prefrontal cortex, parietal eye fields, frontal eye fields and cerebellar and midbrain areas which had been identified previously as being involved in vergence eye movements. It is possible that if they had analyzed the occipital cortex near the cuneus and lingual gyrus, they may have found activations there. In fact, a visual analysis of their images

(particularly the sagittal section for before therapy, and sagittal and axial for the follow- up studies) suggests some occipital activations. Finally, this study differs significantly from the Alvarez et al study in terms of the magnitude of disparities used. The largest magnitude of disparity in Alvarez’s is 4 degrees (7 prism diopters, compared to up to 28 prism diopters here). Considering that most occipital regions (early part of the visual system) respond to coarse disparities[115], and the possibility that there were stronger cues (e.g. proximal cues), it is possible that the occipital cortex was overshadowed in activation in their study.

Activations in the Accommodative Vergence Paradigm: On the other hand, the

accommodative vergence eye movements stimulated areas in the occipital lobe (cuneus

and middle occipital gyrus), parietal lobe (precuneus, superior parietal and postcentral

gyri) and frontal lobe (precentral and postcentral gyri). All these areas have been

previously identified as playing roles in vergence eye movements. The superior parietal

gyrus has been confirmed to be involved with processing spatial localization[116], and tumors in the superior parietal lobe have been reported to affect reaching[117]. The postcentral gyrus is the primary sensory area, just behind the precentral gyrus (primary motor area) in the frontal lobe[65]. It could therefore be considered that the occipito-

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parieto-frontal activation is a progression of visual processing from early in the visual

cortex, through processing areas in the parietal lobe, to the motor areas in the frontal lobe.

However, it is also known that connections amongst different cortical lobes can be both feed-forward and feedback (i.e., neural information flow is not unidirectional). It is

therefore possible that processing of accommodation or accommodative vergence stimuli

could be reversed in some of these lobes. For instance, the direction of flow could be

either occipital to parietal or parietal to occipital What these results show is that the

processing of blur stimuli are probably more extensive than the processing of disparity stimulus in normal adult humans.

It is not possible to differentiate activations which were due purely to accommodation from those which were due to accommodative convergence. However, since the cuneus and middle occipital gyrus were also involved in the more “pure” disparity vergence, it is possible that the accommodative vergence is responsible for the cuneal and middle frontal gyral activations. However, this is only a speculation, particularly as the converse argument could be made that the “pure” disparity-based RDS target could also have stimulated the cuneal and middle occipital gyrus by convergence accommodation which occurs with disparity.

Also, it is interesting to note that the multiple-lobe progression of activation seen in the accommodative convergence paradigm (occipital, parietal, frontal) follows the dorsal stream. The dorsal stream visual processing pathway is known to be involved in spatial localization (the “where”). This has two implications. First, it suggests that the accommodative vergence paradigm involves pathways that are beyond the early visual

225 pathway. Second, even though no disparity information was apparent in the accommodative vergence paradigm, it still activated spatial localization pathways. This may be due to a learned association of accommodation with near work (for instance, through the near triad synkinesis). Another possibility is that the accommodation-active condition (viewing 20/30 blue letters through -2.00D lens) may have changed the size of the letters. Accommodation has been reported to lead to perception of smaller images[6].

Also, minus (concave) lenses have minifying effects. In addition the illusion of depth from blue-red combination of colors (chromostereopsis)[118] could have cued depth in the accommodative vergence paradigm. Altogether, some perceptible minification may have resulted in the blue letters being perceived as farther than the red. However, none of these perceptual phenomena had been reported by any subject, including one of the researchers in a pilot scan. However, that would still not explain the observation that even at the occipital lobe, the activations from the accommodative paradigm were more superiorly positioned than those from the disparity (See figures 23, 31 and 62). In addition, it is known that there is no direction for blur cues of magnitude 2 diopters or more[21, 119]. That is to say that the visual system perceives 2 diopters of myopic blur the same as it perceives 2 diopters of hyperopic blur. Therefore, the pattern of activations observed in the accommodative vergence condition may show that in accommodation, one does not know the stimulus direction, so while he/she knows he/she has to do something, he/she does not have any cue about the direction the response must go.

