University of Florida Thesis Or Dissertation Formatting

THE NEURAL CIRCUITRY OF RESTRICTED REPETITIVE BEHAVIOR

BRADLEY JAMES WILKES

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

To my father, Wade Wilkes, for his lifelong support, love, and encouragement

ACKNOWLEDGMENTS

This research was supported by funding from the Dissertation Research Award from the American Psychological Assocation, the Pilot Project Award (Non-Patient

Oriented Clinical/Translational Research) from the Clinical and Translational Science

Institute at the University of Florida, the Robert A. and Phyllis Levitt Award, the Gerber

Behavioral and Cognitive Neuroscience Psychology Research Award, and the Jacquelin

Goldman Scholarship in Developmental Psychology. I would especially like to thank

Drs. Mark Lewis, Marcelo Febo, David Vaillancourt, Luis Colon-Perez, Darragh Devine,

Timothy Vollmer, and Michael King for their support and guidance.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 10

ABSTRACT ...... 12

CHAPTER

1 GENERAL INTRODUCTION AND LITERATURE REVIEW ...... 14

Background ...... 14 Studying Neural Circuitry with Magnetic Resonance Imaging ...... 19 Magnetic Resonance Imaging of Repetitive Behavior in ASD and IDD ...... 25 Magnetic Resonance Imaging in Animal Models with Repetitive Behavior ...... 43 Conclusions ...... 60 Future Directions ...... 67

2 VOLUMETRIC MAGNETIC RESONANCE IMAGING IN AN ANIMAL MODEL OF REPETITIVE BEHAVIOR ...... 75

Introduction ...... 75 Methods ...... 76 Results ...... 82 Discussion ...... 91

3 DIFFUSION WEIGHTED MAGNETIC RESONANCE IMAGING IN AN ANIMAL MODEL OF REPETITIVE BEHAVIOR ...... 99

Introduction ...... 99 Methods ...... 100 Results ...... 105 Discussion ...... 111

4 VOLUMETRIC MAGNETIC RESONANCE IMAGING IN CHILDREN AND ADOLESCENTS WITH AUTISM SPECTRUM DISORDER ...... 119

Introduction ...... 119 Methods ...... 120 Results ...... 125 Discussion ...... 136

5 DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN CHILDREN AND ADOLESCENTS WITH AUTISM SPECTRUM DISORDER ...... 145

Introduction ...... 145 Methods ...... 146 Results ...... 151 Discussion ...... 154

6 GENERAL DISCUSSION ...... 162

Summary and Synthesis of Results ...... 163 Conclusions and Future Directions ...... 168

APPENDIX

A REGIONS OF INTEREST FOR ATLAS BASED VOLUMETRY ...... 173

B SUPPLEMENTARY DATA ...... 177

LIST OF REFERENCES ...... 181

BIOGRAPHICAL SKETCH ...... 203

LIST OF TABLES

Table page

1-1 Summary of studies that observed a correlation between neuroimaging findings and RRB in ASD and IDD...... 71

2-1 List of brain regions which showed a significant volume difference (FDR corrected, p < .05) in SH-B6 and SH-C58 mice ...... 87

2-2 List of brain regions which showed a significant volume difference (FDR corrected, p < .05) in EE-C58 and SH-C58 mice ...... 89

4-1 Number of participant in each group, with mean age and IQ ...... 121

4-2 Effect of diagnosis and gender on age and IQ ...... 125

4-3 List of brain regions (ROIs) which showed a significant volume difference (FDR corrected, p < .05) between ASD and TD participants ...... 132

4-4 List of brain regions (ROIs) which showed a significant volume difference (FDR corrected, p < .05) between female ASD and TD participants ...... 133

5-1 Number of participant in each group, with mean age and IQ ...... 147

5-2 Effect of diagnosis and gender on age and IQ ...... 151

A-1 List of atlases and brain regions used for volumetry in animal studies ...... 173

A-2 List of atlases and brain regions used for volumetry in human studies ...... 175

B-1 List of brain regions which showed a significant volume difference (FDR corrected, p < .05) in SH-B6 and EE-C58 mice ...... 177

B-2 List of brain regions which showed a significant volume difference (FDR corrected, p < .05) between male and female participants ...... 179

LIST OF FIGURES

Figure page

1-1 Structures and connections of brain circuits implicated in RRB in the human and mouse ...... 17

1-2 Cortico-basal ganglia macro-circuits in the human brain ...... 18

2-1 Mean total number of vertical repetitive motor behaviors exhibited by SH- C58, EE-C58, and SH-B6 groups ...... 82

2-2 Heat map of significant F-statistic values for one-way ANOVA of volume differences among animal groups ...... 83

2-3 Significant t-statistic values for post-hoc t-test of volume differences between SH-C58 and SH-B6 mice ...... 84

2-4 t-statistic values for post-hoc t-test of volume differences between SH-C58 and EE-C58 mice ...... 85

2-5 Significant t-statistic values for post-hoc t-test of volume differences between EE-C58 and SH-B6 mice ...... 86

2-6 Significant t-values for the linear model relating volume and repetitive motor behavior including mice from all groups...... 90

2-7 t-values for the linear model relating volume and repetitive motor behavior including only SH-C58 mice...... 91

3-1 Mean total number of vertical repetitive motor behaviors exhibited by SH- C58, EE-C58, and SH-B6 groups...... 105

3-2 Heat map of significant F-statistic values for one-way ANOVA of FA differences between animal groups ...... 106

3-3 Significant t-statistic values for post-hoc t-test of FA differences between SH- C58 and SH-B6 mice ...... 107

3-4 Significant t-statistic values for post-hoc t-test of FA differences between SH- C58 and EE-C58 mice ...... 108

3-5 Significant t-statistic values for post-hoc t-test of FA differences between EE- C58 and SH-B6 mice ...... 109

3-6 Significant t-values for the linear model relating FA and repetitive motor behavior including mice from all groups...... 110

3-7 t-values for the linear model relating FA and repetitive motor behavior including only SH-C58 mice...... 111

4-1 Significant t-statistic values for main effect of diagnosis on volume between ASD and TD participants ...... 127

4-2 Significant t-statistic values for main effect of gender on volume between male and female participants ...... 128

4-3 Significant F-statistic values for interaction between the effects of diagnosis and gender on volume differences between male and female participants ...... 129

4-4 Significant t-statistic values for post-hoc comparison of volume in male ASD and TD participants ...... 130

4-5 Significant t-statistic values for post-hoc comparison of volume in female ASD and TD participants ...... 131

4-6 Significant t-values for the linear model relating volume and ADI-C scores in ASD participants ...... 134

4-7 Significant t-values for the linear model relating volume and ADI-C scores in male ASD participants ...... 135

4-8 t-values for the linear model relating volume and ADI-C scores in female ASD participants ...... 136

5-1 Three-dimensional rendering of white-matter pathway templates depicted from lateral, anterior, and superior viewpoints...... 150

5-2 FWE-corrected FA for ASD and TD participants in tracts between dorsolateral prefrontal cortex and caudate...... 152

5-3 FWE-corrected FA for male and female participants in tracts between putamen and substantia nigra ...... 152

5-4 FWE-corrected FA for male and female participants in tracts between subthalamic nucleus and globus pallidus ...... 153

LIST OF ABBREVIATIONS

ACC anterior cingulate cortex AD axial diffusivity ADI-C Section C of the Autism Diagnostic Interview ADI-R Autism Diagnostic Interview ASD autism spectrum disorder BOLD blood oxygen level dependent CNS central nervous system DMN default mode network DN dentate nucleus DTI diffusion tensor imaging EE environmental enrichment FA fractional anisotropoy FC functional connectivity FDR false discovery rate fMRI functional magnetic resonance imaging FMRP Fragile-X mental retardation protein FXS Fragile-X syndrome GM gray matter GP globus pallidus GPe globus pallidus externa GPI globus pallidus interna HARDI high angular resolution diffusion imaging IDD intellectual and developmental disability IFG inferior frontal gyrus MCP middle cerebellar peduncle MD mean diffusivity MFG middle frontal gyrus MRI magnetic resonance imaging

OCD obsessive compulsive disorder PCC posterior cingulate cortex PMC primary motor cortex PND post-natal day PWS Prader-Willi syndrome RD radial diffusivity ROI region of interest RRB restricted, repetitive behavior rs-fMRI resting state functional magnetic resonance imaging SCP superior cerebellar peduncle SEM standard error of the mean SFG superior frontal gyrus SH standard housing SNc substantia nigra parts compacta SNr substantia nigra STG superior temporal gyrus STN subthalamic nucleus TBSS tract-based spatial statistics TD typically developing TFCE threshold-free cluster enhancement TS Tourette syndrome WM white matter

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE NEURAL CIRCUITRY OF RESTRICTED REPETITIVE BEHAVIOR

Bradley James Wilkes

May 2018

Chair: Mark H. Lewis Major: Psychology

Restricted, repetitive behaviors (RRBs) are patterns of behavior that exhibit little variation in form and have no obvious function. RRBs although transdiagonstic are a particularly prominent feature of certain neurodevelopmental disorders, yet relatively little is known about the neural circuitry of RRBs. Magnetic resonance imaging (MRI) is well-suited to studying the structural and functional connectivity of the nervous system, and is a highly translational research tool. In this dissertation, I first reviewed and synthesized magnetic resonance imaging (MRI) research in Autism Spectrum Disorder

(ASD) and animal models with RRB. These prior neuroimaging studies suggest a critical role for cortico-basal ganglia circuits in the expression of RRB, and provide emerging evidence for a role of the cerebellum. Next, I performed volumetric and diffusion- weighted MRI in C58 inbred mice with high levels of repetitive behavior, and compared them to genetically similar control mice with low levels of repetitive behavior (C57BL/6) as well as C58 mice reared in environmentally enriched housing conditions that display low levels of repetitive behavior. These studies provided evidence of widespread structural alterations in C58 mice that were associated with high levels of repetitive behavior, primarily localized in the cortex, basal ganglia, and cerebellum. Next, I

performed comparable structural MRI investigations using neuroimaging data from a large sample of children and adolescents with Autism Spectrum Disorders (ASD), which included nearly equal numbers of males and females, acquired from the National

Database for Autism Research. I found evidence of structural alterations in the cortex, basal ganglia, and cerebellum of children with ASD, and that differences in these regions also corresponded to higher rates of repetitive behavior in participants with

ASD. Moreover, I found that males and females with ASD show unique patterns of structural alterations associated with RRB, a finding which was not observed in animal model work in RRB. I conclude by providing a general summary and discussion of findings from the studies I conducted, and suggest future directions for MRI research in

RRB, as well as how such studies can benefit from utilizing a bi-directional approach to translational research using MRI.

CHAPTER 1 GENERAL INTRODUCTION AND LITERATURE REVIEW

Background

Restricted, repetitive behaviors (RRBs) are seemingly purposeless patterns of behavior that exhibit little variation in form, interfere with appropriate behavior, and in some cases may even cause direct harm (e.g., self-injury). RRBs encompass a broad variety of behaviors, including motor stereotypies (e.g., hand-flapping or body-rocking), compulsions, rituals, and circumscribed interests (e.g., fixation on trains or electronic devices). RRBs are often categorized as either “lower-order” repetitive motor behaviors, or “higher-order” repetitive behaviors that are associated with insistence on sameness or resistance to change (Turner, 1999). RRBs are present in a number of human conditions and disorders but the broad range of these behaviors are diagnostic for autism spectrum disorder (ASD) and highly prevalent in syndromic and non-syndromic intellectual and developmental disability (IDD; Flores et al., 2011; Mount et al., 2002;

Oakes et al., 2016). It is these disorders of development that will be the focus of this review. As the prevalence of neurodevelopmental disorders, particularly ASD, has dramatically increased over the past two decades (Boyle et al., 2011), RRBs impact a larger portion of clinical populations than ever before. For example, diagnoses of ASD have grown from one in 150 children in the year 2000 to one in 68 children as of 2012

(Christensen et al., 2016). Although behavioral interventions have some degree of success in treating RRB (Boyd et al., 2012; Rapp & Vollmer, 2005a; Rapp & Vollmer,

2005b), pharmacological interventions are currently aimed at treating associated problems (e.g., aggressive behavior) and have no demonstrated efficacy for RRB in

ASD and IDD (Carrasco et al., 2012; King et al., 2013). This shortcoming is largely due to an incomplete understanding of the neural circuitry mediating RRB.

A great deal of previous research directed toward understanding the etiology and pathophysiology of neurodevelopmental disorders has focused on alterations in specific brain regions, targeted neurotransmitter systems, and/or select genes. Although these efforts have made important contributions toward the current state of the science, much is yet to be learned about mechanisms that mediate RRB in neurodevelopmental disorders. As highlighted by Gunaydin & Kreitzer (2016), recent advances in neuroscience have fostered a shift in thinking as to how various clinical disorders and behaviors are mediated, with evidence pointing to subtle alterations across multiple brain regions, neurotransmitter systems, and synaptic processes that converge as neural circuits. ASD, for example, has been conceptualized by some as a brain network connectivity disorder (Just et al., 2004) where the integrated activity of large-scale neuronal networks is impaired. Adopting a neural circuit approach has resulted in great progress in other research areas such as drug-addiction, where convergent circuitry is affected by drugs acting through multiple molecular mechanisms (Kalivas et al., 2006).

Similarly, a circuit-oriented approach is likely to provide great utility towards understanding the complex and heterogeneous phenomena of RRB appearing in ASD and related neurodevelopmental disorders. Although little is currently known about how

RRB is mediated at the level of neural circuits, previous research in neurodevelopmental disorders (Langen et al., 2011b; Leekham et al., 2011; Lewis &

Kim, 2009) and animal models of RRB (Ahmari et al., 2016; Bechard & Lewis, 2012;

Langen et al., 2011a), suggests a critical role for networks involving the basal ganglia.

Previous efforts dedicated to understanding the pathophysiology of RRB in animal models through investigating components of basal ganglia circuitry (e.g., Bechard et al.,

2016; Presti & Lewis, 2005; Tanimura et al., 2008, 2010, 2011), have added much to our understanding of this phenomenon, but little work has been done to place these findings in the broader context of large-scale brain networks. The basal ganglia are a point of convergence for multiple large-scale brain networks, but there are also opportunities for basal ganglia circuits to be modulated by cerebellar networks at the level of cortex, thalamus, and pontine nuclei (see Fig. 1-1). The complexity of these networks is further evidenced by the existence of multiple functionally distinct cortico- basal ganglia macro-circuits (see Fig. 1-2). Magnetic resonance imaging (MRI) provides the best method for non-invasive evaluation of the structure and function of neural circuits in clinical disorders, and has enabled great progress in many areas of clinical research. Moreover, neuroimaging is being increasingly applied to the study of animal models of psychiatric conditions and neurodevelopmental disorders (Lythgoe et al.,

2003; Oguz et al., 2012). Prior neuroimaging reviews have described brain alterations in

ASD, but did not focus on how these alterations map onto specific behavioral domains such as RRB (e.g., Dichter, 2012; Ecker et al., 2015; Mahajan & Mostofsky, 2015;

Müller et al., 2011; Li et al., 2017; Rane et al., 2015). Similarly, although neuroimaging findings from animal models relevant to ASD and IDD have been described (Ellegood et al., 2015; Ellegood & Crawley, 2015), there has been almost no exploration of how these findings pertain to RRB, or other abnormal behaviors, in such models.

Figure 1-1. Structures and connections of brain circuits implicated in RRB in the human (top) and mouse (bottom). Green dots indicate medium spiny neurons of the direct pathway, and red dots indicate medium spiny neurons of the indirect pathway. Solid lines indicate known synaptic connections. The dashed line from subthalamic nucleus to pontine nuclei in the human brain indicates synaptic connections inferred from viral tracer studies in nonhuman primates (Bostan et al., 2010). Lines ending with arrows indicate glutamatergic synapses, whereas lines with flat ends indicate GABA-ergic synapses. Abbreviations: DN, dentate nucleus of the cerebellum; GPe, globus pallidus externa; GPi, globus pallidus interna; PN, pontine nuclei; STN, subthalamic nucleus; SNc, substantia nigra pars compacta; SNr, substantia nigra pars reticulata.

Figure 1-2. Cortico-basal ganglia macro-circuits in the human brain, including motor (green), associative (blue), and limbic (red) loops. These regions are depicted as approximated schematics, rather than precise anatomical divisions. Abbreviations: GPe, globus pallidus externa; GPi, globus pallidus interna; STN, subthalamic nucleus

A small body of neuroimaging findings related to RRB in ASD has been previously reviewed (Traynor & Hall, 2015), but there has been no attempt to synthesize these findings with what is presently known in the literature about the neural circuitry of

RRB from other clinical disorders and from relevant animal models. Thus, there is a pressing need for synthesis of findings from multiple neurodevelopmental disorders and corresponding animal models in order to move the field forward. In this General

Introduction, I aim to (1) critically review and synthesize neuroimaging findings from

RRB in ASD and IDD and corresponding relevant animal models, (2) determine what these findings, taken together, inform us about the specific neural circuitry of RRB, and

(3) suggest future directions for neuroimaging investigations of RRB that have been effectively employed in other areas within neuroscience. Although RRBs are observed in clinical populations other than ASD and IDD (e.g., Tourette syndrome [TS], obsessive compulsive disorder [OCD], drug addiction, frontotemporal dementia), it is beyond the scope of this dissertation to evaluate neuroimaging findings pertinent to RRB in such disorders.

Studying Neural Circuitry with Magnetic Resonance Imaging

The use of MRI in neuroscience was initially focused on gross neuroanatomy

(e.g., volumetrics) and tissue contrast (e.g., neuro-oncology). Specialized applications of MRI, such as functional and diffusion-weighted MRI, have enabled the study of the interconnected organization of the central nervous system (CNS) and have since become essential tools in neuroimaging. Moreover, the same approaches used for imaging in human populations can be applied to animal models research, enabling a highly translational research environment. Approaches used to acquire and analyze neuroimaging data, including those most relevant to studying neural circuitry have been

reviewed elsewhere (e.g., Malhi & Lagopoulos, 2007; Yang et al., 2011). Thus, the remainder of this section will only briefly review common imaging modalities and how they are used to study neural circuitry.

Volumetric MRI

T1 or T2 weighted images are often used for volumetric assessment of brain tissue. Regions of interest (ROIs) can be defined using either manual segmentation with a reference atlas, or registration software to apply previously defined ROIs from an MRI segmentation atlas. Alternatively, statistical assessments of relative volumes across all voxels in the brain may be performed independently of neuroanatomical labeling schemas. In deformation-based morphometry (DBM), images are transformed into a common stereotactic space and averaged to generate a study-specific template. Each subject’s structural scan is then registered to the study-specific template in order to generate the deformation-field necessary to transform the brain from the subject’s native space onto the study-specific template. These deformation fields, which have been demonstrated to correspond with volumetric expansion and contraction, are used as input data for voxelwise statistical analyses of brain volume across groups or conditions (Ashburner et al., 1998). Because statistical comparisons are performed for every voxel, an important component of voxelwise analyses is correction for multiple comparisons (e.g., family-wise error rate, false discovery rate). This approach does not require a priori hypotheses about how tissue may differ between groups, and may reveal subtle or unexpected differences that would not be discernable through assessment of specific pre-defined regions of interest (ROIs).

Diffusion-weighted MRI

Diffusion-weighted MRI relies upon principles of molecular diffusion, whereby molecules move through space in a temperature-dependent fashion. In biological tissue, diffusion-weighted MRI measures the rate of diffusion of water molecules and provides contrast in tissue with different rates of diffusion, mediated by its microstructural composition. For example, water diffuses at a faster rate along the length of an axon than perpendicular to it. This is due to the presence of barriers to diffusion in the perpendicular plane, such as the surrounding myelin sheath, but also the cellular membrane and microtubules along the length of the axonal process (Beaulieu, 2002).

Diffusion weighting is achieved by applying magnetic field gradients across many different orientations and this has the effect of “sensitizing” the image acquisition to displacements of water in each of these directions. The strength of the applied diffusion gradient and time over which it is applied are described with a scalar unit, b, and “b- values” are part of common terminology in describing diffusion MRI acquisition parameters (for greater detail, see Yang et al., 2011). Although a minimum of six uniformly distributed diffusion-weighted directions and one low-diffusion weighted (b0) image are required to estimate diffusion parameters, greater precision is attained with acquisition schemes of at least 20 diffusion weighted images (Jones, 2004).

Diffusion in the brain may be represented as geometric tensors that describe the direction and magnitude of net water diffusion in three orthogonal planes. This use of diffusion-weighted MRI to assess brain tissue is commonly referred to as diffusion tensor imaging (DTI). As indicated above, estimating diffusion tensors benefits greatly from acquisitions with 20 or more diffusion-sensitizing directions. For example, high angular resolution diffusion imaging (HARDI) uses at least 60 diffusion directions, with

both high and low diffusion weightings (i.e., b-values). Although imaging times are increased, developments in multichannel radiofrequency coils have reduced scan time significantly for HARDI acquisitions, keeping these within a reasonable time frame for obtaining data quickly in study subjects (e.g., 5-10 minutes with a 32-channel coil).

Diffusion tractography allows for assessment of WM fiber bundles that are not evident through gross neuroanatomy, such as parsing of WM fibers in the corona radiata based on their origin and efferent targets (Smith et al., 2004). Tractography improves as spatial resolution increases (i.e., smaller slice thickness) and with better estimates of diffusion parameters, achieved through a larger number of diffusion directions and/or higher magnitude of gradient pulses (b-values).

Two of the most common metrics from diffusion-weighted MRI are mean diffusivity (MD) and fractional anisotropy (FA). MD provides a quantification of the average rate of water diffusion across all three orthogonal planes, and is often further broken into axial diffusivity, describing rate of diffusion along the length of the fiber bundle, and radial diffusivity, an aggregate of diffusion in the two minor planes of the fiber bundle. FA describes how much the tensor ellipsoid deviates from a sphere

(isotropic diffusion). Whereas measures of MD and FA are direction independent, the directional components of diffusion tensors (eigenvectors) can be used to make inferences about continuous fiber bundles between adjacent voxels if the tensors are in alignment. These fibers bundles have been demonstrated to correspond with WM bundles in the brain, as water diffusion occurs more freely along the length of axons, whereas cellular membranes and myelination limit diffusion perpendicular to the length of axons (Moseley et al., 1990).

Segmentation approaches allow for quantification of diffusion parameters in particular ROIs, but is most commonly used for WM structures as the interpretation of diffusion parameters in GM is less clear due to its inherently complex fiber and neuronal organization (e.g., neuronal density, degree of arborization, membrane integrity).

Diffusion-weighted imaging can permit inferences about axonal connectivity and WM integrity. Technical limitations currently prevent mapping of axonal processes at the level of single cells in the mammalian nervous system, but single cells have been imaged in Aplysia (Grant et al., 2001), and efforts have been made to corroborate diffusion tensor tractography with WM histology at microscopic resolutions in the human spinal cord (Hansen et al., 2011). For large WM fasciculi (e.g., corona radiata, corpus callosum, internal capsule), assessment of diffusion parameters may be performed after segmentation. A voxelwise approach for assessing large WM fasciculi throughout the brain is referred to as tract-based spatial statistics (TBSS; Smith et al., 2006). This approach uses non-linear registration to align images from multiple individuals into a common coordinate space, followed by derivation of a “mean FA skeleton” of major WM projections using tissue contrast from FA weighted images. Statistical parametric mapping is then performed throughout the mean FA skeleton, allowing for groupwise comparisons. One advantage of TBSS is that limiting analyses to major WM pathways in the brain reduces of the number of voxels that statistical comparisons are performed on, partially alleviating concerns about multiple comparisons. As with DBM, TBSS may reveal subtle differences between groups that would not be discernable through assessment of specific pre-defined ROIs.

Functional MRI

MRI can also be used to indirectly assess brain function by means of detecting differences in signal resulting from the degree of blood oxygenation in neural tissue.

This approach, referred to as functional magnetic resonance imaging (fMRI), depends upon the difference in magnetic susceptibility for oxygenated and deoxygenated blood.

Oxyhemoglobin is paramagnetic and has little interaction with magnetic fields, whereas deoxyhemoglobin is diamagnetic and highly susceptible to influence from magnetic fields. Because the brain is highly vascularized, the signal resulting from neural tissue with different proportions of oxygenated and deoxygenated blood is distinct and can provide contrast in time-series MRI images. After further accounting for changes in cerebral metabolic rates for oxygen, cerebral blood flow and blood volume, this signal is described as the blood oxygenation level dependent (BOLD) signal (Ogawa et al.,

1992). Typically, fMRI paradigms involve BOLD signal measurement during a resting baseline with subsequent measurements during a particular task or state. The BOLD signal differences from baseline can be analyzed for particular regions of interest, or across the whole brain in a voxelwise fashion, to identify areas that are interpreted as having increased or decreased neuronal activation during a particular task or state.

Multiple statistical contrasts between different task/state paradigms then allow for assessing selective BOLD activations relevant to a priori hypotheses. An alternate approach for analyzing fMRI data uses BOLD signal coherence between different brain regions during resting state (resting state fMRI; rs-fMRI) or during a specified task, in order to infer functional connectivity between brain regions. During a resting state,

BOLD signal coherence is typically strongest between regions that comprise the default mode network (DMN). The exact role of the DMN has not been elucidated, but it has

been hypothesized to mediate a “self-centered predictive model of the world” (Raichle,

2015). Although the DMN represents brain regions with the most robust BOLD coherence during a resting state, rs-fMRI can reveal functional connectivity alterations between other regions or circuitry, provided sufficient image resolution.

Magnetic Resonance Imaging of Repetitive Behavior in ASD and IDD

Neuroimaging investigations relevant to RRB have only been performed in ASD,

Fragile X syndrome (FXS) and Prader-Willi syndrome (PWS). Only a single study has reported findings of individuals actively engaging in RRB (skin picking in PWS) during neuroimaging (Klabunde et al., 2015). The remaining imaging findings reviewed here were correlated with a clinical or behavioral metric for RRB, or an event-related fMRI task where aberrant performance may have commonalities with RRB. For a review of neuroimaging findings related to RRB in other clinical disorders (e.g., OCD, TS,

Parkinson’s disease, Huntington’s disease), please see Langen et al. (2011b). For a review of RRB in syndromic IDD that has not been studied with MRI, see Moss et al.

(2009).

Literature searches were conducted for publications that included the following search parameters: neurodevelopmental disorders with RRB (e.g., ASD, FXS, PWS), relevant neuroimaging terminology (e.g., MRI, DTI, fMRI, tractography, resting state functional connectivity), terms relevant to RRB (e.g., repetitive behavior, stereotypy, self-injury), and publication date between the years of 1990 and 2017. The resulting publications were screened to ensure that they met the criteria of correlating imaging findings to a clinical or behavioral metric for RRB, or fMRI during a task where aberrant performance may have commonalities with RRB (e.g., Go/No-go). References in these

publications yielded additional studies that met inclusion criteria and so are reviewed here.

Summary and Interpretation of Literature

Imaging studies that have focused on RRB in ASD and IDD exhibit a large degree of methodological and participant heterogeneity, including dissimilar age groups and imaging protocols. Moreover, there are multiple metrics for characterizing type and severity of RRB. For example, The Autism Diagnostic Interview – Revised (ADI-R) and

Repetitive Behavior Scale – Revised (RBS-R) are parent reports, whereas the Autism

Diagnostic Observation Schedule (ADOS) is an observational assessment performed by a clinician. Much of this work has involved volumetric comparisons, but recently investigations with DTI and rs-fMRI have provided significant contributions to this field.

Table 1 accompanies the findings reviewed here and includes: the names of studies that observed a correlation between imaging findings and RRB, the sample studied, imaging modality, behavioral task (if relevant), primary neuroimaging findings, correlated measure of RRB and direction of that correlation.

Neuroimaging studies in ASD and IDD link structural and functional alterations in a broad range of brain regions and functional networks to RRB. At first glance, it may appear unclear how these systems could all be implicated with regard to one behavioral domain. Considering the heterogeneity of RRB (i.e., motor stereotypies, compulsions, insistence on sameness) and experimental methodologies used (e.g., population, metric of RRB, imaging parameters, and analytic techniques), it is less surprising that such a broad range of findings has been reported.

Alterations to frontal and temporal cortices are the most varied and difficult to interpret in relation to RRB, as these regions are also implicated in social and/or

communication deficits. Superior frontal gyrus alterations have only been linked to RRB in children and adolescents with ASD, including two rs-fMRI studies (Uddin et al., 2013;

Weng et al., 2010) and a single diffusion weighted imaging study (Cheung et al., 2009).

These studies suggest aberrant connectivity between superior frontal gyrus and cingulate cortex, with increased connectivity to the anterior cingulate cortex (ACC) and reduced connectivity to the posterior cingulate. The superior frontal gyrus is thought to be closely linked to self-awareness and interoception, including sensorimotor processing (Goldberg et al. 2006). In the DSM-V, abnormal sensorimotor processing constitutes part of the RRB diagnostic domain for ASD, and altered function of the superior frontal gyrus in RRB may relate to altered sensorimotor processing in this population. Alternatively, involvement of the superior frontal gyrus in RRB may be a secondary effect of impaired social skills in ASD, as this region is linked with self- awareness and impaired social skills, which may be viewed as an improper balance between self-awareness versus awareness of others. In support of this notion, the insular cortex, which is also thought to mediate interoception (Critchley et al., 2004), has been repeatedly implicated in neuroimaging of RRB in ASD and FXS (Cascio et al.,

2014; Kana et al., 2007; Menon et al., 2004;; Uddin et al., 2013; Zhou et al., 2016).

Middle frontal gyrus alterations in relation to RRB (Delmonte et al., 2013;

Klabunde et al., 2015) have been observed in children and adults with ASD. The middle frontal gyrus has been implicated in self-referential processing, but is also thought to be a site of convergence between dorsal and ventral attention networks, mediating shifts in attentional control between exogenous and endogenous stimuli (Japee et al., 2015).

Klabunde et al. (2015) showed increased activity of this region during self-injurious skin

picking. One interpretation is that in PWS, the middle frontal gyrus may not be appropriately contributing to attentional switching for relevant exogenous stimuli, but rather contributes toward maintenance of attentional focus on the self. In ASD, positive correlation of ADI-R RRB scores with functional connectivity between the middle frontal gyrus and caudate (Delmonte et al., 2013) may be similarly interpreted.

