Discovering Motor Phenotypes in Autism Spectrum Disorder: a Cross-Syndrome Approach

UNLV Theses, Dissertations, Professional Papers, and Capstones

12-15-2019

Discovering Motor Phenotypes in Autism Spectrum Disorder: A Cross-Syndrome Approach

Daniel Edwin Lidstone

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Repository Citation Lidstone, Daniel Edwin, "Discovering Motor Phenotypes in Autism Spectrum Disorder: A Cross-Syndrome Approach" (2019). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3821. http://dx.doi.org/10.34917/18608710

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This Dissertation has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. DISCOVERING MOTOR PHENOTYPES IN AUTISM SPECTRUM DISORDER: A CROSS-SYNDROME APPROACH

Daniel E. Lidstone

Bachelor of Arts – Kinesiology and Physical Education Wilfrid Laurier University 2013

Master of Science – Exercise Science Appalachian State University 2015

A dissertation in partial fulfillment of the requirements for the

Doctor of Philosophy – Interdisciplinary Health Sciences

The Graduate College

University of Nevada, Las Vegas December 2019 Dissertation Approval

The Graduate College The University of Nevada, Las Vegas

August 13, 2019

This dissertation prepared by

Daniel E. Lidstone entitled

Discovering Motor Phenotypes in Autism Spectrum Disorder: A Cross-Syndrome Approach is approved in partial fulfillment of the requirements for the degree of

Doctor of Philosophy – Interdisciplinary Health Sciences The Graduate College

Janet Dufek, Ph.D. Kathryn Hausbeck Korgan, Ph.D. Examination Committee Chair Graduate College Dean

Brach Poston, Ph.D. Examination Committee Member

Jing Nong Liang, Ph.D. Examination Committee Member

Brendan Morris, Ph.D. Graduate College Faculty Representative

ii ABSTRACT

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with a behavioral phenotype characterized by persistent deficits in social communication and social interaction accompanied by restricted, repetitive patterns of behaviors, interests, or activities. Currently in the US, approximately 2.5% of children have a diagnosis of ASD. The etiology of ASD is complex, however the disorder does have a strong genetic basis. Specific genetic mutations can lead to neuroanatomical and neurophysiological changes during development resulting in a behavioral phenotype that falls along the ASD spectrum and may result in a diagnosis of ASD.

The severity of ASD-specific behaviors falls on a continuum and co-occurring psychiatric disorders are common – adding to the complexity of the disorder. In addition to specific gene mutations implicated in the diagnosis of ASD, specific brain regions are also implicated in ASD that are different from those observed in other common neurodevelopmental disorders – such as

Attention-Deficit Hyperactivity Disorder. Studying the neuroanatomical footprint of ASD is a relatively new area of research fueled by the desire to bridge the gap between brain structure and function. Several brain regions implicated in the core social/communication deficits and repetitive behaviors associated with ASD are also involved in various aspects of motor control.

These brain areas include cortical regions such as the primary motor cortex (M1), primary somatosensory cortex (S1), inferior parietal lobule (IPL), and subcortical structures that include the cerebellum and basal ganglia. These neuroanatomical findings are bolstered by several studies detailing a wide range of motor deficits in children and adults with ASD. Therefore, studying motor control may provide another means to study the neurological underpinnings of

ASD. However, the meaningfulness of nearly all studies detailing motor control deficits in children with ASD is limited due to comparisons limited to a single typically developing (TD)

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control group. Therefore, the specificity of motor deficits in children with ASD is not well understood since intellectual and behavioral deficits – not specific to children with ASD – may also contribute to the observed motor deficits between children with ASD and TD controls. To overcome this limitation, the current dissertation project employs a cross-syndrome design that includes two additional clinical control groups of children with Fetal Alcohol Spectrum Disorder

(FASD) and Attention-Deficit Hyperactivity Disorder (ADHD) with similar intellectual and behavioral impairments as children with ASD. Utilizing this novel approach, motor deficits specific to children with ASD may be identified, allowing for the generation of new hypotheses about the neurological underpinnings of ASD.

To bridge the gap between neuroscience and motor control in the study of ASD it is important to understand what findings from both fields of research reveal about ASD. Therefore, an extensive literature review (Chapter 1) is warranted to orient the reader to what is currently known about the underlying neurology and motor deficits associated with ASD. To detail the progression of knowledge about the neuroanatomical deficits associated with ASD, the literature review will funnel from general to more specific findings from animal-models of ASD and human patient studies. Following the neuroanatomical review, a detailed overview of findings from motor control studies on individuals with ASD will be reviewed and discussed in relation to the key neuroanatomical findings in children with ASD.

The overall purpose of this dissertation was to identify motor features specifically impaired in children with ASD using a cross-syndrome design. This dissertation explores the three different motor tasks that previous studies have shown to be impaired in children with ASD compared to TD controls. To examine the specificity of previously observed deficits, motor features were extracted from: (1) a precision-grip force tracking task; (2) a postural maintenance

task; and (3) a manual dexterity task and compared between children with ASD and children with FASD, ADHD, and TD controls. The first study (Chapter 2) examines group differences in isometric precision-grip static force output features in children with ASD, FASD, ADHD, and

TD controls. In this study, grip-force output was maintained at 15% of maximal voluntary contraction (MVC) and no group differences were observed for: (1) relative force accuracy; (2) relative variability; (3) complexity; or (4) frequency structure of the force signal. However, the relative proportion of low frequency oscillations (0-1 Hz) was significantly associated with force accuracy, variability, and complexity in the ASD-group only. In the second study (Chapter 3), dynamic force control features were examined using a ramp-up (0-25% MVC) and ramp-down

(25-0% MVC) task. Compared to the TD group, the children with ASD demonstrated significantly: (1) greater relative error during ramp-up and ramp-down; (2) lower ramp-up force- complexity; and (3) greater relative error during transition between ramp-up and ramp-down phases. In the third study (Chapter 4), postural sway features during quiet stance and unipedal stance time were examined. Compared to the FASD, ADHD, and TD groups, the children with

ASD demonstrated significantly: (1) greater postural sway area and (2) mediolateral (ML) sway magnitude. Furthermore, children with ASD group demonstrated significantly greater anteroposterior (AP) sway velocity between the TD and FASD groups, and lower ML sway complexity compared to the FASD group only. For unipedal stance, TD children had greater stance times compared to all clinical groups. However, postural sway area was associated with unipedal stance times only in the ASD group. In the fourth study (Chapter 5), manual dexterity of the dominant and non-dominant was examined. Children in the ASD group showed significantly: (1) worse dominant hand dexterity compared to TD controls and (2) worse non- dominant hand dexterity compared to children in the FASD and TD groups. Finally, hand

performance asymmetry was significantly lower children with FASD than children without

FASD.

In summary, this dissertation uses a cross-syndrome approach to identify motor features specifically impaired in children with ASD. Throughout the dissertation, several ASD-specific motor features were identified that align with current knowledge of neuroanatomical deficits associated with ASD. Furthermore, identification of ASD-specific motor features using biomechanics techniques may provide a means to quantitatively study the effects of various pharmacological, behavioral, and non-invasive brain stimulation interventions in clinical settings. Therefore, studying the motor system in children with ASD may have clinical importance due to challenges in quantifying changes in behaviors associated with ASD. In this dissertation, several ASD-specific motor features are identified that can be measured quickly in clinical settings. Further research is required to examine the clinical utility of quantitative motor testing in children with ASD.

ACKNOWLEDGEMENTS

First, I would like to extend my gratitude to the members of my dissertation committee,

Drs. Janet Dufek (Chair), Brach Poston, Jing Liang, Brendan Morris for their support and guidance. Dr. Dufek, thank you for the mentorship and support you provided throughout my

Ph.D. experience that allowed me to branch outside my comfort zone and participate in interdisciplinary research. I would also like to thank you for promoting educational opportunities that helped me acquire new knowledge important for my academic career. Finally, your advocacy in support of this dissertation project was crucial to its success. I would also like to acknowledge Dr. Mohamad Trabia, Jeffery Markle, and Conner Neilson from the Department of

Mechanical Engineering for designing and printing the precision-grip apparatus used in this dissertation. Dr. Trabia, I would like to extend my appreciation for your mentorship over the past three years and for allowing me to participate in interdisciplinary clinical research with the

Department of Mechanical Engineering.

Next, I would like to acknowledge the UNLV Medicine Ackerman Autism Center and the staff that welcomed and supported me throughout my time at the center. The research support provided by the Ackerman Center staff including Dr. Caitlin Cook, Irina Abramians (Psy.D.),

Faria Z. Miah, Elizabeth Cooper, Roberto Aguayo Jr., Ruben Martin-Angulo, Holly Summers,

Madison Eliot, and Taniyah Roach was pivotal to the success of this project. Furthermore, the observation opportunities, mentorship, support, and knowledge provided by Dr. Julie Beasley

(Ph.D.), Dr. Colleen Morris (M.D.), and Dr. Mario Gaspar De Alba (M.D.) contributed greatly to my development as a researcher studying neurodevelopmental disorders in an interdisciplinary clinical environment. Finally, this project would not have been possible without the parents and kiddos that participated in this project.

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Lastly, I would like to thank my mother, Roseanne Lidstone, for her continual support in all aspects of my life. To my fiancée, Celina Szymanski, thank you for all your love and support throughout this journey. I can’t wait to start the next chapter of our lives together.

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TABLE OF CONTENTS

ABSTRACT ...... iii LIST OF TABLES ...... xii LIST OF FIGURES ...... xvi CHAPTER 1 ...... 1 1.1 Introduction ...... 1 1.2 Searching for The Neuroanatomical Correlates of Autism ...... 7 1.2.1 Syndromic ASD Implicates Several Genes that Produce ASD ...... 7 1.2.2 Purkinje Cell-specific Gene Mutations Produces the ASD-specific Behavioral Phenotype ...... 12 1.2.3 The ASD-Specific Neuroanatomical Profile – Insights from ASD Patient Data ...... 14 1.2.4 Insights from Chemogenetic-Mediated Neuron Silencing Techniques of Purkinje Cells in the Cerebellum ...... 22 1.2.5 Basal Ganglia and Cerebellum Communicate and Mediate Repetitive Behaviors in ASD ...... 27 1.2.6 Concluding Remarks ...... 33 1.3 Visuomotor Integration Deficits in ASD ...... 34 1.3.1 Mirror Neuron System Dysfunction in ASD ...... 38 1.3.2 Deficits Integrating Visual Input into Internal Models in ASD ...... 42 1.3.3 Concluding Remarks ...... 58 1.4 Force Control of Grasping: Implications for Studying Grasping Circuitry in Children with ASD ...... 59 1.4.1 fMRI Findings Suggests Different Circuitry is Involved in Isometric and Dynamic Force Control ...... 59 1.4.2 fMRI Findings Suggests Different Circuitry is Involved in Force Generation vs. Force Relaxation ...... 61 1.4.3 Intracortical Inhibition and Force Relaxation ...... 62 1.4.4 Power Spectra of Frequencies of Force Fluctuations are Associated with Impaired Force Control ...... 66 1.5 Fetal Alcohol Spectrum Disorder: Neuroanatomical and Motor Control Perspectives on the Disorder ...... 67 1.5.1 Introduction ...... 67 1.5.2 Brain Motor Areas are Sensitive to Prenatal Alcohol Exposure ...... 72 1.5.3 The Effects of Ethanol on the Developing Cerebellum ...... 74 1.5.4 FASD Motor Deficits ...... 79

1.6 References ...... 82 CHAPTER 2 ...... 114 Significance of the Chapter ...... 115 Abstract ...... 116 2.1 INTRODUCTION ...... 117 2.2 METHODS ...... 119 2.2.1 Participants ...... 119 2.2.2 Procedures ...... 122 2.2.3 Statistical Procedures ...... 127 2.3 RESULTS ...... 127 2.3.1 Motor Performance...... 127 2.3.2 Partial Correlations ...... 131 2.4 DISCUSSION ...... 133 2.5 CONCLUSION ...... 138 2.6 REFERENCES ...... 139 CHAPTER 3 ...... 145 Significance of the Chapter ...... 146 Abstract ...... 147 3.1 INTRODUCTION ...... 148 3.2 METHODS ...... 151 3.2.1 Participants ...... 151 3.2.2 Experimental Procedures ...... 152 3.2.3 Statistical Procedures ...... 158 3.3.1 Ramp-Up vs. Ramp-Down ...... 159 3.3.2 Transition vs. Post-Transition Period ...... 163 3.4 DISCUSSION ...... 165 3.5 CONCLUSIONS ...... 168 3.6 REFERENCES ...... 170 CHAPTER 4 ...... 175 Significance of the Chapter ...... 176 Abstract ...... 177 4.1 INTRODUCTION ...... 178 4.2. METHODS ...... 181 4.2.1 Participants ...... 181 4.2.2 Experimental Procedures ...... 183 x