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Activations in the Proximal Vergence Paradigm: Proximal vergence eye movements activated regions in the occipital (cuneus and middle occipital gyrus) and parietal

(precuneus, inferior parietal lobule and superior parietal lobule) lobes. The main lobes that were activated by the proximal vergence stimulus are similar to those of the accommodative vergence stimulus. On the voxel-wise analysis with FSLView, frontal lobe activations were also identified on the proximal vergence paradigm. Similar to the accommodative vergence stimulus, the proximal vergence stimulus also activated the dorsal stream pathway. This makes sense because the dorsal stream pathway is known for locating where objects are located in space and the spatial relations of other objects in space. Proximal vergence is vergence stimulated in response of the perception of nearness of an object. Therefore, it makes sense that the proximal vergence would involve the dorsal stream pathway. Sakata et al have shown that the inferior parietal lobule responds differently to perception of depth, and is sensitive to motion in depth[120, 121]. In a recent study, Hasebe et al[86] found activations in the inferior parietal lobule in an fMRI vergence study where they created disparity using targets that moved closer than the point of fixation.

Activations in the Gross/Voluntary Vergence Paradigm: Finally, the gross/voluntary vergence eye movements activated brain areas in the occipital (cuneus, lingual gyrus and middle occipital gyrus), the middle temporal gyrus in the temporal lobe, frontal

(precentral and middle central gyri) and culmen and declive in the cerebellum. The gross/voluntary paradigm is the only one which activated the temporal lobe and the cerebellum in the cluster-based analysis. It is also the stimulus which resulted in the most

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extensive in terms of spatial areas of activation. It did not have any visual stimulus;

subjects were just told to cross their eyes. All the subjects reported being able to do so,

and this was confirmed on video tracking during the scanning. However, people are

known to use different strategies, some apparently crossing their eyes without the need

for a stimulus (that is, by imagining something near) and some looking at the tips of their

noses. None of the subjects in this study reported executing voluntary vergence by

looking at their noses, or using any other visual cue. It is not clear whether voluntarily

crossing eyes draws on all the cues to vergence or only proximal cues. However, the

middle and superior temporal gyri have been known to be involved in visual imagery

such as imagining grasping objects with a virtual hand[122], besides their roles in language. Therefore, this may help to explain the activation in the paradigm in which

some people may move their eyes in response to an imaginary near object.

Overlaps in Neural Activations: All the vergence stimuli activated the cuneus and middle

occipital gyrus in the occipital lobe. In addition, both accommodative and proximal

convergence eye movements activated the precuneus in the parietal lobe. Similarly, the

accommodative and voluntary vergence stimuli both activated the precentral gyrus in the

frontal lobe, and the disparity and voluntary vergence stimuli activated the lingual gyrus

in the occipital lobe.

Overlaps between Accommodative Vergence & Disparity Vergence Activations: While

the accommodative vergence stimulus activated occipital and parietal lobes, the disparity

vergence stimulus activated only areas in the occipital lobe. Therefore, the overlaps

occurred only in the occipital cortex. However, the overlaps between these two stimuli 228

were mostly in the right occipital lobe. No obvious reasons are apparent for this laterality

in the overlaps between the accommodative and disparity vergence activations. To open the disparity loop, it was necessary to occlude one eye; the right eye was occluded anaglyphically during the accommodative active condition in the accommodative vergence paradigm. This was fashioned after the Müller’s paradigm. In the classic

Müller’s paradigm, one eye is occluded, and accommodation is simulated in the unoccluded, fixating eye. The accommodation stimulates accommodative convergence.

Others researchers have shown that although accommodative convergence is binocular,

there is attenuation in the vergence movement of the fixating eye[123, 124]. The movement in the fixating eye can be about just 10% of that in the occluded eye[125].