The inferior frontal gyrus, superior temporal gyrus, and middle temporal gyrus have been related to RRB in ASD and FXS using structural MRI (Rojas et al., 2006;

Gothelf et al., 2008), event-related fMRI (Kana et al., 2007; Menon et al., 2004; Schmitz et al., 2006), and rs-fMRI (Di Martino et al., 2011). These cortical regions contribute to various aspects of language and communication, typically lateralized to the left hemisphere (Ardila et al. 2016). The right inferior frontal gyrus, however, has been demonstrated to play a critical role in response inhibition, particularly during Go/No-go tasks (Aron et al., 2004), and findings from Kana et al. (2007) showed reduced activation of the right inferior frontal gyrus during a Go/No-go task in adults with ASD.

Nonetheless, many of the studies discussed here have implicated left lateralized alterations to these brain regions in neurodevelopmental disorders with RRB. As social and communication deficits are diagnostic for ASD and common in FXS, it may be that correlations with RRB observed in these studies could also be driven by greater global symptom severity in these neurodevelopmental disorders. In fact, many of these studies also found a similar brain-behavior correlation for measures of social and communication deficits (e.g., Rojas et al., 2006). An alternate viewpoint, however, that some forms of RRB may drive social and communication deficits, has been supported in a longitudinal study by Larkin et al. (2016) on RRB and its relation to language and

cognitive development in young typically developing (TD) children. Their findings showed that young children who showed greater sensorimotor (i.e., lower-order) RRB at

26 months of age had poorer theory of mind and receptive verbal ability at 51 months of age. This relationship was not evident for higher-order RRBs such as restricted interests or insistence on sameness. Thus, in ASD, it may be that correlations observed between measures of RRB and altered structure/function of these cortical regions is strongly influenced by items pertaining to lower-order RRBs.

No volumetric alterations of the ACC have been found in relation to RRB.

However, RRBs have been found to correlate with altered FA in WM near the ACC in

ASD (Thakkar et al., 2008) and altered functional connectivity during rs-fMRI in participants with ASD and FXS (Uddin et al., 2013; Zhou et al., 2016), suggesting that the ACC is aberrantly connected within larger circuits. Using a sparse regression algorithm, repetitive scores on the ADI-R in children with ASD were predicted by the degree of functional connectivity within the ACC circuit known as the ‘salience-network’

(i.e., ACC, frontal cortex, insula, and thalamus). These connectivity differences were further used to train a classification algorithm to identify a participant’s diagnostic group with 78% accuracy (Uddin et al., 2013). The functional consequences of such altered connectivity are evident in many event-related fMRI studies pertinent to RRB (Klabunde et al., 2015; Menon et al., 2004). The ACC is a node within DMN circuitry, and evidence supports its critical role in response-monitoring and outcome expectation (Hoffmann &

Falkenstein, 2012). The structural and functional neuroanatomy of the ACC, particularly its caudal aspects, place it as a likely contributor to the regulation of motor commands

and/or cognitively salient information issued to the basal ganglia, which these studies suggest may be improperly mediated in neurodevelopmental disorders with RRB.

By far the most frequently reported brain alterations linked to RRB in ASD and

IDD involve nuclei of the basal ganglia. Enlargement of caudate volume, or greater caudate growth rate in younger individuals, are the most consistently reported findings linked to clinical metrics of RRB in ASD and FXS (Estes et al., 2011; Hollander et al.,

2005; Gothelf et al., 2008; Langen et al., 2007; Langen et al., 2014; Qiu et al., 2016;

Rojas et al., 2006; Sears et al., 1999). These studies mostly indicate that larger caudate volume is associated with greater RRB severity (Hollander et al., 2005; Gothelf et al.,

2008; Langen et al., 2014; Rojas et al., 2006; Sears et al., 1999), but some studies provide contrary evidence of no correlation (Langen et al., 2007) or that greater caudate volume/growth rate is linked to lower RRB severity (Estes et al., 2011; Qiu et al., 2016).

This apparent contradiction may be explained through a developmental perspective, as studies that found a negative correlation between caudate volume/growth rate and RRB were carried out in young children (Estes et al., 2011; Qiu et al., 2016), whereas positive correlations with RRB were found in adolescents and/or adults (Hollander et al.,

2005; Gothelf et al., 2008; Langen et al., 2014; Rojas et al., 2006; Sears et al., 1999).

Using the Go/No-go paradigm, fMRI studies indicated reduced task-related activation of the caudate in participants with FXS, and that better task performance was positively correlated with degree of caudate activity (Hoeft et al., 2007). Studies using rs-fMRI further indicate altered function of the caudate in the form of reduced cortico- striatal functional connectivity in ASD (Delmonte et al., 2013) and PWS (Pujol et al.,

2015). The direction of correlations identified in these studies, however, does not

correspond. As these rs-fMRI studies were performed in different neurodevelopmental disorders (i.e., ASD, FXS, PWS), different age groups, and using different RRB metrics

(i.e., ADI-R, Social Communication Questionnaire, Child Behavior Checklist), it is difficult to determine which factor might explain between-study variability for this behavioral relationship.

Enlargement of the putamen (Hollander et al., 2005; Sears et al., 1999) and greater putamen growth rate (Langen et al., 2014) were both related to greater RRB severity, as was covariance of volume of the putamen with other limbic structures

(Eisenberg et al., 2015). A single DTI study using tractography identified a negative correlation between FA of fiber bundles originating in the putamen with performance on a Go/No-go task in ASD (Langen et al., 2012). Event-related functional alterations of the putamen pertinent to RRB include reduced activity of the putamen during a Go/No-go task in FXS (Menon et al., 2004). Greater cortico-putamen functional connectivity was linked to higher RRB severity in ASD (Di Martino et al., 2011), whereas reduced functional connectivity of the putamen to globus pallidus (GP) was correlated with greater self-picking behaviors in PWS (Pujol et al., 2015). Pujol et al. (2015) did not distinguish between internal and external portions of the GP, however. Thus it is unclear whether such findings support the hypothesis of reduced activity of the indirect pathway in the expression of RRB (Bechard et al., 2016; Bechard & Lewis, 2012; Lewis & Kim,

2009; Tanimura et al., 2008, 2010, 2011).

Volume of the GP was negatively correlated with higher-order RRB in ASD

(Estes et al., 2011), but this finding was not significant after accounting for total brain volume. A large multi-site investigation of subcortical volumes in ASD by Turner et al.

(2016) also found no relationship between GP volume and RRB. As discussed above, functional connectivity between the putamen and GP were negatively correlated with self-picking behaviors in PWS (Pujol et al., 2015). Other than these three studies, there have been no specific findings linking the GP to RRB in neuroimaging studies of neurodevelopmental disorders. Moreover, none of these studies has distinguished internal and external portions of the GP. Although whole-brain analytic approaches

(e.g., DBM) have the capacity to identify such brain-behavior relationships in the GP and its subdivisions, insufficient image resolution may contribute to lack of findings in this area and will be discussed further in Magnetic Resonance Imaging of Repetitive

Behavior in ASD and IDD, Advantages and limitations.

Despite being one of the most consistently reported anatomical abnormalities related to ASD (see review by Mosconi et al., 2015), the cerebellum has only been linked to RRB in a small number of human neuroimaging studies of ASD (Cheung et al.,

2009; D’mello et al., 2015; Rojas et al., 2006; Wolff et al., 2017). These studies showed that GM smaller volumes within lobules I-IV and crus of the cerebellum corresponded to higher RRB, whereas greater volume of the vermis (lobules VIIB and VIIIA) corresponded to higher RRB (D’mello et al., 2015; Rojas et al., 2006). Of relevance to these studies, reduced volume of the cerebellar vermis in children with ASD was found to be correlated with greater RRB as measured by an observer during children’s exploration of a novel environment, although neuroimaging and behavioral assessments were performed over a year apart (Pierce & Courchesne, 2001). This study did not, however, find a significant relationship between vermis volume and RRB scores from the ADI or ADOS. Regarding cerebellar WM, greater RRB was associated with reduced

FA of the middle cerebellar peduncle, superior cerebellar peduncle, and intra-cerebellar

WM (Cheung et al., 2009; Wolff et al., 2017).

The cerebellum receives inputs from a large variety of regions in the cortex and thalamus (see Dum & Strick, 2003), so it is difficult to conjecture whether cerebellar pathology is primarily related to RRB, or whether these effects are secondary to alterations in other brain systems. Findings from Wolff et al. (2017), however, suggest that cerebellar pathology in children later diagnosed with ASD may be present as early as 6 months. Interestingly, a recent HARDI tractography study in healthy adults found evidence of fiber bundles projecting from the subthalamic nucleus (STN) to cerebellar cortex, as well as fiber bundles from the dentate nucleus to substantia nigra (SNr;

Milardi et al., 2016). This poses the interesting idea that in addition to canonical projections from STN to SNr, that there may be a parallel loop through the cerebellum

(i.e., STN  cerebellar cortex  dentate nucleus  SNr). These tractography findings are supported by viral tracing work in non-human primates that demonstrated disynaptic connections between STN and cerebellar cortex (Bostan et al., 2010) as well as the striatum and dentate nucleus of the cerebellum. Although neuroimaging studies have not yet implicated STN alterations in relation to RRB in neurodevelopmental disorders,

STN abnormalities have been implicated in animal models of RRB (Bechard et al.,

2016; Grabli et al., 2004; Tanimura et al., 2008, 2010, 2011). Furthermore, high frequency stimulation of the STN has effectively reduced stereotypy in rodents (Aliane et al., 2012), non-human primates (Baup et al., 2008), and in severe obsessive- compulsive disorder (OCD; Mallet et al., 2008). These pathways provide a potential

mechanism through which basal ganglia and cerebellar alterations linked to RRB may be related.

Although a majority of the studies discussed here reported lateralized findings in relation to RRB in ASD and IDD (e.g., D’mello et al., 2015; Hollander et al., 2005; Rojas et al., 2006; Pujol et al., 2015; Qiu et al., 2016; Sears et al., 1999), it is unclear how these findings map onto the topography of RRB. Generally speaking, the investigations covered in this review have found that in the cortex there are both left and right lateralized alterations that correlated with RRB. Subcortically, however, there is a predominance of right lateralized alterations that correlate with RRB (Di Martino et al.,

2011; Hoeft et al., 2007; Hollander et al., 2005; Pujol et al., 2015; Qiu et al., 2016; Sears et al., 1999).

There has been little distinction regarding brain regions involved in higher-order versus lower-order RRB. Most studies have correlated brain alterations with total RRB scores from the ADI-R, ADOS, or RBS-R, without further subdividing those scales.

Studies that further distinguished between higher and lower-order RRB primarily found correlations with higher-order constructs (e.g., insistence on sameness, ritualistic behavior), but not those pertaining to lower-order RRB (Hollander et al., 2005; Langen et al., 2014; Qiu et al., 2016; Sears et al., 1999). Although tasks of response inhibition, such as Go/No-go, may appear closer in construct to lower-order RRBs, they should not be considered as equivalent. Parallel cortico-basal ganglia macro-circuits (motor, associative, and limbic) are topographically organized in structures of the basal ganglia

(Alexander et al., 1986; Groenewegen et al., 2003; Karachi et al., 2005), and although it is reasonable to conjecture that higher and lower-order RRBs may correlate more

strongly with particular macro-circuits, there has been no attempt to elucidate this distinction.

Investigations of repetitive behavior in neurological and psychiatric conditions corroborate similar neural circuitry to that implicated in ASD and IDD. Diffusion imaging studies in pediatric OCD have revealed significant correlations of symptom severity with global WM integrity and regional WM integrity of the superior longitudinal fasciculus, internal capsule, and corpus callosum (for a review see Koch et al., 2014). Greater functional connectivity in portions of cortico-basal ganglia circuitry has also been found in both pediatric (Tian et al., 2016) and adult (Beucke et al., 2013) populations with

OCD. Furthermore, over-connectivity of cortico-striatal circuitry was associated with greater tic severity in Tourette syndrome (Worbe et al., 2015).

The brain-behavior relationships described in this section reflect that RRB scores can account for approximately 10 to 15 percent of the variance in regional volume for structures like the caudate and putamen. RRB scores appear to have a stronger relationship with measures of brain connectivity captured in rs-fMRI and diffusion imaging experiments, where they account for between 12 to 47 percent of total variance in these imaging metrics. It is worth noting that the strength of these brain-behavior relationships further support the notion that the structure and function of broader neural circuits may provide greater insights into RRB than the study of isolated brain regions.

Together these neuroimaging findings provide evidence of structural and functional alterations relating to RRB. These alterations differ across disorders, involve multiple cortical sub-regions and the cerebellum, but converge on structures of the basal ganglia. At a broad level, the convergence of neuroimaging findings on structures

of the basal ganglia is logical, considering the neuroanatomical convergence of cortico- basal ganglia circuits. The ACC, which was most frequently tied to RRB through event- related fMRI studies pertinent to RRB (i.e., response inhibition), is also neuroanatomically situated to modulate cortico-basal ganglia inputs, and likely plays a meaningful role in the mediation of RRB. As will be discussed in section 4, these findings with RRB corroborate similar investigations in animal models of RRB.

Advantages and limitations

The use of MRI provides the most direct and feasible approach for studying neuroanatomy and neural circuitry in human populations. Relatively few investigations, however, have correlated brain alterations in neurodevelopmental disorders to behavioral and clinical metrics pertinent to RRB, and even fewer have adopted a circuit- oriented approach. The use of functional and diffusion weighted MRI has expanded the scope of neuroimaging investigations of RRB in neurodevelopmental disorders beyond isolated brain regions and morphology, enabling the study of structure and function in complex neural circuits. These investigations have identified multiple brain regions and neural circuits that appear to play a role in the expression of RRB in neurodevelopmental disorders, but there is still much work to be done to identify the specificity of such alterations across disorders, ages, and different topographies of

RRB.

One important opportunity afforded by neuroimaging paradigms is longitudinal study designs, as multiple scans can be performed on the same individual with no known detrimental effects. The previously described longitudinal investigations by

Langen et al. (2014) and Qiu et al. (2016) on striatal development in children with ASD and their findings of increased striatal growth rate correlated with clinical metrics of RRB

would not have been revealed with cross sectional study designs. Despite these two important studies, additional longitudinal investigations in neurodevelopmental disorders with RRB are critically needed, as they have the potential to identify sensitive periods in brain growth and development in relation to behavior.

There are additional limitations inherent in human neuroimaging. One of these limitations relates to noise artifacts produced by motion, ranging from respiration to larger movements, such as shifting the body for comfort. Fortunately, this difficulty has been long-recognized by the neuroimaging community and there are numerous approaches for motion correction (Maclaren et al. 2013) for both structural and functional imaging paradigms. Motion correction approaches have not sufficiently advanced to enable correction of very large movements, however, prohibiting activity- dependent investigations of some lower-order motor RRBs (e.g., body-rocking). The inability to correct for large movements has also limited inclusion of low-functioning individuals in MRI investigations, as they are unlikely to comply with instructions to remain motionless for extended periods of time. MRI investigations of RRB in ASD focus almost exclusively on high-functioning individuals and severely limit the generalizability of findings. Nonetheless, appropriate considerations in study-design, largely drawing from the field of behavior analysis, can enable successful neuroimaging of individuals with more pronounced intellectuality disability. For example, Nordahl et al.

(2016) outlined an effective protocol for acquiring quality MRI data from low-functioning children with ASD by utilizing a pre-scan parent interview, video modeling of the intended procedures, a pediatric friendly environment (i.e., space-theme), and practice sessions in a mock-scanner.

Another limitation of neuroimaging in neurodevelopmental disorders with RRB relates to image resolution, which is directly proportional to both magnetic field strength and acquisition time. Although image resolution has been improved by increasing the field strength of magnets used for human MRI devices and improvements in transmit/receive radio frequency (RF) coil systems, acquisition times for scans used in both research and clinical settings can limit the upper boundaries of image resolution.

Scans with longer durations may become uncomfortable for participants and are typically not used in research settings as participant attrition or more severe motion artifacts may occur. Additional consideration regarding acquisition time must be given for diffusion weighted scans, as a minimum of 6 diffusion weighted directions are required to resolve diffusion parameters for each voxel, causing scan time to be at least

6 times longer than for a non-diffusion weighted image with comparable acquisition parameters. In practice, a minimum of 45 diffusion weighted directions has been suggested to appropriately characterize diffusion parameters in tissue with complex microstructural organization, such as crossing fibers (Tournier et al., 2013).

Consequently, diffusion weighted sequences typically have lower spatial resolution than non-diffusion weighted sequences in order to maintain practical acquisition times.

Nevertheless, diffusion imaging methods are improving rapidly due to developments in gradient and RF coil hardware, sequence design, and analysis software (Tournier et al.,

2011).

Despite improvements in image resolution, characterizing the structure and function of smaller subcortical nuclei can remain challenging. This has been a particularly salient limitation in the study of RRB in neurodevelopmental disorders, for

which cortico-basal ganglia circuitry is strongly implicated. For example, a recent investigation found that functional imaging of the STN during a stop-signal reaction task produced discernible activation patterns using a specialized sequence on a 7T system, but not with a comparable sequence performed on a 3T system or commonly used sequences for cortical fMRI at 7T (de Hollander et al., 2017). A recent diffusion weighted imaging investigation on a 7T system also successfully characterized neural circuits in the basal ganglia and thalamus at high resolution with a large number of diffusion directions and feasible acquisition times in neurotypical individuals (Lenglet et al., 2012). Less detailed assessments of cortico-striatal connectivity in ASD (Langen et al. 2012), cortico-striato-pallido-thalamic connectivity in Tourette syndrome (Worbe et al.

2015), and globus pallidus interna (GPi) connectivity in dystonia (Rozanski et al., 2013), however, demonstrate the feasibility of basal ganglia tractography in relevant clinical populations on 3T MRI systems. Detailed tractography of basal ganglia circuits in the context of RRB in ASD and IDD has not been performed, and such studies are greatly needed.

Limitations relating to study populations and clinical or behavior metrics relevant to RRB are also of great importance. There is a strong population bias in investigations of RRB in neurodevelopmental disorders. The majority of these investigations have been in ASD, and moreover, in high-functioning individuals. This bias may be partially explained by the population distribution of neurodevelopmental disorders with RRB, with

ASD being most prevalent. Although the implicated neural circuitry appears to have some degree of overlap across disorders, this population bias dramatically limits the generalizability of findings. Some investigators suggest that alterations to neural

circuitry are dependent on the particular topography of RRB (Eisenberg et al., 2015), which could account for further differences between neurodevelopmental disorders and among disorders with heterogeneous expression of RRB (e.g., ASD).

Another important factor in this body of work is the age of research participants.

Some of the investigations discussed in this review have specifically focused on either child or adult populations, but many include a range of ages that span across multiple developmental stages. Those studies that included participants of broader age ranges often did not have the statistical power to determine age-related effects, and as a result may have failed to identify brain alterations pertinent to RRB in their study population.

Significant developmental heterogeneities have been identified in the expression of

RRB in ASD, including age-related effects on the expression of RRB subtypes (e.g., sensorimotor vs insistence on sameness; Richler et al., 2010). As such, more rigorous control of age in experimental populations may serve to benefit future neuroimaging investigations of RRB in neurodevelopmental disorders.

Some neuroimaging findings that were correlated with RRB were also correlated with social and/or communication deficits in individuals with ASD, such as functional connectivity of the superior frontal gyrus with posterior cingulate cortex (Weng et al.,

2010). Although the overlap of these correlations may make interpretation of such brain- behavior relationships difficult in the context of those experiments, they were not specifically designed to parse out the relative contribution of RRB to the observed neuroimaging measures while controlling for social and/or communication deficits. It is certainly possible to utilize statistical methods to critically investigate the relationship

between brain alterations and RRB, while controlling for other factors such as social and/or communication deficits.

In terms of clinical metrics of RRB, a majority of the studies discussed here have correlated their findings with section C of the ADI-R (Restricted, Repetitive, and

Stereotyped Patterns of Behavior). Although this clinical scale is useful as a diagnostic tool, its specificity for ASD limits its application to other neurodevelopmental disorders with RRB. The RBS-R has also been has been successfully used to characterize RRB in other neurodevelopmental disorders (Bodfish et al., 2000; Flores et al. 2011; Oakes et al., 2016), and has capacity to distinguish subtypes of RRB. Neuroimaging studies have found significant brain-behavior relationships using both RBS-R total scores

(Eisenberg et al., 2015; Wolff et al., 2013, 2015, 2017), as well as particular subtypes of

RRB (e.g., self-injurious behavior; Wolff et al., 2013). In addition to brain-behavior relationships with RRB described in neurodevelopmental disorders, neuroimaging correlations with scales relevant to RRB have been described in other conditions. For example, scores on the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) showed positive correlation with functional connectivity of the putamen in individuals with OCD

(Beucke et al., 2013).

Several scales that have been used to characterize RRB in neurodevelopmental disorders have not yet been used in neuroimaging studies of RRB. The Social

Communication Questionnaire (SCQ; Rutter et al., 2003), Diagnostic Interview for

Social and Communication Disorders (DISCO; Wing et al., 2002), and Repetitive

Behavior Questionnaire (RBQ; Turner, 1995) all contain items relevant for characterizing RRB and have been used in populations with neurodevelopmental

disorders. Use of these measures alongside neuroimaging may enable more comprehensive explorations of brain-behavior relationships with RRB, as well as greater generalizability across neurodevelopmental disorders.

The Go/No-go paradigm of response-inhibition used as a stand-in for RRB during fMRI investigations accounts for a large portion of neuroimaging research on

RRB. Although performance on such tasks have been shown to correlate with RRB severity in ASD (Mostert-Kerckhoffs et al., 2015), a meta-analysis of fMRI studies on response inhibition suggests that many brain networks that are observed to show greater activity during Go/No-go tasks may actually correspond to cognitive constructs

(e.g., working memory, attentional shifts) that are engaged by more complex implementations of the Go/No-go task (see Criaud & Boulinguez, 2013). Moreover, there are commonly used behavioral tasks of response inhibition that have not been studied with fMRI in neurodevelopmental disorders. For example, fMRI experiments using the stop-signal reaction task have not been performed in neurodevelopmental disorders, despite evidence that children with ASD show behavioral deficits on it (de

Vries & Geurts, 2014). Inclusion of a broader range of behavioral tasks in fMRI research may help disentangle the complex involvement of a wide-variety of brain regions in

RRB.

Lastly, despite the unparalleled advantages of directly characterizing brain alterations and their relationship with RRB in neurodevelopmental disorders, findings from these studies in humans cannot be followed up with more detailed characterizations and manipulations of the affected neural circuitry at cellular, molecular, and genetic levels of analysis. Fortunately, neuroimaging in combination with

other techniques for manipulating and characterizing neural circuitry in animal models of neurodevelopmental disorders (e.g., optogenetics, viral tracers) provides the possibility for more rigorous experimental control in the study of RRB.

Magnetic Resonance Imaging in Animal Models with Repetitive Behavior

Due to ethical and methodological considerations in human research, animal models are predominantly used to study the neurobiological underpinnings of RRB.

Animal models of RRB can be induced through a variety of insults to the central nervous system (e.g., lesion or genetic mutants), pharmacological manipulations (e.g., psychostimulants), impoverishment of early environment/experience, and use of particular inbred mouse strains (for a review, see Bechard & Lewis, 2012). As in human neurodevelopmental disorders, presentation of RRB is not uniform across animal models. Some animal models primarily display lower-order RRBs, such as motor stereotypies, as in the excessive self-grooming in multiple lines of Shank3 mutant mice

(Wang et al., 2011). Other animal models also display behaviors that reflect higher- order RRB, such as reversal learning deficits in C58/J mice (Whitehouse et al., 2017), which may serve as a model for ‘insistence on sameness’ behavior. This discussion will center on animal models with RRB and MRI findings in those models. I will forego discussion of animal models with RRB that have not been investigated with MRI.

There has been little work aimed toward understanding common or disparate mechanisms mediating RRB among these animal models. Although the targeted genetic and molecular alterations used to generate some of these animal models were based on genetic studies of human neurodevelopmental disorders (e.g., Shank3,

15q11-13), there is an incomplete understanding of how these isolated manipulations converge at cellular and neural circuit levels to mediate RRB. MRI is well-suited to the

task of characterizing alterations of brain structure and function that mediate RRB across these animal models, as well as translational research to human disorders.

Furthermore, MRI findings in animal models with RRB provide a useful foundation for targeted cellular, molecular, and genetic level investigations using other research methodologies.

The field of preclinical neuroimaging research in RRB is in its infancy, however.

Only two of the neuroimaging studies animal models with RRB have correlated imaging and a behavioral measurement of RRB (Allemang-Grand et al., 2017; Ellegood et al.,

2013). This lack of specificity is reflected by the fact that many these animal models were not investigated with the goal of understanding the pathophysiology of RRB per se, but rather to understand how genetic mutations associated with neurodevelopmental disorders alter brain morphology. This limitation severely restricts interpretation of brain alterations described in many of these models, as they cannot be conclusively linked to

RRB expression or severity. Due to this limitation and the limited scope of neuroimaging work performed in these models, neuroimaging studies in animal models with RRB will be discussed even in the absence of specific correlations with behavioral measures of

RRB. The findings discussed in this section will not be presented in tabular format, however, as the majority of experiments only implemented volumetric comparisons and have no behavioral data to correlate with imaging findings.

Summary and Interpretation of Literature

The predominant approach for studying animal models with RRB with MRI has been through ex vivo structural assessments using regional volumetrics or DBM. A large amount of evidence regarding neuroanatomical alterations in animal models with

RRB comes from a study of 26 mouse models of ASD (Ellegood et al., 2015), although

not all models included in that study exhibit an RRB phenotype. As many of the models discussed subsequently include findings from Ellegood et al. (2015), it should be noted that some of these findings were gleaned from figures included in that paper, and were not explicitly stated by the authors as statistically significant.

DTI experiments, including those utilizing tractography, have only been performed in BTBR and Fmr1 mouse models of RRB (Dodero et al., 2013; Ellegood et al., 2010; Ellegood et al., 2013; Haberl et al., 2013; Sforazinni et al., 2016). Although

Ellegood et al. (2013) performed behavioral assessment in BTBR mice prior to imaging, brain-behavior correlations were only performed with volumetric MRI findings and not

DTI metrics. There are also small number of studies that have used fMRI in an animal model with RRB (Dodero et al., 2013; Haberl et al., 2015; Sforazzini et al. 2016;

Squillace et al., 2014;), none of which includes a behavioral measure in relation to imaging findings. A majority of this work has been carried out in animal models with

RRB resulting from targeted genetic mutations, with more recent work emerging from inbred strains with RRB. Neither animal models with RRB induced pharmacologically or by specific early environmental insults have been investigated with MRI.

Mice with mutations to the Mecp2 gene have been used as a model of Rett syndrome and display forelimb stereotypies (Moretti et al. 2005). Compared to control mice with normal Mecp2 expression, male mice with hemizygous Mecp2 truncation (-/Y) had larger relative volume in the cerebellar vermis (Steadman et al., 2014). Female mice with heterozygous Mecp2 truncation (-/+) had greater volume in the vermis and cerebellar cortex (lobules IV – VII), whereas female mice with homozygous Mecp2 truncation (-/-) showed more widespread volumetric enlargements in the cerebellum,

including vermis and a greater number of regions in the cerebellar cortex (lobules III –

X). Subsequent investigation of Mecp2 hemizygous male (-/Y) mice confirmed prior findings of increased volumes in the cerebellum, but also showed other volumetric differences, including greater volumes in the brainstem and smaller volumes in frontal cortex, striatum, stria terminalis, corpus callosum, and thalamus (Ellegood et al., 2015).

An investigation of the relationship between Mecp2 phenotypes and neuroanatomical alterations was performed by Allemang-Grand et al. (2017).

Independent of phenotype, all Mecp2 models studied showed reduced volumes in the cortex (frontal, motor, somatosensory, and cingulate), striatum, globus pallidus, and thalamus. Effects on the cerebellum were dependent on the type and functional severity of the mutation, where greater severity of mutation corresponded to greater enlargements within the cerebellum. Moreover, linear models relating neuroanatomy to a phenotype severity score found negative correlations between severity and volume of the cingulate, motor and somatosensory cortices, striatum, globus pallidus, and basal forebrain. These findings correspond well with morphological alterations in human imaging studies of Rett syndrome (Reiss et al, 1993; Carter et al., 2008; Dunn et al.,

2002; Naidu et al., 2001), and provide preliminary evidence that such alterations may relate to RRB in this population.

Mice with a knockout (KO) of the Fmr1 gene, the causal factor of FXS, display increased self-grooming when expressed on the C57BL/6 genetic background but not on the FVB background (Pietropaolo et al., 2011). Fmr1 KOs on the C57BL/6 background showed reduced volume of cerebellar WM and deep cerebellar nuclei, with a notable trend toward reduced volume of the striatum (Ellegood et al., 2010), although

no differences were observed in DTI metrics from the same study. One study performed

DTI and rs-fMRI to examine structural and functional connectivity in the cortex and basal ganglia of Fmr1 KO mice on the C57BL/6 background (Haberl et al., 2015), but did not include analyses in the cerebellum. Their findings showed reduced structural connectivity (i.e., FA) in the corpus callosum and brain-wide reductions in functional connectivity, including connections between cortical association regions (i.e., visual, auditory, motor, somatosensory), as well as between striatum and primary somatosensory cortex. These findings are consistent with the notion of dysfunctional cortico-striatal circuits in animal models that display RRB.

Mice with only one copy of the 16p11.2 gene, deletions of which have been found in individuals with ASD, display increased locomotion and stereotypic climbing behaviors (Horev et al., 2011). Portman et al. (2014) showed that juvenile mice with only one copy of the 16p11.2 gene (16p11.2 +/-) have smaller absolute volumes of the striatum, but this difference did not remain after controlling for total brain volume.