4.2.3 Statistical Procedures ...... 186 4.5 CONCLUSION ...... 197 4.6 REFERENCES ...... 199 CHAPTER 5 ...... 203 Significance of the Chapter ...... 204 Abstract ...... 205 5.1 INTRODUCTION ...... 206 5.2 METHODS ...... 208 5.2.1 Participants ...... 208 5.2.2 Procedures ...... 210 5.2.3 Statistical Procedures ...... 212 5.3 RESULTS ...... 212 5.4 DISCUSSION ...... 216 5.5 CONCLUSION ...... 219 5.6 REFERENCES ...... 221 APPENDIX 1 ...... 226 APPENDIX 2 ...... 231 APPENDIX 3 ...... 232 APPENDIX 4 ...... 233 APPENDIX 5 ...... 234 CURRICULUM VITAE ...... 235

LIST OF TABLES

Table 1: Syndromes and associated gene mutations with highest prevalence of syndromic

ASD diagnosis (Phelan & McDermid, 2012; Richards, Jones, Groves, Moss, & Oliver, 2015;

Siu & Weksberg, 2017; Zafeiriou et al., 2013)...... 8

Table 2: Genes associated with ASD that regulate synaptic homeostasis (Ebert &

Greenberg, 2013; Gioveda et al., 2014; Toro et al., 2010; Zhou et al., 2006). BDNF = Brain

Derived Neurotrophic Factor...... 9

Table 3: Syndromic and non-syndromic gene mutations producing ASD-like behavioral phenotypes and altered Purkinje cell (PC) morphological and electrophysiological outcomes in PC-specific (mutation on PC protein 2/L7 promoter) and global ASD-mutant mouse models...... 13

Table 4: ASD-specific structural deficits in cerebellar vermis. MRI=Magnetic Resonance

Imaging; ASD=Autism Spectrum Disorder; TD=typical development; DS=Down’s syndrome; FSX=Fragile X syndrome; ADHD=Attention-Deficit Hyperactivity Disorder;

VBM=voxel-based morphometry; IHC=Immunohistochemistry. (×=increase from controls; Ù=no change from controls; Ø=decrease from controls; -=not measured). aPurkinje cell density assessed...... 17

Table 5: ASD-specific structural deficits in cerebellar vermis correlated with ASD-specific behaviors. (×=reduced area/volume/PC density associated with increase in behavior; Ø= reduced area/volume/PC density associated with reduced behavior or performance; -=not examined). aPurkinje cell density assessed...... 18

Table 6: ASD-specific structural deficits in cerebellar hemispheres; LF-ASD=low- functioning ASD; FSX=Fragile X syndrome; ASDELD=ASD with expressive language delay;

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MRI=Magnetic Resonance Imaging; VBM=voxel-based morphometry. (×=increase from controls; Ù=no change from controls; Ø=decrease from controls; -=not measured). aPurkinje cell density assessed...... 21

Table 7: ASD-specific structural deficits in cerebellar hemispheres correlated with ASD- specific behaviors. (×=reduced area/volume associated with increase in behavior; Ø= reduced area/volume associated with reduced behavior or performance; - =not examined).

...... 22

Table 9: fMRI activation findings examining regions of activation specific to static and dynamic precision-grip force tasks in right-handed healthy participants (Neely et al., 2013).

...... 60

Table 10: fMRI activation findings from Spraker et al. (2009) on force generation and force relaxation using a precision-grip force tracking task in right-handed healthy participants

(Spraker et al., 2009). These findings indicate specific force generation and relaxation networks...... 62

Table 11: Review of neuroanatomical structures sensitive to prenatal alcohol exposure.

Focus on brain regions involved in motor control...... 72

Table 12: Effects of PAE at the structural, electrophysiological, and molecular level of the developing cerebellum...... 75

Table 13: FASD-specific structural deficits in cerebellar vermis. MRI=Magnetic Resonance

Imaging; FASD=Fetal Alcohol Spectrum Disorder; FAS=Fetal Alcohol Syndrome;

FAE=fetal alcohol effects; ARND=alcohol-related neurobehavioral disorder;

PAE=prenatal exposure to alcohol; SE/AE=static encephalopathy/alcohol exposed;

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NB/AE=neurobehavioral disorder/alcohol exposed. (Ù=no change from controls;

Ø=decrease from controls)...... 77

Table 14: ADHD-specific structural deficits in cerebellar vermis. MRI=Magnetic

Resonance Imaging; ADHD=Attention-Deficit Hyperactivity Disorder. (×=increase from controls; Ù=no change from controls; Ø=decrease from controls; -=not measured)...... 78

Table 16: Demographic characteristics of participants in ASD, FASD, ADHD, and TD groups...... 121

Table 17: Partial correlations (covarying for age) between normalized power in each frequency band and performance variables (rRMSE, SNR, and SampEn). P values are shown in brackets and significant correlations are in bold (p ≤ 0.05)...... 132

Table 18: Demographic characteristics of participants in ASD, FASD, ADHD, and TD groups...... 152

Table 19: Mean (M) and standard deviation (SD) for relative error (rRMSE) during ramp- up and ramp-down. Age-adjusted mean (Madj) and standard errors (SE) are also shown. 161

Table 20: Mean (M) and standard deviation (SD) for sample entropy (SampEn) during ramp-up and ramp-down. Age-adjusted mean (Madj) and standard errors (SE) are also shown...... 162

Table 21: Mean (M) and standard deviation (SD) for relative error (rRMSE) during transition and post-transition phases. Age-adjusted mean (Madj) and standard errors (SE) are also shown...... 164

Table 22: Demographic characteristics of participants in ASD, FASD, ADHD, and TD groups...... 182

Table 23: Participant Demographics...... 209

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Table 24: 9-Hole Pegboard test times in seconds...... 215

Table 8: Summary of studies demonstrating ASD deficits to integrate visual input into motor commands. pIFG = posterior inferior frontal gyrus, PMd = dorsal pre-motor area,

PMv = ventral pre-motor area, IPL = inferior parietal lobule, pSTS = posterior superior temporal sulcus...... 226

Table 15: Summary of findings of motor deficits in children with Fetal Alcohol Spectrum

Disorder (FASD) vs. controls (CON)...... 228

LIST OF FIGURES

Motor phenotypes are not well defined in ASD with most research focusing on understanding the genetic mutations that lead to ASD and the neurophenotypes of ASD.

Identifying motor phenotypes of ASD, that are associated with the neurological and behavioral phenotypes of ASD, may help in clinical settings to objectively (1) identify effective medication dose/combinations, (2) quantify the effectiveness of behavioral or motor-based interventions, (3) examine the effectiveness of non-invasive brain stimulation protocols, and (4) improve diagnostic practices...... 5

Purkinje Cell (PC) produced (1) decreased PC firing rate, (2) increased left-IPL firing rate, and (3) increased ASD-related behaviors. DREADD-mediated RCrusI PC excitation in a

Tsc1 ASD-mouse model (1) increased PC firing rate, (2) decreased left-IPL firing rate, and

(3) increased social preference...... 23

Figure 3: Proposed ascending and descending pathways between cerebellum and basal ganglia (Alexander & Crutcher, 1986; Bostan et al., 2010; Bostan & Strick, 2018; Hoshi et al., 2005; Jwair, Coulon, & Ruigrok, 2017; Milardi et al., 2016). Other existing connections that were not traced in these studies (using retrograde transneural transport) are shown in

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gray. These connections (gray) provide other ways cerebellum can influence the function of basal ganglia...... 30

“X” over the inverse model is used to highlight that cerebellum output does not mediate changes in muscle activation and therefore may not be involved in the inverse model (Coltz et al., 1999; Pasalar et al., 2006; Roitman, 2005; Yavari et al., 2016b). However, simple spike output from several PCs predict future sensory states (position, movement direction, and speed) ~100ms prior to movement onset showing involvement in forward model computations (Coltz et al., 1999; Liu, 2002; Roitman, 2005)...... 36

Figure 5: Right posterolateral cerebellum (RCrusI/II) and left Mirror Neuron System (IPL, pSTS, and PMv) circuitry that may be implicated in visuomotor integration deficits in

ASD. Red = output from dentate nuclei (DN); Blue = input to cerebellum via pontine nuclei

(PN). (Clower et al., 2001; Iacoboni & Dapretto, 2006; Rozzi, Ferrari, Bonini, Rizzolatti, &

Fogassi, 2008; Stoodley et al., 2018)...... 37

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Figure 6: Review of neuroanatomical, neuropsychological, behavioral, and motor outcomes associated with brain damage resulting from prenatal alcohol exposure. FAS = Fetal

Alcohol Syndrome; ARBD = Alcohol-Related Birth Defect; ARND = Alcohol-Related

Neurodevelopmental Disorder. (Derauf et al., 2009; Mattson et al., 2011; Riccio &

Sullivan, 2016)...... 71

Figure 7: Experimental procedure. (A) precision-grip apparatus, (B) MVC visual stimulus,

(C) force regulation target (green) and cursor (white), (D) sample data force output from a single participant, (E) force signal (D) decomposed into the frequency domain...... 123

Figure 8: Mean and standard errors of motor performance data for MVC, rRMSE, SNR, and SampEn. *p < 0.05...... 128

Figure 9: Best three trials (lowest rRMSE) for each participant in ASD, FASD, ADHD, and

TD groups. Green = best trial for each participant, Blue = 2nd best trial for each participant, Red = 3rd best trial for each participant...... 129

Figure 10: Proportion of power in each frequency band (0-1 Hz, 0-4 Hz, 4-8 Hz, 8-12 Hz) normalized to total power between 0-12 Hz...... 130

Figure 11: Partial correlations in the ASD group (covarying for age) between 0 to 1 Hz normalized power and (A) rRMSE (r = 0.45, p = 0.03), (B) SNR (r = -0.42, p = 0.04), and (C)

SampEn (r = -0.42, p = 0.050)...... 132

Figure 12: Experimental procedure. (A) precision-grip apparatus, (B) MVC visual stimulus, (C) force regulation target (green) and cursor (white), (D) sample data force output from a single participant...... 154

Figure 13: Visual angle for force tracking task...... 155

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Figure 14: Each participants trial with the lowest total error (rRMSE) shown for each group (ASD, FASD, ADHD, and TD)...... 159

Figure 15: Relative accuracy (rRMSE) mean ± standard errors shown for ramp-up and ramp-down. * p < 0.05; ** p = 0.002...... 160

Figure 16: Sample entropy (SampEn) mean ± standard errors shown for ramp-up and ramp-down...... 162

Figure 17: Relative error (rRMSE) mean ± standard errors shown for transition and post- transition phases. * p < 0.05; ** p < 0.0005...... 164

Figure 18: Postural sway area between groups. *p = 0.04, **p = 0.012, ***p=0.006...... 187

Figure 19: Non-parametric Kruskal-Wallis H test performed on unipedal stance times.

Reference line indicates 45-s test cut-off. *p = 0.041, **p = 0.022, ***p=0.0002...... 188

Figure 20: Balance-impaired sub-group analysis. (A) unipedal stance time; (B) postural sway area, (C) correlation between postural sway area and unipedal stance time between

ASD (solid circles, dashed regression line) and Clinical Controls (white diamond, solid regression line) (ASD: r = -0.452, p = 0.078; Clinical Controls: r = -0.008, p = 0.975)...... 189

Figure 21: Anteroposterior (AP) and mediolateral (ML) RMS sway magnitude between groups...... 190

Figure 22: Anteroposterior (AP) and mediolateral (ML) mean velocity of sway between groups...... 191

Figure 23: Anteroposterior (AP) and mediolateral (ML) sample entropy (SampEn) between groups using m = 2 and tolerance r = 0.2. *p = 0.01, **p = 0.003...... 192

Figure 24: Experimental set-up for (A) handedness assessment and (B) 9-hole pegboard test...... 210

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Figure 25: 9-hole pegboard time mean ± standard errors for dominant and non-dominant hands...... 214

Figure 26: Hand performance asymmetry between children with FASD and ASD, ADHD, and TD groups...... 215

CHAPTER 1

Comprehensive Literature Review

1.1 Introduction

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) affecting 1 in

59 children with diagnoses biased towards boys at a prevalence ratio of 4 (Baio et al., 2018). In the US, total costs per year for children with ASD are estimated between $11.5 to $60.9 billion

US dollars (Lavelle et al., 2014) and the cost of supporting an individual with ASD during his/her lifetime is $1.4 to 2.4 million USD (Buescher, Cidav, Knapp, & Mandell, 2014). Children with ASD display a complex core symptomology that include social-interaction deficits, communication challenges, and restricted and repetitive behaviors or interests. Furthermore, children with ASD often demonstrate atypical sensory processing (Klin et al., 2009) and mild to severe motor impairments independent of intellectual disability (Fournier, Hass, Naik, Lodha, &

Cauraugh, 2010; Green et al., 2009). The search for ASD-specific neuroanatomical profile responsible for the complex array of symptoms in ASDs is a primary focus for researchers developing targeted pharmacological, non-invasive brain stimulation, and behavioral/motor- based interventions.