This is possibly what happened in this study too: the left eye was anaglyphically

occluded, both eyes converged, but more so in the right eye. Also, the accommodative

vergence activations were more superior in spatial location than the disparity vergence

activations. Besides this, more extrastriate areas were activated in the accommodative

vergence. Overall, this shows that while disparity is probably processed early in the

visual system, the processing for accommodative vergence continues to higher processing

centers, including those in the dorsal stream and frontal lobes. The earlier processing of

disparity vergence, which synkinetically stimulate convergence accommodation may help

to give direction to accommodation, as blur stimuli above 2 diopters are known to have

no perceived direction[21]. Thus, the overlaps between disparity and accommodative

vergence quite early in the visual sensory processing pathways provide a possible

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mechanism for the accommodation-disparity vergence cross-talk in the dual interaction model.

Overlaps between Accommodative Vergence & Proximal Vergence Activations: The

accommodative vergence and proximal vergence stimuli both activated occipital and

parietal lobes in the cluster-based analysis. Therefore, their overlapping activations

occurred at the middle occipital gyrus (Brodmann 18) and cuneus (Brodmann Area 19) in

the occipital lobe, as well as in the precuneus of the parietal lobe (See table 17). Both

paradigms therefore appeared to share common pathways in the dorsal stream. While it

makes physiological sense for the perceptual (top-down) proximal system to be involved

with the “where” visual processing stream, it is difficult to explain why a reflexive

(bottom-up) accommodative system in the same schema that uses spatial localization.

One way to tie these two processes which appear to differ in their perceptual correlates is

to consider the accommodative vergence system as a partly conditioned reflex.

Accommodation is stimulated when near objects things are blurred. It is also stimulated

with knowledge of proximity (proximal accommodation). Therefore, it is possible that

when proximal vergence is activated, proximal accommodation is also activated as part

of the accommodative response, even in the absence of blur[21]. As such,

accommodation, and by extension, accommodative convergence relies on the nearness of

a visual target, thus stimulating the dorsal stream pathways.

Accommodative cues of 2 diopters or more have no sign (that is, whether it is positive or

negative)[21]. Therefore, the stimulation of the putative dorsal stream by the

accommodative vergence paradigm could mean that although the accommodative system

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(in this disparity open-loop condition) knows it has to respond, it does not know which direction the response has to go.

Jones[126] has argued that proximal convergence is not an independent vergence component, but a response that is secondary to proximal accommodation by way of an

AC/A-like cross-link. Similarly, McLin and Schor[127] have argued that proximal accommodation drives proximal convergence, and not the other way round. However,

Hofstetter[12] argued that proximal accommodation and proximal convergence are independent of each other, nor do they have a mutual mechanism. Although there are overlaps in the activations by the two eye movement systems, there is no evidence that one drives the other. Despite their considerable overlaps, the activations are still distinct.

It is not likely that the proximal responses obtained in this study were driven primarily by proximal accommodation. The use of DOG targets reduces this possibility. Therefore, the results of this study show that proximal vergence and accommodative vergence are largely independent.

Overlaps between Accommodative Vergence & Gross/Voluntary Activations: Besides

the cuneus (Brodmann Area 19) and the middle occipital gyrus, both accommodative

vergence and gross/voluntary vergence also stimulate the precentral gyrus in the frontal

lobe. The precentral gyrus is the primary motor area, involved in the coordination of

motor activities. The overlaps in the occipital activations probably suggests common areas of early visual processing. However, the frontal activation shows that both stimuli have moved on from the afferent pathway to the efferent pathway. Thus, these two

231 stimuli appear to be the pair that travels the farthest together in the afferent to efferent paths.

Overlaps between Disparity Vergence & Proximal & Disparity and Gross/Voluntary

Vergence Activations: Similar to the disparity-accommodative vergence overlaps, the disparity and proximal stimuli also share common activations in the middle occipital gyrus and cuneus in the occipital lobe. Also, the disparity vergence stimuli shares those same common activations with the gross/voluntary. In addition to the middle occipital gyrus and cuneus, the two also activate the lingual gyrus in common. This pattern of common activations is probably because disparity vergence is mostly processed so early in the visual system in an area which does not differentiate among the various stimuli.