Controlling for total brain volume, 16p11.2 +/- mice showed smaller volume in the GP, nucleus accumbens, and several regions of frontal cortex that project to the basal ganglia. A separate investigation in adult 16p11.2 heterozygous mice showed significant neuroanatomical differences for 11 of 62 of the brain regions measured, controlling for total brain volume (Ellegood et al., 2015). These included smaller volume of striatum and larger volumes of the midbrain, fornix, hypothalamus, periaqueductal grey, and superior colliculus

Two models with RRB phenotypes result from mutations of the gene coding for the β3 subunit of the GABAA receptor on the q11-13 region of chromosome 15, which

has been associated with ASD, PWS, and Angelman syndrome. Homozygous 15q11-13

KOs display repetitive circling behaviors (DeLorey et al., 1998), whereas mice with

15q11-13 duplication show behavioral inflexibility (Nakatani et al., 2009). Although

15q11-13 homozygous KO mice have not been studied with MRI, mice with a 15q11-13 duplication showed significant neuroanatomical differences for 15 of 62 brain regions, including smaller relative volumes for thalamus, hypothalamus, midbrain, basal forebrain, medial septum, superior cerebellar peduncle, and inferior and superior colliculi (Ellegood et al., 2015).

Mice with a deletion of the Shank3 gene, which encodes a synaptic scaffolding protein associated with ASD, show reduced social behaviors, engage in excessive self- grooming, and have dysfunctional glutamatergic synapses (Peça et al., 2011; Yang et al., 2012; Wang et al., 2011). Shank3 KO mice on a C57BL/6 background show significantly reduced total brain volume, but after controlling for multiple comparisons none of the observed differences in relative brain volume remained for the 62 brain regions investigated (Ellegood et al., 2015). Mice with a KO of the neurexin-1α gene, a presynaptic cell adhesion protein associated with ASD, exhibit excessive self-grooming and impaired pre-pulse inhibition, a measure of sensorimotor gating (Etherton et al.,

2009). Data figures from Ellegood et al. (2015), although not explicitly discussed in text, show that heterozygous neurexin-1α KOs have enlarged striatal volume, whereas homozygous neurexin-1α KOs have reduced striatal volume, and both manipulations result in reduced volume of the superior cerebellar peduncle.

Mice with a deletion of the integrin β3receptor (Itgβ3) gene, which has been linked to increased 5-HT levels in ASD, display increased self-grooming and reduced

preference for novel social interactions (Carter et al., 2011). Compared to control mice with normal Itgβ3 expression, homozygous Itgβ3 KO mice show significant neuroanatomical differences in 39 out of 62 brain regions, including larger relative volume of amygdala and parieto-temporal lobe, as well as smaller relative volume of frontal lobe, striatum, GP, internal capsule, thalamus, corpus callosum, cerebellar cortex and deep nuclei (Steadman et al., 2014; Ellegood et al., 2015).

Parvalbumin is a calcium binding protein, and individuals with ASD show reductions in parvalbumin containing interneurons in the medial prefrontal cortex

(Hashemi et al., 2016). Parvalbumin KO mice have deficits in social interactions and ultrasonic vocalizations, which are used to mimic the communication deficits diagnostic for ASD. Parvalbumin KO mice also show deficits in reversal learning, another measure of “higher-order” RRB (Wöhr et al., 2015). MRI in Parvalbumin KO mice showed that they had smaller neocortical volume and greater cerebellar volume as juveniles, but that these differences did not persist into adulthood (Wöhr et al., 2015). Volumetric comparisons with more detailed neuroanatomical distinctions have yet to be performed in this model.

Specific teratogens known to induce CNS damage have also been implicated as causal factors in a sub-population of ASD. Rats that have been exposed to valproic acid

(VPA) in utero display locomotor stereotypy, reduced acoustic prepulse inhibition, and decreased number of social behaviors (Schneider & Przewlocki, 2005). Frisch et al.

(2009) showed that rats that have been exposed to VPA in utero show reduced total brain volume, as well as reduced relative brain volumes of cortex and brainstem. A separate investigation of rats exposed to VPA in utero demonstrated reduced volume of

the hippocampus, as well as greater T2 relaxation time for hippocampus, thalamus, amygdala, and striatum (Petrenko et al., 2013).

The animal model of RRB most rigorously characterized through MRI is the inbred BTBR mouse strain. The BTBR strain, which exhibits agenesis of the corpus callosum, recapitulates many features of ASD, including impaired social behavior and

RRB in the form of intense bouts of spontaneous self-grooming (McFarlane et al., 2008;

Ellegood et al., 2013), as well as deficits in reversal learning during operant tasks

(Amodeo et al., 2012). Ellegood et al. (2013) demonstrated that relative to total brain volume, inbred BTBR mice have significant volumetric differences for 20 out of 62 brain regions as compared to control strains with low incidence of repetitive behaviors

(C57BL/6 and FVB). BTBR mice showed smaller cerebral white matter and ventricular volumes than either control strain, reduced cerebral grey matter compared to B6 mice, but increased brainstem and olfactory volumes compared to both control strains. The cerebellum of BTBR mice was smaller than FVB controls, but larger than B6 controls.

DTI was used to assess FA differences in a voxelwise fashion. As expected due to callosal agenesis in BTBR mice, there was reduced FA in regions where the corpus callosum would be, but also reduced FA of the external capsule and increased FA in hippocampal white matter, although these findings were not correlated with self- grooming. Moreover, whole-brain DBM was used to assess voxel clusters where volume differences significantly correlated with higher self-grooming behaviors, but not reduced sociability, across all animals included in the study. The authors note that this analytic strategy did not result in a “barbell” effect driven purely by volume differences between strains (Ellegood et al., 2013). Clusters with relatively reduced volume were

found within the striatum, GP, thalamus, hippocampus (dentate gyrus), entorhinal cortex, internal capsule, and stria medullaris. In contrast, voxel clusters with enlarged volume correlated with higher self-grooming behaviors, but not reduced sociability, were found within the hippocampus (CA3), cerebellar cortex, and arbor vita of the cerebellum.

These findings provide robust evidence of brain alterations specific to RRB, rather than non-specific strain differences of the BTBR model.

Dodero et al. (2013) used TBSS and diffusion tractography, and rs-fMRI to investigate BTBR neuropathology. They confirmed previous evidence of callosal agenesis and abnormal hippocampal commissure, but also revealing the presence of large rostro-caudal WM “Probst” bundles. This study selected seed regions for deterministic tractography that were first revealed by TBSS, which is limited to the large- scale WM “skeleton” of the brain. It remains unclear whether BTBR mice have abnormal connectivity between structures that have been identified as having volumetric differences, such as fiber bundles arriving at or projecting from GP. Rs-fMRI revealed that BTBR mice have reduced basal cerebral blood volume in thalamus, nucleus accumbens, cingulate, and somatosensory cortex, and increased activity in the hypothalamus and dorsal hippocampus.

In a similar investigation in BTBR mice and C57BL/6 controls, Sforazzini et al.

(2016) performed DTI and rs-fMRI in order to perform tractography and resting state functional connectivity analyses, respectively. Tractography verified callosal agenesis and rostro-caudal “Probst” bundles in BTBR mice. Interhemispheric functional connectivity analyses revealed that BTBR mice had reduced interhemispheric connectivity for striatum, cingulate, insular, frontal association regions, primary and

secondary motor cortex, but greater interhemispheric connectivity within hippocampal, occipital and temporal regions. Immunohistochemistry with a viral tracer was also performed and supported interhemispheric connectivity abnormalities found with rs- fMRI. There was also evidence of bilateral differences in functional connectivity, particularly underconnectivity of striato-thalamo-cortical circuits in BTBR mice, but histochemical approaches were not used to verify this subset of findings.

One additional study to utilize fMRI in BTBR mice was a targeted fMRI investigation of dopaminergic function. BTBR mice were shown to lack the fMRI BOLD response seen in control mice in response to the dopamine transporter blocker GBR

12909 (Squillace et al., 2014). This experiment, however, was targeted toward better understanding of altered reward-processing seen in ASD, and had no specific relation to the RRB phenotype seen in BTBR mice.

Cortical aberrations in animal models of RRB differ across models, but alterations in volume and function were often localized to frontal and temporal cortical regions (Ellegood et al., 2015; Haberl et al., 2015; Portman et al., 2014). In addition, clustering analysis carried out by Ellegood et al. (2015) on neuroanatomical differences across ASD-relevant mouse models, many of which show RRB, revealed that cortical changes were closely linked to basal ganglia changes. Altered structure and function of frontal and temporal cortices were similarly implicated in neuroimaging studies of neurodevelopmental disorders with RRB (see section 3.1). As in human neurodevelopmental disorders, some of these animal models also display abnormal social and/or communication phenotypes in addition to RRB. In the absence of behavioral correlations, it is impossible to determine whether these frontal and temporal

cortical alterations in animal models with RRB specifically relate to their RRB phenotype. Parsing how these alterations relate to behavior for these cortical regions will be difficult, considering the less comprehensive mapping of structure-function relationships in the rodent cortex.

Analogous to human neurodevelopmental disorders with RRB, the most frequently reported findings in animal models with RRB were alterations of structures within the basal ganglia. The most consistent finding in animal models with RRB was reduced volume of the striatum (Ellegood et al., 2010; Ellegood et al., 2015; Portman et al., 2014), which was also negatively correlated with self-grooming behavior in BTBR mice (Ellegood et al., 2013). The direction of this volume difference is, however, opposite of that observed in neurodevelopmental disorders (i.e., enlargement of the striatum). It is difficult to speculate how opposite effects on volume might result in similar behavioral outcomes between human neurodevelopmental disorders and animals models with RRB, as such a volumetric divergence has not been documented between other human disorders and corresponding animal models.

Reduced volume of the GP was also demonstrated in animal models with RRB

(Ellegood et al., 2015; Portman et al., 2014; Steadman et al., 2014) and in BTBR mice was negatively correlated with self-grooming behavior (Ellegood et al., 2013). This finding is consistent with human neuroimaging data from a large multi-site investigation in ASD, which showed reduced volume of the GP, despite no observed correlation with

RRB (Turner et al., 2016). It is possible that the GP plays a different role in the expression of RRB in human neurodevelopmental disorders and animal models. These species-related differences in GP function are evidenced by the fact that high frequency

stimulation of the entopeduncular nucleus (rodent equivalent to human GPi) did not attenuate Parkinsonian symptoms in a rodent model (Fischer et al., 2015), yet the GPi is one of the major sites for deep brain stimulation used to improve motor symptoms of

Parkinson’s disease in humans. Further neuroimaging investigations are needed to confirm this correlation in BTBR mice, as well as possible correlations of RRB with GP volume in other relevant animal models of RRB.

Differences in cerebellar anatomy have been repeatedly associated with animal models that include RRB, but the direction of these findings has been inconsistent across models. A number of relevant animal models showed clusters of reduced volume within the cerebellum, including Fmr1, 15q11-13, Shank3, and Itgβ3 mice (Ellegood et al., 2010, 2015). Mecp2 and Parvalbumin KO mice, however, showed enlargement within the cerebellum (Ellegood et al., 2015; Steadman et al., 2014; Wöhr et al., 2015).

BTBR mice were shown to have volumetric enlargement of the cerebellar cortex and arbor vita, correlated with greater self-grooming behaviors (Ellegood et al., 2013). The cerebellum is often implicated in MRI studies of neurodevelopmental disorders, especially in ASD, and these studies suggest that RRB corresponds to smaller volumes within cerebellar GM or reduced FA of cerebellar WM (Cheung et al., 2009; D’mello et al., 2015; Rojas et al., 2006; Wolff et al., 2017). Considering animal and human studies together, there is greater correspondence for the notion of reduced cerebellar volumes in relation to RRB.

Advantages and Limitations

In animal models, RRB results from a variety of genetic manipulations, administration of pharmacological agents (e.g., amphetamine, cocaine), impoverishment of early environment/experience (Mason & Rushen, 2006), and

generation of specific inbred mouse strains. Some of these models represent highly salient features of RRB in human neurodevelopmental disorders (e.g., Mecp2 mice).

Other models, however, display RRBs that do not align well with RRBs observed in corresponding human neurodevelopmental disorders (e.g., Fmr1 mice). A majority of these animal models center around sensorimotor, or lower-order RRBs, but there are select animal models of RRB that also display analogs for higher-order RRB, such as reversal learning deficits. A critical shortcoming of neuroimaging investigations of animal model with RRB has been the lack of correlation between brain alterations and behavioral indices of RRB. Only two studies have correlated their neuroimaging findings with behavioral indices of RRB in an animal model (Allemang-Grand et al. 2017;

Ellegood et al., 2013), and no study thus far has explored the relationship between brain alterations and analogs of higher-order RRB. Although the animal imaging studies discussed in this review have contributed an important early step toward understanding brain changes in neurodevelopmental disorders with RRB, this critical gap severely limits interpretation of findings. The current state of animal model research and parallel investigations in clinical populations warrant more detailed and rigorous neuroimaging investigations of RRB in these animal models.

It is also important to note that many of the animal models with RRB discussed in this review were investigated with the aim of understanding ASD pathology in general.

Thus, these models display abnormal social behaviors in addition to RRB. In the absence of behavioral correlations, some of the neuroimaging findings from these models may relate to abnormal social behaviors. There are a number of animal models of RRB which have not yet been investigated with neuroimaging approaches, such as

Sapap3 KO mice (Welch et al., 2007), the inbred C58 mouse strain (Muehlmann et al.,

2012), or the outbred deer mouse (Peromyscus maniculatus) (Muehlmann et al., 2015).

MRI investigations are needed for these models, as they have been demonstrated to display robust and specific RRB phenotypes.

Although use of MRI to study animal models of RRB provides opportunities for experimental manipulations that are not possible in human clinical populations, there are some fundamental limitations in this area of research. The most salient of these limitations is that rodent brains (for mice, ~ 450 mm3) are dramatically smaller than human brains (~1300 cm3). The use of higher field strength magnets and longer scan times in animal neuroimaging, however, allow for much greater spatial resolution than is common in human neuroimaging. Despite greater spatial resolution afforded by methodologies used in animal neuroimaging, the smaller total volume of rodent brains makes the relative resolution of animal and human neuroimaging data comparable.

Fortunately, high-resolution animal neuroimaging is approaching the spatial resolution common among histological assays, as evidenced by Johnson et al. (2008), who performed ex vivo imaging of C57BL/6 brains at isotropic resolutions of 21.5 µm.

Although high-resolution structural scans afford similar resolution to histological approaches, diffusion-weighted MRI and tractography provide the unparalleled utility for simultaneous characterizations of microstructural organization across multiple brain regions and the connections between them. Importantly, assessment with MRI and/or

DTI does not preclude histological analyses, which may subsequently be performed on the same tissue. Although the use of neuroimaging in concert with histological approaches for studying neural circuitry in animal models has not been applied to RRB,

these complementary approaches have been effectively demonstrated in the study of traumatic brain injury, drug addiction, and brain development (Laitinen et al., 2015;

Narayana et al., 2014; Takahashi et al., 2012).

Related to image resolution, a number of mouse neuroimaging atlases group together numerous small subcortical structures, some of which are critical components of cortico-basal ganglia circuitry. For example, the high resolution (32 µm) C57BL/6 atlas established by Dorr et al. (2008), which has been utilized by a number of subsequent studies of animal models of RRB, does not include individual segmentations for STN or SNr, both of which are critical components of the cortico- basal ganglia circuitry implicated in RRB. The C57BL/6 basal ganglia atlas generated by the Australian Mouse Brain Mapping Consortium (Ullmann et al., 2014) does not contain segmentation for STN or SNr, but a more recently developed diencephalon atlas from the same group includes segmentation of these structures (Watson et al., 2017).

Similarly, a C57BL/6 atlas at comparable resolution (21.5 µm) established by Johnson et al. (2010) also includes segmentation of the SNr. These atlases demonstrate that assessment of small basal ganglia nuclei (e.g., STN and SNr) in the mouse brain can be performed with neuroimaging methodologies currently in practice, although no neuroimaging studies have yet investigated these structures in animal models with

RRB.

Exploratory analyses, such as DBM or whole-brain volumetrics using pre-defined neuroimaging atlases, comprise a bulk of the neuroimaging investigations in animal models of RRB. Although exploratory neuroimaging investigations are an important component toward understanding the neural circuitry mediating RRB, there is a strong

need for hypothesis based investigations in this area of research. White-matter alterations have been repeatedly implicated in neurodevelopmental disorders, such as

ASD (Ben Bashat et al., 2007; Billeci et al., 2012; Bode et al., 2012; Langen et al.,

2014), but comparable experiments have not been performed in animal models of RRB.

It is likely alterations in white-matter integrity and microstructure contribute to development and expression of RRB, and this relationship could be explored in greater detail with animal models.

There are only three currently published DTI investigations in an animal model with RRB, all using BTBR mice (Dodero et al. 2013; Ellegood et al., 2013; Sforazzini et al., 2016). DTI studies in other animal models of RRB are sorely needed, as well as tractography analyses., Inclusion of behavioral measures and/or histology would also greatly benefit DTI studies in animal models of RRB. Furthermore, although research lacking a priori hypotheses can provide a useful starting point for identifying pathology in animal models of RRB, hypothesis-driven investigations into specific components of the neural circuitry implicated in RRB are needed. Hypothesis-based queries are especially critical for tractography analyses, as ‘seed’ and /or ‘target’ regions are necessary.

fMRI has not been widely used to study animal models of RRB, with the exception of BTBR (Dodero et al., 2013; Sforazzini et al., 2016; Squillace et al., 2014) and Fmr1 mice (Haberl et al., 2015). Importantly, no fMRI study in an animal model of

RRB has correlated brain alterations with behavior. As fMRI investigations are often related to specific behaviors or response patterns, it is not surprising that this neuroimaging approach has not been as widely utilized in animal models of RRB.

Motion artifacts preclude the possibility of acquiring quality fMRI data from an animal

engaging in lower-order RRB, and there are currently no appropriate analogs to high- order RRB in animal models that are suitable to investigation with fMRI. Surprisingly, there are relatively few rs-fMRI studies that have assessed functional connectivity in an animal model with RRB (Dodero et al., Haberl et al., 2015; Sforazinni et al., 2016).

Assessment of functional connectivity with rs-fMRI is well-suited to the study of RRB in animal models, and more such investigations using this approach are needed.

One of the major advantages of studying neurobiology of RRB with MRI in animal models is the capacity to perform targeted histological analyses to investigate mechanistic underpinnings of MRI findings in those models. Furthermore, histological studies using neuronal tracers, such as biotinylated dextran amines or modified viral vectors, provide the capacity to look at specific neuronal projections between structures of interest, and can be compared to tractography data from neuroimaging. This approach has been elegantly demonstrated in BTBR (Sforazzini et al., 2016) and

C57BL/6J control mice (Calabrese et al., 2015).

A number of nonhuman primate models of RRB have been described (Lutz,

2014), and these models have added to our understanding of the neural circuitry of

RRB (Bauman et al., 2008; Baup et al., 2008; Grabli et al., 2004; Martin et al., 2008;

Saka et al., 2004). To our knowledge, however, MRI has only been used in nonhuman primate models of RRB as a tool to identify stereotactic coordinates for targeted chemical lesions (Bauman et al., 2008). Nonhuman primate models are an important intermediate in translational research between rodent models and humans (Watson and

Platt, 2012). Neuroimaging in nonhuman primate models of RRB is needed to better

understand neural circuitry of RRB, especially with regard to neural circuits that share a greater degree of homology to humans than mice, such as the frontal cortex.

Lastly, compared to other approaches for studying brain morphology and circuitry, MRI methodologies are the most highly translational to human research. MRI has set the stage for bidirectional translational research, where findings from clinical populations can also be more thoroughly investigated in corresponding animal models.

The bidirectional translational utility of MRI is especially evident in the area of stroke research. Dijkhuizen et al. (2012) described how loss of interhemispheric functional connectivity following ischemic stroke in humans has been replicated in rats with experimentally induced stroke, and mechanistic details of this process were further investigated with manganese enhanced MRI (van Meer et al., 2010), an in vivo tract- tracing agent not used in human due to potential adverse effects.

Conclusions

RRBs appear across a number of neurodevelopmental disorders, but effective treatments are limited by inadequate understanding of the neural circuitry mediating such behaviors. Neuroimaging investigations are particularly well-suited to the translational study of neural circuitry and have provided insights into altered brain morphology, connectivity, and function that mediate RRB in neurodevelopmental disorders and corresponding animal models.

In ASD and IDD, alterations of the frontal and temporal cortices have been repeatedly correlated with RRB, but were often not unique to RRB. Many of these same frontotemporal regions were also implicated in social and communication deficits, often within the same studies. The goal of these investigations was to relate neuroimaging findings to general ASD symptomatology, rather than targeting brain-behavior

relationships for RRB while controlling for social or communication deficits. The ACC, which has been consistently implicated in fMRI tasks pertinent to RRB, is thought to be involved in the regulation of motor commands and/or cognitively salient information to the basal ganglia (Hoffmann & Falkenstein, 2012).

Alterations in structures of the basal ganglia are by far the most consistently reported imaging findings correlated with RRB among multiple neurodevelopmental disorders. Enlargement of the caudate and putamen, as well as aberrant cortico-striatal connectivity, implicate widespread alterations to a variety of brain networks. Currently there is lack of MRI research into the structure, connectivity, and function of smaller basal ganglia nuclei, and how alterations in these regions pertain to RRB in neurodevelopmental disorders. In particular, the STN has not yet been linked to RRB in imaging studies of neurodevelopmental disorders, despite its well-established role in animal models of RRB (Aliane et al., 2012; Bechard et al., 2016; Baup et al., 2008;

Tanimura et al., 2008, 2010, 2011). This shortcoming is likely methodological in nature, and relates to use of insufficient image resolution or anatomical labeling practices.

A major role of the cerebellum and associated circuitry has begun to emerge from neuroimaging studies of RRB. Alterations to the cerebellum are also one of the most consistently reported pathological findings in post-mortem studies in ASD. The majority of these neuroimaging studies suggest that higher levels of RRB are associated with volumetric reductions in cerebellar GM or reduced WM integrity.

Reduced volume of the cerebellar crus was correlated with higher RRB in multiple studies of children with ASD (Cheung et al., 2009; D’mello et al., 2015; Rojas et al.,

2006; Wolff et al., 2017). The cerebellar crus has been shown to be functionally

connected with regions of the frontal and temporal cortices (Buckner et al., 2011) that have been independently linked to RRB (Kana et al., 2007; Menon et al., 2004; Rojas et al., 2006; ). Moreover, novel pathways connecting the cerebellum and basal ganglia have recently been identified in human neuroimaging studies (Milardi et al., 2016) and support the notion that interactions between the cerebellum and basal ganglia may be important in the mediation of RRB.

Animal models with RRB show similar neuroanatomical alterations to those correlated with RRB in studies of neurodevelopmental disorders. In the only study linking imaging findings with behavior in an animal model of RRB (BTBR mice; Ellegood et al., 2013), higher rates of RRB were linked to reduced volumes in the striatum, GP, and thalamus, but greater volumes in the cerebellar GM and WM. Cortical alterations in animal models with RRB were typically localized to prefrontal and temporal regions, but the direction and magnitude of these changes differed considerably across models. The most frequent and consistently reported findings in animal models with RRB were alterations in structures of the basal ganglia, primarily reduced volumes of the striatum and GP. Alterations in the cerebellum were also frequently reported in animal models of

RRB, and generally showed reduced volumes in both cerebellar GM and WM.

A majority of the brain regions whose morphology, connectivity, or function have been implicated in RRB are components of larger cortico-basal ganglia macro-circuits.

Several cortical regions have been implicated in neuroimaging of RRB, but altered structure and function within the basal ganglia was more consistently reported. Alternate approaches to studying brain morphology and function corroborate the critical role of the basal ganglia in the mediation of RRB (Aliane et al., 2012; Bechard et al., 2016;

Burguière et al., 2013; Baup et al., 2008; Grabli et al., 2004; Tanimura et al., 2008,

2010, 2011). The confluence of large scale brain networks within the basal ganglia makes these structures uniquely positioned, with the potential to moderate the initiation and termination of a broad range of cognitive and motor behaviors by functioning as a central hub among diverse neural networks.

Although many of the same structures are implicated from investigations in neurodevelopmental disorders and animal models of RRB, it is curious that opposite effects are observed for the volume of certain structures, particularly the striatum, between species. As direct relationships between brain volume and function are not fully understood, one possible explanation is that although network-level function of the basal ganglia result in similar behavioral outcomes between species, there are different cellular or molecular level pathologies that lead to structural and functional abnormalities observed in the striatum. An alternate explanation for the opposite effects on striatum volume between neurodevelopmental disorders and animal models with

RRB, is that underlying pathology may result in compensatory changes to related structures or circuits in humans, whereas similar compensatory changes may not have occurred in these animal models. As the majority of the neuroimaging data in RRB are from higher-functioning individuals, it is possible that compensatory brain changes in these individuals may result in improved outcomes, relating to their higher level of intellectual functioning.

Most neuroimaging investigations of RRB have been in individuals with ASD, and findings in this population may not be recapitulated in other neurodevelopmental disorders with RRB, or broader clinical populations with RRB. For example, decreased

volume of the caudate was reported in multiple neuroimaging studies of OCD (see review by Ahmari, 2016). Within ASD, the inconsistency of neuroimaging findings related to RRB may due, in part, to heterogeneity within the population (e.g., comorbid conditions, age differences, medication status).

Differential involvement of cortical regions across studies in clinical populations may be due to both experimental design (i.e., different tasks used in fMRI investigations) and population heterogeneities. Similarly, differential cortical involvement across animal models of RRB is not surprising, as these models result from disruption of diverse genetic systems and display different topographies of RRB. Moreover, pathology in different components of these basal ganglia circuits, such as the STN or

SNr, may exist in neurodevelopmental disorders but remain unobserved due methodological shortcomings (e.g., image resolution, labeling practices). The cerebellum has been clearly implicated in the pathophysiology of ASD, and neuroimaging evidence suggests that cerebellar pathology is also linked to RRB in this population (Cheung et al., 2009; D’Mello et al., 2015; Rojas et al., 2006). The extent to which the cerebellum may be involved in other neurodevelopmental disorders with RRB remains unknown.

There are currently no neuroimaging investigations in neurodevelopmental disorders with RRB that have assessed intra-basal ganglia connectivity using DTI tractography. As the pathophysiology of RRB appears to converge in the basal ganglia, this area of research is critically important. Specifically, one of the major theoretical frameworks for interpreting basal ganglia activity is the differential role of the direct and indirect pathway. As a result of the lack of studies investigating intra-basal ganglia

connectivity, and studies which investigate STN and SNr morphology, there is currently no evidence from neuroimaging to suggest differential involvement of the direct and indirect basal ganglia pathways in neurodevelopmental disorders with RRB. Diffusion imaging investigations in OCD, which shares similar pathophysiology, demonstrate the feasibility of performing striato-pallidal tractography with current acquisition parameters

(Worbe et al. 2015). Similarly, functional connectivity assessment of the STN have been demonstrated in Parkinson’s disease (Baudrexel et al., 2011). Thus, there is a strong need for future investigations of intra-basal ganglia structural and functional connectivity in relation to RRB.

Diffusion imaging investigations, including TBSS and tractography, may reveal circuit pathologies that are not apparent from regional volumetric analyses, and have the potential to link seemingly disparate findings. For example, a recent diffusion imaging investigation performed tractography in healthy adult participants and showed fibers connecting the STN and cerebellar cortex, as well as fibers from the dentate nucleus of the cerebellum to the SNr (Milardi et al., 2016). Moreover, fibers of the cerebellothalamic fiber bundles running through the superior cerebellar peduncle may indirectly affect cortico-basal ganglia circuits via the thalamus, which has been implicated in neurodevelopmental disorders with RRB (Pujol et al. 2015) and animal models (Ellegood et al., 2015; Petrenko et al., 2013). Thus, there is a strong need for additional diffusion imaging investigations to assess structural connectivity in both neurodevelopmental disorders with RRB and corresponding animal models.

Beyond considerations related to neuroimaging paradigms and analytic approaches, there are other factors that are relevant to MRI research in RRB. Linking

brain structure and function to RRB in humans entails the use of a behavioral index.

The behavioral indices most frequently used to associate RRB severity with brain alterations were the ADI-R and ADOS. The ADI-R and ADOS are designed for use in

ASD, and although a large portion of neuroimaging investigations of RRB are performed in this population, the use of behavioral assessments specific to one clinical population limits the generalizability of these findings to other neurodevelopmental disorders. Only five of the neuroimaging investigations discussed here have utilized an alternate behavioral index of RRB, the RBS-R (Dichter et al., 2012; Eisenberg et al., 2015; 2013;

Wolff et al., 2013, 2015, 2017). Wolff et al. (2017) demonstrated that the type of behavioral index used to assess RRB can influence the identification of brain-behavior relationships, as significant relationships between cerebellar WM were only found with the RBS-R, whereas similar comparisons using RRB scores from the ADOS failed to find a relationship. Other work using the RBS-R has shown that some neuroanatomical alterations identified in ASD were linked to specific domains of RRB, such as self- injurious behavior or compulsions (Wolff et al., 2013).

An important consideration affecting the generalizability of findings from neuroimaging investigations of RRB in neurodevelopmental disorders is that of population bias. As there is a great degree of heterogeneity in intellectual function among neurodevelopmental disorders with RRB, research in these populations should attempt to recapitulate this diversity. Unfortunately, due to the necessity for minimal motion during MRI, lower-functioning individuals are often unable to participate in studies of this nature. A recent study by Nordahl et al. (2016), however, demonstrated that appropriate behavioral techniques (e.g., video-modeling, training in mock scans)

can enable the acquisition of high-quality MRI data from low-functioning children with

ASD.