The first step to uncover the ASD-specific neuroanatomical profile is to understand what factors, genetic or environmental, produce ASD-like behaviors. Studies show that genetic

(Abrahams & Geschwind, 2008; Hallmayer et al., 2011; Leblond et al., 2014; Losh, Sullivan,

Trembath, & Piven, 2008; Sandin et al., 2017) and environmental (Al-Hamdan, Preetha,

Albashaireh, Al-Hamdan, & Crosson, 2018; Chomiak, Turner, & Hu, 2013; Newschaffer et al.,

2007) factors both contribute to the etiology of ASD (Siu & Weksberg, 2017). The number of

candidate gene mutations underlying ASD is ~1000 – highlighting the complexity in this field of research (Ronemus, Iossifov, Levy, & Wigler, 2014; SFARI gene). Many of these genes encode proteins implicated in synaptic function and development. Research on syndromic ASD – where a subgroup of individuals with a known genetic syndrome develop ASD – has helped narrow the search for candidate genes associated with ASD and to develop mutant-mouse models with

ASD-like behavioral profiles. Genetic syndromes with high rates of comorbid ASD diagnoses include fragile X syndrome (FSX), Down’s syndrome (DS), Rett’s syndrome, 22q11.2 deletion syndrome, 22q13.3 deletion syndrome, Angelman syndrome, Prader-Willi syndrome, Pitt-

Hopkins-like syndrome 1, neurofibromatosis type 1, Cornelia de, Lange syndrome, Sotos syndrome, CHARGE syndrome, CHD8 mutation, and tuberous sclerosis complex (TSC) (Table

1). These rare genetic syndromes show a high prevalence of syndromic ASD and combined account for 10-20% of all ASD cases (Abrahams & Geschwind, 2008). Furthermore, specific mutations on two X-linked genes that encode neuroglins (NLGN3/NLGN4 - postsynaptic adhesion proteins) have also been identified in siblings with ASD possibly explaining observations of a fourfold increase of ASD prevalence in males (Jamain et al., 2003). Using these findings from genetic studies of ASD, several ASD-mutant mouse models have been developed that reproduce the complex behavioral phenotypes and neurophenotypes observed in human studies of ASD (Kloth et al., 2015; Mabunga, Gonzales, Kim, Kim, & Shin, 2015; Peter et al., 2016; Provenzano, Zunino, Genovesi, Sgadó, & Bozzi, 2012; Tsai et al., 2012). Several genetic ASD-related mutant mouse models, including (FMR1, NLGN3, MeCP2, SHANK3,

CNTNAP, PTEN, Tsc1) and environmental exposure Valproic Acid (VPA) models, replicate the

ASD-specific behavioral phenotype, deficits in cerebellum-dependent learning, and sensorimotor integration deficits (Baudouin et al., 2012; Chomiak et al., 2013; Cupolillo et al., 2016; Kloth et

al., 2015; Koekkoek et al., 2005; Piochon et al., 2014). Furthermore, Purkinje cell (PC)-specific mutant mouse models such as SHANK2 (Peter et al., 2016), PTEN (Cupolillo et al., 2016),

NLGN3 (Baudouin et al., 2012), and Tsc1 (Tsai et al., 2012) replicate ASD-like behavioral traits and late-onset PC death (Cupolillo et al., 2016; Tsai et al., 2012) – one of the most consistent neuroanatomical findings in post-mortem study of ASD patients (Bailey et al., 1998; Fatemi et al., 2002; Palmen, Van Engeland, Hof, & Schmitz, 2004; Whitney, Kemper, Bauman, Rosene, &

Blatt, 2008; Whitney, Kemper, Rosene, Bauman, & Blatt, 2009). Evaluating ASD-specific behavioral outcomes in PC-specific knock out mice has also helped elucidate the role of PC dysfunction to the ASD-specific behavioral phenotype (Cupolillo et al., 2016; Peter et al., 2016;

Tsai et al., 2012). The current review will overview findings from PC-specific ASD-mutant mouse models, from both a neuroanatomical and behavioral perspective, to better understand the link between the cerebellum PC pathology and ASD-specific neuroanatomical and behavioral traits.

Evidence of late-onset PC death in post-mortem study of human and ASD-mutant mouse models points to the cerebellum as a neural correlate contributing to ASD symptomology

(Whitney et al., 2009). Furthermore, 37% of pre-term children with cerebellar hemorrhagic injury show elevated ASD symptoms versus controls (Limperopoulos et al., 2007) and cerebellar posterior vermis hypoplasia is a significant predictor of ASD-like behavioral symptoms (Bolduc et al., 2012, 2011). However, cerebellar abnormalities are not specific to ASD and are observed in children with genetic and idiopathic neurodevelopmental disorders without co-diagnosed ASD

(Manto & Jissendi, 2012; McCorkle et al., 2014). Therefore, specific regions of the cerebellum are likely responsible for the ASD-like behavioral profile (Stoodley, 2014). The cerebellum contains ~50% of the brain’s neurons, and insult to one region of cerebellum could produce

significant deficits in a wide range of behaviors (Andersen, Korbo, & Pakkenberg, 1992;

Herculano-Houzel, 2009). The current review will highlight specific regions of the cerebellar cortex that may be responsible for ASD-like symptomology using: (1) post-mortem studies of the cerebellum; (2) findings from ASD-mutant mouse models; (3) structural imaging; and (4) functional imaging studies. Particular attention will be given to the posterior-inferior vermis

(vermal lobules VI-VII), lobule IX, right Crus I, and right Crus II as they relate to ASD-core symptomology (Courchesne et al., 1994; D’Mello, Crocetti, Mostofsky, & Stoodley, 2015;

Stoodley et al., 2018). Repetitive behaviors and restricted interests in ASD will also be discussed in relation to the indirect pathway of the basal ganglia – a pathway with indirect cerebrocerebellar connections (Bostan, Dum, & Strick, 2010; Hoshi, Tremblay, Féger, Carras, &

Strick, 2005; Milardi et al., 2016; Moers-Hornikx et al., 2011).

Cerebellar regions implicated in the ASD-specific neuroanatomical profile and the regions they connect with participate in motor control. In ASD mouse models, recent work has shown significant improvements in motor learning following a behavioral intervention that supports a link between brain areas that mediate motor and non-motor functions in ASD

(Bachmann et al., 2019). Furthermore, several studies have highlighted the robust relationships between the severity of the ASD-specific phenotype and motor proficiency (Hirata et al., 2014;

LeBarton & Landa, 2019; MacDonald, Lord, & Ulrich, 2014). Therefore, motor tasks that preferentially activate the identified ASD-specific neuroanatomical regions may improve functioning within these circuits and reduce the severity of the ASD-specific phenotype.

Likewise, targeted behavioral interventions that reduce the severity of the ASD-specific phenotype may improve motor function in children with ASD. In summary, this review will highlight specific neuroanatomical and neurophysiological mechanisms that mediate the

behavioral phenotype of ASD and assist in identifying an ASD-specific motor phenotype (Figure

1).

Figure 1: Schematic of proposed interactions between ASD-specific genotype and the physical manifestations of ASD-specific gene mutations = phenotypes (behavioral, neurological, and motor). Interventions aimed at improving function within a single ASD-specific phenotype should interact with other domains due to shared mediating circuitry. Motor phenotypes are not well defined in ASD with most research focusing on understanding the genetic mutations that lead to ASD and the neurophenotypes of ASD. Identifying motor phenotypes of ASD, that are associated with the neurological and behavioral phenotypes of ASD, may help in clinical settings to objectively (1) identify effective medication dose/combinations, (2) quantify the effectiveness of behavioral or motor-based interventions, (3) examine the effectiveness of non-invasive brain stimulation protocols, and (4) improve diagnostic practices.

Although the ASD-specific behavioral phenotype and neurology are well studied in the literature, the ASD-specific motor phenotype has received much less attention. For example, findings from Stoodley et al. (2014) show that gray matter abnormalities in cerebellum are specific to behavioral phenotypes, showing abnormalities differ between ASD, ADHD, and

Dyslexia (Stoodley, 2014). However, similar approaches to Stoodley et al. (2014) have not been applied to the study of motor control in children with ASD. Therefore, to overcome this limitation, the current dissertation proposes a cross-syndrome approach to examine the specificity of motor deficits in children with ASD. The cross-syndrome approach may help bridge the gap between ASD-specific neuroanatomical findings and the motor deficits observed in this population.

The purpose of this literature review is two-fold: (1) to review evidence supporting an

ASD-specific neurophenotype and (2) to evaluate findings from motor control studies supporting the ASD-specific neurophenotype.

1.2 Searching for The Neuroanatomical Correlates of Autism

1.2.1 Syndromic ASD Implicates Several Genes that Produce ASD

Although genetic and epigenetic contributions to ASD diagnosis is not a primary concern of this literature review (detailed reviews on these topics are reviewed here: Siu & Weksberg,

2017; Zafeiriou, Ververi, Dafoulis, Kalyva, & Vargiami, 2013), a brief synopsis of genetic contributions to ASD diagnosis will be reviewed here. The reader is also directed to an exhaustive and continually updated list of candidate ASD genes found on the SFARI gene database.

ASD is a genetic-based neurodevelopmental disorder with a heritability estimates greater than 90% (Freitag, 2007) and genetic syndromes contribute to 10-20% of all ASD cases

(Abrahams & Geschwind, 2008; Iossifov et al., 2014). ASD co-diagnosed with a known genetic syndrome is termed syndromic ASD whereas ASD diagnosed without a known cause is termed idiopathic ASD. The genetic markers that increase risk of idiopathic ASD diagnosis are not well defined, in stark contrast to genes implicated in syndromic ASD diagnosis (Freitag, 2007).

Several genetic syndromes associated with high-risk of syndromic ASD diagnosis and their associated genetic mutations have been identified (Table 1).

Table 1: Syndromes and associated gene mutations with highest prevalence of syndromic ASD diagnosis (Phelan & McDermid, 2012; Richards, Jones, Groves, Moss, & Oliver, 2015; Siu & Weksberg, 2017; Zafeiriou et al., 2013).

Syndromic ASD Syndrome Gene Mutation Risk (%) CHD8 CHD8 85% Rett’s MeCP2 61% Sotos NSD1 55% Choen’s VPS13B 53% 22q13.3 deletion Phelan-McDermid SHANK3 50% syndrome Cornelia de Lange NIPBL 43% Tuberous Sclerosis TSC1/TSC2 36% Complex (TSC) 15q11-13 maternal Angelman 34% deletion, UBE3A Fragile X FMR1 30% CHARGE CHD7 30% 22q11.2 deletion DGCR8 20-40% 15q11-13 paternal Prader-Willi 19-36% deletion, SNRPN Neurofibromatosis NF1 18% Type 1 Down’s 22q22.2 duplication 16% Pitt-Hopkins-like CNTNAP2 - syndrome 1

ASD-mutant mouse models have been extremely valuable in replicating the complex behavioral phenotypes observed in ASDs and for examining the effectiveness of pharmacological interventions at reducing the severity of symptoms (Ey, Leblond, & Bourgeron,

2011; Lázaro & Golshani, 2015). Although several hundred syndromic and non-syndromic genes have been implicated in ASD diagnosis (Iossifov et al., 2014; Liu et al., 2014; Ronemus et al.,

2014), it is important to understand on which neuronal functions they converge. Strong evidence supports the hypothesis that gene mutations associated with ASD converge on synaptic

homeostasis functions (Ebert & Greenberg, 2013; Gioveda, Corradi, Fassio, & Benfenati, 2014;

Kelleher & Bear, 2008; Toro et al., 2010) (Table 2).

Table 2: Genes associated with ASD that regulate synaptic homeostasis (Ebert & Greenberg, 2013; Gioveda et al., 2014; Toro et al., 2010; Zhou et al., 2006). BDNF = Brain Derived Neurotrophic Factor.