This implies that the cuneus and middle occipital gyrus may be part of a main depth sorting center. They receive the different depth stimuli, assess them grossly, and pass them on to unique higher processing centers with more specialization. Stimuli with common characteristics may be sent along the same path. It should be noted again that this is speculative; the direction of the visual processing cannot be determined with functional imaging studies.

In addition, voluntary vergence activations should be assessed with care. Many subjects use different and unique strategies to be able to voluntarily converge. While some appear to be able to converge voluntarily without any stimulus, many converge voluntarily by looking at their noses, or imagining focusing on something close. It is therefore possible that some of these strategies influenced the activations observed. While the analyses of the group functional images made statistical adjustments for individual peculiarities,

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there is no way of knowing if the voluntary activations were due entirely to the voluntary

stimulus. For instance, some of the activations at or near the primary motor cortex could

have arisen from movement of facial muscles that occurred as some subjects attempted to

voluntarily cross their eyes and maintain the crossed eyes.

Overlaps between Proximal Vergence & Gross Vergence Activations: The proximal and

gross/voluntary shared the largest extent of overlapping activations, by visual

examination of the color-rendered activation maps. On the cluster-based analysis, the

overlaps were found in cuneus and middle occipital gyrus, which was also activated by

all the other stimuli. This may not appear to be a lot of overlaps, but it should be noted

that the unit of overlaps are clusters. Therefore, although their overlaps were very large,

they were mostly in the occipital cortex. The expectation was that these two stimuli

would both activate pathways in the dorsal stream and the frontal cortex due to their

apparent control of conscious control (proximal) and will (voluntary).

Additional Activations and Overlaps: The overlaps described above were based only on

the cluster-based analyses. Also, multiple appearances of an area are listed only once. For

instance, if the cuneus was activated multiple times in the overlap between two stimuli, it

is still mentioned once. This reduces the appreciation for the extent of overlaps. The

extent of overlaps between the stimuli are best appreciated by looking at the color- rendered overlaps overlaid on each other, as shown in figures 62 to 95.

Also, some of the areas which were activated by the various conditions did not have

names in Talairach Client. As described in the methods section, either the nearest gray

matter or structures within 1mm were found. Because of the lack of direction for such

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search findings, the nearest gray matter or structures were omitted in the qualitative

comparison of the overlap using the list of hits. This would further reduce the number of

overlaps. Sometimes, the same name may have different designations in terms of

Brodmann Area, and may thus have different functions.

Therefore, an additional approach was used to identify overlaps. This second approach was based on activated voxels, rather than the activated clusters. In this second approach, the two stimuli being compared were overlaid on top of each other. Each stimulus had a different color. Therefore, overlaps become easy to see by visual inspection using the differences in color. When any voxel is clicked within an area of overlap, that voxel is identified by a cross-hair, its X, Y, Z coordinate and its name in Talairach Daemon (the one in Talairach Client).

This more comprehensive list of overlaps, their Brodmann Area designations and their coordinates is provided in table 18. It shows that the overlap between the accommodative and gross/voluntary stimulus is larger, and although it can all be found in the cuneus, their locations range from Brodmann Areas 17, 18, 19, 31. It also shows further activation in the superior parietal lobe (Brodmann Area 37).

Furthermore, the areas of activation overlap between the proximal and gross/voluntary stimuli expand to include areas in the parietal lobe (Brodmann Area 7), culmen and declive in the cerebellum as well as middle temporal gyrus (Brodmann Area 39) and the parahippocampal gyrus in the right limbic lobe. The parahippocampal gyrus has been identified in medium-term spatial memory[128]. This may explain its activation in

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gross/voluntary vergence which is activated by some people who imagine looking at

something at near when they cross their eyes voluntarily.