Future Directions

As the majority of neuroimaging investigations specific to RRB in neurodevelopmental disorders have been performed in ASD, there is a critical need to expand investigations into the neural circuitry of RRB in other neurodevelopmental disorders. Despite convergent findings related to basal ganglia circuitry, it is possible that RRB in different neurodevelopmental disorders may not be mediated by identical neural circuitry alterations. In addition, it is possible that neural circuitry alterations in neurodevelopmental disorders with RRB are dependent on the particular topography of

RRB (Eisenberg et al., 2015; Wolff et al., 2013). Future investigations would be well- served by exploring the relationship of neural circuitry alterations with indices of repetitive behavior that have the capacity to parse out subdomains of RRB. Despite its relative brevity, the RBS-R is an assessment with the capacity to identify subtypes of

RRB and would provide great utility for exploring relationships between RRB and neural circuitry. In addition to use in populations with ASD, the RBS-R has been used in FXS

(Oakes et al., 2016), PWS (Flores et al. 2011), and non-syndromic intellectual disability

(Bodfish et al., 2000). Thus, increased use of the RBS-R as an index of RRB can enable more detailed explorations of relationships between neuroanatomy and RRB, as well as greater generalizability across populations.

The development of more complex motion correction algorithms in concert with more improved approaches for tracking motion may improve the ability to collect usable

MRI data from lower-functioning individuals. For example, Todd et al. (2015) used an optical camera to capture head movements and perform real-time changes to the image

field of view during image acquisition, so that the same portion of the participant’s brain remains in the same space of the acquired image despite head movements.

Neuroimaging investigations in animal models of RRB have mostly been restricted to volumetric comparisons and there has been minimal focus on how regional alterations associated with RRB might combine at the level of neural circuitry. For example, it is currently unknown how volumetric alterations in one brain region effects

WM efferents, or the extent to which such alterations modify directly and indirectly connected brain regions. In order to understand the complex pathophysiology of RRB, it is critical to understand how regional alterations might affect structure and function of the larger neural circuits to which they contribute. In addition to DTI and rs-fMRI approaches, analyzing networks of anatomical covariance can lend to a better understanding of how volumetric alterations can effect larger neural circuits (Evans,

2013).

Animal neuroimaging methodologies are well-developed in other lines of investigations, and the feasibility of neural circuit / network level investigations in an animal model of RRB has been demonstrated (Dodero et al., 2013; Sforazzini et al.,

2016). Additional investigations of structural and functional connectivity in both animal models of RRB and neurodevelopmental disorders are needed to determine the extent to which such alterations are involved in the development and expression of RRB.

There is also a need to expand neuroimaging investigations to other models of

RRB such as Sapap3 knockouts. Additional neuroimaging investigations in mouse lines that exclusively display RRB without overlap with other phenotypes (e.g., social

deficits), such as the Sapap3 knockout, may provide clarity as to which neuroanatomical and circuit pathologies are specific to RRB.

A unique opportunity in the study of neural circuitry in animal models is that the same animal’s brain may be imaged in vivo, ex vivo, and assessed via histology.

Despite this opportunity, very few studies have incorporated these multiple levels of investigation. Only one investigation in an animal model of RRB has used histology in concert with neuroimaging metrics (BTBR; Sforazzini et al., 2016). There are a number of additional examples of this approach from other lines of investigation. Takahashi et al. (2011) compared tractography streamlines to histology (Luxol Fast Blue + Cresyl

Violet) for cortical projection fibers in the cat brain, and found that the distribution and orientation of streamlines corresponded to those inferred from histology. Narayana et al.

(2014) assessed WM differences resulting from chronic cocaine treatment and found that reduced FA in the splenium, fimbria, and internal capsule corresponded with reduced expression of multiple white-matter proteins. Hammelrath et al. (2016) utilized diffusion weighted images in combination with myelin staining (Black Gold II) to assess development of cortical WM in B6 mice and found that significant age dependent changes in myelin staining corresponded with T2 in the regions analyzed. Calabrese et al. (2015) performed DTI scans at an isotropic resolution of 43 µm in the B6 mouse and compared probabilistic tractography to neuronal tracer data available from the Allen

Brain Atlas mouse brain connectome, finding weak but significant correlations for 98% of the 976 generated fiber bundles. These studies demonstrate that investigations using neuroimaging in concert with other methodologies have the potential to greatly enhance our understanding of brain alterations and circuit pathologies.

Prior neuroimaging work in ASD and animal models of RRB suggest that brain alterations mediating RRB converge on circuits involving the basal ganglia and cerebellum. The findings presented in Chapters 2-5 strongly support this notion, but also expand upon these findings and implicate an important role of the STN and GP, and by extension the indirect basal ganglia pathway. I have also provided suggestions for future neuroimaging investigations in neurodevelopmental disorders and animal models with RRB. These include, but are not limited to, more detailed neuroanatomical labeling approaches, multimodal neuroimaging investigations (e.g., structural and functional connectivity), comparative neuroimaging investigations between clinical populations and corresponding animal models, and use of neuroimaging in concert with more traditional neuroscience techniques. These approaches have the potential to greatly enhance our understanding the brain alterations and circuit pathologies which mediate RRB, and provide promising targets for therapeutic interventions.

Table 1-1. Summary of studies that observed a correlation between neuroimaging findings and RRB in ASD and IDD. Brain Study Population Imaging Task Primary Finding RRB Region Modality Correlation Superior Cheung ASD / DTI - No group difference, (-) Frontal et al., children but reduced SFG FA Gyrus 2009 correlated with ADI-C (SFG) Weng et ASD / rs- - Reduced fc between (-) al., 2010 adolescents fMRI SFG and PCC Uddin et ASD / rs- - Increased fc between (+) al., 2013 children fMRI SFG and ACC Middle Delmonte ASD / rs- - Increased fc between (+) Frontal et al., adults fMRI MFG and caudate Gyrus 2013 (MFG) Klabunde PWS / Task Skin- Increased MFG (+) et al., children & fMRI picking BOLD during skin- 2015 adults behavior picking Inferior Rojas et ASD / Struct - No group diff in IFG (-) Frontal al., 2006 children & ural volume, but L-IFG Gyrus adults MRI volume correlated (IFG) with ADI-R in ASD group Schmitz ASD / Task Go/No-go Reduced L-IFG (-) et al., adults fMRI BOLD during 2006 successful No-go trials Kana et ASD / Task Go/No-go Reduced R-IFG (-) al., 2007 adults fMRI BOLD during Go/No- Go rs-MRI - Reduced fc between (-) R-IFG and ACC, insula Superior Rojas et ASD / Struct - No group diff in (+) Temporal al., 2006 children & ural volume, but bilateral Gyrus adults MRI STG volume (STG) correlated with ADI-R in ASD group Di ASD / rs- - Higher fc between (+) Martino et children fMRI RV-Putamen and R- al., 2011 STG Higher fc between (-) LD-Putamen and R- pons Precentral Cheung ASD / DTI - Reduced M1 FA (+) gyrus et al., children between groups, but (PMC) 2009 greater M1 FA within ASD correlated with ADI-C

Table 1-1. Continued Brain Study Population Imaging Task Primary Finding RRB Region Modality Correlation Anterior Thakkar ASD / DTI - Reduced R-ACC FA (-) Cingulate et al., adults Cortex 2008 (ACC) Zhou et ASD / rs- - Reduced fc L-ACC to (-) al., 2016 children & fMRI R-insula adults Uddin et ASD / rs- - Increased fc within (+) al., 2013 children fMRI Salience Network (ACC, SFG, thalamus, insula)

Menon et FXS / Task Go/No-go Reduced ACC BOLD (-) al., 2004 adults fMRI during Go/No-go Klabunde PWS/ Task Skin- Greater ACC BOLD (+) et al., children & fMRI picking during skin-picking 2015 adults behavior Insula Kana et ASD / Task Go/No-go Reduced insula (-) al., 2007 adults fMRI BOLD during Go/No- go Cascio et ASD / Task Visual Greater L-insula (+) al., 2014 children & fMRI stimuli BOLD for “Own” adolescents related to minus “Other” stimuli circumscrib ed interests Zhou et ASD / rs- - Reduced fc between (-) al., 2016 children & fMRI R-insula and L-ACC adults Uddin et ASD / rs- - Increased fc in (+) al., 2013 children fMRI Salience Network (ACC, SFG, thalamus, insula) Menon et FXS / Task Go/No-go Insula BOLD (+) al., 2004 adults fMRI correlated with FMRP levels in FXS group Caudate Sears et ASD / Struct - Enlarged caudate (+) al., 1999 adolescents ural volume, - correlated & adults MRI with ADI Estes et ASD / Struct - Enlarged (Caudate + (-) al., 2011 children ural Putamen) volume MRI Hollander ASD / Struct - Enlarged R-caudate (+) et al., adults ural volume 2005 MRI Rojas et ASD / Struct - Enlarged R-Caudate (+) al., 2006 children & ural volume adults MRI

Table 1-1. Continued Brain Study Population Imaging Task Primary Finding RRB Region Modality Correlation Caudate Langen et ASD / Struct - No diff at two time (+) (cont.) al., 2014 children ural points, but caudate MRI greater growth rate Qiu et al., ASD / Struct - Growth rate of R- (-) 2016 young ural caudate children MRI Gothelf et FXS / Struct - Greater bilateral (+) al., 2008 children ural caudate volume MRI Menon et FXS / Task Go/No-go No group differences, (+) al., 2004 adults fMRI but caudate BOLD + correlated with FMRP levels in FXS Hoeft et FXS / Task Go/No-go Reduced R-caudate (+) al., 2007 children fMRI BOLD during Go/No- go Delmonte ASD / rs- - Increased fc between (+) et al., adults fMRI R-caudate and R- 2013 MFG Pujol et PWS / rs- - Reduced fc between (-) al., 2015 adults fMRI R-caudate and medial prefrontal cortex, correlated with ordering compulsions Putamen Estes et ASD / Struct - No putamen group (-) al., 2011 children ural difference. Striatum MRI volume (C+PU) correlated with ADOS Hollander ASD / Struct - Enlarged bilateral (+) et al., adults ural putamen volume 2005 MRI Langen et ASD / Struct - Growth rate of (+) al., 2014 children ural putamen within ASD MRI group Eisenberg ASD / Struct - Covariance of (+) et al., adolescents ural volume between 2015 & adults MRI putamen, pallidum, accumbens, amygdala, Langen et ASD / DTI & Go/No-go Trend for reduced FA (-) al., 2012 adults fMRI of putamen tracts, correlated with Go/No-go performance

Table 1-1. Continued Brain Study Population Imaging Task Primary Finding RRB Region Modality Correlation Putamen Menon et FXS / Task Go/No-go Reduced putamen (-) (cont.) al., 2004 adults fMRI BOLD during Go/No- go Di ASD / rs-fMRI - Increased fc between (+) Martino et children R-putamen and R- al., 2011 STG Di ASD / rs-fMRI - Increased fc between (-) Martino et children R-putamen and R- al., 2011 pons Pujol et PWS / rs- - Reduced fc R- (-) al., 2015 adults fMRI putamen to L-GP and thalamus, correlated with self-picking behaviors Globus Estes et ASD / Struct - Enlarged R-GP (+) Pallidus al., 2011 adolescents ural volume (GP) & adults MRI Pujol et PWS / rs- - Reduced fc R- (-) al., 2015 adults fMRI putamen to L-GP and thalamus Cerebellum Cheung ASD / DTI - No group difference, (-) et al., children but reduced FA in L- 2009 cerebellum correlated with ADI-C Rojas et ASD / Struct - Reduced L- (-) al., 2006 children & ural cerebellum volume adults MRI Wolff et ASD / DTI - Increased FA in SCP (+) al., 2017 young and MCP correlated children with lower-order RRB. Abbreviations: ASD, Autism spectrum disorder; PWS, Prader-Willi syndrome; FXS, Fragile-X syndrome; FMRP, Fragile X mental retardation protein; MRI, magnetic resonance imaging; fMRI, functional MRI; BOLD, blood oxygen level dependent; rs-fMRI, resting-state fMRI; fc, functional connectivity; DTI, diffusion tensor imaging; FA, fractional anisotropy

CHAPTER 2 VOLUMETRIC MAGNETIC RESONANCE IMAGING IN AN ANIMAL MODEL OF REPETITIVE BEHAVIOR

Introduction

Restricted, repetitive behaviors (RRBs) are observed in a variety of human conditions and are diagnostic for ASD. Animal models of RRB can be induced through a variety of insults to the central nervous system (e.g., lesion or genetic mutations), pharmacological manipulations (e.g., psychostimulants), impoverishment of early environment/experience, and use of particular inbred mouse strains (for a review, see

Bechard & Lewis, 2012). The C58/J inbred mouse strain (hereafter referred to as C58) exhibits a robust repetitive motor behavior phenotype (Moy et al., 2008; Muehlmann et al., 2012; Ryan et al., 2010), which is not shared by genetically similar C57BL/6 mice

(hereafter referred to as B6). The repetitive motor behavior of C58 mice is highly robust, and the behaviors exhibited (vertical jumping and backward somersaulting) do not resemble an excess of otherwise adaptive species-specific behaviors, such as self- grooming. Because the behavior phenotype of C58 mice is not the result of a targeted manipulation (e.g., genetic knockout, pharmacological disruption), this model may more closely resembles repetitive behaviors exhibited in non-syndromic ASD than other animal models. Moreover, C58 mice reared in environmentally enriched (EE) housing conditions exhibit lower rates of repetitive behavior compared to C58 mice reared in standard housing (SH) conditions (Muehlmann et al., 2012).

Magnetic resonance imaging (MRI) has been used to characterize a number of animal models with repetitive behavior (see Chapter 1), but only two prior studies have included a behavioral measure of RRB and related neuroimaging findings to behavior

(Allemang-Grand et al., 2017; Ellegood et al., 2013). The C58 mouse model of

repetitive behavior has not previously been studied with MRI, and such investigations are needed as they can provide a better understanding of the mechanisms underlying non-syndromic expression of RRB. In this study I utilized MRI to assess brain morphology in SH-reared C58 mice with high rates of repetitive motor behavior compared to both SH-reared B6 mice and EE-reared C58 mice with low rates of repetitive motor behavior. Deformation-based morphometry (DBM) was used to assess brain-wide volumetric alterations between these groups. In addition, the relationship between volumetric alterations and repetitive motor behaviors was investigated in the same animals.

Methods

Animal Housing

All animal procedures were approved by the Institutional Animal Care and Use

Committee at the University of Florida and followed the NIH Guidelines for the Care and

Use of Laboratory Animals. All mice used in this study were generated from a breeding colony located in the Psychology building at the University of Florida. B6 mice (n=10, 5 males and 5 females) were weaned at post-natal day (PND) 21, and animals of the same sex were separated into standard housing cages containing between 4 and 6 mice. C58 mice were weaned at PND 21, and animals of the same sex were separated into either SH (n=17, 9 males and 8 females) or EE (n=10, 6 males and 4 females) housing conditions, containing between 4 and 6 mice, and remained in the same housing condition until behavioral assessment and sacrifice between PND 63-70. As both SH-B6 mice and EE-C58 mice display relatively low amounts of RRB, a greater number of animals were included in the SH-C58 group in order to provide a wider range of RRBs for investigation of brain-behavior relationships.

SH cages were shoebox-style plastic cages (29 x 18 x 13 cm) furnished with

Sani Chip bedding and Nestlets for nest construction. EE cages were large dog kennels

(122x 81 x 89 cm) modified to have three levels, interconnected with ramps. EE cages were furnished with a large hut and running wheel as permanent fixtures, as well as tunnels and a variety of toys that were exchanged on a weekly basis to provide novel stimuli. Birdseed was scattered throughout EE cages (~2 oz/wk) in order to promote foraging behavior. In both SH and EE housing conditions, food (rodent chow) and water were available ad libitum. All mice, whether EE or SH, were maintained at 70-75º F and

50-70% humidity under at 12:12 light:dark cycle.

Behavioral Assessment

C58 mice reared in standard housing conditions display high rates of spontaneous hind-limb jumping and backward somersaulting. For all mice, the frequency of repetitive motor behavior was assessed using an automated system during their 12 hour dark-cycle on PND 63, a time at which repetitive motor behaviors are asymptotic in C58 mice (Muehlmann et al., 2012). This system consists of software

(Labview, National Instruments) that records and time-stamps each break of a photo- beam array (Columbus Instruments). The vertical height of the photobeams was chosen so that all four limbs of the mouse must leave the floor in order to be counted. Animals were placed into individual testing chambers (28 x 22 x 25 cm) with food and water 1 hour prior to their dark-cycle (8 PM) for habituation, and repetitive motor behaviors were recorded for 12 hours, until the beginning of their light cycle (8 AM). In addition, video recordings were taken and sampled for validation of automated behavioral measures.

Magnetic Resonance Imaging

Following assessment of repetitive motor behavior, mice were deeply anesthetized with gaseous isoflurane and transcardially perfused with 60 ml of 10% neutral buffered formalin. Following perfusion, mice were decapitated and the skin, lower jaw, muscles, and cartilaginous tissue removed. The remaining tissue was then post-fixed in 10% neutral buffered formalin for 7 days at 4º C before being transferred to phosphate buffered saline for at least 48 hours prior to imaging.

Imaging was performed at the Advanced Magnetic Resonance Imaging and

Spectroscopy (AMRIS) facility at the University of Florida in Gainesville. Tissue samples were immersed in FC-40 (fluorinert) and placed inside an 18mm diameter glass tube designed for nuclear magnetic resonance (NMR). A custom plastic fixture was inserted into the glass tube to stabilize the sample and reduce motion artifacts due to vibration.

Brains were scanned inside of the skull in order to keep the structure of the nervous system intact. Images were acquired on an Advance III Bruker 750 MHz (17.6 tesla) spectrometer using an 18 mm shielded NMR probe (DOTY) using a T2-weighted 3D fast low angle shot (FLASH) gradient echo sequence with flip angle =30º, TR=160 ms,

TE=20 ms, with 2 averages. The field of view was 16x13x9.75 mm, with a 213x173x130 matrix, resulting in an isotropic resolution of 75µm.

Data Analysis

Deformation-based morphometry

Deformation-based morphometry (DBM) is a neuroimaging analysis technique used to infer volumetric expansion and contraction without the need for a priori hypotheses about where such differences may occur (Ashburner & Friston, 1998). This approach is well-suited to the study of the neural circuitry of RRB in C58 mice, as

relatively little is known about neuroanatomical alterations associated with RRB in this animal model. I employed a DBM analysis pipeline similar to that used by Ellegood et al.

(2013, 2015) in order to determine volumetric differences in SH-C58 mice compared to both SH-B6 mice and EE-C58 mice, as well as how volume alterations correspond to repetitive motor behavior in the same animals.

Removal of tissue outside the brain (i.e., skull, muscle, cartilage) was performed using an automated software tool (PCNN3D) in MATLAB (Chou et al., 2011), with subsequent quality control using ITK-Snap software (Yushkevich et al., 2006). Images from each animal were aligned to a common stereotactic space by performing linear (6 parameter) registration to a manually selected representative brain using Advanced

Registration Tools (ANTs; Avants et al., 2010). Images were then corrected for signal inhomogeneity using N3 bias correction (Sled et al., 1998). A population template representing the average anatomy of all animals from the study was then generated using multivariate template construction in ANTs. The non-linear deformation matrix used to transform each animal’s brain from its native morphology onto the population template was used to create an image that contains Jacobian determinant values of that transformation matrix at each voxel, which were then log-transformed to maintain assumptions of normality for statistical testing. The Jacobian determinant values in each voxel represent a ratio of relative volume change between the original image (i.e., individual animal) and the target image (i.e., population template), where values between 0 and 1 indicate a relatively smaller volume, and values greater than 1 indicate relatively larger volume. After log-transformation, voxels with negative values represent smaller volume and voxels with positive values represent greater volume, relative to the

population template. These Jacobian determinant values have been empirically demonstrated to correspond with volumetric expansion and contraction (Ashburner &

Friston, 1998). The log-transformed Jacobian images for each animal were used as input data for a single factor (group; SH-C58, SH-B6, and EE-C58) voxel-wise analysis of variance (ANOVA). Preliminary analysis revealed no significant effect of sex, and thus sex was not included as a factor in the final analytic model. This statistical model was applied using Randomise, a statistical analysis tool in the FMBRIB Software Library

(FSL; Jenkinson et al., 2012). The model was run through 5000 random permutations with threshold-free cluster enhancement (TFCE; Smith & Nichols, 2009). Additional correction for multiple comparisons was performed using the false discovery rate (FDR) method, with an FDR of 20% (Benjamini & Hochberg, 1995). This FDR was chosen based upon the exploratory nature of the current experiment, as well as because it is an additional control for multiple comparisons to that provided by multiple permutation testing of the linear model, which is necessary for TFCE.

Atlas-based volumetry

As a complementary technique to assess brain volume differences between groups, I also performed volumetric comparisons using regions of interest (ROIs) derived from mouse MRI atlases generated by the Australian Mouse Brain Mapping

Consortium. These atlases include segmentation of cortex (Ullmann et al; 2013), basal ganglia (Ullmann et al., 2014), cerebellum (Ullmann et al., 2012), and diencephalon

(Watson et al., 2017). Due to the large number of anatomical regions included in these segmentation atlases, several regions of interest were merged together or excluded from this analysis in order to limit the number of comparisons performed. Segmentation of the substantia nigra was added to the basal ganglia atlas, as it was not originally

included by the authors (Ullmann et al., 2014), and only segmentation of the subthalamic nucleus was included from the diencephalon atlas (Watson et al., 2017).

The ROIs included for this atlas-based volumetric comparison are listed in Appendix A

(Table A-1). Non-linear registration with ANTs was used to apply segmentation images for these ROIs onto the population template image (as described under Deformation- based morphometry). The deformation matrices created during template construction were used to calculate the inverse transformation matrices for each animal, and ROI segmentation from the population template was then applied onto native-space images for each animal using those inverse transformation matrices. The percent difference in volume between the population template and each animal was then calculated for each

ROI, and used as input data for a single factor (group; SH-C58, SH-B6, and EE-C58)

ANOVA. Correction for multiple comparisons was performed using 20% FDR.

Brain-behavior relationships

Brain-behavior relationships were investigated on a whole-brain voxel-wise basis with FSL’s Randomise, using a linear model relating the log-transformed Jacobian determinant images (i.e., volumetric expansion or contraction) to repetitive behavior scores for each animal. This approach for relating behavior to volumetric alterations is similar to that employed by Ellegood et al. (2013) and Allemang-Grand (2017). Two separate brain-behavior models were generated, one which included all animals in the study sample (n=37) and a second which included only SH-C58 mice (n=17). The additional SH-C58 only model was included due to the potential concern that strain differences may drive behavioral correlations. These statistical models were analyzed using Randomise, a statistical analysis tool in FSL. The model was run through 5000

random permutations with TFCE. Additional correction for multiple comparisons was performed using a 20% FDR.

Results

Repetitive Motor Behavior

A three group one-way ANOVA revealed that repetitive motor behaviors were significantly different among groups [F(2, 34) = 19.6, p < 0.0001] (Fig. 2-1). Post-hoc comparisons performed with Welch’s t-test, as the groups had unequal variances, revealed that SH-C58 mice had significantly higher repetitive motor behavior than EE-

C58 mice [t(16.5) = 5.67, p<0.0001 ] and SH-B6 mice [t(16.1) = 5.97, p<0.0001]. There was no difference in repetitive motor behavior between EE-C58 and SH-B6 mice

[t(12.4) = 1.74, p=.11].

Figure 2-1. Mean total number of vertical repetitive motor behaviors exhibited by SH- C58, EE-C58, and SH-B6 groups. Significant differences were observed between SH-C58 and EE-C58 mice (p < .0001), as well as SH-C58 and SH- B6 mice (p < .0001).

Deformation-based Morphometry

One-way ANOVA with 20% FDR correction revealed a significant main effect of group (i.e., SH-C58, EE-C58, or SH-B6) for voxels throughout the brain (Fig. 2-2), including cortex, basal ganglia, thalamus, and cerebellum. Voxels that showed a significant main effect of group were further explored with post-hoc comparisons between the groups. More detailed anatomical descriptions are provided for group differences identified through post-hoc comparisons.

Figure 2-2. Heat map of significant F-statistic values for one-way ANOVA of volume differences among animal groups (FDR corrected, p<.05).

Comparing SH-C58 and SH-B6 mice (Fig. 2-3), post-hoc tests revealed that SH-

C58 mice showed volumetric enlargement within anterior commissure, corpus callosum, septal nuclei, medial thalamic nuclei, hippocampus (CA 1-3), posterior cingulate cortex, cerebellar cortex (lobules I – V, IX), paraflocculus, middle cerebellar peduncle (MCP), and brainstem. SH-C58 mice also showed reduced volumes within the frontal association cortex, orbital cortex, primary and secondary motor cortex, anterior

cingulate cortex, somatosensory cortex, insular cortex, auditory cortex, entorhinal cortex, dentate gyrus, striatum, nucleus accumbens, globus pallidus, entopeduncular nucleus, internal capsule, lateral thalamic nuclei, subthalamic nucleus, hypothalamus, midbrain, Crus I and II, cerebellar white matter (arbor vitae), and deep cerebellar nuclei

(medial and lateral).

Figure 2-3. Significant t-statistic values for post-hoc t-test of volume differences between SH-C58 and SH-B6 mice (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes.

For the post-hoc comparison of SH-C58 and EE-C58 mice no significant findings remained after 20% FDR correction. Uncorrected data (p<.05; Fig. 2-4) showed that compared to EE-C58 mice, SH-C58 mice have increased volume in the thalamus, subthalamic nucleus, hippocampus, midbrain, cerebellar cortex (lobules II, III and IV/V), and deep cerebellar nuclei (lateral, medial, and interposed). SH-C58 mice also showed reduced volume in the orbital cortex, anterior commissure, the interstitial nucleus of the

posterior limb of the anterior commissure, primary motor cortex, somatosensory cortex, anterior commissure, amygdala, posterior cingulate cortex, and corpus callosum.

Figure 2-4. t-statistic values for post-hoc t-test of volume differences between SH-C58 and EE-C58 mice (uncorrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes.

For the comparison of EE-C58 and SH-B6 mice (Fig. 2-5), significant findings were highly similar to those differences observed between SH-C58 and SH-B6 mice.

Due to the high degree of similarity between these comparisons, detailed anatomical descriptions are not provided here. It is worth noting, however, that the magnitude of the differences observed between EE-C58 and SH-B6 are generally smaller than those observed between SH-C58 and SH-B6 mice.

Figure 2-5. Significant t-statistic values for post-hoc t-test of volume differences between EE-C58 and SH-B6 mice (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes.

Atlas-based Volumetry

Atlas-based volumetry with 20% FDR correction revealed a significant main effect of group in a majority of the brain regions investigated. ROIs that showed a significant main effect of group were further explored with post-hoc comparisons between the groups.

Compared to SH-B6 mice, SH-C58 mice had significant volume differences in a large number of brain regions, which are listed in Table 2-1. Notably, many of these regions are components of larger cortico-basal ganglia-thalamo-cortical circuits. In addition, the cerebellar cortex and deep cerebellar nuclei were also found to have volume differences between SH-C58 and SH-B6 mice.

Table 2-1. List of brain regions which showed a significant volume difference (FDR corrected, p < .05) in SH-B6 and SH-C58 mice SH-B6 mean SH-C58 mean Delta p value Brain Region (% difference (% difference (% difference) from template) from template) Amygdalostriatal 7.19 -14 21.2 1.25E-04 transition area Anterior cingulate 7.83 -12.2 20 4.97E-04 Anterior commissure 10.9 2.69 8.18 1.99E-02 (anterior limb) Anterior commissure 10.7 -4.07 14.7 3.71E-03 (posterior limb) Auditory Cortex 4.59 -7.99 12.6 2.70E-05 Central amygdaloid 7.57 -9.28 16.8 1.72E-04 nucleus Claustrum 0.162 -7.73 7.89 8.86E-04 Copula of the pyramis -7.13 8.95 -16.1 4.05E-02 Corpus callosum 14.7 5.22 9.5 2.33E-02 Corpus callosum 15.2 5.38 9.79 1.93E-02 Dorsal cochlear nuclei 8.47 -4 12.5 2.17E-02 Dorsal fornix 12.6 -5.16 17.7 2.23E-03 Dorsal hippocampal 12.8 -4.84 17.6 2.33E-03 fissure Entopeduncular nucleus 15.9 -20.5 36.4 3.04E-07 Entorhinal cortex 7.22 -4.05 11.3 4.01E-04 Extended amygdala 10.6 -11.2 21.8 2.35E-06 Flocculus -3.69 15.5 -19.2 2.43E-02 Forceps minor 13.5 -2.26 15.8 4.01E-03 Fornix 10.6 -0.564 11.1 1.82E-02 Frontal association area 10.5 -2.16 12.7 9.21E-03 Globus pallidus 15.4 -14.3 29.7 2.75E-08 Insular Cortex 6.18 -1.17 7.35 3.70E-05 Internal capsule 15.6 -4.62 20.2 1.40E-05 Interposed nucleus 6.12 -11.7 17.8 1.36E-04 Interstitial nucleus of the 14.1 -9.92 24 3.37E-05 post limb Lateral preoptic area 0.87 -13.6 14.5 4.01E-02 Lateral striatal stripe 9.27 -5.26 14.5 2.29E-03 Lobule I -13.4 8.8 -22.1 1.09E-02 Lobule II -13.4 13.8 -27.2 2.23E-05 Lobule III -7.27 1.91 -9.17 3.60E-02 Lobule VIII -12.4 9.56 -22 1.55E-02 Lobule IX -12.8 10.9 -23.7 4.27E-03 Magna island of Calleja 3.67 -20.6 24.3 1.08E-03 Medial longitudinal 17 -11.9 28.9 3.22E-02 fasciculus

Table 2-1. Continued SH-B6 mean SH-C58 mean Delta p value Brain Region (% difference (% difference (% difference) from template) from template) Medial nucleus 3.02 -11.6 14.7 7.97E-05 Middle cerebellar 6.35 0.934 5.41 4.75E-02 peduncle Nigrostriatal bundle 19 -3.84 22.8 5.67E-07 Nucleus accumbens 1.83 -10.9 12.7 1.36E-03 Nucleus of diagonal -1.23 -8.83 7.6 4.58E-02 band Orbital Cortex 8.05 -6.64 14.7 1.08E-03 Paraflocculus -19 16.7 -35.6 2.19E-03 Parietal association area 3.2 -2.6 5.8 1.94E-02 Piriform cortex 9.46 3.02 6.44 3.10E-04 Posterior cingulate 1.58 -4.83 6.41 4.11E-03 Primary motor cortex 3.24 -6.64 9.88 1.44E-03 Secondary motor cortex 6.42 -3.58 10 3.15E-03 Somatosensory cortex 3.5 -5.98 9.48 8.67E-04 Stria terminalis 10.1 -14.7 24.8 1.99E-04 Striatum 7.16 -6.25 13.4 7.64E-06 Substantia innominate -0.849 -13.8 12.9 1.88E-02 Substantia nigra 5.92 -12.6 18.5 8.64E-05 Subthalamic nucleus 2.9 -19.5 22.4 4.75E-05 Superior medullary -10.8 7.03 -17.9 8.79E-03 velum Temporal association 7.9 -6.42 14.3 4.54E-07 area Ventral pallidum 4.96 -10.7 15.7 9.78E-03 Ventral spinocerebellar 18.4 -2.95 21.4 2.90E-05 Visual cortex 1.31 -3.14 4.45 9.82E-03

Regions which showed a significant difference (FDR corrected, p<.05) between

EE-C58 mice and SH-C58 mice are listed in Table 2-2. SH-C58 mice had significantly reduced volumes in the nucleus accumbens, anterior commissure (anterior and posterior limbs), and interstitial nucleus of the posterior limb, but increased volume in the medial nucleus of the cerebellum.