Gene Synaptic Mechanisms Transports mRNA into FMR1 dendrites Regulates activity- MeCP2 dependent induction of BDNF transcription Regulate protein synthesis- TSC1/TSC2, NF1, PTEN dependent plasticity of synapses NLGN3, NLGN4X NRXN, Code for synaptic adhesion CNTNAP2 molecules Postsynaptic synaptic SHANK2 and SHANK3 density scaffold proteins

Several syndromic and non-syndromic gene mutations implicated in ASD diagnosis, such as FMR1, MeCP2, TSC1/TSC2, NF1, PTEN, NLGN3, NLGN4X NRXN, CNTNAP2, SHANK2, and SHANK3, control synapse development and function (Kelleher & Bear, 2008). Dysregulation of tumor suppressor genes (TSC1/TSC2, NF1, PTEN) that regulate protein synthesis may contribute to findings of synaptic dysfunction (Tsai et al., 2012), macrocephaly (Butler et al.,

2005; Courchesne, Campbell, & Solso, 2011), and overconnectivity of local but underconnectivity long-range connections in ASD (Courchesne & Pierce, 2005; Nebel et al.,

2016). Furthermore, genes that code for presynaptic neurexin (NRXN) cell adhesion molecules

(CAMs), postsynaptic neuroligin (NLGN3, NLGN4X) CAMs, neuron-glia CAMs (CNTNAP2), and scaffolding proteins (SHANK 2/3) on the postsynaptic density that interact with PSD95 and

anchor CAMs are implicated in ASD (Kelleher & Bear, 2008; Peñagarikano & Geschwind,

2012; State & Levitt, 2011). Together, these findings suggest regulation of the synapse may contribute to ASD diagnosis. However, it is unclear why synapse dysfunction leads to the ASD- behavioral phenotype of (1) social and communication deficits and (2) repetitive and restricted interests and behaviors.

Genes regulating the expression of arginine vasopressin (AVP) and its receptors (V1aR) have also attracted interest from researchers. These neuropeptides are implicated in social behaviors, communication, and repetitive behaviors in mammals (Hammock, 2015; Insel,

O’Brien, & Leckman, 1999). Recent findings in non-social primates show that AVP concentration in cerebrospinal fluid (CSF) is a key marker of sociability (Parker et al., 2018).

Furthermore, AVP concentrations in the CSF of children with ASD are significantly lower than in controls without ASD (Oztan et al., 2018; Parker et al., 2018). AVP concentrations in the CSF of children also (1) successfully distinguishes children diagnosed with ASD from controls and is

(2) negatively associated with ASD symptom severity and communication deficits in male children with ASD (Oztan et al., 2018; Parker et al., 2018). The bias in the relationship between

AVP concentrations and ASD symptom severity in males-only, may be explained by AVPs sexually dimorphic behavioral effects and higher concentrations of AVP in males vs. females

(Hammock, 2015). Therefore, disruption in AVP signaling may partially contribute to the male- bias in ASD diagnoses (4:1 odds ratio) (Baio, 2014). AVP also plays a significant role in brain development with the cerebellum being the most affected brain region (Boer, 1985). AVP knockout mice show a 15% reduction in net weight of the cerebellum vs. control mice (Boer,

1985). Furthermore, the cerebellum – specifically the fastigial nucleus – is involved in the regulation of AVP release (Sved, Scott, & Kole, 1985). Recent findings suggest that AVP may

also participate in the dendritogenesis and synaptogenesis of Purkinje cells of the cerebellum

(Vargas et al., 2009). Together, these findings show that AVP plays an important role in cerebellar development and in-turn the cerebellum regulates the release of AVP. In summary, disrupted AVP signaling may contribute to abnormal brain growth, development of core ASD symptoms, and male-bias in ASD diagnosis.

Although several cortical regions underlying ASD diagnosis have been identified – including the: (1) frontal lobe; (2) amygdala; and (3) cerebellum (Amaral, Schumann, &

Nordahl, 2008) – anatomical disruption of the cerebellum and its Purkinje cells (PC) are one of the most consistent neuroanatomical findings in ASD patients (Bailey et al., 1998; Fatemi et al.,

2002; Palmen et al., 2004; Whitney et al., 2008, 2009). Generating ASD-mutant mouse models with PC-specific gene mutations can help elucidate the effects of ASD-associated gene mutations on PC morphology and electrophysiology. Furthermore, if PC-specific expression of gene mutations in mouse models produces the ASD-behavioral phenotype, then the role of PC- specific dysfunction in ASD can be examined. Fortunately, several PC-specific ASD-mutant mouse models including TSC1, SHANK2, NLGN3, PTEN, and FMR1 have been developed with detailed evaluation of PC-specific morphology, electrophysiology, and corresponding behavioral phenotypes. The next section will outline neuroanatomical and electrophysiological effects of gene mutations expressed only in the PCs of the cerebellum. The findings from these studies show electrophysiological and morphological deficits of the PC, late-onset PC death, and replication of the ASD-specific behavioral phenotype.

1.2.2 Purkinje Cell-specific Gene Mutations Produces the ASD-specific Behavioral Phenotype

One of the most consistent neuroanatomical findings in ASD is late-onset PC death

(Bailey et al., 1998; Fatemi et al., 2002; Palmen et al., 2004; Whitney et al., 2008, 2009). In PC- specific PTEN and TSC1 tumor-suppressor gene mutations, increased late-onset PC death and reduced PC density are observed compared to wild-type mice (Table 3) (Angliker, Burri,

Zaichuk, Fritschy, & Rüegg, 2015; Cupolillo et al., 2016; Tsai et al., 2012). Furthermore,

SHANK2, PTEN, NLGN3, TSC1, and FMR1 mutations produce electrophysiological deficits at the PC that may contribute to deficits in cerebellum-mediated learning and contribute to ASD- like behaviors (Table 3) (Angliker et al., 2015; Baudouin et al., 2012; Cupolillo et al., 2016;

Koekkoek et al., 2005; Peter et al., 2016; Rothwell et al., 2014; Tsai et al., 2012). The PCs are therefore a neural correlate for ASD-like behaviors including deficits in social-interaction and increased repetitive/stereotyped behaviors. Furthermore, other behaviors associated with the complex ASD-specific behavioral phenotype – such as impaired motor learning, motor coordination deficits, hyperactivity, increased vocalizations, and increased anxiety – are produced by altering synaptic function of the PC (Table 3). A UBE3A-global mutation also results in changes in PC-specific morphology, electrophysiology, and produces the ASD-like behaviors (Table 3) (Nakatani et al., 2009; Piochon et al., 2014).

PC-specific knock PC-specific Behavioral Phenotype of PC- Reference out Mouse Model Neurophysiological Effects specific mutants Electrophysiology Ø Social-interaction SHANK2-PC specific × Irregular simple-spike × Repetitive behaviors (Peter et al., 2016) Non-syndromic firing pattern (lobules I-V, Ø Motor learning IX-X) PC morphology × Number of CF synapses on PC × Repetitive behaviors (Baudouin et al., NLGN3- PC specific Electrophysiology Ø Motor coordination 2012; Rothwell et al., Non-syndromic × Baseline LTD × Hyperactivity 2014) × CF-evoked EPSC Ø PF-evoked EPSC PC survival × Late-onset PC death (lobules IV-V and IX) PC morphology Ø Social-interaction PTEN-PC specific × Size of PC soma (Cupolillo et al., × Repetitive behaviors Syndromic Electrophysiology 2016) Ø Motor coordination Ø PC spontaneous firing frequency × PF EPSC Ø CF EPSC PC survival × Late-onset PC death Ø Density (>11 wks) Ø Social-interaction PC morphology × Repetitive behaviors TSC1-PC Ø Soma size Ø Motor coordination (only at (Angliker et al., 2015; specific × Dendritic tree high age) Stoodley et al., 2018; Syndromic Electrophysiology × Vocalizations Tsai et al., 2012) Ø Spontaneous firing Ø Cognitive flexibility frequency Ø Motor learning Ø Excitability (mediated by Ø PC input resistance) × Motor stereotypy PC morphology × Repetitive behaviors Ø CF pruning UBE3A-global × Anxiety (Nakatani et al., 2009; Electrophysiology Syndromic × Activity Piochon et al., 2014) × Baseline PF-LTD × Vocalizations

Ø Behavioral flexibility PC morphology × Length of spine head and FMR1-PC Ø Cerebellum-mediated spine neck (Koekkoek et al., specific learning (eye-blink Electrophysiology 2005) Syndromic conditioning) × PF-LTD × Rate of CF pruning

Although one single gene mutation cannot explain ASD diagnosis, together these findings show both syndromic and non-syndromic gene mutations – implicated in ASD diagnosis

– converge on synapse homeostasis functions producing: (1) PC morphological changes; (2) PC- specific synaptic dysfunction; and (3) the ASD-specific behavioral phenotype. However, how do globally expressed gene mutations in the CNS produce ASD-like behaviors? To better understand the region specificity of cerebellum deficits in ASD, the following section will summarize findings from studies that have examined whether expression of ASD genes in only

PCs produce the ASD-specific behavioral phenotype. These studies allow researchers to examine the contribution of the cerebellum and its PCs to the ASD-specific behavioral phenotype.

1.2.3 The ASD-Specific Neuroanatomical Profile – Insights from ASD Patient Data

1.2.3.1 Cerebellar Vermis

The cerebellar vermis receives mossy fiber afferents from the spinal cord and regulates postural control and gait via efferent output from fastigial nuclei, and the paravermal region – adjacent to the vermis – regulates fine movements of the distal limbs via efferent output from interposed nuclei (Manto et al., 2012; Schoch, Dimitrova, Gizewski, & Timmann, 2006). The anterior vermis receives large somatosensory input (lobules I-V) and is involved in regulating axial muscle tone, posture, and gait (Manto et al., 2012). Inputs from cortical motor areas to the cerebellar vermis have also been identified (Coffman, Dum, & Strick, 2011). Lesions to vermal lobules II-IV leads to ataxia of posture and gait (Schoch et al., 2006), lesions to vermal lobules

III-IV leads to lower limb ataxia, whereas lesions to IV-V and VI leads to upper limb ataxia

(Grimaldi & Manto, 2012). Removal of the posterior vermis (lobules VI-X) also results in severe impairments in tandem gait with relatively normal regular gait and quiet stance (Bastian, Mink,

Kaufman, & Thach, 1998). The vermis is also involved in oculomotor functions via efferent output from fastigial nuclei and oculomotor vermis (vermal lobules VI-VII, IX, X) (Grimaldi &

Manto, 2012; Voogd, Schraa-Tam, Van Der Geest, & De Zeeuw, 2012).

One of the most consistent vermal abnormalities in ASD is in the posterior vermis – specifically the posterior superior vermis (lobules VI-VII) (Stanfield et al., 2008) (Table 4). As shown in Table 4, significant reductions in the vermal area of the posterior superior vermis (VI-

VII) in individuals with ASD has been reported (Akshoomoff et al., 2004; Courchesne et al.,

1994; Courchesne, Yeung-Courchesne, Hesselink, & Jernigan, 1988; Hashimoto et al., 1995;

Kates et al., 1998; Kaufmann et al., 2003; Pierce & Courchesne, 2001; Webb et al., 2009).

Furthermore, a meta-analysis of 12 studies examining the area of vermal lobules VI-VII showed the reduction in area was likely real for younger subjects with ASD whereas area reductions in older individuals were partially related to IQ (Stanfield et al., 2008). One study identified two subgroups of individuals with ASD with one subgroup showing significant reductions in VI-VII vermal area and the other subgroup showing significant increases in VI-VII vermal area

(Courchesne et al., 1994). Structural deficits in vermal lobules VI-VII are related to core ASD behaviors including reduced exploratory behaviors (Pierce & Courchesne, 2001), increased repetitive and stereotyped behaviors (D’Mello et al., 2015; Pierce & Courchesne, 2001), and deficits in social interaction and communication (D’Mello et al., 2015) (Table 5). Vermal lobule

VII is also involved in emotional processing (Stoodley & Schmahmann, 2009), a function that is also impaired in individuals with ASD (Cassidy et al., 2016).