Afferent or Efferent Activations: Neural activation is due to one of two reasons in

visuomotor processing; processing of the visual information, or planning and control of

the eye movement itself. Processing occurs at the afferent stage, and the motor planning and execution at the efferent stage. All stimulate occipital lobes (containing the processing areas in the primary visual and associated visual areas). All premotor areas may be considered to be afferent, motor and post-motor efferent. However, the distinction between afferent and efferent areas in the areas identified, or even the direction and order of activation is not possible based on this study. For instance, the

processing paths have been organized into feedforward and feedback neural processing

paths[65]. From the retino-geniculate pathway, two different pathways are known to exist

to the primary visual cortex-one directly from the LGN, and a second, indirect one from

the lateral geniculate nucleus in the thalamus, through the pulvinar, then back to visual

cortex. These direct and indirect pathways to the visual cortex cannot be seen on fMRI

imaging studies such as this. Therefore, the pathways and overlaps identified in this

study should not be used to deduce pathways. They should be used as complementary

evidence for results of other studies using different strategies like transcranial magnetic

stimulation, electrophysiology, lesion studies, etc.

That is important because it would help to answer the question of whether the vergence

sub-types ultimately amount to the same vergence response, irrespective of where they

draw their neural control from. As suggested by Mitchell[8], it may be that ultimately,

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vergence response is the same, whether driven by blur, disparity or proximal/cognitive

cues, and that the various cues only show differences in ability to draw the response. On

the other hand, it is also possible that the classical view of vergence sub-components drawing on separate innervations is the correct one. So far, the results of this study cannot address that question. What the results show is that the different stimuli are unique in terms of the extent of overlaps in activation of different areas of the visual system. The

evidence of a final common pathway beginning from the brainstem to the oculomotor

nuclei and oculomotor plants[20, 34] suggest that both views may be correct in part.

There is also evidence that people with CI tend to use different neural control areas and

(perhaps more attentional,) and different extents of known areas compared to normal

controls[37]. Evidence from this lab has shown that after therapy, there is a marked

change in the activated areas so that the pattern of activation becomes more focused and

less diffuse. This may mean that CI patients try to make up for their loss/absence of

normal neural control by recruiting other, less effective areas. In that case, the point of

recruiting those “non-traditional” areas would be to achieve the same end. The

recruitment of parts of such areas which are normally dedicated for attention may

impinge on the normal function of the attentional areas, helping to explain symptoms

such as forgetfulness and loss of attention in CI.

Limitations of the Study

It is difficult to say whether the BOLD activations are due to phasic or tonic control, or

both. In other words, one cannot tell if the BOLD responses observed here are due to the

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transient eye movement responses or steady-state responses. It is not known how long the cerebral blood flow due to the initial, fusion-initiating component of vergence lasts, and whether they overlap with cerebral blood flow due to fusion-maintaining component of disparity vergence. When the transient BOLD responses dissipate for the steady-state

BOLD responses to take over, or if those are different is difficult to say. According to

Mays et al[3], neurons encoding vergence velocity, even the most phasic neurons may

have tonic components in extreme vergence angles. Beside the accommodative vergence

paradigm, the proximal, voluntary, or even the 28 prism diopters of disparity may be

extreme.

Studies which employ strategies that have very high temporal resolutions may be needed

to differentiate the phasic or tonic components of vergence eye movements and their

hemodynamic response profiles. In addition, the various vergence paradigms had

different vergence demands, with accommodative vergence the lowest and proximal

vergence the highest. The convergence demand for voluntary vergence in this study is

indeterminate. These differences in vergence demand may have contributed to the

differences in magnitude of activations observed.

Conclusion

The results from this study show that the various vergence components appear to have

unique patterns and extent of activation in the visual processing and control centers of the

brain. They activate the occipital, parietal, temporal and frontal lobes in varying amounts.

The disparity stimulus appears to predominantly activate the occipital cortex, with a few

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cerebellar activations. The accommodative vergence stimulus activates neurons in an

occipito-parieto-frontal pathway network. The proximal vergence stimulus mainly

activates neurons in an occipito-parietal network of neurons. The gross/voluntary

stimulus appears to draw the widest extent of neural control by activating neurons in the

occipital, parietal, frontal, and cerebellar cortical areas.

The accommodative vergence stimulus activates the occipital cortex too, but at higher

areas in the visual processing stream compared to disparity. It also appears to go farther

than the disparity vergence stimulus in the visual processing hierarchy by activating more

extrastriate areas. However, the activation of common areas in the visual cortex by the

accommodative and disparity vergence stimuli may serve as part of the mechanism by

which the two oculomotor systems interact.