Table 2-2. List of brain regions which showed a significant volume difference (FDR corrected, p < .05) in EE-C58 and SH-C58 mice EE-C58 mean SH-C58 mean Delta p value Brain Region (% difference (% difference (% difference) from template) from template) Anterior commissure 10 2.7 7.6 6.83E-03 (anterior limb) Nucleus accumbens -5 -11 5.9 8.23E-03 Anterior commissure 4.8 -4.1 8.9 7.27E-03 (posterior limb) Interstitial nucleus of the 0.56 -9.9 10 2.50E-03 post limb Medial nucleus -19 -12 -7.8 9.30E-03

Comparisons between EE-C58 and SH-B6 mice are not reported here, but can be found in Appendix B (Table B-1). Differences between EE-C58 and SH-B6 mice are influenced by factors of both strain and housing condition, and moreover, these two groups showed no difference in rates of repetitive motor behavior.

Brain-behavior Relationships

For the linear model relating volume alterations to repetitive motor behavior across all animals (n=37), there was a significant (FDR corrected, p<.05) positive relationship (i.e., volumetric enlargement associated with greater repetitive motor behavior) within the corpus callosum, septal nuclei, medial aspects of the dorsal striatum, hippocampus (CA 1-3), posterior cingulate, cerebellar cortex (lobules I–V, IX,

X), paraflocculus, middle cerebellar peduncle, lateral nucleus of the cerebellum, and brainstem. There was a significant negative relationship (i.e., volumetric reduction associated with greater repetitive motor behavior) within the frontal association cortex, orbital cortex, primary and secondary motor cortex, anterior cingulate cortex, somatosensory cortex, insular cortex, auditory cortex, entorhinal cortex, dentate gyrus, anterior portions of the striatum, nucleus accumbens, globus pallidus, entopeduncular

nucleus, internal capsule, lateral nuclei of the thalamus, hypothalamus, and brainstem

(Fig. 2-6).

Figure 2-6. Significant t-values (FDR corrected, p<.05) for the linear model relating volume and repetitive motor behavior including mice from all groups. Red- yellow colors indicate larger volumes associated with higher repetitive motor behavior, whereas blue-light blue colors indicate smaller volumes associated with higher repetitive motor behavior.

For the linear model relating volume alterations to repetitive motor behavior within only SH-C58 mice (n=17), no significant voxels remained after correction for multiple comparisons. Uncorrected data (p<.05) showed that larger volume was associated with greater repetitive motor behavior within the nucleus accumbens, lateral hypothalamus, zona inserta, and lobule II of the cerebellum. In contrast, smaller volumes were associated with greater repetitive motor behavior in the somatosensory cortex, cortical-amygdala transition zone, insular cortex, visual cortex, lateral striatum, globus pallidus, external capsule, and medial thalamus (Fig. 2-7).

Figure 2-7. t-values (uncorrected, p<.05) for the linear model relating volume and repetitive motor behavior including only SH-C58 mice. Red-yellow colors indicate larger volumes associated with higher repetitive motor behavior, whereas blue-light blue colors indicate smaller volumes associated with higher repetitive motor behavior.

Discussion

Work in animal models with RRB has implicated a critical role of basal ganglia, particularly the indirect pathway, in mediating such behaviors (Baup et al., 2008;

Bechard et al., 2016; Grabli et al., 2004; Tanimura et al., 2008, 2010, 2011). There is a growing literature of volumetric alterations in mouse models with RRB, but exceedingly few studies have characterized volumetric alterations in relation to behavioral measures of RRB (Allemang-Grand et al., 2017; Ellegood et al., 2013). Despite this shortcoming, work in mouse models with RRB has shown volumetric alterations in the basal ganglia, but also in regions of the cortex, thalamus, and cerebellum (see Chapter 1).

In this study, I performed MRI to assess volumetric alterations in SH-C58 mice with high levels of repetitive motor behavior compared to SH-B6 and EE-C58 mice with

low levels of repetitive motor behavior. This is the first MRI investigation in the C58 mouse model of repetitive behavior. Importantly, this study also included a behavioral measure of RRB which was related to neuroimaging findings, which has only been performed in two previous neuroimaging studies of an animal model with RRB. Utilizing two complementary approaches for assessing brain volume, I sought to balance concerns of statistical power in voxel-wise analyses with correction for multiple comparisons, and the potential for atlas-based volumetry to overlook significant volumetric differences that occur in only a portion of a segmented ROI.

Using these complementary approaches for volumetric assessment, I found significant brain-wide alterations between SH-C58 mice and SH-B6 mice. Compared to

SH-B6 mice, SH-C58 mice showed widespread volumetric reduction in frontal cortical regions, basal ganglia, lateral thalamus, and deep cerebellar nuclei, but volumetric enlargement in posterior cortical regions, medial thalamus, and cerebellar cortex. Atlas- based volumetry revealed the same pattern of differences between SH-C58 and SH-B6 mice as those observed with DBM. No significant differences between SH-C58 mice and EE-C58 mice were observed with DBM after correction for multiple comparisons.

Atlas-based volumetry with correction for multiple comparisons, however, did reveal significant differences between SH-C58 and EE-C58 mice. This atlas-based approach showed that SH-C58 mice have reduced volume in the anterior commissure and interstitial nucleus of the posterior limb of the anterior commissure, and greater volume in the medial nucleus of the cerebellum. These significant findings from the atlas-based comparison of SH-C58 and EE-C58 mice were in line with the uncorrected differences observed with DBM between SH-C58 and EE-C58 mice.

Considering post-hoc comparisons for all three groups, certain brain regions in

EE-C58 mice may represent an intermediate volume phenotype between the more pronounced differences observed between SH-C58 and SH-B6 mice. Within these regions, the SH-C58 to EE-C58 comparison (Fig. 2-4) showed effects in the opposite direction as those identified in the SH-C58 to SH-B6 comparison (Fig. 2-3), but comparison of EE-C58 to SH-B6 (Fig. 2-5) animals showed effects in the same direction as those in the SH-C58 to SH-B6 comparison (Fig. 2-3). Areas in which an intermediate volume was observed in EE-C58 mice included the anterior commissure, interstitial nucleus of the posterior limb of the anterior commissure, lateral thalamus, subthalamic nucleus, and deep cerebellar nuclei. Alterations to the anterior commissure and deep cerebellar nuclei have not previously been related to RRB in animal models or in human studies in ASD. Prior work has established a critical role of the subthalamic nucleus in repetitive motor behavior in C58 mice (Lewis et al., 2018), and these other structures may be useful targets for future investigations. Areas which showed the same direction of volume alteration in comparisons of SH-C58 to SH-B6 (Fig. 2-3) and EE-C58 to SH-

B6 (Fig. 2-5) are less likely to be important mediators of repetitive motor behavior, but could relate to other behavioral differences between groups (e.g., resistance to change, social behaviors).

Volumetric differences that were most consistent with prior work in animal models with RRB were reduced volume in the globus pallidus and enlarged volumes in midline cerebellar cortex, which were identified from DBM and atlas-based comparison of SH-C58 and SH-B6 mice, as well as both linear models relating volume to behavior.

Reduced volume of the globus pallidus was reported in multiple animal models with

RRB (Allemang-Grand et al., 2017; Ellegood et al., 2013, 2015; Portman et al., 2014), and importantly in the only other two animal neuroimaging studies that related volumetric alterations to repetitive behavior (Allemang-Grand et al., 2017; Ellegood et al., 2013). Reduced volume of the GP has been reported in human neuroimaging studies in ASD (Estes et al., 2011; Turner et al., 2016), although Turner et al., (2016) failed to find a significant brain-behavior relationship with ADI-R section C scores of

RRB. Reduced activation of the GP in during repetitive skin-picking was also reported in

PWS (Pujol et al., 2015). Enlargement of the midline cerebellar cortex has been reported in multiple animal models with RRB (Allemang-Grand et al., 2017; Ellegood et al., 2013, 2014; Steadman et al., 2014), but a significant brain-behavior relationship was not identified in the only two studies that included a behavioral measure (Allemeng-

Grand et al., 2017, Ellegood et al., 2013). Enlargement of midline cerebellar cortex has, however, been associated with higher RRB in individuals with ASD (D’mello et al., 2016;

Rojas et al., 2006).

In order to provide greater information about the relationship of the neuroanatomical alterations observed in this study to RRB, two linear models relating voxel-wise volumetric alterations to repetitive motor behaviors were generated. The first model included all animals in the study sample (n=37), irrespective of group, whereas the second model included only SH-C58 mice (n=17). The model which included all study animals showed a highly similar pattern of volumetric enlargement and reduction related to repetitive motor behaviors as the volumetric differences observed between

SH-C58 and SH-B6 mice identified without consideration of behavior. This pattern of

finding suggests that this linear model including all animals may be biased by group differences (i.e., bimodal distribution) in both behavior and brain morphology.

The model that included only SH-C58 mice found no significant brain-behavior correlations after correction for multiple comparisons. Uncorrected data, however, showed a limited pattern of volume alterations associated with repetitive behavior.

Uncorrected data (p<.05) from this SH-C58 only model showed that higher repetitive behavior was associated with larger volume within in the nucleus accumbens, lateral hypothalamus, zona inserta, and lobule II of the cerebellum, but smaller volume within the somatosensory cortex, cortical-amygdala transition zone, insular cortex, visual cortex, lateral striatum, globus pallidus, external capsule, and medial thalamus.

Interpretation of these uncorrected results, however, must be taken with caution due to the presence of false positives. Although a fairly wide distribution of repetitive motor behaviors were present SH-C58 mice (see Fig. 2-1), within-group variation of brain volume was restricted to a smaller range. Thus, a larger sample of SH-C58 mice may be needed to elucidate significant (FDR corrected) within-group volume alterations associated with repetitive motor behavior using this approach. Alternately, stratification of SH-C58 mice into subgroups (e.g., median split) based on their repetitive motor behavior could be used to assess volume alterations associated with different rates of repetitive motor behavior within SH-C58 mice. It is also possible that other approaches for characterizing brain structure and function, such as DTI and rs-fMRI, may be more sensitive at identifying brain-behavior relationships specific to repetitive motor behavior in C58 mice.

The volumetric investigations performed here did not have the ability to distinguish between the effects of strain and housing condition, as no EE-B6 group was included. Although rearing B6 mice in EE has no significant effect on their rates of repetitive motor behavior, the effects of EE elucidated from inclusion of this additional group may be informative for understanding how EE attenuates RRB in C58 mice. The longitudinal effects of EE on brain volume in B6 mice has been investigated previously

(Scholz et al., 2015) and showed relatively similar patterns of alterations as those observed between SH-C58 and EE-C58 mice in this study.

Taken together, these findings suggest that volume differences in the cortex, basal ganglia, and cerebellum of C58 mice are associated with their repetitive behavior phenotype. Many of the findings described in this study are in-line with those from prior neuroimaging investigations in animal models with RRB, such as reduced volume in the striatum and globus pallidus. This study, however, also found novel evidence for volume differences related to repetitive behavior in the cingulate cortex and sub-regions of the cerebellum (e.g., lobules I-V, IX, X). Such differences in brain volume likely reflect underlying differences in tissue structure or organization, such as number of neurons or glial cells, relative cellular density, or degree of dendritic arborization. Follow-up histological analyses may provide insight as to which of these factors contribute to the observed differences in regional brain volume.

An important consideration of the present work is that the repetitive behaviors exhibited by C58 mice (i.e., vertical jumping and backward somersaulting) do not represent an excess of otherwise appropriate species-specific behaviors, such as repetitive self-grooming. As such, the repetitive behaviors exhibited by C58 mice more

closely represent the types of aberrant repetitive behaviors observed in ASD and related conditions, and thus the brain-behavior relationships observed in this study may provide novel insights into volume differences and their relationship to RRB in those clinical populations. One of the limitations of the present study is that only measured repetitive motor behaviors (vertical jumping / somersaulting) and some of the observed volumetric differences could relate to other topographies of RRB, such resistance to change behaviors. Additionally, although DBM captures brain-wide volumetric alterations, atlas-based analyses targeted the cortex, basal ganglia, and cerebellum.

Atlas-based investigations were localized to these areas based on the preponderance of evidence for alterations associated with these regions in both animal models with

RRB and in ASD (see Chapter 1). Thus, the complementary nature of these two approaches only extends to cortex, basal ganglia, and cerebellum. Lastly, factors underlying the observed volumetric differences (e.g., neuronal density) were not investigated, although such histological analyses are possible in the same tissue following imaging.

Future investigations in the C58 model of RRB, as well as other relevant animal models, would benefit from in vivo imaging, which also enables fMRI and corresponding analyses, such as functional connectivity. Analyses of functional connectivity have been performed in BTBR mice (Sforazzini et al., 2016), but no other animal model of RRB.

Moreover, in vivo imaging in animal models with RRB can enable longitudinal studies that can help determine how the development of RRB over the lifespan corresponds to neuroanatomical alterations. Considering interventions that attenuate RRB, such as environmental enrichment, pre-test / post-test designs could help elucidate the

developmental brain changes corresponding with the attenuation of RRB. It is also important that future studies utilizing animal models of RRB explore additional RRB topographies (e.g., excessive self-grooming, resistance to change), as human conditions with RRB display a wide range of such behaviors. C58 mice have also been demonstrated to have deficits in reversal learning tasks, which can serve as a model of resistance to change in ASD and IDD. Future investigations in C58 mice would benefit from assessment of both repetitive motor behaviors and resistance to change, as only

C58 and BTBR mice (Amodeo et al., 2012) have been reported to capture aspects of both motor and cognitive RRBs seen in ASD.

CHAPTER 3 DIFFUSION WEIGHTED MAGNETIC RESONANCE IMAGING IN AN ANIMAL MODEL OF REPETITIVE BEHAVIOR

Introduction

As discussed in Chapter 1, restricted, repetitive behaviors (RRBs) are observed in a variety of human conditions and are diagnostic for ASD. Animal models with RRB can be induced through a variety of central nervous system insults. Among the animal models with RRB that have been studied using magnetic resonance imaging (MRI), most have been studied outside the context of RRB. Only two prior studies in such models have included a behavioral measure of RRB and related neuroimaging findings those behaviors (Allemang-Grand et al., 2017; Ellegood et al., 2013). Diffusion tensor imaging (DTI), a specialized application of MRI, has become increasingly utilized to study structural connectivity in clinical populations and corresponding animal models.

Although DTI has been used in a small number studies to investigate connectivity related to RRB in ASD, and a small number of animal models with RRB (Dodero et al.,

2013; Ellegood et al., 2013; Haberl et al., 2015), no previous study has used DTI to specifically study RRB in an animal model. DTI studies can provide information regarding structural connectivity in the brain, and inferences drawn from DTI studies in animal models can be further explored using histological approaches.

In Chapter 2, I assessed volumetric alterations in the C58 mouse model of RRB and their relationship to RRB. In the present study, I used DTI to further characterize structural alterations in the brains of the same C58 mice, as indexed by fractional anisotropy (FA). As described in Chapter 1, FA describes the “shape” in which random water movement can occur in tissue and can be used to generate inferences about tissue structure, particularly in highly organized tissue such as white matter (WM). The

repetitive motor behavior of C58 mice is highly robust, and the behaviors exhibited

(vertical jumping and backward somersaulting) do not resemble an excess of otherwise adaptive species-specific behaviors, such as self-grooming. Because the behavior phenotype of C58 mice is not the result of a targeted manipulation (e.g., genetic knockout, pharmacological disruption), this model may more closely resembles repetitive behaviors exhibited in non-syndromic ASD than other animal models. For an additional comparison, I also performed DTI in C58 mice reared in environmentally enriched (EE) housing conditions, which show lower rates of repetitive behavior compared to C58 mice reared in standard housing (SH) conditions (Muehlmann et al.,

2012). In addition to groupwise comparisons, I also investigated the relationship between FA and repetitive motor behaviors exhibited by these same animals.

Methods

Animal Housing

All animal procedures were approved by the Institutional Animal Care and Use

Committee at the University of Florida and followed the NIH Guidelines for the Care and

Use of Laboratory Animals. All mice used in this study were generated from a breeding colony located in the Psychology building at the University of Florida. B6 mice (n=10, 5 males and 5 females) were weaned at post-natal day (PND) 21, and animals of the same sex were separated into standard housing cages containing between 4 and 6 mice. C58 mice were weaned at PND 21, and animals of the same sex were separated into either SH (n=17, 9 males and 8 females) or EE (n=10, 6 males and 4 females) housing conditions, containing between 4 and 6 mice. As both SH-B6 mice and EE-C58 mice display relatively low amounts of RRB, a greater number of animals were included

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in the SH-C58 group in order to provide a wider range of RRBs for investigation of brain-behavior relationships.

SH cages were shoebox-style plastic cages (29 x 18 x 13 cm) furnished with

Sani Chip bedding and Nestlets for nest construction. EE cages were large dog kennels

(122x 81 x 89 cm) modified to have three levels, interconnected with ramps. EE cages were furnished with a large hut and running wheel as permanent fixtures, as well as tunnels and a variety of toys that were exchanged on a weekly basis to provide novel stimuli. Birdseed was scattered throughout EE cages (~2 oz/wk) in order to promote foraging behavior. In both SH and EE housing conditions, food (rodent chow) and water were available ad lib. All mice, whether EE or SH, were maintained at 70-75º F and 50-

70% humidity under at 12:12 light:dark cycle.

Behavioral Assessment

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Magnetic Resonance Imaging

Imaging was performed at the Advanced Magnetic Resonance Imaging and

Brains were scanned inside of the skull in order to keep the structure of the nervous system intact. Images were acquired on an Advance III Bruker 750 MHz (17.6 tesla) spectrometer using an 18 mm shielded NMR probe (DOTY) using a 2D diffusion weighted spin echo sequence, with repetition time (TR)=3.8 s, echo time (TE)=22.96 ms, pulse duration (δ)=2ms, pulse spacing (∆)=25ms, and 3 averages. The field of view was 16x13 mm, with a 128x104 matrix, and slice thickness of 125 µm, resulting in an isotropic resolution of 125µm. A multi-shell diffusion weighting scheme with a total of 60 diffusion directions was used, including two b=0 s/mm2 volumes, eight b=600 s/mm2 volumes, and fifty-two b=3000 s/mm2 volumes.

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Data Analysis

Voxel-based fractional anisotropy

Voxel-based analyses of neuroimaging data are used to assess differences in neuroimaging metrics across the whole brain by using nonlinear registration methods to align scans from each subject, so that each voxel represents the same anatomical space (Ashburner & Friston, 2000). In this experiment, a voxel-based approach was used to asses FA throughout the brain. Voxel-wise analyses are well-suited to exploratory animal models, and relatively little is known about how diffusion magnetic resonance imaging (dMRI) metrics relate to RRB in animal models. In order to determine alterations in FA among C58 mice, I employed an analysis pipeline similar to that described in Chapter 2.

Diffusion weighted images were corrected for motion and eddy current distortions using affine registration to a reference volume (first b0 image) with the FSL eddy correct function. Data from FSL’s eddy correct function was then used to rotate the diffusion- weighting directions (i.e. b-vectors), in order to properly estimate the diffusion tensor and diffusion parameters in each voxel after correcting for the distortions caused by motion and eddy currents. Skull stripping was performed manually with ITK-snap, followed by tensor element reconstruction with the DTIFIT function in FMRIB’s Diffusion

Toolbox (FDT). Whole-brain FA images from each animal were aligned to a common stereotactic space by performing linear (6 parameter) registration to a manually selected representative brain using ANTs (Avants et al., 2010). A population template representing the average FA of all animals from the study was then generated using multivariate template construction in ANTs. FA images for each animal were then non- linearly registered to the population template FA image, so that each voxel represented

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the same anatomical space across all animals. These FA images for each animal were used as input data for a single factor (group; SH-C58, SH-B6, and EE-C58) voxel-wise analysis of variance (ANOVA). Preliminary analysis revealed no significant effect of sex, and thus sex was not included as a factor in the final analytic model. This statistical model was applied using Randomise, a statistical analysis tool in the FMBRIB Software

Library (FSL; Jenkinson et al., 2012). The model was run through 5000 random permutations with threshold-free cluster enhancement (TFCE; Smith & Nichols, 2009).

Additional correction for multiple comparisons was performed using the false discovery rate (FDR) method, with an FDR of 20% (Benjamini & Hochberg, 1995). This FDR was chosen based upon the exploratory nature of the current experiment, as well as because it is an additional control for multiple comparisons to that provided by multiple permutation testing of the linear model, which is necessary for TFCE.

Brain-behavior relationships

Brain-behavior relationships were investigated on a whole-brain voxel-wise basis with FSL’s Randomise, using a linear model relating FA in each voxel to repetitive behavior scores. This approach for relating behavior to FA is similar to that employed by

Ellegood et al. (2013) and Allemang-Grand (2017), but with FA rather than volumetric deformation. This was done for two separate models, one which included all animals in the study sample (n=37) and a second which included only SH-C58 mice. The additional SH-C58 only model was included due to the potential concern that strain differences may drive behavioral correlations. These statistical models were analyzed using Randomise, a statistical analysis tool in FSL. The model was run through 5000 random permutations with TFCE. Additional correction for multiple comparisons was performed using a 20% FDR.

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Results

Repetitive Motor Behavior

A three group one-way ANOVA revealed that repetitive motor behaviors were significantly different among groups [F(2, 34) = 19.6, p < 0.0001] (Fig. 3-1). Post-hoc comparisons performed with Welch’s t-test, as the groups had unequal variances, revealed that SH-C58 mice had significantly higher repetitive motor behavior than EE-

C58 mice [t(16.5) = 5.67, p<0.0001 ] and SH-B6 mice [t(16.1) = 5.97, p<0.0001]. There was no difference in repetitive motor behavior between EE-C58 and SH-B6 mice

[t(12.4) = 1.74, p=.11].

Figure 3-1. Mean total number of vertical repetitive motor behaviors exhibited by SH- C58, EE-C58, and SH-B6 groups. Significant differences were observed between SH-C58 and EE-C58 mice (p < .0001), as well as SH-C58 and SH- B6 mice (p < .0001).

Voxel-based Fractional Anisotropy

A three group one-way ANOVA with FDR correction revealed FA differences among groups in regions throughout the brain (Fig. 3-2). Detailed anatomical

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descriptions of voxels that showed a significant main effect for group are not provided here, as these descriptions are given for post-hoc comparisons between groups.

Figure 3-2. Heat map of significant F-statistic values for one-way ANOVA of FA differences between animal groups (FDR corrected, p<.05).

For the comparison of SH-C58 and SH-B6 mice (Fig. 3-3), post-hoc tests revealed that SH-C58 mice showed significantly greater FA within frontal association cortex, motor and somatosensory cortices, ventromedial striatum, nucleus accumbens, hypothalamus, amygdala, hippocampus, cerebellar cortex, and brainstem. SH-C58 mice showed reduced FA within the fimbria, corpus callosum, dorsolateral striatum, globus pallidus (GP), internal capsule, external capsule, substantia nigra (SNr), nigrothalamic tract, and cerebellar white matter (e.g., arbor vitae, SCP, MCP).

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Figure 3-3. Significant t-statistic values for post-hoc t-test of FA differences between SH-C58 and SH-B6 mice (FDR corrected, p<.05). Red-yellow colors indicate greater FA, whereas blue-light blue colors indicate reduced FA.

For the comparison of SH-C58 and EE-C58 mice (Fig. 3-4), post-hoc tests revealed that SH-C58 had significantly reduced FA within frontal association regions, motor and somatosensory cortices, striatum, GP, amygdala, thalamus, hypothalamus, internal capsule, subthalamic nucleus, hippocampus, SNr, and throughout the cerebellum and brainstem. No voxels with significantly greater FA in SH-C58 mice were observed.

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Figure 3-4. Significant t-statistic values for post-hoc t-test of FA differences between SH-C58 and EE-C58 mice (FDR corrected, p<.05). Blue-light blue colors indicate reduced FA. No voxels with significantly greater FA in SH-C58 mice were observed.

For the comparison of EE-C58 and SH-B6 mice (Fig. 3-5), post-hoc tests revealed that EE-C58 had significantly increased FA within frontal association regions, motor and somatosensory cortices, striatum, GP, amygdala, thalamus, hypothalamus, internal capsule, subthalamic nucleus, hippocampus, SNr, and throughout the cerebellum and brainstem. No voxels with significantly greater FA in SH-C58 mice were observed.

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Figure 3-5. Significant t-statistic values for post-hoc t-test of FA differences between EE-C58 and SH-B6 mice (FDR corrected, p<.05). Red-yellow colors indicate increased FA. No voxels with significantly lower FA were observed.

Brain-behavior Relationships

For the linear model relating FA to repetitive motor behavior across all animals

(n=37), there was a significant (FDR corrected, p<.05) negative relationship (i.e., reduced FA associated with greater repetitive motor behavior) in the orbital cortex, anterior cingulate, somatosensory cortex, corpus callosum, striatum, GP, internal capsule, external capsule, fimbria, hippocampus, hippocampal commissure, SNr, nigrothalamic tract, lobule II, WM throughout the cerebellum, and brainstem. There were no significant relationships between increased FA and higher levels of repetitive motor behavior in the model containing all animals.

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Figure 3-6. Significant t-values (FDR corrected, p<.05) for the linear model relating FA and repetitive motor behavior including mice from all groups. Blue-light blue colors indicate reduced FA associated with higher repetitive motor behavior. No significant relationship between repetitive motor behavior and increased FA was observed.

For the linear model relating FA to repetitive motor behavior within only SH-C58 mice (n=17), no significant voxels remained after correction for multiple comparisons.

Uncorrected data (p<.05) showed that reduced FA was associated with greater repetitive motor behavior within the orbital cortex, striatum, anterior limb of the posterior commissure, and posterior cingulate. There was no observed relationships between increased FA and higher levels of repetitive motor behavior in the model containing only

SH-C58 mice.

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Figure 3-7. t-values (uncorrected, p<.05) for the linear model relating FA and repetitive motor behavior including only SH-C58 mice. Blue-light blue colors indicate reduced FA associated with higher repetitive motor behavior. No relationship between repetitive motor behavior and increased FA was observed.

Discussion

Although volumetric MRI has been more widely used to study animal models with

RRB, DTI studies are limited to BTBR and Fmr1 mouse models (Dodero et al., 2013;

Ellegood et al., 2010; Ellegood et al., 2013; Haberl et al., 2013; Sforazinni et al., 2016), and no previous DTI study has explored brain-behavior relationships to RRB in animal models. In this study, I compared SH-C58 mice with high levels of repetitive motor behavior to two separate groups with low levels of repetitive motor behavior (i.e., SH-B6 mice and EE-C58 mice) and incorporated analyses of brain-behavior relationships.

Thus, the study performed here expands significantly upon prior DTI work in animal models with RRB. In addition, this is the first DTI study in the C58 mouse model of repetitive behavior.

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Voxel-wise analysis showed significant differences in FA in a number of brain regions and WM pathways between SH-C58 mice and SH-B6 mice. For the comparison of SH-C58 and SH-B6 mice, SH-C58 mice showed greater FA in anterior cortical GM, many of which are important in sensorimotor function. SH-C58 mice also showed greater FA in ventromedial regions of the striatum that include efferents fibers traveling toward the GP, as well as GM in regions of the cerebellar cortex which are important in refinement of motor control. Other regions of higher FA in SH-C58 mice were observed in nucleus accumbens, hippocampus and cerebellum. For a number of these same GM regions, SH-C58 mice also showed reduced FA compared to EE-C58 mice. Thus, in these GM regions EE-C58 mice show the highest FA, followed by SH-C58 mice, and

SH-B6 mice showed the lowest FA.

In contrast reduced FA was observed in a number of major WM pathways of SH-

C58 mice, compared to SH-B6 mice. These WM pathways included the corpus callosum, internal capsule, fimbria, nigrothalamic tract, and cerebellar WM (e.g., arbor vitae, MCP, SCP). The corpus callosum, internal capsule, nigrothalamic tract, and SCP are all important WM pathways related to motor execution and refinement. In these same WM pathways, SH-C58 mice also had lower FA than EE-C58 mice. Compared to

SH-B6 mice, however, EE-C58 mice had greater FA in these same WM pathways.

Thus, in these WM pathways EE-C58 mice showed the highest FA, followed by SH-B6 mice, and SH-C58 mice showed the lowest FA. Reduced FA was also observed in basal ganglia GM regions, including dorsolateral aspects of striatum which receive sensorimotor cortical afferents, as well as the GP and SNr.

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In order to investigate the relationship of FA to RRB in these animals, two linear models relating voxel-wise FA to repetitive motor behaviors were generated. The first model included all animals included in the study sample (n=37), irrespective of group, whereas the second model included only SH-C58 mice (n=17). The model including all animals found only a significant negative relationship between FA and repetitive motor behavior (i.e., lower FA, greater repetitive motor behavior). Among GM regions, this relationship was observed in the orbital cortex, anterior cingulate, somatosensory cortex, hippocampus, substantia nigra, and cerebellar lobule II. Among WM pathways, this relationship was observed in the corpus callosum, internal capsule, external capsule, fimbria, hippocampal commissure, nigrothalamic tract, cerebellar WM (MCP, arbor vitae), and brainstem.