Vermal area reductions in the anterior lobe (I-V) and posterior inferior lobule (VIII-X) are a less consistent finding than in the posterior superior vermis (VI-VII). In the anterior vermal lobules, significant reduction of area has been observed (Hashimoto et al., 1995; Webb et al.,

2009) with one study showing a significant increase in area (Akshoomoff et al., 2004). No study to our knowledge has detected reduced gray matter volume in the anterior vermal lobules, although reduced gray matter volume of the anterior vermis is related to social interaction deficits in individuals with ASD (D’Mello et al., 2015). In contrast to findings from the anterior vermis, more robust gray matter volume reductions have been found in vermal lobules VIII-X in individuals with ASD (Riva et al., 2013; Rojas et al., 2006; Stoodley, 2014). A meta-analysis of

17 voxel-based morphometry (VBM) studies, involving individuals with ASD, identified significant gray matter reductions in vermal lobule IX (Stoodley, 2014). Reductions in PC density have also been found in posterior inferior vermis of ASD subjects in vermal lobule IX

(Wegiel et al., 2013) and X (Skefos et al., 2014; Wegiel et al., 2013). In vermal lobules VIII-X, reduced gray matter volume was related to reciprocal social communication (Riva et al., 2013) and increased stereotyped and repetitive behaviors (D’Mello et al., 2015); with reduced PC density associated with social eye contact deficits (Skefos et al., 2014). Together, these findings show the posterior vermis (VI-X) is implicated in ASD with the anterior vermal lobes spared, in contrast to findings in fetal alcohol spectrum disorders (FASD) (Astley et al., 2009; Cardenas et al., 2014; O’Hare et al., 2005; Sowell et al., 1996). The posterior vermis, including lobules VI-

VII and IX-X, is involved with oculomotor functions (Grimaldi & Manto, 2012) and may explain findings of abnormal gaze, saccade accuracy, and smooth pursuit in individuals with ASD

(Schmitt, Cook, Sweeney, & Mosconi, 2014; Takarae, Minshew, Luna, Krisky, & Sweeney,

2004; Wegiel et al., 2013). Reduced vermal area in lobules VI-VII and VIII-X were also shown to be related to the ASD-specific behavioral phenotype (Table 5).

Table 4: ASD-specific structural deficits in cerebellar vermis. MRI=Magnetic Resonance Imaging; ASD=Autism Spectrum Disorder; TD=typical development; DS=Down’s syndrome; FSX=Fragile X syndrome; ADHD=Attention-Deficit Hyperactivity Disorder; VBM=voxel-based morphometry; IHC=Immunohistochemistry. (×=increase from controls; Ù=no change from controls; Ø=decrease from controls; -=not measured). aPurkinje cell density assessed.

ASD vs. Controls Study ASD-Specific Findings Cerebellar Vermis Area, Volumetric Measures, or PC density Measurement Original Studies Age (years) Groups I-V VI-VII VIII-X Tool ASD (Courchesne et al., 1988) 6-30 MRI Ù Ø Ù TD ASD (Courchesne et al., 1994) 2-40 MRI Ù ×/Ø - TD ASD (Hashimoto et al., 1995) 0.5-20 MRI Ø Ø Ø TD Monozygotic (Kates et al., 1998) 7.5 MRI Ù Ø Ù Twins ASD (Levitt et al., 1999) 12 MRI Ù Ù Ø TD ASD Diagnostic (Riva, 2000) 6-12 Case study - Ø Ø Testing (Pierce & Courchesne, ASD 3-8 MRI Ù Ø - 2001) TD ASD DS+ASD (Kaufmann et al., 2003) 7 DS MRI Ù Ø Ù FXS+ASD FXS ASD (LFA, HFA, (Akshoomoff et al., 2-5 PDD-NOS) MRI × Ø - 2004) TD ASD (Rojas et al., 2006) 7-44 VBM Ù Ù Ø TD (Scott, Schumann, ASD (LFA, HFA, Goodlin-Jones, & 7.5-18.5 ASP) MRI Ù Ù Ù Amaral, 2009) TD ASD (Webb et al., 2009) 3-4 DD MRI Ø Ø Ù TD ASD (Wegiel et al., 2013) 4-66 IHCa - - Ø TD ASD (Riva et al., 2013) 2-10 VBM Ù Ù Ø TD ASD Stereological (Skefos et al., 2014) 5-56 - Ù Ø TD Assessmenta ASD (D’Mello et al., 2015) 8-12 VBM - - × TD Meta-Analyses ASD (Stanfield et al., 2008) 3-30 MRI Ù Ø Ø TD ASD (Stoodley, 2014) 6.5-30 ADHD VBM Ù Ù Ø DYS

Correlation Cerebellar Vermis ASD-like Area, Volumetric Measures, or PC density vs. Study Behaviors ASD Behaviors ØTotal ØI-V ØVI-VII ØVIII-X Vermis Exploratory - - Ø - (Pierce & Behaviors Courchesne, 2001) Repetitive - - × - Movements Social Ø - - - Communication (Webb et al., 2009) Motor Ø - - - Function Reciprocal (Riva et al., 2013) Social - - - Ø Communication Social Eye (Skefos et al., 2014)a - - - Ø Contact Social - Ø Ø - Interaction Stereotyped - - × × (D’Mello et al., Behaviors 2015) Repetitive - - × × Behaviors Communication - - Ø -

1.2.3.2 Posterolateral Cerebellum – Crus I/II

Structural deficits in the posterolateral cerebellum have primarily been localized to the right Crus I (RCrus I) region (Stoodley, 2014; Yang et al., 2016a). Rcrus I gray matter volume reduction has been the most consistent finding in the cerebrocerebellar region (D’Mello et al.,

2015; Rojas et al., 2006; Stoodley, 2014; Wilson, Tregellas, Hagerman, Rogers, & Rojas, 2009;

Yang et al., 2016). However, other regions including bilateral Crus I and II have also been implicated in ASD (Table 6). Evidence for gray matter volumetric reduction in Crus I and II of the ansiform lobules may explain deficits in higher level cognitive, planning, language, and social behaviors in ASD (Stoodley, 2014; Stoodley, MacMore, Makris, Sherman, &

Schmahmann, 2016; Stoodley & Schmahmann, 2009). Rcrus II is another region with several studies reporting gray matter reductions in ASD groups compared to controls (D’Mello et al.,

2015; Riva et al., 2013; Wilson et al., 2009). The volumetric reduction in gray matter in the Crus lobules appear to be right lateralized, however, gray matter reduction has been reported in left

Crus I (LCrus I) (Rojas et al., 2006) and LCrus II (Riva et al., 2013). Two meta-analyses – that included 745 participants with ASD in a combined 29 studies – showed that Rcrus I has reduced gray matter volume in individuals with ASD compared to TD controls (Stoodley, 2014; Yang et al., 2016). Furthermore, in individuals with ASD, reduced gray matter volume correlated with reduced social and communication scores (D’Mello et al., 2015) and increased repetitive and stereotyped behaviors (D’Mello et al., 2015; Rojas et al., 2006) (Table 7). However, social and communication deficits in ASD groups have also been related to gray matter volume reduction in bilateral Crus II (D’Mello et al., 2015; Riva et al., 2013) with increased repetitive behaviors related to gray matter volume reduction in bilateral lobules VIIIB and Rcrus II (D’Mello et al.,

2015). Furthermore, significant reduction in PC density in Rcrus I/II, but not for lobules IV-VI, has been observed in post-mortem study of ASD cerebella vs. controls (Skefos et al., 2014).

Additional support for L/R Crus I structural deficit comes from a meta-analysis of resting-state fMRI in individuals with ASD that showed bilateral Crus I hypoactivation (Wang et al., 2018).

Laidi et al. (2017), did not find any significant differences in R/L Crus I between adults with

ASD and TD controls, however in the ASD group, R/L Crus I volume was significantly

correlated with eye avoidance behavior (Laidi et al., 2017). Together, these findings show that structural deficits in the posterolateral cerebellum are related to severity of core ASD symptoms with the most consistent finding being reduced gray matter volume in Rcrus I. The following section will discuss a novel technique that has enabled researchers to selectively silence PCs in cerebellar regions thought to be implicated in ASDs (Badura et al., 2018; Stoodley et al., 2018).

Findings from these studies provide direct evidence that PC silencing in Rcrus I/II and hemispheric lobules VI and VII reproduces the ASD-specific behavioral phenotypes in mouse models.

Table 6: ASD-specific structural deficits in cerebellar hemispheres; LF-ASD=low- functioning ASD; FSX=Fragile X syndrome; ASDELD=ASD with expressive language delay; MRI=Magnetic Resonance Imaging; VBM=voxel-based morphometry. (×=increase from controls; Ù=no change from controls; Ø=decrease from controls; -=not measured). aPurkinje cell density assessed.

ASD vs. Controls Study ASD-Specific Findings Cerebellar Hemisphere Volumetric Measures Age Measurement Original Studies Groups RcrusI LcrusI RcrusII LcrusII (years) Tool (Rojas et al., ASD 7-44 VBM Ø Ø Ù Ù 2006) TD

ASD (Wilson et al., ~ 30 FXS VBM Ø Ù Ø Ù 2009) TD

LF-ASD (Riva et al., 2013) 2-10 VBM Ù Ù Ø Ø TD (Skefos et al., ASD Stereological 5-56 Ø - Ø - 2014) TD Assessmenta (D’Mello et al., ASD 8-13 VBM Ø Ù Ø Ù 2015) TD (D’Mello, Moore, ASD Crocetti, 8-13 ASD VBM Ø Ù Ø Ù Mostofsky, & ELD Stoodley, 2016) TD

(Laidi et al., ASD 18-64 Structural MRI Ù Ù - - 2017) TD

Meta-Analyses

ASD

(Stoodley, 2014) 6.5-30 ADHD VBM Ø Ù Ù Ù

DYS

ASD (Yang et al., 2016) ~ 32 VBM Ø - - - COM

Correlation Cerebellar Hemisphere

Area or Volumetric Measures vs. ASD Behaviors ASD-like Study ØRcrus I ØLCrus I ØRcrus II ØLCrus II Behaviors Repetitive and (Rojas et al., 2006) Stereotyped × - - - Behaviors Reciprocal Social (Riva et al., 2013) - - Ø Ø Communication Social and Communication Ø - Ø - Scores (D’Mello et al., 2015) Repetitive and Stereotyped × - × - Behaviors Eye Avoidance (Laidi et al., 2017) × × - - Behaviors

1.2.4 Insights from Chemogenetic-Mediated Neuron Silencing Techniques of Purkinje Cells in the Cerebellum

Based on findings from D’Mello et al. (2015) and Stoodley et al. (2014), Rcrus I has been proposed as a neuroanatomical correlate for ASD-specific behaviors. A recent comprehensive investigation of Rcrus I showed that this region, but not LCrus I, mediates ASD-related behaviors and is functionally connected with left inferior parietal lobule (left-IPL) (Stoodley et al., 2018) (Figure 2). Stoodley et al. (2018), showed that individuals with ASD, and a Tsc1 ASD- mutant mouse model, show abnormal connectivity between Rcrus I and left-IPL that is restored using chemogenetic-mediated excitation of PCs in Rcrus I. Following stimulation of PCs in

Rcrus I, the Tsc1 ASD-mutant mouse model showed an increase in preference for social novelty without any changes in gross motor, vision, and repetitive behaviors (Stoodley et al., 2018). No 22

changes in social behaviors were observed when LCrus I was stimulated, demonstrating that changes in social behaviors were specific to Rcrus I. These findings show that Rcrus I mediate the social communication deficits in ASD, and stimulation of the PCs in this region of an ASD- mutant mouse model rescues social impairment. However, stimulation of PC activity in Rcrus I did not rescue repetitive behaviors in the Tsc1 mouse model (Stoodley et al., 2018). This finding provides evidence that repetitive behaviors may be mediated by different circuitry, possibly involving basal ganglia (Wilkes & Lewis, 2018). Furthermore, the fact that stimulation of PCs in

Rcrus I rescues behavioral deficits in ASD may support the use of non-invasive neuromodulation techniques in individuals with ASD (discussed in Section 1.6).

Figure 2: (A) ASD-patients and the Tsc1 ASD-mouse model showed abnormal connectivity between Right Crus I (RCrusI) and Left Inferior Parietal Lobule (left-IPL). (B) Designer receptors exclusively activated by designer drugs (DREADD) mediated inhibition of Purkinje Cell (PC) produced (1) decreased PC firing rate, (2) increased left-IPL firing rate, and (3) increased ASD-related behaviors. DREADD-mediated RCrusI PC excitation in a Tsc1 ASD-mouse model (1) increased PC firing rate, (2) decreased left-IPL firing rate, and (3) increased social preference.

A study by Badura et al. (2018), used a similar chemogenetic perturbation method as

Stoodley et al. (2018), although cerebellar inhibitory interneurons were targeted instead of PCs.

Furthermore, whereas Stoodley et al. (2018) targeted right and left CrusI, Badura et al. (2018) targeted multiple posterior cerebellar regions implicated in ASD (lobules VI, VII, RCrus I/II).

Their findings showed that novelty seeking, exploratory, and repetitive grooming behaviors were regulated by lobule VII, whereas social behaviors were regulated by RCrus I/II (Badura et al.,

2018). Furthermore, supporting the findings of Stoodley et al. (2018), RCrus I was also involved in novelty seeking behaviors in conjunction with lobule VII. Badura et al. (2018) also showed significant involvement of RCrus I on cerebellar-mediated learning via eyeblink conditioning

(Badura et al., 2018). Together, Stoodley et al. (2018) and Badura et al. (2018) – using different neuronal targets to silence PC output – demonstrate that chemogenetic-mediated modulation of

PC excitability in cerebellar regions implicated in ASD reproduce the ASD-specific behavioral phenotype in mouse models.