There appears to be evidence that the processing of depth information begins in the primary visual cortex. The processing of disparity stimulus appears to have occurred at

areas that are also used to process the other vergence stimuli. The more volitional the

stimulus, the higher the tendency to activate processing centers in the parietal, frontal and

temporal lobes.

Besides the disparity signal which appears totally reflexive in these subjects with normal

binocular vision, all the other stimuli appear to be volitional to varying degrees. Even the

accommodation stimulus appeared to stimulate volitional visual processing areas,

contrary to its retinotopic nature which is expected to make it reflexive like disparity.

Possible conditioned association of accommodation to near work through the synkinesis

which also includes convergence and pupillary constriction may explain why the

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accommodative vergence stimulus also draws processing or control from the dorsal

stream elements. Furthermore, blur cues which are 2 diopters or more in magnitude lack

directionality. Thus, subjects may unconsciously need to determine the correct direction

for accommodative responses (that is, whether to increase or relax accommodation in

response to the blur cues). This may help explain why dorsal stream elements (the

“where” system) are activated.

It is not possible to determine directionality in processing depth information from this

study. It cannot be determined from fMRI whether the processing begins from the

occipital cortex and goes towards parieto-frontal regions, or vice versa. Previous research

which shows that there is feedback and feedforward interactions between “early” and

“late” visual processing areas in the cortex make this determination more challenging in this study.

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Chapter 6: Overall Summary and Conclusion

Comparing the Results between the Clinical and Imaging Studies

The imaging suggests that the convergence components share common neural control pathways even though each also retains unique neural activations. On the other hand, the clinical study suggests that the vergence components draw on independent neural paths under open-loop conditions.

Based on stimulus accommodative convergence, there was no correlation between

accommodative vergence and disparity vergence. Also, proximal vergence appeared

negatively correlated with gradient accommodative convergence, but that correlation was

probably an artifact of the methods used to measure both accommodative and proximal

vergences. Furthermore, the clinical study showed a non-meaningful negative correlation

between proximal vergence and disparity vergence.

In the functional imaging study, each of the convergence paradigms appeared to activate

the middle occipital gyrus (Brodmann Area 18) or cuneus (Brodmann Area 19) of the

occipital lobe. Beyond this, each of the convergence stimuli appeared to activate unique

cortical areas.

Further, while the clinical study showed no correlation between accommodative vergence

(obtained by the gradient AC/A) and disparity vergence in play, the imaging study

showed overlaps between the two in the middle occipital gyrus and cuneus (Brodmann 240

Area 19). These overlaps may help explain the cross-talk between accommodation and

convergence.

Finally, the proximal vergence and gross/voluntary vergence stimulus may potentially

activate the most extensive network of neurons. This may suggest that they are very close

in terms of their effects or neural paths. Their common volitional or perceptual top-down

processing is how they may be related clinically. They are still significantly different in

terms of areas they may potentially co-activate. That may be due to differences in individual strategies for activating voluntary/gross versus proximal vergence. While some subjects are reported to simulate voluntary/gross convergence by imagining or thinking of near objects, others just look at the tips of their noses. Some other people also appear to do it effortlessly without any direct or imagined visual stimulus. These variations in strategies may explain the total lack of overlaps between proximal and voluntary/gross convergence eye movements. Nevertheless, these results offer some evidence for classifying voluntary convergence as similar to proximal convergence.

However, it should be noted that these overlaps denote potential co-activations. In other words, it is possible that each of those potential overlapping areas are multimodal in their sensitivity to stimuli. That is, the observation in this study that the middle temporal gyrus and cuneus of the occipital cortex is activated by each of the stimuli may simply imply that those two areas have no preference in terms of depth cues. They may rather possess the capacity to process any cue to depth. It does not necessarily mean that if both accommodative and disparity vergences are in a stimulus, cuneus and middle occipital gyrus will be equally activated, or activated at all.

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Implications for Current Models of Neural Control of Accommodation and Convergence

The current understanding of the neural basis of vergence eye movements under open- loop disparity shows a relationship between accommodation and disparity vergence.