Regarding the SH-C58 only model, the limited number of voxels that showed a negative relationship between FA and repetitive motor behavior did not survive correction for multiple comparisons. Although these voxels identified in the SH-C58 only model were localized to the same areas that were significant in the model including all mice, it is possible that they were false positives as they did not survive FDR correction.

A larger within-group sample of SH-C58 may be needed to determine if higher rates of repetitive motor behavior map onto lower FA in this group.

Reduced FA in WM is commonly thought to reflect reduced integrity of the pathway, and lower anisotropy has been demonstrated to relate to factors such as reduced axonal thickness, density, or myelination (Beauileu, 2002). The reduced FA observed in WM pathways of SH-C58 mice may similarly reflect impaired WM integrity due to such underlying factors. FA differences in GM regions are more difficult to

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interpret due to the complex fiber and neuronal organization of GM tissue, but may reflect underlying factors such as differences in neuronal density, dendritic arborization, or integrity of neuronal membranes. In the population template generated for voxel-wise analysis of FA, a mild degree of FA contrast can be observed in the cortex (see Figs. 3-

2 through 3-7), and may partially reflect layers of pyramidal neurons. It is possible that the increased FA in cortical GM observed in SH-C58 mice could reflect greater density of pyramidal neurons, reduced dendritic arborization, or reduced membrane permeability. Combining DTI with follow-up histological analyses could help reveal the factors underlying FA differences in C58 mice.

FA differences between SH-C58 mice compared to both low-repetitive behavior groups, as well as the relation of FA to behavior among all animals, suggest reduced FA within major WM pathways is related to higher rates of repetitive motor behavior. Many of these pathways are important components of sensorimotor cortical-basal ganglia macro circuits. Other WM pathways with reduced FA that were found to have a significant relationship to RRB are involved in carrying information between the hemispheres (e.g., corpus callosum, anterior commissure, hippocampal commissure,

MCP). Reduced FA in inter-hemispheric WM pathways are also observed BTBR mice, although the relationship between RRB and FA in BTBR mice was not explored in those studies (Dodero et al., 2013; Ellegood et al., 2013). Alterations in FA of the corpus callosum have, however, been implicated in RRB in ASD (Fitzgerald et al., 2016; Wolff et al., 2017). Thus, inter-hemispheric connectivity may also play an important role in

RRB.

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Many areas that showed FA differences in the present study also showed volume differences in the same animals (see Chapter 2). It is important to note that the voxel- wise analyses performed for FA utilize non-linear registration such that each voxel represents the same region of anatomical space across all animals. Given the differences in volume between strain (SH-C58 and SH-B6) and housing conditions (SH-

C58 and EE-C58), a particular voxel in these DTI analyses may represent slightly different amounts of tissue, despite correspondence in relative anatomical space. Areas of increased FA motor and sensory cortices of SH-C58 also showed reduced volume, as compared to SH-B6 mice. Increased FA, which reflects a more directionally organized tissue microstructure, may reflect fewer numbers of cortical interneurons or glial cells, both of which could be reflected by reduced volume. In contrast, regions that showed decreased FA and decreased volume in SH-C58 mice could reflect other patterns of microstructural alterations. For example, reduced FA and volume in the GP of SH-C58 mice could reflect fewer recurrent fibers from STN, which would result in less tissue in that space as well as reduced directional organization due to fewer WM fibers.

Histological studies are needed to better understand how volumetric alterations, FA differences, and tissue composition relate.

Work in animal models of RRB has implicated a critical role of basal ganglia, particularly the indirect pathway, in mediating such behaviors (Baup et al., 2008;

Bechard et al., 2016; Grabli et al., 2004; Tanimura et al., 2008, 2010, 2011). The relationship between reduced FA and higher RRB observed in this study support the important role of broad cortico-basal ganglia networks in mediation of such behaviors, but further suggest that the cerebellum may also play an important role. Moreover, the

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patterns of reduced FA found to relate to RRB in this study recapitulate patterns of reduced FA observed to relate to RRB in ASD (see Chapter 5 and Langen et al., 2012).

Other studies in ASD have observed similar patterns of reduced FA in major WM pathways, but did not investigate how such alterations relate to RRB (see Mahajan &

Mostofsky, 2015). Regarding other clinical disorders with RRB, reduced FA of frontostriatal fibers was also found to correlate with symptom severity in individuals with OCD

(Nakamae et al., 2014).

One of the limitations in this DTI study, was that only FA was assessed among groups. Although significant differences were observed with FA, other relevant DTI metrics, such as mean diffusivity (MD) and constituent components (axial and radial diffusivity), may provide additional information about alterations in C58 mice and their relationship to RRB. Moreover, atlas-based analyses of FA were not performed in this study, as FA differences were hypothesized to primarily localize to WM, and these atlases used here contained a large number of GM regions compared to WM pathways.

This study only measured repetitive motor behaviors (vertical jumping / somersaulting) and some of the observed differences in FA could relate to other topographies of RRB, such resistance to change behaviors. Lastly, factors underlying the observed FA differences (e.g., neuronal/axonal density, membrane permeability, and myelination) were not investigated, although such histological analyses are possible in the same tissue following imaging.

Another approach for characterizing WM with DTI, tractography, was not utilized here. Preliminary tractography analyses in these animals revealed that cortico-basal ganglia pathways of interest were either not appropriately localized with regard to

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known fiber localization from viral tracer studies performed in mice (e.g., cortico-striatal fibers), or could not be differentiated from each other (e.g., direct versus indirect basal ganglia pathways). Nevertheless, the use of tractography to characterize other WM pathways may be useful in future investigations of C58 mice, particularly with regard to follow-up investigations into cerebellar alterations in these animals.

Due to the limited number of studies utilizing DTI in animal models with RRB, there is room for a great deal of future work in this area. Investigations in C58 mice and other animal models of RRB would benefit from inclusion of other DTI metrics (e.g., MD,

AD, RD), as FA has been the primary focus of all DTI studies performed in these models. Atlas or ROI based approaches would also be of value for DTI in C58 mice and other animal models with RRB, as differences in FA and other DTI metrics may also occur in GM tissue. For within-group analyses of C58 mice, larger sample sizes may be needed to elucidate whether different rates of RRB correspond to differences in FA and other DTI metrics. Alternately, targeted investigations in a limited subset of regions

(e.g., motor aspects of cortico-basal ganglia loops) may help to ameliorate the problem of multiple comparisons and require less statistical power than brain-wide investigations.

Future studies in animal models of RRB would also benefit from a combination in vivo fMRI and ex vivo DTI, as these complementary analyses can enable inferences about both functional and structural connectivity. In animal models of RRB, functional connectivity analyses have only been performed in BTBR mice (Sforazzini et al., 2016), and no study has yet utilized a combination of fMRI and DTI in such models. DTI

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followed by histological analyses (e.g., neuronal density, myelination) may also be useful for revealing cellular factors underlying differences observed with DTI.

DTI studies could also help determine how the development of RRB over the lifespan corresponds to alterations in FA and other diffusion imaging metrics. If a longitudinal approach is taken, such studies may have to sacrifice spatial or angular resolution in consideration of long scan times that may negatively impact animal welfare. Alternately, a cross-sectional approach could be taken for assessment of DTI metrics at different developmental time-points. Considering interventions that attenuate

RRB, such as environmental enrichment, pre-test / post-test designs could help elucidate the developmental brain changes corresponding with the attenuation of RRB.

It is also important that future studies utilizing animal models of RRB explore additional RRB topographies (e.g., excessive self-grooming, resistance to change), as human conditions with RRB display a wide range of such behaviors. C58 mice have also been demonstrated to have deficits in reversal learning tasks, which can serve as a model of resistance to change in ASD and IDD. Future investigations in C58 mice would benefit from assessment of both repetitive motor behaviors and resistance to change, as only C58 and BTBR mice (Amodeo et al., 2012) have been reported to capture aspects of both motor and cognitive RRBs seen in ASD.

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CHAPTER 4 VOLUMETRIC MAGNETIC RESONANCE IMAGING IN CHILDREN AND ADOLESCENTS WITH AUTISM SPECTRUM DISORDER

Introduction

As discussed in Chapter 1, restricted, repetitive behaviors (RRBs) are diagnostic for autism spectrum disorder (ASD). In individuals with ASD, RRBs can significantly impair functional behavior and contribute to problem behaviors. Although pharmacological treatments are used in individuals with ASD, they target related issues such as comorbid anxiety, depression, or aggressive behavior. There are currently no effective biologically-based treatments of RRB in ASD. This shortcoming is largely due to an incomplete understanding of the neural circuits and underlying mechanisms that mediate RRB in ASD.

One of the predominant approaches for studying neural circuitry in clinical populations is magnetic resonance imaging (MRI). MRI has been widely utilized to assess differences in brain volume, morphology and connectivity in ASD. Volumetric alterations in ASD, independent of RRB, have been described elsewhere (Ecker et al.,

2015; Mahajan & Mostofsky, 2015; Li et al., 2017). Briefly, the most consistent volumetric findings in individuals with ASD are larger total brain volume in children and adolescents, localized differences in the frontal, temporal, and cingulate cortices, as well as subcortical differences in the amygdala, caudate, and cerebellum. Fewer studies, however, have investigated how such differences in brain volume relate to

RRB. These studies suggest that RRB is most consistently linked to volumetric alterations in the caudate and putamen (see Chapter 1). Other studies of brain volume and RRB have found volumetric correlations with RRB in individuals with ASD, despite

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lack of group differences between ASD and TD individuals in the same regions (e.g

Rojas et al., 2006).

Although these studies have improved our understanding of morphological and connectivity alterations associated with RRB in ASD, many such studies have been limited by factors such as small sample sizes and demographic bias (e.g., majority males). In this study, I used structural MRI data from a large sample of children and adolescents with ASD and typically developing (TD) controls to investigate brain-wide volumetric alterations, their relationship to RRB, and whether such alterations were differentially expressed by males and females with ASD. The neuroimaging dataset used in this study was acquired from the National Database for Autism Research

(NDAR), a data repository funded and curated by the National Institutes of Health (NIH).

Methods

Participant Data

Participant data were acquired from NDAR (collection ID #2021). Selection criteria within this dataset were that each participant had a high-resolution structural image and score for the Differential Ability Scales – II, which was used to assess intelligence quotient (IQ). For individuals in the ASD group, a diagnostic assessment

(i.e., Autism Diagnostic Observation Schedule, Autism Diagnostic Interview – Revised) was required for confirmation of diagnosis status, but these assessments were not given to TD participants and thus were not required for inclusion in the TD group.

Participants whose data met these initial inclusion criteria were also checked for quality of image data (e.g., no missing volumes or major motion artifacts) before inclusion in the final dataset. Application of these criteria resulted in a dataset including 192

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individuals, of which 96 were individuals with ASD and 96 were TD individuals (table 4-

1).

Table 4-1. Number of participant in each group, with mean age and IQ Mean Age (Years) IQ Group N Gender (± SD) (± SD) ASD 96 M (n=46), F (n=50) 12.6 (± 2.81) 102 (± 20) TD 96 M (n=52), F (n=44) 13.3 (± 2.97) 110 (± 16)

T1-weighted structural images were acquired across four sites, using identical acquisition parameters (see Irimia et al., 2017): (1) The Center for Translational

Developmental Neuroscience, Child Study Center, Yale School of Medicine, New

Haven, CT; (2) The Nelson Laboratory of Cognitive Neuroscience, Boston Children’s

Hospital, Harvard Medical School, Boston, MA; (3) The Center on Human Development

& Disability, Seattle Children’s Hospital, University of Washington School of Medicine,

Seattle, WA; (4) Staglin IMHRO Center for Cognitive Neuroscience, David Geffen

School of Medicine, University of California, Los Angeles, CA. All scans were acquired on a 3T Siemens Magnetom Trio using a T1-weighted magnetization-prepared rapid acquisition gradient-echo (MPRAGE) sequence with 256 single-shot interleaved sagittal slices, flip angle=7º, TR=2530 ms, TE=3.31 ms, TI=1100 ms. The field of view was 256 mm, with a 256x256 matrix, resulting in an isotropic resolution of 1 mm.

Data Analysis

Population parameters

A 2x2 analysis of variance (ANOVA) was used to assess the effects of diagnosis

(ASD or TD) and gender on the population variables of age (in months) and IQ. Only individuals with ASD were given the Autism Diagnostic Interview – Revised (ADI-R),

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and thus RRB scores from section C of the ADI-R were compared with a t-test between males and females with ASD.

Deformation-based morphometry

Deformation-based morphometry (DBM) is a neuroimaging analysis technique used to assess volumetric differences between groups across the whole brain, without the need for a priori hypotheses about where such differences may occur (Ashburner &

Friston, 1998). This approach is well-suited to the study of brain morphology and its relationship to RRB in ASD, as subtle alterations within larger brain structures (e.g., head of the caudate) have been shown to correspond with RRB (Rojas et al., 2006). In order to determine alterations in brain morphology in children and adolescents with ASD compared to their TD counterparts, and how alterations in morphology correspond to

RRB in children and adolescents with ASD, I employed a DBM analysis pipeline similar to that used in Chapter 2.

T1-weighted structural images from each child were skull-stripped using FSL’s

Brain Extraction Tool (BET) to remove tissue outside of the central nervous system

(CNS). Images from each participant were then linearly registered to a common space

(i.e., MNI space) using Advanced Registration Tools (ANTs; Avants et al., 2010). Each brain was then corrected for signal inhomogeneity using N3 correction (Sled et al.,

1998) from MINC toolkit. A population template representing the average anatomy of all individuals in the study was then generated using multivariate template construction in

ANTs. The non-linear deformation matrix used to transform each individual’s brain from its native morphology onto the population template was used to create an image that contains Jacobian determinant values of that transformation matrix at each voxel, which were then log-transformed to maintain assumptions of normality for statistical testing.

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The Jacobian determinant values in each voxel represent a ratio of relative volume change between the original image (i.e., individual participant) and the target image

(i.e., population template), where values between 0 and 1 indicate a relatively smaller volume, and values greater than 1 indicate relatively larger volume. After log- transformation, voxels with negative values represent smaller volume and voxels with positive values represent greater volume, relative to the population template. These

Jacobian determinant values have been empirically demonstrated to correspond with volumetric expansion and contraction (Ashburner & Friston, 1998).

The log-transformed Jacobian images for each individual were used as input data for a 2x2 voxel-wise ANOVA, using diagnosis (ASD or TD) and gender (male or female) as factors, and including the covariates of age and IQ. This statistical model was applied using Randomise, a statistical analysis tool in the FMBRIB Software Library (FSL;

Jenkinson et al., 2012). The model was run through 5000 random permutations with threshold-free cluster enhancement (TFCE; Smith & Nichols, 2009). Additional correction for multiple comparisons was performed using the false discover rate (FDR) method, with an FDR of 20% (Benjamini & Hochberg, 1995). This FDR was chosen based upon the exploratory nature of the current experiment, where the potential for false positives has limited negative consequences, as well as the additional control for multiple comparisons resulting from permutation testing of the linear model for TFCE.

Atlas-based volumetry

As a complementary technique to assess brain volume differences between groups, I also performed volumetric comparisons using regions of interest (ROIs) derived from human MRI segmentation atlases. These atlases include segmentation of cortex (Desikan et al., 2006), basal ganglia (Keuken et al., 2014), and cerebellum

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(Diedrichsen et al., 2009). The ROIs included for this atlas-based volumetric comparison are listed in Appendix A (Table A-2). Non-linear registration was used to apply segmentation images from these atlases onto the population template described previously under Deformation-based morphometry. The deformation matrices created during template construction were used to calculate the inverse transformation matrices for each participant, and ROI segmentation from the population template was then applied onto native-space images for each participant using those inverse transformation matrices.. The relative brain volume for each ROI was used as input data for a 2x2 ANOVA, with the factors of diagnosis (i.e., ASD or TD) and gender. Correction for multiple comparisons was performed using a 20% FDR.

Brain-behavior relationships

The relationship between volume and RRB was explored on a whole-brain voxel- wise basis with FSL’s Randomise, using a linear model relating the log-transformed

Jacobian determinant images (i.e., volumetric expansion or contraction) to scores from section C of the ADI-R. Section C of the ADI-R (hereafter referred to as ADI-C) includes

12 items specific to RRBs, including those from both sensorimotor and insistence on sameness/resistance to change domains. Scores from the ADI-C were only available for individuals with ASD, and thus this analysis was carried out within ASD participants only. ASD participants data were screened for completeness of the ADI-C, and the remaining 68 participants (29 male, 39 female) were included in this linear model relating volume and ADI-C scores. This approach for relating behavior to volumetric alterations is similar to that employed by Rojas et al. (2006). Due to the observed gender differences in other analyses, additional models were generated for male only

(n=29) and female only (n=39) groups of ASD participants. In all models, age and IQ

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were included as covariates. These statistical models were analyzed using Randomise, a statistical analysis tool in FSL. The model was run through 5000 random permutations with TFCE. Additional correction for multiple comparisons was performed using a 20%

FDR.

Results

Population Parameters

ANOVA revealed no significant main effect of diagnosis (ASD or TD), gender, or interaction for the population variable of age. Regarding IQ, a significant main effect of diagnosis was observed [F(3, 188) = 9.43, p<.01] (see Table 4-2), where the ASD group had lower IQ than the TD group (see Table 4-1).

Table 4-2. Effect of diagnosis and gender on age and IQ Factor Variable F statistic p-value Age 2.42 0.12 Diagnosis IQ 9.43 * <0.01 Age 0.72 0.40 Gender IQ 0.01 0.92 Age 2.44 0.12 Interaction IQ 0.12 0.74 * Significant effect of factor

Within ASD participants, no significant difference on ADI-C scores was observed between males (M=6.45, SD=2.59) and females (M=5.79, SD=2.57); t(66)=1.04, p=0.30.

Deformation-based Morphometry

Utilizing DBM data, ANOVA with correction for multiple comparisons revealed significant main effects of both diagnosis (Fig. 4-1) and gender (Fig. 4-2). For the presentation of the following results, differences were observed bilaterally unless otherwise specified. The main effect of diagnosis revealed that individuals with ASD had increased volumes within the inferior frontal gyrus (IFG; pars opercularis), orbital cortex,

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anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), subcallosal cortex, midline precentral gyrus (primary motor cortex), left lateral occipital cortex, precuneus, cuneal cortex, and occipital pole. In addition, individuals with ASD showed reduced volumes within frontal pole, right middle frontal gyrus (MFG), paracingulate gyrus, insular cortex, temporal pole, planum temporale (Wernicke’s area), left temporal and occipital fusiform, hippocampus, parahippocampal gyrus, PCC, right lingual gyrus, caudate, putamen, right globus pallidus externa (GPe), right globus pallidus interna

(GPi), right subthalamic nucleus (STN), lobule V, lobule VI, lobule VIIb, Crus I, Crus II, vermis (VI, VIIb, VIIIa, VIIIb) and deep cerebellar nuclei. With regard to WM regions, significant volume reductions were also observed in cortical WM, anterior commissure, internal capsule, corpus callosum (genu and splenium), and cerebellar WM (superior cerebellar peduncle and arbor vitae).

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Figure 4-1. Significant t-statistic values for main effect of diagnosis on volume between ASD and TD participants (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes. These results are displayed on the population-specific template brain.

The main effect of gender revealed that males had increased volumes within the subcallosal cortex, insula cortex, opercular cortex, temporal pole, precentral and postcentral gyrus (lateral aspects), temporal cortex (superior, middle, and inferior gyri), temporal and occipital fusiform, hippocampus, parahippocampal gyrus (anterior division), PCC, lateral occipital cortex, precuneus, cuneal cortex, lingual gyrus, supracalcarine cortex, occipital pole, lobules I-IV, lobule VI, right crus I, lobules VII-X, and pons. In addition, males also had decreased volumes within the frontal pole, superior frontal gyrus (SFG), MFG, IFG, paracingulate gyrus, ACC, juxtapositional lobule cortex (supplementary motor), precentral and postcentral gyrus (medial aspects), middle temporal gyrus (MTG; temporo-occipital part), parahippocampal gyrus (posterior division), caudate, putamen, GPe, GPi, STN, throughout the thalamus and midbrain, crus I, crus II, and vermis (VI). With regard to WM regions, significant volume reductions in males were also observed in cortical WM, internal capsule, corpus callosum (body), and cerebellar WM (arbor vitae).

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Figure 4-2. Significant t-statistic values for main effect of gender on volume between male and female participants (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes. These results are displayed on the population-specific template brain.

No significant interaction between diagnosis and gender remained after correction for multiple comparisons. Uncorrected data (p<.05) for the interaction between diagnosis and gender are presented (Fig. 4-3), however, as the pattern of volumetric differences observed between ASD and TD participants, particularly in the basal ganglia, were dramatically different than those previously reported. Moreover, a significant interaction between diagnosis and gender was observed using atlas-based volumetry (see following section).

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Figure 4-3. Significant F-statistic values for interaction between the effects of diagnosis and gender on volume differences between male and female participants (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes. These results are displayed on the population-specific template brain.

Post-hoc comparisons between ASD and TD individuals were performed for male-only (Fig. 4-4) and female-only (Fig. 4-5) groups, and revealed strikingly different patterns of volumetric alterations. When compared to TD males, males with ASD had increased volume within the right IFG (pars opercularis), precentral gyrus (primary motor cortex), ACC, right superior parietal lobule, and right lateral occipital cortex.

Males with ASD also had decreased volume within the left MFG, left IFG (pars triangularis), right postcentral gyrus, PCC, precuneus, parahippocampal gyrus (posterior division), lingual gyrus, right lobules I-V, lobule VI, crus I, crus II, vermis (I-IV), and deep cerebellar nuclei. Males with ASD also showed volumetric reductions in cortical and

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cerebellar WM, but less widely distributed than observed in the comparison including both genders.

Figure 4-4. Significant t-statistic values for post-hoc comparison of volume in male ASD and TD participants (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes. These results are displayed on the population-specific template brain.

When compared to TD females, females with ASD had increased volume within the frontal pole, SFG, MFG, left IFG (pars triangularis), ACC, PCC, precuneus, cuneal cortex, left superior parietal lobule, and occipital pole. Females with ASD also had reduced volumes within orbital cortex, MFG, right IFG (pars opercularis), precentral gyrus, paracingulate gyrus, temporal pole, temporal cortex (superior, middle, and inferior gyri), planum temporale (Wernicke’s area), insular cortex, parahippocampal gyrus, hippocampus, caudate, putamen, GPe, GPi, right STN, lateral thalamus, supramarginal gyrus, angular gyrus, lingual gyrus, lobules I-IV, lobule V, lobule VI, crus

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I, left crus II, right lobule VIIa, and lobule VIIb. Females with ASD also showed widespread volumetric reductions in WM throughout frontal and temporal cortices, as well as in the internal capsule, corpus callosum, and cerebellar WM.

Figure 4-5. Significant t-statistic values for post-hoc comparison of volume in female ASD and TD participants (FDR corrected, p<.05). Red-yellow colors indicate larger volumes, whereas blue-light blue colors indicate smaller volumes. These results are displayed on the population-specific template brain.

Atlas-based Volumetry

Atlas-based volumetry revealed significant main effects of diagnosis, gender, and an interaction between diagnosis and gender for a number of brain regions. The significant main effect of diagnosis revealed that individuals with ASD had significantly smaller volumes in the parahippocampal gyrus, planum temporale (Wernicke’s area), temporal fusiform cortex, cerebellar crus I, lobule VI, and vermis VIIb (Table 4-1). The

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main effect of diagnosis did not show any regions of significant volumetric enlargement in ASD participants.

Table 4-3. List of brain regions (ROIs) which showed a significant volume difference (FDR corrected, p < .05) between ASD and TD participants, adjusted for total brain volume. ASD mean TD mean Delta p value Brain Region (% total brain (% total brain (% difference) volume) volume) Parahippocampul Gyrus .152 .158 -3.80 .027 (posterior division) Planum Temporale .099 .104 -4.81 .038 (Wernicke’s area) Temporal Fusiform .356 .372 -4.30 .020 Cortex (posterior division) Cerebellar Crus I .914 .948 -3.59 .034 Lobule VI .489 .513 -4.68 .019 Vermis VIIb .117 .120 -2.50 .040

The significant main effect of gender is not described in detail here, as nearly all

ROIs investigated showed a significant main effect of gender. A full list of regions which showed a main effect of gender for this atlas-based analysis can be found as supplementary data in Appendix B (Table B-2). A significant interaction between diagnosis and gender was observed for the parahippocampal gyrus (posterior division), planum polare (Heschl’s gyrus), precentral gyrus, GPe, and GPi. Post-hoc comparisons of ASD and TD participants by gender revealed that although females with ASD showed volume differences in a number of ROIs, males with ASD had no significant ROI volume differences compared to TD males. For females with ASD, reduced volume was observed in ROIs for the parahippocampal gyrus (posterior divisin), planum polare

(Heschl’s gyrus), planum temporale (Wernicke’s area), temporal-occipital fusiform

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cortex, GPe, GPi, striatum (caudate + putamen), and lobule VI of the cerebellum (see table 4-4).

Table 4-4. List of brain regions (ROIs) which showed a significant volume difference (FDR corrected, p < .05) between female ASD and TD participants adjusted for total brain volume. ASD mean TD mean Delta p value Brain Region (% total brain (% total brain (% difference) volume) volume) Parahippocampul gyrus .158 .174 -.016 .003 (posterior division) Planum polare .114 .122 -.008 .021 (Heschl’s gyrus) Planum temporale .101 .109 -.008 .035 (Wernicke’s area) Temporal-occipital .342 .368 -.026 .049 fusiform cortex Globus pallidus .128 .137 -.009 .047 (external) Globus pallidus .047 .051 -.004 .026 (internal) Striatum (caudate + 1.55 1.65 -0.10 .031 putamen) Lobule VI .504 .538 -.034 .050

Brain-behavior relationships

For the linear model relating volume alterations to ADI-C scores across all individuals with ASD (n=68; Fig. 4-6), there was a significant (FDR corrected, p<.05) positive relationship (i.e., volumetric enlargement associated with greater ADI-C scores) within the insula, parahippocampal gyrus (anterior division), central opercular cortex, left temporal fusiform (posterior division), PCC, precuneus, cuneal cortex, intracalcarine cortex, occipital pole, putamen, GPe, medial thalamus, and corpus callosum (splenium).

A significant (FDR corrected, p<.05) negative relationship (i.e., volumetric reduction

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associated with greater ADI-C scores) was observed within the left lateral ventricle, left lobule VI, and left crus I.

Figure 4-6. Significant t-values (FDR corrected, p<.05) for the linear model relating volume and ADI-C scores in ASD participants. Red-yellow colors indicate larger volumes associated with higher ADI-C scores, whereas blue-light blue colors indicate smaller volumes associated with higher ADI-C scores. These results are displayed on the population-specific template brain.

For the linear model relating volume alterations to ADI-C scores within ASD males (n=29; Fig. 4-7), there was a significant (FDR corrected, p<.05) positive relationship within the frontal pole (superior portion), SFG, ACC, insular cortex, planum polare, precentral gyrus (primary motor cortex), postcentral gyrus (primary somatosensory cortex), left MTG (posterior division), parahippocampal gyrus (anterior and posterior), hippocampus, right lateral occipital cortex, caudate, putamen, GPe, GPi,

STN, substantia nigra (SNr), medial thalamus, lobules I-IV, left lobule V, right crus I, crus II, and lobule VIIb. Notably, there was also significant positive relationship for WM

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throughout the cortex, basal ganglia, and the superior cerebellar peduncle. A significant negative relationship was observed within the frontal pole (inferior portion), orbital cortex, left opercular cortex (frontal and central), left IFG, MTG (anterior division), MTG, left supramarginal gyrus, and left crus I.

Figure 4-7. Significant t-values (FDR corrected, p<.05) for the linear model relating volume and ADI-C scores in male ASD participants. Red-yellow colors indicate larger volumes associated with higher ADI-C scores, whereas blue- light blue colors indicate smaller volumes associated with higher ADI-C scores. These results are displayed on the population-specific template brain.

For the linear model relating volume alterations to ADI-C scores within females with ASD (n=39; Fig. 4-8), no significant findings remained after correction for multiple comparisons. Uncorrected data (p<.05) showed a positive relationship within the frontal pole, SFG, juxtapositional lobule cortex (supplementary motor area), frontal opercular cortex, insular cortex, MTG, left occipital fusiform gyrus, PCC, precuneus, cuneal cortex, right occipital pole, putamen, GPe, left lobule V, corpus callosum (splenium),

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and third ventricle. Uncorrected data (p<.05) showed a negative relationship within

MFG, left orbital cortex, ACC, right parietal opercular cortex, left superior temporal gyrus

(STG), right supramarginal gyrus (anterior division), right angular gyrus, lateral occipital cortex (superior and inferior), left occipital pole, caudate, lobule V, crus I, crus II, lobules

VII-IX, deep cerebellar nuclei, and the lateral ventricles.

Figure 4-8. t-values (uncorrected, p<.05) for the linear model relating volume and ADI-C scores in female ASD participants. Red-yellow colors indicate larger volumes associated with higher ADI-C scores, whereas blue-light blue colors indicate smaller volumes associated with higher ADI-C scores. These results are displayed on the population-specific template brain.

Discussion

Despite their clinical importance, there has been limited focus on structural alterations underlying RRB in ASD. In this study, I used structural MRI data from a large sample of children and adolescents with ASD to investigate brain-wide volumetric

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differences from TD controls, the relationship of volume to RRB in individuals with ASD, and whether such findings were differentially expressed in males and females with ASD.

Prior work investigating volumetric alterations in ASD, without consideration of

RRB, have consistently reported differences in the frontal, temporal, and cingulate cortices, as well as subcortical differences in the amygdala, caudate, and cerebellum

(Ecker et al., 2015; Mahajan & Mostofsky, 2015; Li et al., 2017). Volumetric investigations targeting RRB in ASD have suggested that greater volume of the caudate and putamen is associated with greater RRB, whereas reduced volume in the cerebellum is associated with greater RRB (see Chapter 1, Magnetic Resonance

Imaging of Repetitive Behavior in ASD and IDD). These prior studies have been limited by factors such as small sample sizes and demographic bias (e.g., males only).