Dysfunctional connectivity between RCrus I and left-IPL may explain findings from psychology and motor control paradigms showing ASD impairments in imitation (Jack,

Englander, & Morris, 2011), praxis (Dewey, Cantell, & Crawford, 2007; Mahajan, Dirlikov,

Crocetti, & Mostofsky, 2016), sensorimotor integration (Proville et al., 2014), visuomotor integration (Mosconi et al., 2015; Wang et al., 2015), biological motion processing (Jack, Keifer,

& Pelphrey, 2017), language comprehension (Bzdok et al., 2016; Lesage, Hansen, & Miall,

2017; Stoodley & Schmahmann, 2009), and facial processing (Rigby, Stoesz, & Jakobson,

2018). Crus I also participate in higher order cognitive functions and is strongly connected with prefrontal cortex (Balsters et al., 2010; Stoodley & Schmahmann, 2009). The IPL and posterior superior temporal sulcus (pSTS) are functionally connected with one another and comprise the

mirror neuron system (MNS) (Iacoboni & Dapretto, 2006). Crus I is also functionally connected to both structures (Jack et al., 2017; Jack & Morris, 2014). Jack & Morris (2014), showed that connectivity between RCrus I and the MNS is significantly associated with Theory of Mind deficits in children with ASD (Jack & Morris, 2014). Furthermore, ASD-specific increases in left-IPL gray matter volume (Mahajan et al., 2016) and reduced white matter integrity (Yang et al., 2018) compared to TD controls are also observed, supporting dysfunctional circuitry between left-IPL and RCrus I. Reduced white matter volume in the left superior longitudinal fasciculus, a tract that connects left-IPL to premotor region, is also observed in children with ASD and is related to the severity of motor deficits (Hanaie et al., 2016) and sensory processing disorder in

ASD (Pryweller et al., 2014). Abnormally greater activation in left-IPL has also been observed during a task involving object recognition and location detection (DeRamus, Black, Pennick, &

Kana, 2014). Finally, significant increases in gray matter volume in the left primary somatosensory cortex (S1) has also been shown to be specific to ASD diagnosis (Mahajan et al.,

2016) and may contribute to abnormal sensory sensitivity (Riquelme, Hatem, & Montoya, 2016), action-perception coupling (Stewart H. Mostofsky & Ewen, 2011), and fine-motor deficits

(Mahajan et al., 2016; Riquelme et al., 2016) in children with ASD. The IPL, S1, and M1 comprise the frontal-parietal network and anatomical abnormalities within one region of this network (ex. IPL) may contribute to deficits to other interconnected regions. Increased gray matter volume within the left IPL (Mahajan et al., 2016) – that may be produced from RCrus I structural abnormality and reduced inhibitory projections onto left-IPL – combined with abnormal gray matter volume within the left S1 in children with ASD (Mahajan et al., 2016) may explain findings of visuomotor integration deficits and sensory hypo/hypersensitivity that are commonly reported in children with ASD. The IPL and S1 are also functionally connected with

one another and S1 exchanges somatosensory information with IPL that is important for action- perception during action-observation (Keysers, Paracampo, & Gazzola, 2018; Valchev, Gazzola,

Avenanti, & Keysers, 2016). To better understand whether deficits within the action-perception network originate from structural deficits in RCrus I, chemogenetic silencing techniques on

RCrus I PCs in animal models (Badura et al., 2018; Stoodley et al., 2018) should be combined with an evaluation of other interconnected brain regions implicated in ASD diagnosis – such as

S1 (Mahajan et al., 2016). Therefore, experiments that build on the findings from Stoodley et al.

(2018) and Badura et al. (2018) may help determine whether structural deficit in RCrus I can explain the ASD-specific neurophenotype and behavioral phenotype observed in children with

ASD.

Although RCrus I is identified as a neuroanatomical correlate for social behaviors in

ASD (D’Mello et al., 2015, 2016; Stoodley, 2014; Stoodley et al., 2018), LCrus I deficits may also contribute to social and communication deficits. LCrus I deficits may contribute to early language delay (ELD) in ASD (D’Mello et al., 2016), Theory of Mind deficits (Kana et al.,

2015), and mirror neuron system (MNS) deficits via its projections to RpSTS – albeit to a lesser extent than RCrus I (Jack & Morris, 2014; Sokolov, 2018; Sokolov et al., 2012). However, findings suggest that children with ASD show stronger rightward lateralization of motor, language, auditory, sensorimotor, visual, executive, attentional, and visuospatial circuit connectivity in right-handed individuals compared to TD controls – with the degree of lateralization correlated with the severity of ASD symptoms (Cardinale, Shih, Fishman, Ford, &

Müller, 2013; Floris et al., 2016). These findings point to left hemispheric dysfunction in ASD

(Fein, Humes, Kaplan, Lucci, & Waterhouse, 1984). Left hemispheric dysfunction may explain the strong rightward lateralization and the dysfunction of left IPL and RCrus I circuitry in

individuals with ASD. Future research should examine the region specificity of cerebellar dysfunction to examine whether the right-laterality of Crus I dysfunction is a consistent finding in ASD, or a specific neurophenotype of ASD (Peterson et al., 2006; Segovia et al., 2014).

In conclusion, Stoodley et al. (2018) provides convincing evidence that RCrus I mediates social communication deficits in ASD and affects upstream activity with left-IPL possibly leading to sensorimotor integration deficits. These findings support data from D’Mello et al.

(2015) and Stoodley et al. (2014), showing RCrus I gray matter reduction (D’Mello et al., 2015;

Stoodley, 2014) and left-IPL/S1 structural abnormalities (Hanaie et al., 2016; Mahajan et al.,

2016) co-occur and are specific to ASD diagnosis. Furthermore, Stoodley et al. (2018) provides evidence that (1) cerebellar tDCS (ctDCS) increases functional connectivity between RCrus I and left-IPL areas and that (2) modulating the activity of PCs in RCrus I rescues social behaviors in an ASD-mutant mouse model (Stoodley et al., 2018). These findings provide the foundation for investigations into the effects of ctDCS of RCrus I as a potential non-pharmaceutical treatment for children with ASD. Lastly, repetitive behaviors were not found to be mediated by

RCrus I, however, findings from Badura et al. (2018) show lobule VII may mediate deficits in novelty-seeking, exploratory, and repetitive behaviors (Badura et al., 2018). Furthermore, lobule

VII is strongly connected with the indirect pathway of the basal ganglia and imbalances in activity between direct and indirect pathways of basal ganglia may contribute to repetitive behaviors in ASD (Wilkes & Lewis, 2018).

1.2.5 Basal Ganglia and Cerebellum Communicate and Mediate Repetitive Behaviors in ASD

One of the core behaviors within the ASD-phenotype are repetitive behaviors that may also include self-injurious behaviors. Self-injurious behaviors are reported in approximately 50%

of ASD cases (Baghdadli, Pascal, Grisi, & Aussilloux, 2003). In the previous section, RCrus I was identified as a structure that mediates social and communication deficits in ASD (Badura et al., 2018; Stoodley et al., 2018). Furthermore, lobule VII (Badura et al., 2018), posterior vermis

(Pierce & Courchesne, 2001), and Crus I/II (D’Mello et al., 2015) were also related to repetitive and novelty-seeking behaviors in ASD. However, the role of the cerebellum as a mediating structure for repetitive behaviors in ASD is not clear. It is more likely that repetitive behaviors in

ASD are produced by imbalances between movement facilitation and inhibition that are mediated by the direct and indirect pathways of the basal ganglia, respectively (Wilkes & Lewis,

2018). In support of the role of basal ganglia in mediating repetitive behaviors, Estes et al.

(2011) found that lower basal ganglia volumes were significantly related to repetitive behaviors in children with ASD (Estes et al., 2011). Furthermore, high-frequency stimulation of subthalamic nuclei (STN) – a structure within the indirect pathway – reduces repetitive behaviors without modulating social behaviors in ASD-mutant mouse models (Chang et al., 2016).

Cerebellum morphology may be related to repetitive behaviors in ASD since the indirect pathway receives input from the cerebellar hemispheres (Hoshi et al., 2005) and is also the target of projections from subthalamic nuclei (STN) (Bostan et al., 2010; Milardi et al., 2016). The evidence discussed in the current section will argue that the basal ganglia, specifically the subthalamic nuclei (STN) of the indirect pathway, mediates repetitive behaviors in ASD.

Evidence of connections between the cerebellum and the basal ganglia will also be discussed to explain previous findings of cerebellar structural deficits correlating with repetitive behaviors in

ASD.

Using retrograde transneural transport of rabies virus into putamen of the striatum and external globus pallidus (GPe), Hoshi et al. (2005) found disynaptic and trisynaptic connections

from cerebellar deep nuclei (DCN) to the indirect pathway of basal ganglia (Hoshi et al., 2005).

Injections into putamen were transported to the thalamus (1st-order neuron) and then to the DCN

(2nd-order neuron) (distribution of virus: 67% dentate, 29% interpositus, and 4% fastigal).

Injections into GPe resulted in transneural transport to the striatum (1st-order neuron), the thalamus (2nd-order neuron), and to contralateral DCN (3rd-order neuron) with very similar distributions of the virus in DCN (distribution of virus: 69% dentate, 14% interpositus, and 17% fastigal) (Hoshi et al., 2005). GPe projects to subthalamic nuclei – a structure part of the indirect pathway that excites inhibitory input to thalamus via GPi and SNr – and therefore influences activity of the indirect pathway. In the indirect pathway, (1) GABAergic neurons in the striatum receive glutamatergic inputs, (2) GABAergic output from striatum onto GPe is increased, (3)

GABAergic output to STN from GPe is reduced, and (4) glutamatergic output from STN onto

GABAergic GPi and substantia nigra pars reticulata (SNr) increases GABAergic output onto thalamic nuclei thereby reducing glutamatergic excitatory output from the thalamus to cortex

(Alexander & Crutcher, 1986). Therefore, the function of the indirect pathway in motor circuitry is to fine tune movements via increased inhibitory tone on the thalamus mediated by STN output onto GPi and SNr.

The connections between cerebellum are not one-way connections since the basal ganglia also project to cerebellum via STN (Bostan et al., 2010). Using the same retrograde transneural transport technique as Hoshi et al. (2005), Bostan et al. (2010) injected rabies virus into the cerebellar cortex of monkeys (Crus II and hemispheric lobule HVIIB). The virus travelled to pontine nuclei (2nd-order neuron) and to STN (1st-order neuron) (Bostan et al., 2010). This finding shows that the indirect pathway of basal ganglia projects to cerebellar cortex and can modulate its output. In humans, Milardi et al. (2016) confirmed the existence of the (1) STN-

ponto-cerebellar pathway in humans and the dento-thalamo-striato-pallidal (GPe) pathway using diffusion weighted tractography (DTW) (Milardi et al., 2016). Milardi et al. (2016) also identified direct connections between (1) dentate nuclei to ipsilateral substantia nigra (SN)

(dento-nigral pathway) and (2) dentate nuclei to globus pallidus interna (GPi) (dento-pallidal pathway), although direction of pathways could not be inferred using DWT (Milardi et al.,

2016). A summary of these findings is illustrated in Figure 3, that shows how the cerebellar hemispheres may modulate basal ganglia (ascending pathway) and how the cerebellar hemispheres may be modulated by basal ganglia (descending pathway) (Figure 3).

A more recent study from Jwair et al. (2017) found significantly more STN neurons labelled with rabies virus following injections into vermal lobule VII compared with injections in the cerebellar hemisphere (Crus IIb). Although their findings confirm the existence of a disynaptic connection between STN and cerebellar cortex, initially reported in Bostan et al.

(2010), vermal lobule VII may be a more prominent target of STN compared to the cerebellar hemisphere (Crus II) (Jwair et al., 2017). If the indirect pathway of the basal ganglia has reduced activation in ASD via reduced cerebellar output and ascending pathway activity, it is possible that this may promote reduced indirect pathway activity via STN to cerebellar cortex.

Hypothetically, the reduced activity of STN to cerebellar cortex (Crus II and vermal lobule VII) may then produce structural/functional abnormalities in the posterior superior vermis (VI-VII).