McLin and Schor[14] and Schor and Horner[10] have provided evidence to show that convergence insufficiency is characterized by increased fast disparity vergence, decreased slow disparity vergence and high convergence accommodation. This high

CA/C drives down fast blur accommodation and this decreases accommodative convergence as a consequence. In addition, Mannen[129] has provided evidence to show that decreased accommodative vergence is related to increased proximal vergence under disparity open loop conditions. These imply a relationship between disparity vergence and accommodative vergence, and between accommodative and proximal vergences.

Unlike accommodation and disparity, feedback for proximal vergence is not established; proximal vergence is considered by some researchers to be very rapid, and thus probably utilizes extraretinal feed-forward control[19, 20].

The functional imaging study reported in this report provides corroborating evidence to support previous studies which showed that areas in the occipital cortex appear to be involved in the central processing for depth. The cuneus and middle occipital gyrus were activated by each of the stimuli in the functional imaging study. Therefore, the evidence suggests that these areas may be involved in the central processing for depth.

However, the results of the current studies also provide evidence that suggest a significant departure from current understanding of the neural control of accommodation, disparity and proximal vergence eye movements. The disparity open-loop conditions in 242

the clinical study showed no relationships among accommodative, disparity and proximal

vergence eye movements. The resulting conceptualization of the neural control of

vergence eye movements and accommodation is shown in models below.

In summary, the results of the clinical study suggest that there is no relationship among the open-loop vergence measures. However, previous research suggests there is a

relationship between open-loop accommodation and disparity vergences. Mannen[45] has provided evidence to show that low open-loop accommodative vergence is associated with high open-loop proximal vergence. In addition, there is a common area in the posterior cortex that has been posited to be the central depth processing unit. Finally, proximal vergence is known to be very rapid, and feedback control for proximal vergence is not established.

Based on these, two models for neural control for vergence eye movements under open- loop disparity follow. The first model (figure 98) is based on the current understanding of the neural control of vergence eye movements.

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Figure 104: Model of the neural control of accommodation and vergence eye movements based on Schor’s studies

In this study, the clinical, open-loop study showed a lack of relationships. However, the functional imaging study showed areas of possible overlap among all the vergence paradigms, and between the various pairs of vergence paradigms. Therefore, a second model was made based on the results of these two studies which account for the lack of relationships noted in the clinical study but also allow for the overlaps seen in the imaging study.

The second model is shown in figure 99 below.

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Figure 105: Model of neural control of accommodation and vergence eye movements based on the results of this study

Based on evidence suggesting that proximal vergence is suited for initiating the vergence response[13], especially in situations where the initial depth cue is high, proximal vergence can be considered to be the initial mechanism for initiating vergence response.

However, proximal vergence is fast, and only makes gross estimates of the depth demand. Once proximal vergence has decreased the level of the initial depth to a certain level, visual (retinal) feedback error signals are sent to a central depth processing center

(perhaps in the visual cortex) in a closed-loop feedback. From there, the accommodative and disparity vergence sub-systems are activated to accommodate and converge respectively. These two sub-systems reflexively activate their cross-links to affect each other, and to keep the eyes in the new vergence position. 245

When the initial depth stimulus is not too high, the process can be considered the same,

except that it begins with visual feedback information, instead of proximal inputs. The

estimates from retinotopically-sensed signals depth signals is more accurate, and this is fed back to the central processing center. Based on this new model, the sequence of events described above can be summarized as follows.

Proximal vergence initiates the vergence response to reduce the demand. The remaining stimulus error is retinotopically sensed and sent to a central processing center by way of a feedback loop. From the central processing center, signals are sent to the accommodation and disparity vergence processing centers to engage the accommodation and fusional vergence mechanisms, and to keep the eye in a steady state.

It must be emphasized that this conception is for open-loop conditions. It is not clear whether the difference observed in the two models above persists under naturalistic closed-loop accommodative and vergence conditions when the two sub-systems actively interact.

Altogether, the results of this study provide additional evidence that can help to further the understanding of the neural basis of vergence eye movement in health and disease.

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