The current study included a large study sample with nearly equal number of males and females. Two complementary approaches were used for comparing brain volume, DBM and atlas-based volumetry, both of which controlled for total brain volume.

Using these complementary approaches, I found significant volumetric alterations between ASD and TD participants, but also a significant interaction between diagnosis and gender.

DBM identified several anatomical differences that were not observed in regions that were included in atlas-based volumetry. The differences observed between these two approaches highlight strengths and weakness of both methods. DBM can identify volume differences that occur in only a portion of an atlas ROI, but suffers from more stringent correction for multiple comparisons due to comparisons in each voxel. Atlas- based comparisons of volume allow for hypothesis-based investigations in limited

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anatomical regions, which can partially alleviate the problem of multiple comparisons, but may not capture alterations that localize to subregions within a segmented ROI.

Using DBM, participants with ASD were found to have volumetric enlargements in primary motor cortex, ACC, PCC, and occipital cortical regions, but no such findings were found using atlas-based volumetry. DBM also revealed widespread volumetric reductions in ASD participants within both GM and WM of the frontal, temporal, and insular cortex, basal ganglia (caudate, putamen, GPe, GPi, STN), and throughout the cerebellum. In contrast, atlas-based volumetry revealed a more limited set of ROIs with reduced volumes, including the parahippocampal gyrus, Wernicke’s area, fusiform cortex, and limited regions of the cerebellum (lobule VI, crus I, vermis VIIb).

Some of the differences between ASD and TD participants observed in this study were in line with those previously reported, such as enlargement of the ACC, and reduced volumes in the corpus callosum and cerebellum. Although localized to similar brain regions, a number of findings in the present study were in the opposite direction than those previously reported; in particular, areas of reduced volume observed in the temporal cortex and basal ganglia, particularly the caudate and putamen. This pattern of findings, as well as the significant interaction between diagnosis and gender observed in atlas-based volumetry analyses, led to post-hoc comparisons of male and female samples.

Males with ASD showed a limited set of volumetric differences compared to TD males, including increased volumes in GM of the frontal cortex, and reduced volumes in occipital cortex and cerebellum. In contrast, females with ASD showed much more widespread differences when compared to TD females, including enlargement in frontal

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cortical regions, but reduced volume in temporal cortex, basal ganglia, cerebellum, cortical WM, and the corpus callosum. Particular to the basal ganglia, whereas males with ASD showed no volume differences compared to TD males, females with ASD showed reduced volume throughout the basal ganglia compared to TD females. Among prior studies that indicated enlargement of caudate or putamen, some did not control for total brain volume (Estes et al., 2011; Sears et al., 1999) or included adults with ASD

(Hollander et al., 2005; Rojas et al. 2006). These differences could partially explain the pattern of results observed between ASD and TD individuals from this large sample of children and adolescents that included nearly equal numbers of males and females.

In order to provide greater detail regarding the relationship of RRB to the neuroanatomical alterations found in this study, a linear model relating voxel-wise volumetric alterations to a clinical measure of RRB (i.e., ADI-C) was used. This analyses of brain-behavior relationships showed that volumetric alterations related to

RRB were limited to fewer anatomical regions than were observed in comparisons excluding behavior. The model which included both males and females with ASD found that ADI-C scores showed a positive relationship to volumes in the putamen, GPe, medial thalamus, corpus callosum (splenium), and midline structures in posterior cortical areas (PCC, precuneus, cuneal cortex). A negative relationship between volume and ADI-C scores (i.e., smaller volumes, higher ADI-C scores) was observed in the left lateral ventricle and left cerebellar GM (lobule VI, crus I).

Additional analyses performed in only males or females with ASD showed significant brain-behavior relationships in ASD males, but brain-behavior relationships in females with ASD did not survive correction for multiple comparisons. In males with

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ASD, larger volumes were associated with higher ADI-C scores in frontal cortex, basal ganglia (all structures), thalamus, and portions of the cerebellum, whereas smaller volumes were associated with higher ADI-C scores in the orbital cortex, left IFG, MTG, operculum, and crus I. One of the most striking patterns in this brain-behavior investigation was that males with ASD showed enlargement of WM throughout the cortex and basal ganglia in association with higher ADI-C scores.

Although females with ASD had no significant volume differences associated with

ADI-C scores after FDR correction, some discussion of uncorrected findings (p<.05) is now included in order to better inform the pattern of results observed when relating volume to ADI-C scores within all ASD participants, and provide contrast to findings in the brain-behavior analysis that included only males with ASD. In previous work, the most consistent volumetric alteration associated with RRB within ASD participants was enlargement of the caudate. In the present study, although volumetric enlargement of the caudate was associated with higher ADI-C scores in males with ASD, reduced volume of the caudate was associated with higher ADI-C scores in females with ASD. In both males and females with ASD, however, increased volume in the putamen and

GPe, as well as reduced volume in cerebellar crus I, was associated with higher ADI-C scores.

The gender differences observed in this study shed light on the previous work on volumetric alterations in ASD, as well as how such alterations relate to RRB. In particular, although individuals with ASD show reduced volumes in multiple basal ganglia nuclei compared to TD individuals, this pattern is driven by females with ASD.

Considering the main effects of gender and interaction between diagnosis and gender,

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another way to frame these findings is that males and females with ASD do not exhibit similar gender differences to those observed between TD males and females within the basal ganglia, where TD males have smaller relative brain volume in these basal ganglia nuclei. A similar pattern of results, where ASD males and females do not exhibit gender differences in brain volume similar to TD controls, has been reported for cortical white matter and the opercular cortex by a previous investigation (Beacher et al., 2012).

This novel finding of atypical gender differences in the basal ganglia of individuals with

ASD adds substantially to the limited number of neuroimaging studies of gender in ASD

(see review by Chen & Van Horn, 2017).

Taken together, the findings observed in this study suggest that although individuals with ASD have reduced relative volume in the caudate and putamen compared to TD individuals, analyses within ASD individuals showed that larger relative volumes in the putamen and GPe are associated with higher rates of RRB. Reduced volume in the cerebellar cortex, specifically Crus I, was observed in both males and females with ASD as compared to TD males and females, and was found to relate to higher ADI-C scores in both males and females with ASD. In contrast, a number of regions were found to have a different relationship between volume and RRB between

ASD males and females. For example, within ASD males greater caudate volumes were associated with higher RRB, but in females with ASD smaller caudate volumes were associated with higher RRB. Thus, findings from this study suggest that alterations to the putamen, GPe, and cerebellar crus I are most relevant to RRB in both males and females with ASD, but that for other brain regions (e.g., caudate), differential mechanisms may underlie RRB in males versus females with ASD.

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An important advantage of the present study was the large study sample which included nearly equal numbers of males and females for both ASD and TD groups.

Moreover, neuroimaging studies in ASD utilizing NDAR and other similar databases

(e.g., the Autism Brain Imaging Data Exchange) provide the important opportunity for replication and validation using multiple approaches for studying brain structure.

Additional data (e.g., DTI, rs-fMRI) from the same participants included in this study could be used for further investigations into the relationship between RRB and alterations in brain structure, function, and connectivity.

One of the limitations of the volumetric investigations performed here is that although voxel-wise and atlas based comparisons were both performed, the human atlases utilized included only GM structures in the cortex, basal ganglia, and cerebellum. Thus, these analyses did not overlap for some subcortical structures (e.g., thalamus, brainstem), or WM pathways. Although DBM revealed WM differences in individuals with ASD, as well as gender differences between males and females with

ASD, these findings should be further investigated using additional analytic approaches.

Another limitation related to DBM versus atlas-based analyses is that DBM enabled detection of lateralized volume differences in ASD individuals for a number of brain regions (e.g., right GPe, GPi, and STN), but the cortical and basal ganglia atlases used here did not distinguish between left and right hemispheres for segmentation of these brain regions. Although the cerebellum atlas did contain left and right lateralization, we chose to merge left and right hemisphere cerebellar data to be more in line with cortical and basal ganglia atlases. Thus, some discrepancy between the findings observed with

DBM and atlas-based volumetry may be due to this limitation.

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Another limitation of the present study relates to the behavioral index of RRB – the ADI-C. Although widely utilized in neuroimaging studies of RRB in ASD, the ADI-C contains only 12 items with a limited range of severity rating. Moreover, many of these items overlap in content, such as “complex mannerisms” and “hand and finger mannerisms”, or “difficulties in changes with routine” and “resistance to trivial changes in environment”. This narrow range of scores and potential overlap for certain items limits the “resolution” at which the relationship between brain alterations and RRB, as well the degree to which relationships with particular subtypes of RRB can be explored.

Future volumetric investigations in ASD should consider the potential effect of gender on brain volume, as the work presented here identified a significant interaction between gender and ASD diagnosis on brain volume. As these data are available through NDAR, additional, complementary approaches for characterizing structural alterations in the same study sample could be performed. Some examples of analyses that would complement those performed here are cortical thickness, covariance of volume (Evans et al., 2013), and surface based morphometry (e.g., Schuetze et al.,

2016). The brain-behavior analyses performed in this study were limited to within-group comparisons among ASD participants, as the ADI-R was only given to individuals with

ASD. Future studies would benefit from using additional behavioral assessments to capture features of RRB in both ASD and TD populations. Some examples of additional behavioral assessments that have been utilized in both ASD and TD individuals are the

Repetitive Behavior Scale – Revised (RBS-R), Social Communication Questionnaire

(SCQ), and Sensory Profile. Moreover, investigation of brain-behavior relationships using these additional behavioral indices could enable more detailed understanding

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about how structural alterations in the brain correspond with subtypes of RRB, such as motor stereotypies, restricted interests, and insistence on sameness.

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CHAPTER 5 DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN CHILDREN AND ADOLESCENTS WITH AUTISM SPECTRUM DISORDER

Introduction

As discussed in Chapter 1, restricted, repetitive behaviors (RRBs) are diagnostic for autism spectrum disorder (ASD). Although no particular etiology has been identified for individuals with non-syndromic ASD, there has been increasing focus on hypotheses regarding brain connectivity in ASD, and how disrupted connectivity patterns relate to core symptoms of ASD. Early post-mortem studies in ASD postulated local over- connectivity and long distance underconnectivity of the frontal cortex (Courchesne &

Pierce, 2005). Subsequent investigations into connectivity differences in ASD have largely utilized MRI to assess structural and functional connectivity, using diffusion tensor imaging (DTI) and resting-state fMRI (rs-fMRI), respectively. Evidence from both

DTI and rs-fMRI studies support the notion of altered connectivity in ASD. Functional connectivity studies support mixed patterns of both under and over-connectivity, for both local and long-distance circuits. DTI studies, however, generally support the notion of reduced connectivity, whether in local or distributed brain circuits (Ameis & Catani,

2015; Hull et al., 2017; Mahajan & Mostofsky, 2015; Mash et al., 2017; Müller et al.

2011; Rane et al., 2015). Connectivity differences in ASD have frequently been studied in the context of social and communication deficits (see review by Ameis & Catani,

2015), with fewer studies have focused on how altered connectivity relates to RRB (see

Chapter 1).

The small number of neuroimaging studies that have applied DTI to study RRB in

ASD have generally shown that reduced fractional anisotropy (FA) in WM of the cortex, basal ganglia, and cerebellum are associated with RRB (Cheung et al., 2009; Langen et

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al., 2012; Thakkar et al., 2008). RRB has also been linked to higher FA in the precentral gyrus of children and adolescents with ASD (Cheung et al., 2009). Additionally, Wolff et al. (2017) found that higher FA in the corpus callosum of 6-month old infants predicted rates of RRB at 2 years of age in infants that go on to develop an ASD diagnosis.

Although these studies have improved our understanding of connectivity alterations associated with RRB in ASD, they have been few in number, largely focused on males, and had small sample sizes.

In this study, I utilized DTI to assess free-water corrected FA within cortico-basal ganglia and cerebello-thalamo-cortical pathways in a large dataset of individuals with

ASD, the relationship of RRB to FA in these pathways, and whether males and females with ASD show differences in these measures. These pathways were selected based on the large amount of evidence linking RRB with alterations in cortex, basal ganglia, and cerebellum in ASD (see Chapter 1). This work contributes significantly to the relatively small number of neuroimaging investigations that have utilized DTI to study

RRB in ASD (Cheung et al., 2009; Langen et al., 2012; Thakkar et al., 2008; Wolff et al.,

2015, 2017). The neuroimaging dataset used in this study was acquired from the

National Database for Autism Research (NDAR), a data repository funded and curated by the National Institutes of Health (NIH).

Methods

Participant Data

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1).

Table 5-1. Number of participant in each group, with mean age and IQ Mean Age (Years) IQ Group N Gender (± SD) (± SD) ASD 94 M (n=44), F (n=50) 12.7 (± 2.81) 102 (± 20) TD 96 M (n=52), F (n=44) 13.3 (± 2.97) 110 (± 16)

Diffusion-weighted images were acquired across four sites, using identical acquisition parameters (see Irimia et al., 2017): (1) The Center for Translational

Developmental Neuroscience, Child Study Center, Yale School of Medicine, New

Haven, CT; (2) The Nelson Laboratory of Cognitive Neuroscience, Boston Children’s

Hospital, Harvard Medical School, Boston, MA; (3) The Center on Human Development

& Disability, Seattle Children’s Hospital, University of Washington School of Medicine,

Seattle, WA; (4) Staglin IMHRO Center for Cognitive Neuroscience, David Geffen

School of Medicine, University of California, Los Angeles, CA. All scans were acquired on a 3T Siemens Magnetom Trio using a diffusion-weighted sequence with 60 interleaved transverse slices, flip angle=7º, TR=9000 ms, TE=93 ms, δ=0.69 ms, and

100% phase resolution. The field of view was 190 mm, with a 96x96 matrix, and 2 mm

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slice thickness. A diffusion weighting scheme with a total of 64 diffusion directions was used, including two b=0 s/mm2 volumes and sixty-two b=1000 s/mm2 volumes.

Data Analysis

Population parameters

A 2x2 analysis of variance (ANOVA) was used to assess the effects of diagnosis

(ASD or TD) and gender on the population variables of age (in months) and IQ. Only individuals with ASD were given the Autism Diagnostic Interview – Revised (ADI-R), and thus RRB scores from section C of the ADI-R were compared with a t-test between males and females with ASD.

Evaluation of white matter pathways

Diffusion weighted images were corrected for motion and eddy current distortions using affine registration to a reference volume (first b0 image) with the eddy correct function in FMBRIB Software Library (FSL). Data from FSL’s eddy correct function was then used to rotate the diffusion-weighting directions (i.e. b-vectors), in order to properly estimate the diffusion tensor and diffusion parameters in each voxel after correcting for the distortions caused by motion and eddy currents. Removal of non-CNS tissue was performed with FSL’s Brain Extraction Tool (BET) on the first b0 image and then applied to all remaining diffusion-weighted volumes. The focus of this investigation was cortico- basal ganglia and cerebello-thalamo-cortical pathways. Due to the proximity of these pathways to the ventricles, partial volume effects of cerebro-spinal fluid (CSF) may impair estimation of diffusion parameters in voxels. Free-water elimination (FWE) is a post-processing technique for diffusion MRI that has been demonstrated to improve estimation of diffusion parameters in tissue near the ventricles (Pasternak et al., 2009).

FWE was performed using a custom MATLAB script (Ofori et al., 2015; Planetta et al.,

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2016), followed by tensor element reconstruction with the DTIFIT function in FMRIB’s

Diffusion Toolbox (FDT) to generate FWE-corrected FA maps for each participant.

Templates for the WM pathways of interest were generated using the methods outlined by Archer et al. (2017). Briefly, these tract templates were generated using probabilistic tractography (FSL; probtrackx2) with slice-level thresholding in high- resolution DTI data from 100 subjects (54 males, 46 females) included in the Human

Connectome Project (http://www.humanconnectcomeproject.org). The tracts between the following brain regions, for both left and right hemispheres, were included: [1]

Dorsolateral prefrontal cortex (DLPFC) and caudate; [2] Primary motor cortex, upper- extremity (PMC-U) and putamen; [3] Primary motor cortex, lower-extremity (PMC-L) and putamen; [4] Putamen and substantia nigra (SNr); [5] Subthalamic nucleus (STN) and globus pallidus (GP); [6] Superior cerebellar peduncle (SCP) and PMC-U. These tracts are depicted in Figure 5-1.

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Figure 5-1. Three-dimensional rendering of white-matter pathway templates depicted from lateral (left), anterior (center), and superior (right) viewpoints. Tracts between the following brain regions were included: [1] Dorsolateral prefrontal cortex and caudate – pink; [2] Primary motor cortex (upper-extremity) and putamen – blue; [3] Primary motor cortex (lower-extremity) and putamen – light blue; [4] Putamen and substantia nigra – green; [5] Subthalamic nucleus and globus pallidus – red; [6] Superior cerebellar peduncle and primary motor cortex (upper-extremity) – yellow.

FWE-corrected FA was evaluated in both left and right hemispheres for each of these six tracts, resulting in a total of 12 tracts. A 2x2 ANOVA was used to assess the effects of diagnosis (ASD or TD) and gender on FWE-corrected FA in each of the 12 tracts, adjusting for covariates of age and IQ.

Brain-behavior relationships

In order to investigate the relationship between RRB and FWE-corrected FA in the WM pathways of interest, linear models relating section C of the Autism Diagnostic

Interview – Revised and FWE-corrected FA, with covariates of age and IQ, were generated for each of the 12 tracts. Moreover, this was done separately for all ASD participants (n=67), as well as for male-only (n=28) and female-only (n=39) subgroups of ASD individuals. This approach was utilized rather than traditional bivariate correlation (i.e., Pearson’s) because of potential effects of age and IQ on FWE- corrected FA, as well as the similarity to methods employed for exploring brain-behavior relationships in Chapters 2, 3, and 4. These linear models were evaluated in SPSS, and corrected for multiple comparisons using a 20% FDR.

Section C of the ADI-R (hereafter referred to as ADI-C) includes items specific to

RRBs, including those from both sensorimotor and insistence on sameness/resistance to change domains. Scores from the ADI-C were only available for individuals with ASD, and thus these correlations were only investigated within ASD participants. ASD

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participants with DTI data were screened for completeness of the ADI-C, and data from the remaining 67 participants (28 male, 39 female) were included in these correlations.

Results

Population Parameters

ANOVA revealed no significant main effect of diagnosis (ASD or TD), gender, or interaction for the population variable of age. Regarding IQ, a significant main effect of diagnosis was observed [F(3, 188) = 9.43, p<.01] (see Table 5-2), where the ASD group had lower IQ than the TD group (see Table 5-1).

Table 5-2. Effect of diagnosis and gender on age and IQ Factor Variable F statistic p-value Age 2.42 0.12 Diagnosis IQ 9.43 * <0.01 Age 0.72 0.40 Gender IQ 0.01 0.92 Age 2.44 0.12 Interaction IQ 0.12 0.74 * Significant effect of factor

Within ASD participants, no significant difference on ADI-C scores was observed between males (M=6.25, SD=2.40) and females (M=5.79, SD=2.57); t(65)=0.76, p=0.47.

Evaluation of White Matter Pathways

ANOVA revealed a significant main effect of diagnosis for both left [F(3, 186) =

8.10, p<0.01] and right [F(3, 186) = 6.41, p=0.01] lateralized tracts between DLPFC and caudate (Fig. 5-2). A significant main effect of gender was observed for left [F(3, 186) =

13.9, p<0.01] and right [F(3, 186) = 28.1, p<0.01] tracts between putamen and SNr (Fig.

5-3), as well as left [F(3, 186) = 16.7, p<0.01] and right [F(3, 186) = 16.1, p<0.01] tracts between STN and GP (Fig 5-4). No significant interaction between diagnosis and gender was observed for any of the evaluated tracts.

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Figure 5-2. FWE-corrected FA (mean and SEM) for ASD and TD participants in left and right tracts between dorsolateral prefrontal cortex and caudate.

Figure 5-3. FWE-corrected FA (mean and SEM) for male and female participants in left and right tracts between putamen and substantia nigra

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Figure 5-4. FWE-corrected FA (mean and SEM) for male and female participants in left and right tracts between subthalamic nucleus and globus pallidus

Brain-behavior Relationships

A significant linear relationship between ADI-C scores and FWE-corrected FA, adjusting for covariates of age and IQ, was observed for both left (p<.05, adjusted

R2=.15) and right (p<.05, adjusted R2=.15) tracts between PMC-L and putamen. A positive trend was observed for the linear model relating FWE-corrected FA and ADI-C scores in the left hemisphere tract between DLPFC and caudate (p=.06, adjusted

R2=.13). For all of these WM pathways, greater FWE-corrected FA was associated with higher ADI-C scores within ASD participants.

The relationship between ADI-C scores and FWE-corrected FA was further explored in subgroups of males and females with ASD. After adjusting for the covariates of age and IQ, no significant relationships were observed in either male or female ASD subgroups. If age and IQ were left out of the model, only males with ASD had significant correlations between ADI-C scores and FWE-corrected FA in the left (p<.01, R2=.24)

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and right (p<.05, R2=.17) tracts between PMC-L and putamen, as well as the left PMC-

U to putamen tract (p<.05, R2=.15).

Discussion

There has been limited focus on connectivity alterations underlying RRB in ASD.

In this study, I used DTI data from a large sample of children and adolescents with ASD and TD controls to investigate FWE-corrected FA in select WM pathways, their relationship to RRB, and whether such alterations were differentially expressed by males and females with ASD. This is the first DTI investigation in ASD to assess intra- basal ganglia WM pathways (e.g., putamen and SNr, GP and STN), as well as how alterations in such pathways may relate to RRB. Moreover, this is the first DTI investigation in ASD to utilize FWE-correction for FA, which is especially important for investigations of WM in proximity to the ventricles. Thus, this investigation provides a substantial contribution toward understanding the neural circuitry of RRB in ASD, as smaller basal ganglia nuclei (e.g., GP, STN) have been demonstrated to play a critical role in mediation of RRB in other clinical conditions, as well as animal models of RRB

(see Chapter 1).

The current study included a large sample with nearly equal number of males and females. The effects of diagnosis and gender on FWE-corrected FA were assessed in select WM pathways between the following brain regions: [1] DLPFC and caudate; [2]

PMC-U and putamen; [3] PMC-L and putamen; [4] Putamen and SNr; [5] STN and GP;

[6] SCP and PMC-U. Tract templates for these WM pathways were generated from high-resolution DTI data from the Human Connectome Project using probabilistic tractography methods outlined by Archer et al. (2017).

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Using this approach, I found a significant main effect of diagnosis on FWE- corrected FA in the tract between DLPFC and caudate for both the left and right hemisphere. Here individuals with ASD showed reduced FWE-corrected FA compared to TD individuals. The DLPFC (including middle and inferior frontal gyri) and connections to the caudate are thought to play an important role response inhibition

(see Zandbelt & Vink, 2011). Moreover, altered connectivity between DLPFC and caudate has been shown to occur in other clinical populations with deficits in response inhibition, such as attention-deficit hyperactivity disorder (Shang et al., 2013) and obsessive-compulsive disorder (Koch et al., 2014).

A significant main effect of gender was observed in left and right lateralized tracts between putamen and SNr, as well as between STN and GP, such that males showed higher FA than females. This pattern of higher FA in males was observed between TD males and females as well as ASD males and females. The greater FA observed in males for these basal ganglia pathways is consistent with a prior DTI investigation of gender differences during childhood and adolescence, performed in TD individuals

(Simmonds et al., 2014). There was no interaction between diagnosis and gender in any of the investigated pathways. The main effect of gender was not further explored in this study, as it applied to both ASD and TD populations equally, and was not central to the focus of the investigation.

In order to provide greater detail regarding the relationship of RRB to FA in these

WM pathways, linear models relating FWE-corrected FA and a clinical measure of RRB

(i.e., ADI-C), adjusting for covariates of age and IQ, were generated for each pathway.

These analyses were only performed within ASD participants who also had complete

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scores for the ADI-C (n=67). These analyses of brain-behavior relationships showed that higher ADI-C scores showed a significant positive relationship with FWE-corrected

FA (i.e., higher ADI-C, higher FA) in the tract between PMC-L and putamen in both hemispheres. It is curious that a similar relationship was not observed for the tract between PMC-U and putamen, as many motor RRBs in ASD involve the upper extremities. A positive and marginally significant (p=.06) association between FWE- corrected FA and ADI-C scores was also observed for the tract between DLPFC and caudate in the left hemisphere. When ASD participants were subdivided into male-only

(n=28) and female-only (n=39) subgroups, no significant correlations between FWE- corrected FA and ADI-C scores were found for either gender group when age and IQ were included as covariates. If age and IQ were left out of these models, only ASD males showed significant correlations between ADI-C score and FA in tracts between

PMC-L and caudate, as well as left PMC-U and caudate.

Prior DTI work investigating WM pathways in ASD have generally found evidence of reduced FA, primarily localized to WM pathways in frontal cortex and the corpus callosum (Ameis & Catani, 2015; Hull et al., 2017; Mahajan & Mostofsky, 2015;

Mash et al., 2017; Müller et al. 2011; Rane et al., 2015). Reduced FA of cortico-striatal inputs has also been shown in ASD (Bode et al., 2012; Delmonte et al., 2013; Langen et al., 2012). Findings from the present study provides supporting evidence for reduced FA between ASD and TD individuals in cortico-striatal inputs, specifically the DLPFC to caudate.

In a prior DTI investigation of gender differences in ASD, although TD males had higher FA than females in the corpus callosum, cingulum, and corona radiata, these

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differences were not observed between ASD males and females (Beacher et al., 2012).

The current study did not investigate FA in the pathways reported by Beacher et al.

(2012), but found gender effects on FA in cortico-basal ganglia WM pathways that were observed between both TD males and females and ASD males and females. Thus, for the cortico-basal ganglia WM pathways studied here, it appears that individuals with

ASD exhibit similar gender differences as those observed in TD individuals.

Far fewer studies have investigated the relationship of DTI metrics to RRB within

ASD individuals. One such study found that higher rates of RRB are associated with reduced FA near the inferior frontal gyrus and cerebellum (Cheung et al., 2009). Other studies showed that RRB was associated with reduced FA in WM near the anterior cingulate cortex (Thakker et al., 2008) and in fibers emanating from the putamen, identified through tractography (Langen et al., 2012). The current study did not investigate WM near the ACC or cerebellar cortex, but did observe that FA was positively correlated with ADI-C scores in the tract between PMC-L and putamen.

Cheung et al. (2009) similarly found that FA near the PMC positively correlated with

ADI-C scores in ASD individuals. Moreover, the negative correlation observed by

Langen et al. (2012) between FA and RRB within fibers of the putamen, were not specific to those connected with PMC. The current study also found a marginally significant positive correlation (p=.06) of ADI-C scores with FA in the fibers between

DLPFC and caudate, which is inconsistent with previous reports that found either lower

FA (Cheung et al., 2009) or no difference (Langen et al., 2012) for similar WM regions in relation to RRB. Due to the hypothesized involvement of connectivity within basal ganglia in RRB (e.g., indirect pathway; see Chapter 1), it was surprising that no

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significant correlations with ADI-C were observed for pathways between GP and STN or putamen and SNr.

The DTI investigations performed here complement the structural investigations described in Chapter 4, which identified volumetric alterations in similar brain regions for these same individuals. Reduced volume in areas of the DLPFC (e.g., middle and inferior frontal gyri) and nearby cortical WM was observed in individuals with ASD. Also in these regions, correlations to ADI-C scores within ASD participants found that reduced volume (Chapter 4), and although only marginally significant, reduced FA were associated with higher RRB. The DLPFC and caudate are components of the associate cortico-basal ganglia macro-circuit, and the observed relationship between ADI-C scores likely relate to the expression of cognitive subtypes of RRB, such as insistence on sameness or resistance to change.

For the pathways between PMC and putamen, although no significant differences in FA were observed between ASD and TD individuals, there was a significant positive relationship between FA and ADI-C scores in this pathway within

ASD participants. As discussed in Chapter 4, there was also a significant positive relationship between volume of the putamen and ADI-C scores in individuals with ASD.

The PMC and putamen are components of the motor cortico-basal ganglia macro- circuit, and as such the observed correlation between ADI-C scores with both volume

(Chapter 4) and connectivity in these regions likely relate to the expression of motor

RRBs.

Altered FA is often considered an indicator of aberrant structural connectivity, reflecting factors such as differences in white matter integrity (i.e., myelination), but may

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also correspond to differences in other micro-structural properties, such as microtubule density. Although the experiments performed here cannot determine the microstructural underpinnings of the observed differences in FA, targeted experiments in animal models of ASD or RRB may help elucidate how differences in FA and other DTI metrics correspond with microstructural alterations in brain tissue.

One of the limitations of the DTI investigations performed here was that the WM pathway templates have not been validated with corresponding probabilistic tractography analyses in individuals with ASD. Although these WM pathway templates have been validated in adult populations, it is possible that fibers between these brain regions follow a different topography in TD children and adolescents, as well as children and adolescents with ASD. In addition, the WM pathways assessed here were chosen due to their hypothesized relevance to RRB. Thus, group differences between ASD and

TD individuals that occur outside these pathways, and potential relationships to RRB, were not captured by this investigation.

Another limitation of the present study relates to the behavioral index of RRB – the ADI-C. Although widely utilized in neuroimaging studies of RRB in ASD, the ADI-C contains only 12 items with a limited range of severity rating. Moreover, some of these items overlap in content, such as “complex mannerisms” and “hand and finger mannerisms”, or “difficulties in changes with routine” and “resistance to trivial changes in environment”. This narrow range of scores and potential overlap for certain items limits the “resolution” at which the relationship between brain alterations and RRB, as well the degree to which relationships with particular subtypes of RRB can be explored.