Structural abnormalities in the posterior superior vermis (VI-VII) were discussed in earlier sections as the most prevalent neuroanatomical finding in the cerebellar vermis of ASD patients

(Stanfield et al., 2008). Furthermore, reduced area and gray matter volume of the posterior superior vermis (VI-VII) is related to core ASD symptoms including repetitive behaviors

(D’Mello et al., 2015; Pierce & Courchesne, 2001). In addition to evidence provided from transneural transport studies, the existence of the descending pathway is also supported by the finding that high-frequency stimulation of STN normalizes ipsilateral cerebellar activation in patients with Parkinson Disease (PD) (Grafton et al., 2006). Furthermore, activation in cerebellar cortex granule cells are also observed in response to reward presentation – a primary function of basal ganglia – revealing a role of cerebellum in reward-anticipation (Wagner, Kim, Savall,

Schnitzer, & Luo, 2017). Together, these findings show that cerebellar projections to the indirect pathway of the basal ganglia are not one-way. The STN of the indirect pathway projects to Crus

II, paravermal region of VII, and vermal lobule VII with most fibers projecting to vermal lobule

VII of the posterior superior vermis (Bostan et al., 2010; Hoshi et al., 2005; Jwair et al., 2017).

With evidence supporting the existence of connections between the cerebellum and the indirect pathway of basal ganglia reviewed, the following paragraphs will discuss the role of the indirect pathway in mediating repetitive behaviors in ASD.

Evidence for the role of the STN in stereotypy and repetitive behaviors in ASD have been shown in several mouse models (Chang et al., 2016; Tanimura, King, Williams, & Lewis, 2011;

Tanimura, Vaziri, & Lewis, 2010). In Chang et al. (2016), high-frequency stimulation (HFS) of the STN suppressed repetitive behaviors in two ASD-mouse models (Mecp2 and Shank3). In both ASD-mouse models, STN-HFS did not alter social interaction deficits but did significantly reduce repetitive behaviors (Chang et al., 2016). STN HFS in PD patients also does not alter social cognitive abilities, showing its effects are specific to repetitive behaviors (Enrici et al.,

2017). The mechanism responsible for reduced repetitive behaviors in the ASD-mouse models following STN-HFS is likely the enhanced STN excitation of GABAergic SNr and GPi outputs onto thalamic nuclei (indirect pathway). Increased activity of SNr and GPi would reduce abnormal thalamic spike patterns leading to suppression of repetitive behaviors. Further evidence of the role of STN on repetitive behaviors comes from studies assessing STN metabolic activity in deer mice with high rates of stereotypic behavior (Tanimura et al., 2011, 2010). Tanimura et al. (2010, 2011), found that metabolic activity in the indirect pathway (SNpr, SNpc, STN), assessed by cytochrome oxidase (CO), was significantly correlated with severity of repetitive behaviors in the mice (Tanimura et al., 2011, 2010). Together, these findings show the indirect pathway of the basal ganglia may mediate repetitive behaviors in individuals with ASD.

However, what role does the cerebellum play in abnormal indirect pathway activity?

Abnormal indirect pathway activity may be related to developmental diaschisis from late- onset PC death in the cerebellum (Wang, Kloth, & Badura, 2014). Future studies should examine the effects of neuronal silencing in RCrus I/II of the cerebellum on STN in developing mice.

Such studies would clarify the role of RCrus I on the development of the ASD-specific behavioral phenotype and upstream effects on other cortical structures during postnatal development. The findings may further the hypothesis of cerebellar mediated developmental diaschisis in ASD (Wang, Kloth, & Badura, 2014) and explain cerebellum structure correlations with repetitive behaviors in ASD.

1.2.6 Concluding Remarks

In this section, evidence supporting the role of cerebellum in mediating the ASD-specific behavioral phenotype has been reviewed. The evidence reviewed supports late-onset PC death and abnormal PC morphology as the most consistent neuroanatomical findings in ASD.

Furthermore, the PC-specific contribution to the ASD-specific behavioral phenotype has also been confirmed using ASD-mutant mouse models. ASD-mutant mouse models with PC-specific gene mutations reproduce findings of (1) late-onset PC death and (2) ASD-specific behaviors. To confirm the region specificity of cerebellar contributions to the ASD-specific behavioral phenotype, meta-analyses of ASD MRI imaging studies and chemogenetic methods in mice showed: (1) RCrus I/II mediate ASD-specific social behaviors and (2) lobule VII mediates ASD- specific repetitive and exploratory behaviors (Badura et al., 2018; Stoodley, 2014; Stoodley et al., 2018). Further evidence supports reduced activation of the STN in repetitive behaviors in

ASD, that may be mediated by reduced cerebellar output to the indirect pathway. With specific targets within the cerebellum identified that mediate the ASD-specific behavioral phenotype, targeted interventions that modulate the activity of these regions should be examined.

Furthermore, considering the overlap of cerebellar and basal ganglia structures implicated in

ASD with motor circuitry, motor tests that assess the integrity of these regions may improve diagnostic tests for ASD and provide reliable assessments of improvement in these circuits following interventions.

1.3 Visuomotor Integration Deficits in ASD

The previous section identified structural deficits of the right posterolateral cerebellar cortex, STN of basal ganglia, and the left IPL as possible neuroanatomical correlates of core

ASD symptomology. The IPL and the posterolateral cerebellum overlap with higher order cognitive and motor circuitry and may explain the strong correlations observed between motor and social and communication deficits in children with ASD (Hirata et al., 2015; Mody et al.,

2017). Furthermore, the IPL and posterolateral cerebellum are functionally connected (Clower,

West, Lynch, & Strick, 2001; Glickstein, 2000; Moulton et al., 2017; Stoodley et al., 2018). One of the main functions of IPL is sensory-motor transformations (Cohen & Andersen, 2002) that, when combined with outgoing motor commands from M1, allow the cerebellum to predict future sensory consequences through forward modelling (Coltz, Johnson, & Ebner, 1999; Liu, 2002;

Pasalar, Roitman, Durfee, & Ebner, 2006; Roitman, 2005). The cerebellum also detects errors between predicted and actual sensory consequences via climbing fiber projections from the inferior olive that may recalibrate sensory representations in IPL (Clower et al., 1996, 2001;

Crottaz-herbette, Fornari, & Clarke, 2014; Küper et al., 2014; Martin, Keating, Goodkin,

Bastian, & Thach, 1996; Rossetti et al., 1998) and forward models within the cerebellum (Rondi-

Reig, Paradis, Lefort, Babayan, & Tobin, 2014; Tseng, Diedrichsen, Krakauer, Shadmehr, &

Bastian, 2007).

When planning a goal-directed movement to the location of a target in space, the initial position of the effector must be mapped into a common reference frame. The IPL integrates visual, somatosensory, auditory, and vestibular inputs (Andersen, 1997) and therefore can compute the spatial location of targets and initial effector locations in an eye-centered common reference frame (Cohen & Andersen, 2002). Therefore, the IPL is important for motor planning and providing state estimates. Initial state estimates (IPL) may be combined with outgoing motor commands (primary motor cortex - M1) in a forward model – located within the cerebellum – to predict future sensory states (Ishikawa, Tomatsu, Izawa, & Kakei, 2016; Rondi-Reig et al.,

2014). Direct evidence shows that cerebellar output commands feedback to cerebellar cortex

(Crus I/II) via a corollary discharge pathway and may combine with afferent sensory input for predictive state estimate computations (Houck & Person, 2015). Predicted state estimates – computed within the cerebellum – can then be compared to intended actions from premotor (PM) areas to update motor commands that achieve the desired state (inverse model). Current evidence suggests that the cerebellum does not participate in producing motor commands, with its output being consistent with participation in forward modelling (Coltz et al., 1999; Liu, 2002; Roitman,

2005; Yavari et al., 2016a). Single cell recordings of PCs in monkeys show that PC discharges are not related to muscle activation, showing that these PCs do not participate in the inverse model (Coltz et al., 1999; Pasalar et al., 2006; Roitman, 2005). However, PCs in the lateral and posterolateral cerebellum are modulated approximately ~100ms prior to the onset of movement and are tuned to the predicted position and direction of movement (Coltz et al., 1999; Liu, 2002;

Roitman, 2005). Together, these findings show that the cerebellum likely does not participate in inverse modelling of movement but provides sensory predictions via forward modelling to modulate motor output in the absence of delayed sensory feedback (Figure 4).

Figure 4: Proposed internal model adapted from Desmurget et al. (2000) and Ishikawa et al. (2016). Afferent sensory input from inferior parietal lobule (IPL) enters contralateral cerebellar cortex via projections from pontine nuclei (PN). Sensory input is integrated with efferent copy of outgoing motor command by forward model. Errors in forward model predictions are computed by comparison of actual vs. predicted sensory states in inferior olive (IO). IO provides teaching signal to forward model via climbing fiber inputs to individual PCs inducing long-term depression (LTD) on PCs contributing to prediction error. Predicted sensory states from the forward model are integrated with desired sensory states in an inverse model that generates motor commands to achieve desired states. An “X” over the inverse model is used to highlight that cerebellum output does not mediate changes in muscle activation and therefore may not be involved in the inverse model (Coltz et al., 1999; Pasalar et al., 2006; Roitman, 2005; Yavari et al., 2016b). However, simple spike output from several PCs predict future sensory states (position, movement direction, and speed) ~100ms prior to movement onset showing involvement in forward model computations (Coltz et al., 1999; Liu, 2002; Roitman, 2005).

Combined involvement of IPL and posterolateral cerebellum suggest that visuomotor integration deficits should be a hallmark finding in individuals with ASD. In support of this hypothesis, several studies have identified deficits in visuomotor functions in ASD in tasks involving the IPL and the cerebellum (Table 8, Appendix 1). Furthermore, in support of our hypothesis (Figure 1), deficits in visuomotor processing should be related to ASD-specific social and communication deficits due to overlapping circuitry in the posterolateral cerebellum. A summary of visuomotor integration deficits in individuals with ASD including the neuroanatomical structures that mediate performance in each task is shown below (Table 8,

Appendix 1).

In individuals with ASD, motor function is strongly linked to social function providing evidence that motor networks implicated in the core ASD symptomology of social and communication deficit (Hirata et al., 2015; Mody et al., 2017). Based on evidence that will be presented later in this section, visuomotor impairments appear disrupted in individuals with

ASD. Visuomotor integration deficits implicate: (1) the mirror neuron system (MNS) and (2) the posterolateral cerebellum. The circuitry implicated in visuomotor integration deficits in ASD is shown below (Figure 5).

Figure 5: Right posterolateral cerebellum (RCrusI/II) and left Mirror Neuron System (IPL, pSTS, and PMv) circuitry that may be implicated in visuomotor integration deficits in ASD. Red = output from dentate nuclei (DN); Blue = input to cerebellum via pontine nuclei (PN). (Clower et al., 2001; Iacoboni & Dapretto, 2006; Rozzi, Ferrari, Bonini, Rizzolatti, & Fogassi, 2008; Stoodley et al., 2018).

1.3.1 Mirror Neuron System Dysfunction in ASD

The mirror neuron system (MNS), is a network of neurons, located in the pSTS, IPL, and

PMv (Iacoboni, 2009). The MNS processes visual input of other’s motor actions and allows for the observer to understand the performers intentions through activation of the associated motor networks in the observer’s brain. The cerebellum is also a structure involved in action- observation processing and is functionally connected to pSTS and IPL (Jack & Morris, 2014;

Kana et al., 2015). The MNS is important for learning through observation and understanding the intentions of others – both of which are important for learning motor skills and engaging in quality social interactions. Deficits in MNS function have been shown in children with ASD using electrophysiological measures (Gizzonio et al., 2015; Kana et al., 2015; Kaur et al., 2018;

Oberman et al., 2005, 2008) and impaired long-range connectivity in the brains of individuals with ASD may also affect integration of visual and motor areas (Aoki, Abe, Nippashi, &

Yamasue, 2013; Kana et al., 2015; Nebel et al., 2016).

It is suggested that during action-observation, the pSTS, a higher order visual processing area (Grossman & Battelli, 2005; Jack et al., 2011), provides visual descriptions of the actions of others and integrates this information with the IPL to code these actions into the motor domain.

The mirror neuron areas in the IPL then integrate this information with the PMv mirror neurons that interpret the goal of the action. Evidence suggests the mirror network is important for social function, with increased activation in the mirror network positively correlated to empathic concern scores (Kaplan & Iacoboni, 2006). The following sections will discuss the MNS as it relates to (1) biological motion processing, (2) imitation, and (3) praxis, all of which may mediate impaired social and communication behaviors in individuals with ASD.

Biological Motion Processing

Deficits in imitation and performance of skilled gestures may be linked to biological motion processing deficit, possibly implicating the pSTS in the disorder (Blake, Turner, Smoski,

Pozdol, & Stone, 2003; Freitag et al., 2008; Nackaerts et al., 2012; Robertson et al., 2014;

Thurman, van Boxtel, Monti, Chiang, & Lu, 2016; Yang et al., 2016b). Rigby and colleagues

(2018), observed adults with ASD show deficits in processing dynamic expressive faces compared to IQ matched TD controls, with prolonged facial recognition response times related to social deficits and reduced empathetic skills in the ASD group (Rigby et al., 2018). Furthermore, there was no difference in response times between the ASD and TD groups when responding to static facial expressions. These findings are in line with Lainé et al. (2011), that found when biological motion speed is reduced, children with ASD improve imitation abilities of body movements and facial expressions (Lainé et al., 2011).