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DTI investigations are an important counterpart to other neuroimaging approaches for characterizing brain structure (e.g., volumetrics), as they can provide important information about WM pathways and generate inferences about connectivity, which may be further investigated using other approaches (e.g., rs-fMRI, electro- encephalography). Importantly, subsequent DTI investigations may be performed in the same dataset as was used in this study. For example, tract-based spatial statistics

(TBSS), a voxel-wise approach for assessing the core “WM-skeleton” throughout the brain would complement the analysis performed here. Moreover, rs-fMRI data are available from many of the same participants included in this dataset, and functional connectivity analyses would complement DTI investigations into structural connectivity in this population.

The DTI experiment performed here found no significant interactions between gender and ASD diagnosis in the WM pathways explored, but future work in this area should consider the potential effect of gender on such measures. As demonstrated in

Chapter 4, males and females with ASD show different patterns of brain volume alterations compared to their TD counterparts, and volumetric alterations correlate with

RRB differentially in males and females with ASD.

Although the brain-behavior analyses performed in this study were limited to within-group comparisons among ASD participants, other behavioral assessments that include information relevant to RRB would benefit future DTI investigations in ASD.

Some examples of additional behavioral assessments include the Repetitive Behavior

Scale – Revised (RBS-R), Social Communication Questionnaire (SCQ), and Sensory

Profile. Importantly, these assessments are also commonly given to TD participants, in

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addition to participants with ASD. Investigation of brain-behavior relationships using these additional behavioral indices could enable more detailed understanding about how altered WM pathways in the brain correspond with RRB in ASD, including motor and cognitive subtypes.

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CHAPTER 6 GENERAL DISCUSSION

Restricted, repetitive behaviors (RRBs) are inflexible and non-functional behavior patterns that interfere with adaptive behaviors. RRBs are observed in many forms, with varying levels of severity, across many human and animal conditions. Although RRBs impact many clinical populations, the presentation of multiple forms of RRB is diagnostic for autism spectrum disorder (ASD), and represents a significant problem in related disorders with intellectual and developmental disability (IDD). Despite the efficacy of certain behavioral interventions in attenuating RRB (Boyd et al., 2012; Rapp & Vollmer,

2005a; Rapp & Vollmer, 2005b), effective biologically-based treatments for RRB in ASD and IDD are lacking. This shortcoming is largely due to an incomplete understanding of the neurobiological mechanisms mediating RRBs. Much of the prior research into the etiology and pathophysiology of RRB has focused on alterations at the level of genes, cellular processes, and individual brain regions. Despite the important contributions of research at these other levels of analysis, modern neuroscience has increasingly adopted a circuit or network level approach for understanding the complex nature of many clinical conditions.

Magnetic resonance imaging (MRI) is the most widely utilized approach for studying the neural circuitry of RRB in clinical populations. Moreover, MRI is well-suited to translational studies of clinical disorders and corresponding animal models. The overarching goal of this dissertation was to provide an updated framework for understanding neural circuitry of RRB, informed by work from both clinical populations and animal models with RRB. I first reviewed and synthesized prior MRI work on RRB in

ASD and IDD, as well as corresponding animal models with RRB (Chapter 1). I

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expanded upon these prior investigations by performing volumetric MRI (Chapter 2) and

DTI (Chapter 3) experiments in the C58 animal model of RRB. Importantly, these studies in C58 mice evaluated brain-wide structural alterations and their relationship to

RRB. This work in an animal model of RRB provides a significant contribution toward understanding the broader neural networks of RRB, as most neuroimaging studies in animal models with RRB have not included evaluation of brain-behavior relationships.

Additionally, I performed comparable brain-wide volumetric (Chapter 4) and targeted

DTI (Chapter 5) investigations in a large sample of children and adolescents with ASD, which included a nearly equal number of females. Similarly, the relationship between structural brain alterations and RRB was evaluated in this sample. These complementary studies of structural brain alterations associated with RRB in both an animal model and clinical population provide significant translational value toward understanding the pathophysiology of RRB.

Summary and Synthesis of Results

Structural alterations (MRI and DTI) in SH-C58 mice with high levels of repetitive motor behavior were compared to both SH-B6 and EE-C58 mice with low levels of repetitive motor behavior. Importantly, these studies also included a behavioral measure of RRB which was related to neuroimaging findings, which has previously only been performed in relation to volumetric MRI data in studies of animal models with RRB

(Allemang-Grand et al., 2017; Ellegood et al., 2013). I found evidence of significant brain-wide volumetric alterations between SH-C58 mice and SH-B6 mice. Compared to

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volumetric findings are highly consistent to those observed in other animal models with

RRB, such as BTBR, Mecp2, Fmr1, Itgβ3, and 16p11.2 mouse models, particularly with regard to the basal ganglia. Moreover, altered FA in SH-C58 mice was observed in a majority of these same regions, and generally showed increased FA in gray matter

(GM) regions and reduced FA in white matter (WM).

In contrast, EE-C58 mice showed a more limited pattern of volumetric differences compared to SH-C58 mice, despite considerable differences in repetitive motor behavior. The localization and magnitude of these volumetric changes in EE-C58 were in line with those observed in a prior investigation of EE in B6 mice (Scholz et al., 2015).

FA differences in EE-C58 mice, however, were observed within many more regions of the brain. Compared to SH-C58 mice, EE-C58 mice showed only reduced FA, which was broadly distributed throughout both GM and WM of the cortex, basal ganglia, and cerebellum. No previous study has explored the effects of EE using DTI, and thus these novel findings should be further explored and validated in future investigations.

Correlations between repetitive motor behavior and volume were explored using a model which included all study animals, as well as an additional model that included only SH-C58 mice. The model that included all animals showed significant correlations between volume and repetitive motor behavior that were highly similar to the volume- only differences observed between SH-C58 and SH-B6 mice, and thus may have been heavily influenced by strain differences in volume. The volume-behavior correlations performed within only SH-C58 mice did not survive FDR correction for multiple comparisons, however, uncorrected data suggested that repetitive motor behavior was associated with reduced volumes in sensorimotor regions of the cortex, striatum, globus

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pallidus (GP), and thalamus, but increased volume in the nucleus accumbens and cerebellar vermis. The only two investigations which explored volume-behavior relationships in animal models with RRB found similar patterns of volume alterations.

These studies in both Mecp2 (Allemang-Grand et al., 2017) and BTBR (Ellegood et al.,

2013) mice showed that RRB was associated with reduced volume in motor and somatosensory cortices, striatum, and GP. In addition, increased volume in the cerebellar vermis was found to relate to RRB, but only in BTBR mice (Ellegood et al.,

2013).

Correlations between repetitive motor behavior and FA were also explored using both a model that included all study animals and a SH-C58 only model. The model that included all animals showed only negative relationships between FA and repetitive motor behavior, which were localized to major WM pathways of the basal ganglia and cerebellum, as well as the corpus callosum. The FA-behavior correlations performed within only SH-C58 mice did not survive FDR correction for multiple comparisons, and uncorrected data showed a limited number of voxels with reduced FA, localized in orbital cortex, striatum, posterior commissure, and posterior cingulate cortex. No prior study has used DTI to investigate brain-behavior relationships in an animal model with

RRB.

I also performed brain-wide volumetric MRI (Chapter 4) and targeted DTI

(Chapter 5) investigations to assess structural alterations in children and adolescents with ASD, as well as how such alterations corresponded to RRB within ASD participants. All analyses performed in Chapters 4 and 5 controlled for covariates of age and IQ. Compared to TD children and adolescents, those with ASD showed

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enlargement within select regions of the frontal, parietal, and occipital cortices, as well as regions of reduced volume distributed throughout the basal ganglia, cerebellum, and cortical WM (see Chapter 2). Although many of these findings are in-line with those previously observed in ASD, reduced volume within basal ganglia structures is unprecedented in volumetric studies of ASD, and further discussion of this novel observation is presented below.

The study sample included nearly equal numbers of males and females, which enabled the investigation of gender effects and their potential interaction with diagnosis.

A significant interaction between diagnosis and gender was observed for a large number of brain regions. Post-hoc comparisons revealed that males and females with

ASD show a unique pattern of volume differences compared to their gender-matched

TD counterparts. Volumetric differences between ASD and TD males were localized in regions of the cortex and cerebellum. Volumetric differences between ASD and TD females were observed as well as in the cortex and cerebellum, but reduced volume was observed throughout the basal ganglia. Distinctive patterns of structural alteration between males and females with ASD has been explored in a limited number of previous studies (see review by Chen & Van Horn, 2017), but the work presented in

Chapter 4 extends such findings to the basal ganglia.

Correlations between RRB and volume were explored within ASD participants, and also in subgroups of males and females with ASD. When ASD participants were grouped together, ADI-C scores were associated with greater volume in the putamen,

GPe, corpus callosum, and midline parietal regions, as well as reduced volumes in cerebellar cortex. These findings are highly similar to those observed in prior research

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relating volume and RRB in individuals with ASD, but interestingly did not extend to the caudate, which has been the most consistently reported structural alterations linked to

RRB in ASD (see Chapter 1). When separated by gender, males and females with ASD showed distinct patterns of volume correlations with ADI-C scores. Notably, opposite effects were observed in the caudate, such ADI-C scores were associated with greater volumes in the caudate of males with ASD, but reduced volumes in the caudate of females with ASD. In both males and females with ASD, higher ADI-C scores linked to enlargement of the putamen and GPe, and reduced volume in the left cerebellar crus I.

The DTI investigation performed in Chapter 5 targeted specific WM pathways in cortico-basal ganglia and cerebello-thalamo-cortical circuits, rather than utilizing a brain- wide approach. Individuals with ASD showed reduced FA, corrected for free-water, in both left and right lateralized tracts between the dorsolateral prefrontal cortex (DLPFC) and caudate. This finding is consistent with prior literature in ASD showing reduced FA in fronto-striatal pathways compared to TD individuals (Bode et al., 2012; Delmonte et al., 2013; Langen et al., 2012). No significant interaction between diagnosis and gender was observed in these WM pathways, unlike volumetric findings from Chapter 4.

Correlations between RRB and FA in these pathways were explored within ASD participants using scores from the ADI-C. Although significant correlations between FA and ADI-C scores were observed when ASD participants were considered as a whole, no significant correlations remained in male-only and female-only subgroups. When considered together, ASD participants showed a significant correlation between ADI-C scores and increased FA in the tracts between primary motor cortex lower extremity

(PMC-L) and putamen. A similar finding was observed by Cheung et al., (2009), where

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no differences between ASD and TD individuals was observed for FA near the PMC, but ASD individuals showed a significant positive correlation between ADI-C scores and

FA near the PMC. Taken together, these findings suggest an involvement of the motor cortico-basal ganglia macro-circuit in the expression of RRB in ASD. Although this study observed marginally significant association between ADI-C scores and increased FA in the tract between DLPFC and caudate for the left hemisphere, prior literature has suggested that RRB is associated with reduced FA in WM near the DLPFC (Cheung et al., 2009). It is, however, difficult to make direct comparisons of these prior studies to the findings observed in Chapter 5, due to methodological differences (i.e. tractography versus tract-based spatial statistics). Although there are a limited number of DTI studies in animal models with RRB (Dodero et al., 2013; Ellegood et al., 2010; Ellegood et al.,

2013; Haberl et al., 2013; Sforazinni et al., 2016), these studies generally implicated reduced FA in similar brain regions as those observed in ASD.

Conclusions and Future Directions

In Chapter 1, I provided several suggestions regarding important future directions for neuroimaging research in RRB. The work presented in Chapters 2 through 5 addresses many of these suggestions. I expanded upon prior work in animal models with RRB to include the C58 mouse model and performed both volumetric MRI and DTI, which has been performed in a limited number of prior investigations (see Chapter 1).

Importantly, I included a behavioral measures of RRB and related RRB to neuroimaging findings. I also investigated the effects of environmental enrichment (EE) in C58 mice, which attenuated their repetitive motor behavior and resulted in alterations in brain structure, as compared to standard housed (SH) C58 mice.

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I expanded on prior neuroimaging work related to RRB in ASD by investigating a large sample of children and adolescents with nearly equal numbers of males and females. Females with ASD are severely under-represented in neuroimaging studies, and this work provides a substantial contribution toward understanding brain alterations in females with ASD, as well as how such alterations relate to RRB. Moreover, this study did not exclude participants with lower IQ, but rather controlled all analyses for IQ, further extending the generalizability of the observed findings.

Taken together, findings from these studies support prior work implicating volume and connectivity alterations in cortico-basal ganglia circuits, and significantly contribute to emerging evidence supporting an important role of the cerebellum in RRB. The confluence of large scale brain networks within the basal ganglia makes these structures uniquely positioned, with the potential to moderate the initiation and termination of a broad range of cognitive and motor behaviors by functioning as a central hub among diverse neural networks. Moreover, pathways connecting the cerebellum and basal ganglia identified in human neuroimaging studies (Milardi et al.,

2016) support additional ways in which these brain regions may interact, which may be relevant for better understanding RRB.

In particular, volumetric alterations in cerebellar crus I and vermis were shown in both C58 mice and in individuals with ASD. Importantly, the reduced volume of cerebellar crus and vermis observed in individuals with ASD was also found to correlate with RRB. The cerebellar crus has been shown to be functionally connected with regions of the frontal and temporal cortices (Buckner et al., 2011) that have been independently linked to RRB (Chapter 4; Kana et al., 2007; Menon et al., 2004; Rojas et

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al., 2006). Moreover, a recently published study provides powerful evidence for the role of crus I in mediation of RRB, utilizing functional connectivity analyses in both an animal model and individuals with ASD (Stoodley et al., 2017).

There is still much to be done in the field of neuroimaging of RRB. In particular, future studies should further expand investigations in animal models with RRB to include in vivo neuroimaging. Resting-state fMRI studies provide a powerful counterpart to investigations of structural connectivity afforded by DTI, and can only be performed in vivo. Resting state fMRI has only been used to study the BTBR model of RRB

(Sforazzini et al., 2016), but no brain-behavior relationships were assessed. There are still a number of relevant animal models of RRB that have not yet been investigated using neuroimaging, such as Sapap3 mice. Another important opportunity afforded by work in animal models of RRB is the ability to perform complementary analyses using neuroimaging data and histology. This has been performed, again, only in BTBR mice in order to assess inter-hemispheric connectivity, or lack thereof (Sforazzini et al., 2016).

Many additional complementary analyses may be performed using neuroimaging and histology, which could help elucidate factors underlying structural differences such as reduced volume or fractional anisotropy.

With regard to neuroimaging of RRB in clinical populations, there is still great need to expand these investigations to populations outside of ASD, as well as provide contrasts between different populations with RRB. Such studies may investigate the similarities and/or differences between individuals with ASD, Rett syndrome, Fragile-X syndrome, obsessive-compulsive disorder, and Tourette syndrome. There is also great need to further expand upon the behavioral indices used to investigate brain-behavior

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relationships in neuroimaging studies of RRB. As discussed in Chapters 1, 4, and 5, additional behavioral assessments such as the Repetitive Behavior Scale – Revised

(RBS-R), Social Communication Questionnaire (SCQ), and Sensory Profile may dramatically improve our understanding of how different forms of RRB correspond to alterations in brain structure, function, and connectivity. Moreover, MRI investigations into the broader domain of behavioral inflexibility may shed light on some of the findings that have been related to RRB in these clinical conditions.

MRI is a powerful tool, enabling bidirectional translational research. In this framework, findings from clinical populations can be validated and more thoroughly investigated in corresponding animal models, or mechanisms identified in animal model research may be investigated in clinical populations. Moreover, exploratory investigations may be performed in parallel with both clinical populations and animal models in order to identify convergent pathophysiology. Such approaches can also provide greater confidence in the validity of experiments conducted by researchers who primarily work with either clinical populations or animal models.

A recent study by Stoodley et al. (2017) elegantly demonstrates the value of bidirectional translational research. These investigators found convergent evidence of cerebellar dysfunction in a mouse model of ASD and individuals with ASD, then used those convergent findings to generate and test an experimental manipulation designed to disrupt cerebellar network connectivity in otherwise neurotypical mice and humans.

They found evidence that the experimentally induced disruption of cerebellar connectivity in both animal and human groups closely resembled the inherently

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disrupted connectivity patterns observed in the mouse model of ASD and in a clinical population with ASD.

Similar to the previous example, the present studies identified convergent evidence from both an animal model and individuals with ASD suggesting an important role of the basal ganglia and cerebellum in relation to RRB. Follow-up investigations may further utilize parallel investigations in C58 mice and individuals with ASD to explore connectivity in these regions (e.g., resting state fMRI), or experimentally manipulate these regions in C58 mice using techniques such as optogenetics to determine how such manipulations affect behavior. These targeted investigations could further elucidate the mechanisms by which these brain regions and their corresponding neural circuits mediate RRB in ASD.

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APPENDIX A REGIONS OF INTEREST FOR ATLAS BASED VOLUMETRY

Table A-1. List of atlases and brain regions used for volumetry in animal studies Atlas Brain Region Cortex Anterior cingulate (Ullmann et al. 2013) Posterior cingulate Auditory Cortex Corpus callosum Cingulum Claustrum Piriform cortex Dorsal fornix Dorsal hippocampal fissure Orbital Cortex Forceps minor Frontal association area Insular Cortex Lateral olfactory tract Parietal association area Primary motor cortex Secondary motor cortex Entorhinal cortex Subiculum Somatosensory cortex Temporal association area Visual cortex Secondary visual cortex Basal ganglia Anterior commissure (anterior limb) (Ullmann et al., 2014) Nucleus accumbens Anterior commissure (posterior limb) Amygdalostriatal transition area Basal nucleus of meynert Corpus callosum Central amygdaloid nucleus Striatum Extended amygdala Entopeduncular nucleus Fornix Bed Nucleus of Stria Terminalis Globus pallidus Nucleus of diagonal band Internal capsule Magna island of Calleja Interstitial nucleus of the post limb of the anterior

commissure Lateral hypothalamus

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Table A-1. Continued. Atlas Brain Region

Basal ganglia Lateral preoptic area (Ullmann et al., 2014) Lateral striatal stripe Lateral ventricle Medial forebrain bundle Medial septal nucleus Nigrostriatal bundle Optic tract Substantia innominate Stria terminalis Olfactory tubercle Ventral pallidum Subthalamic nucleus Substantia nigra Cerebellum Lobule 1 (Ullmann et al., 2012) Lobule 2 Lobule 3 Lobule 4/5 Lobule 6 Lobule 7 Lobule 8 Lobule 9 Lobule 10 Simple lobule Crus 1 of the ansiform lobule Crus 2 of the ansiform lobule Paramedian lobule Copula of the pyramis Paraflocculus Flocculus Medial longitudinal fasciculus Superior cerebellar peduncle Middle cerebellar peduncle Inferior cerebellar peduncle Decussation of the superior cerebellar peduncle Ventral spinocerebellar tract Medial nucleus Lateral nucleus Superior medullary velum Dorsal cochlear nuclei Ventral cochlear nuclei Interposed nucleus Dorsal acoustic stria

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Table A-2. List of atlases and brain regions used for volumetry in human studies Atlas Brain Region Basal ganglia Striatum (caudate + putamen) (Keuken et al., 2014) Globus pallidus externa Globus pallidus interna Subthalamic nucleus Substantia nigra Cortex Frontal Pole (Desikan et al., 2006) Insular Cortex Superior Frontal Gyrus Middle Frontal Gyrus Inferior Frontal Gyrus, pars triangularis Inferior Frontal Gyrus, pars opercularis Precentral Gyrus Temporal Pole Superior Temporal Gyrus, anterior division Superior Temporal Gyrus, posterior division Middle Temporal Gyrus, anterior division Middle Temporal Gyrus, posterior division Middle Temporal Gyrus, temporooccipital part Inferior Temporal Gyrus, anterior division Inferior Temporal Gyrus, posterior division Inferior Temporal Gyrus, temporooccipital part Postcentral Gyrus Superior Parietal Lobule Supramarginal Gyrus, anterior division Supramarginal Gyrus, posterior division Angular Gyrus Lateral Occipital Cortex, superior division Lateral Occipital Cortex, inferior division Intracalcarine Cortex Frontal Medial Cortex Supplementary Motor Cortex Subcallosal Cortex Paracingulate Gyrus Cingulate Gyrus, anterior division Cingulate Gyrus, posterior division Precuneous Cortex Cuneal Cortex Frontal Orbital Cortex Parahippocampal Gyrus, anterior division Parahippocampal Gyrus, posterior division Lingual Gyrus Temporal Fusiform Cortex, anterior division Temporal Fusiform Cortex, posterior division Temporal Occipital Fusiform Cortex

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Table A-2. Continued. Atlas Brain Region

Cortex Occipital Fusiform Gyrus (Desikan et al., 2006) Frontal Operculum Cortex Central Opercular Cortex Parietal Operculum Cortex Planum Polare Heschl's Gyrus (includes H1 and H2) Planum Temporale Supracalcarine Cortex Occipital Pole Cerebellum Lobules I-IV Lobule V Lobule VI Vermis VI Crus I Vermis Crus I Crus II Vermis Crus II Lobule VIIb Vermis VIIb Lobule VIIIa Vermis VIIIa Lobule VIIIb Vermis VIIIb Lobule IX Vermis IX Lobule X Vermis X

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APPENDIX B SUPPLEMENTARY DATA

Table B-1. List of brain regions which showed a significant volume difference (FDR corrected, p < .05) in SH-B6 and EE-C58 mice SH-B6 mean EE-C58 mean Delta p value Brain Region (% difference (% difference (% difference) from template) from template) Amygdalostriatal 7.2 -11 18 4.75E-04 transition area Anterior cingulate 7.8 -6.5 14 1.05E-04 Auditory Cortex 4.6 -4.6 9.2 2.29E-04 Basal nucleus of meynert -12 8.9 -21 3.86E-02 Central amygdaloid 7.6 -9.3 17 3.67E-04 nucleus Claustrum 0.16 -7.1 7.3 2.44E-03 Corpus callosum 15 9.4 5.8 3.35E-02 Decussation of the 11 -5.4 16 9.61E-03 superior cerebellar peduncle Dorsal cochlear nuclei 8.5 -7.3 16 1.59E-02 Dorsal fornix 13 -2 15 4.93E-04 Dorsal hippocampal 13 0.26 13 6.62E-03 fissure Entopeduncular nucleus 16 -21 37 8.52E-08 Entorhinal cortex 7.2 -3.9 11 1.91E-03 Extended amygdala 11 -1.9 12 6.02E-03 Forceps minor 14 2.3 11 1.99E-02 Fornix 11 1.6 9 7.10E-03 Frontal association area 11 -0.43 11 6.52E-03 Globus pallidus 15 -12 28 2.11E-07 Inferior cerebellar 2 -8.2 10 4.65E-02 peduncle Insular Cortex 6.2 -1.9 8 1.54E-04 Internal capsule 16 -4 20 1.38E-05 Interposed nucleus 6.1 -16 23 7.04E-06 Interstitial nucleus of the 14 0.56 14 3.68E-03 post limb of the anterior commissure Lateral hypothalamus 13 -4 17 8.40E-04 Lateral nucleus 5.2 -15 20 6.38E-05 Lateral striatal stripe 9.3 -4.4 14 2.73E-04 Lobule I -13 4.3 -18 5.19E-03 Lobule II -13 4.6 -18 1.40E-04 Lobule VIII -12 0.34 -13 2.50E-02 Magna island of Calleja 3.7 -14 17 1.42E-02

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Table B-1. Continued SH-B6 mean EE-C58 mean Delta p value Brain Region (% difference (% difference (% difference) from template) from template)

Medial nucleus 3 -19 22 8.10E-07 Middle cerebellar 6.3 -2.1 8.4 4.52E-04 peduncle Nigrostriatal bundle 19 -7.7 27 2.45E-08 Nucleus accumbens 1.8 -5 6.8 4.09E-02 Orbital Cortex 8.1 -3.4 11 1.23E-03 Paraflocculus -19 15 -34 7.41E-04 Parietal association area 3.2 -4.3 7.5 5.22E-04 Piriform cortex 9.5 0.94 8.5 3.16E-04 Posterior cingulate 1.6 -3.9 5.5 6.15E-03 Primary motor cortex 3.2 -3.8 7.1 1.98E-04 Secondary motor cortex 6.4 -1.6 8 1.91E-05 Somatosensory cortex 3.5 -5 8.5 3.70E-04 Stria terminalis 10 -9.4 19 9.37E-04 Striatum 7.2 -6.4 14 3.72E-06 Substantia nigra 5.9 -8.3 14 1.62E-04 Subthalamic nucleus 2.9 -17 19 5.22E-04 Superior cerebellar 3.7 -6.2 9.9 5.18E-03 peduncle Superior medullary velum -11 4.3 -15 1.18E-02 Temporal association 7.9 -4.5 12 2.49E-06 area Ventral pallidum 5 -3.6 8.5 4.54E-02 Ventral spinocerebellar 18 -3.2 22 2.81E-06 tract Visual cortex 1.3 -2.7 4 1.59E-02

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Table B-2. List of brain regions (ROIs) which showed a significant volume difference (FDR corrected, p < .05) between male and female participants, adjusted for total brain volume. male mean female mean Delta p value Brain Region (% total brain (% total brain (% difference) volume) volume) Angular Gyrus 0.345 0.373 -0.027 0.001 Central Opercular 0.498 0.519 -0.021 0.033 Cortex Cingulate Gyrus, 0.624 0.705 -0.081 <.001 anterior division Cingulate Gyrus, 0.673 0.705 -0.031 0.017 posterior division Crus I 0.888 0.976 -0.088 <.001 Crus II 0.638 0.713 -0.074 <.001 Cuneal Cortex 0.174 0.189 -0.015 0.003 Frontal Medial Cortex 0.211 0.234 -0.022 <.001 Frontal Operculum 0.113 0.123 -0.011 <.001 Cortex Frontal Orbital Cortex 0.800 0.873 -0.072 <.001 Frontal Pole 4.151 4.681 -0.529 <.001 Globus pallidus externa 0.121 0.132 -0.011 <.001 Globus pallidus interna 0.044 0.049 -0.005 <.001 Heschl's Gyrus (includes 0.075 0.082 -0.007 0.005 H1 and H2) Inferior Frontal Gyrus, 0.207 0.234 -0.027 <.001 pars opercularis Inferior Frontal Gyrus, 0.140 0.161 -0.022 <.001 pars triangularis Inferior Temporal Gyrus, 0.093 0.100 -0.007 0.006 anterior division Inferior Temporal Gyrus, 0.255 0.275 -0.021 0.001 posterior division Inferior Temporal Gyrus, 0.294 0.319 -0.025 <.001 temporooccipital part Insular Cortex 0.767 0.841 -0.074 <.001 Intracalcarine Cortex 0.201 0.214 -0.013 0.013 Lateral Occipital Cortex, 0.937 1.002 -0.065 0.001 inferior division Lateral Occipital Cortex, 2.419 2.578 -0.159 <.001 superior division Lingual Gyrus 0.727 0.763 -0.036 0.014 Lobules I-IV 0.193 0.202 -0.010 <.001 Lobule V 0.214 0.228 -0.014 0.032 Lobule VI 0.483 0.520 -0.036 0.005 Lobule VIIb 0.260 0.283 -0.023 0.001

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Table B-2. Continued male mean female mean Delta p value Brain Region (% total brain (% total brain (% difference) volume) volume) Lobule VIIIa 0.255 0.271 -0.016 <.001 Lobule VIIIb 0.228 0.234 -0.005 0.004 Lobule IX 0.185 0.196 -0.010 0.036 Middle Frontal Gyrus 0.874 0.956 -0.082 <.001 Middle Temporal Gyrus, 0.523 0.559 -0.036 0.002 posterior division Middle Temporal Gyrus, 0.393 0.423 -0.030 0.001 temporooccipital part Occipital Fusiform Gyrus 0.285 0.315 -0.030 <.001 Occipital Pole 1.046 1.124 -0.078 <.001 Paracingulate Gyrus 0.700 0.768 -0.068 <.001 Parahippocampal Gyrus, 0.145 0.165 -0.021 <.001 posterior division Planum Polare 0.106 0.118 -0.012 <.001 Planum Temporale 0.099 0.105 -0.006 0.018 Postcentral Gyrus 1.015 1.102 -0.086 <.001 Precentral Gyrus 1.143 1.272 -0.128 <.001 Precuneous Cortex 1.154 1.212 -0.058 0.017 Striatum 1.436 1.596 -0.159 <.001 Subcallosal Cortex 0.313 0.335 -0.022 <.001 Substantia nigra 0.023 0.025 -0.002 <.001 Subthalamic nucleus 0.007 0.007 -0.001 <.001 Superior Frontal Gyrus 0.908 1.019 -0.110 0.001 Superior Parietal Lobule 0.282 0.307 -0.025 <.001 Supplementary Motor 0.336 0.373 -0.038 <.001 Cortex Supramarginal Gyrus, 0.316 0.339 -0.023 0.003 anterior division Supramarginal Gyrus, 0.290 0.311 -0.021 0.003 posterior division Temporal Fusiform 0.354 0.374 -0.020 0.013 Cortex, posterior division Temporal Occipital 0.334 0.354 -0.020 0.005 Fusiform Cortex Temporal Pole 1.310 1.392 -0.082 0.012 Vermis Crus II 0.025 0.027 -0.002 0.004 Vermis VI 0.113 0.124 -0.010 0.002 Vermis VIIIa 0.063 0.068 -0.004 0.022 Vermis VIIIb 0.030 0.033 -0.002 0.008 Vermis IX 0.039 0.042 -0.003 0.007 Vermis X 0.012 0.013 -0.001 0.009

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BIOGRAPHICAL SKETCH

Bradley James Wilkes was born in Orlando, Florida in 1988. In Orlando, he attended Dr. Phillips High School, graduating in the summer of 2006. In the fall of 2006, he began attendance at the University of Florida. In August 2011, Bradley graduated summa cum laude with a Bachelor of Science in neurobiological sciences, and cum laude with a Bachelor of Arts in anthropology. In August 2011, Bradley began a PhD in

Psychology under the mentorship of Dr. Keith White, within the behavioral and cognitive neuroscience area of the Department of Psychology. Bradley was awarded his Master of Science in psychology in 2015. Bradley continued work toward his PhD under the mentorship of Dr. Mark Lewis, with a research focus on the neuroimaging correlates of restricted, repetitive behaviors.

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