Recent evidence shows cerebellar involvement in social processes during biological motion perception tasks (Sokolov et al., 2012). In an fMRI study on 15 children with ASD and

TD controls, Jack & Morris (2014) showed reduced connectivity between Crus I and pSTS that was positively associated with social deficits (Jack & Morris, 2014). Furthermore, Jack et al.

(2017) found cerebellar Crus I/II are involved in action-observation processes, with reduced activation during biological motion perception predictive of social impairment in individuals with ASD (Jack et al., 2017). These findings provide evidence that posterolateral cerebellum deficits might be linked to abnormal pSTS activation during biological motion processing.

Furthermore, the cerebellum and IPL are also structurally connected with one another

(Blakemore & Sirigu, 2003; Clower et al., 2001) and may contribute to biological motion processing deficits observed in individuals with ASD via shared circuitry with pSTS.

Deficits in the MNS may also be responsible for some of the motor and social deficits observed in ASD. If children with ASD have difficulty interpreting the actions of others and integrating the visual input of the actions of others into motor commands, deficits in action imitation are likely to occur. Furthermore, difficulties interpreting the actions and intentions of others may impair the ability to empathize with others (Rigby et al., 2018). To examine MNS function during action-observation, Oberman and colleagues (2005) used EEG measures of mu wave suppression in the PMv region in 11 individuals with ASD and 13 TD controls (6-47 years)

(Oberman et al., 2005). The ASD participants showed a normal mu wave suppression response when observing their own hand performing a grasp; however, an abnormally reduced mu wave suppression response was observed during action-observation of a stranger’s hand. In a follow- up study using younger participants (8-12 years), participants with ASD showed the same abnormal mu wave suppression response to action-observation of a stranger’s hand and normal response to their own hand movements (Oberman et al., 2008). However, there was no difference between the ASD and TD groups when observing hand actions from a familiar performer. These findings show abnormal interpretation or processing of visual information of other’s actions implicating a functional deficit in the MNS. Enticott and colleagues (2012), used a different transcranial magnetic stimulation (TMS) paradigm to assess mirror neuron activity deficit in 34 adults with ASD compared to TD controls and found similar deficits in the MNS (Enticott et al.,

2012). Electromyography recordings of the FDI (contralateral to stimulated hemisphere) showed a significant reduction in percent change in the motor evoked potential (MEP) elicited by TMS stimulation when participants viewed videos of goal-directed hand movements. Furthermore, a larger percent change in MEP (a more typical response to action-observation) was associated with higher social function scores, suggesting deficits in the MNS may contribute to more severe

ASD symptoms. Together, these findings show deficits in the MNS of individuals with ASD, and that abnormal MNS function during action-observation mediates social and communication deficits of the disorder. The following section will assess performance of individuals with ASD during action-imitation tasks that involve transforming sensory-input from observed actions into motor commands (inverse model).

Imitation and Praxis

Behavioral studies have identified ASD specific action-imitation deficits that may be linked to an impaired MNS function (DeMyer et al., 1972; Dziuk et al., 2007; Gizzonio et al.,

2015; Kaur et al., 2018). Some of the earliest reports of body imitation and motor-object imitation deficits came from DeMyer (1972), and showed that children with ASD were significantly worse at imitation compared to TD controls (DeMyer et al., 1972). It is possible, however, that the deficits observed in imitation and praxis result from motor skill deficits that are highly prevalent in children with ASD (Green et al., 2009). To rule out this effect, Dziuk et al.

(2007) assessed motor skill and praxis (motor planning of complex skills) scores in 43 children with ASD and 47 TD controls (8-14 years) to determine whether basic motor skill accounted for deficits in praxis in children with ASD. Basic motor skill could not fully account for the praxis deficits observed in children with ASD compared to TD controls (Dziuk et al., 2007).

Furthermore, praxis scores in children with ASD were significantly correlated to social and communication deficits. Together these results suggest deficits in the mirror network may be characteristic of ASD diagnosis and contribute to the core social and communication deficits of the disorder.

Gizzonio et al. (2015), also studied imitation and praxis deficits in children with ASD (7-

17 years) compared to their siblings and TD children (Gizzonio et al., 2015). They found praxis

errors during pantomime (imagine use of object and demonstrate the action) to: (a) verbal command and (b) imitation of the experimenter. Errors were most prominent during the (a) pantomime to verbal commands and imitation, and (b) imitation of meaningless gestures performed by the experimenter. These findings show that imitation of meaningless gestures is more impaired than imitation of symbolic gestures (ex. “wave good-bye”). During imitation of meaningless tasks, the child had to imagine (“visualize”) the movement sequence and execution of the movement sequence. In the pantomime action of the verbal command and imitation condition, the child had to imagine an object to be manipulated with an end-goal provided and then correctly execute the movement. Imitation processes involve the MNS to visualize and plan the motor task based on sensory-motor transformations and the goal of the movement. In the verbal command condition, the action is not observed by the child requiring the retrieval of internal models to complete the task without an external reference provided by the experimenter performing the task (Gizzonio et al., 2015). The IPL is involved in visuomotor transformation

(Jackson & Husain, 2006) and may contribute to imitation deficits of meaningless gestures in children with ASD. Kaur et al. (2018), also found both gross and fine motor deficits in addition to praxis deficits in high-functioning and low-functioning children with ASD (5-12 years) (Kaur et al., 2018). Together, these findings provide evidence of MNS dysfunction in ASD that may be mediated by dysfunctional circuitry between posterolateral cerebellum and IPL.

1.3.2 Deficits Integrating Visual Input into Internal Models in ASD

Behavioral studies in children diagnosed with ASD show impaired use of visual information to: (1) form action plans and sensory predictions (feedforward control) and (2) correcting errors during ongoing movements (feedback control). As discussed in the previous section, feedforward control involves inverse models (motor commands to achieve desired

sensory states) and forward models (predictions of sensory consequences of outgoing motor commands). Feedback control involves multisensory integration from the periphery to correct errors during ongoing movements. The purpose of this section is to overview studies that provide evidence of impaired integration of visual input into inverse and forward models in individuals with ASD.

1.3.2.1 Visuomotor Integration in Motor Planning

The ability to form and update the internal model during goal-directed tasks involves effective multisensory integration in the IPL to provide accurate visuospatial input to cerebellum for forward model sensory predictions. Current findings suggest individuals with ASD may have impaired ability to integrate visual input to form and update internal models. Deficits in the selection of motor commands, in response to visual stimuli, have been shown in individuals with

ASD using various experimental paradigms. The findings of these experiments will be discussed in this section and point to deficits in effectively using visual input to plan goal-directed movements in ASD.

To test reach-to-grasp motor planning in children with ASD, Hughes (1996) used a “Bar

Game” paradigm and compared children with ASD to children with developmental delay (DD) and TD controls (Hughes, 1996). The children were asked to (1) grasp either the black or white end of a painted rod and (2) place the grasped end of the rod into either a red or blue disc.

Grasping the most distal end of the rod (pointed away from the participant) with an overhand grip resulted in an awkward end position and grasping with an underhand grip resulted in a comfortable end position. Children with ASD completed the placement task with more awkward end-states (thumb-down) than DD and TD children, demonstrating poor integration of visual input into the motor plan.

Fabbri-Destro and colleagues (2009), performed a different goal-directed reach-to-grasp target task to examine action-planning in children with ASD compared to a TD control group

(Fabbri-Destro et al., 2009). The children were asked to complete two movements: (1) reach and grasp object and (2) place object within a container that was either small (condition 1) or large

(condition 2). In contrast to the TD group, the children with ASD did not adjust their reach time in the more difficult condition (small container). These findings suggest children with ASD may have difficulty integrating visual information into motor commands (inverse model) and/or accurately predicting the sensory consequences of outgoing motor commands (forward model).

In young adults with ASD, similar findings of impaired visuomotor information processing have been observed during choice reaction time tasks (Sachse et al., 2013). Sachse et al. (2013), found intact movement execution abilities in simple and choice visual reaction time tasks, however, movement preparation time was significantly prolonged in the ASD vs. TD groups.

Evidence of impaired integration of visual input to predict sensory consequences of motor commands has also been observed in children with ASD using electromyography (EMG) techniques. Schmitz and colleagues applied a load onto a platform attached to the left forearm of young children with ASD and TD controls to examine EMG responses to unexpected vs. voluntary unloading of the platform (Schmitz et al., 2003). During the voluntary unloading condition, the children with ASD used their right hand to remove the load from the platform. The

TD age-matched controls demonstrated biceps brachii inhibition prior to unloading whereas the children with ASD showed a significant latency (~51ms) of biceps brachii inhibition that occurred after unloading. These findings show deficits using efferent copies of motor commands during reaching-to-grasping in the forward model to predict the sensory consequences of unloading the platform. Furthermore, a feedback strategy appears to have been predominant,

with EMG activity occurring following load removal. EMG delay during visually guided goal- directed motor actions has also been observed during feeding in young children with ASD

(Cattaneo et al. 2007). However, see (Pascolo et al., 2010) for alternative view.

In Cattaneo and colleagues, the children were asked to (1) reach, (2) grasp, and (3) bring- to-the-mouth a piece of food from a touch sensitive plate (Cattaneo et al., 2007). Surface EMG from the mylohyioid (MH) muscle, a muscle involved in controlling the tongue, was recorded during the task. In contrast to the TD group, MH EMG activity was delayed in the ASD group during the grasp-to-eat condition with no EMG increase of MH observed during reaching and grasping phases of the task. These findings support deficits in prediction of sensory consequences of a goal-directed feeding action (forward model) in children with ASD. However, these findings might also suggest that movements in children with ASD are planned in sequential steps compared to a global action plan (Fabbri-Destro et al., 2009). These findings may result from inaccurate predictions of sensory states of the forward model from inaccurate sensory input and/or inaccurate sensory predictions of the forward model. It is also possible that the inverse model calculation, using predicted sensory states and desired sensory states to produce motor commands, is inaccurate in individuals with ASD.

Using a visual distractor task, Dowd and colleagues (2012) further demonstrated impaired integration of visual input into motor commands in young children with ASD (Dowd et al., 2012). Dowd used a simple tablet target task, requiring the children to move between two targets (20 mm in diameter) on a touch-screen tablet using a stylus. In the simple condition, the children moved between the two targets with similar performance observed between the ASD and TD groups, albeit the ASD group had more variable movement preparation times.

Interestingly, when a different colored visual distractor was added to the task, flanking the visual

target, the TD group showed longer and more variable movement preparation and execution times. The ASD group did not make any adjustments when the visual distractor was present showing this visual input may not have been integrated in the forward model prediction.

Therefore, the visual distractor did not provide updates to the inverse model in the ASD group.

Recently, a precision-grip force target-matching paradigm was used to assess feedforward control in children and adults with ASD (Mosconi et al., 2015; Wang et al., 2015).

Wang et al. (2015) and Mosconi et al. (2015), using similar experimental procedures, found significantly higher initial force overshoot relative to a fixed visual target at various percentages of maximal voluntary isometric contraction (MVIC) force. Overall, individuals with ASD demonstrated increased primary pulse overshoot compared to TD controls. The initial force overshoot occurred before any corrective motor commands, through feedback control, could provide error corrections. In Mosconi (2015), the primary pulse accuracy was significantly related to handwriting impairment (r=-0.65) and social-communication abnormalities (r=0.41) in the ASD group (Mosconi et al., 2015). These findings provide evidence that visual input of target location and/or forward model computations may be inaccurate, and the magnitude of these errors are related to the development of fine-motor and social skills in individuals with ASD.

Using the same precision-grip isometric force tracing task, Neely and colleagues (2016) observed an overreliance of visual feedback control in individuals with ASD (Neely et al., 2016).

Visual feedback was presented in real-time of the target and force cursor. Neely and colleagues

(2016) assessed force decay following removal of the visual feedback 8-seconds following trial initiation. Participants in the ASD group showed a significant increase in force decay (slope of force) following visual feedback removal that was not observed in the TD control group. This finding may indicate deficits in retrieving motor memories used prior to visual feedback

removal, and/or indicate an over reliance on visual feedback control of motor output vs. feedforward control. Social and communication scores were also related to force error during the visual feedback removal condition, demonstrating a link between overreliance on feedback vs. feedforward control mechanisms and core ASD symptoms.

Further evidence of impaired feedforward control has also been shown using a repetitive

(10 repetition) FITTs aiming task in children with ASD, Aspergers, and TD controls (7-12 years)

(Papadopoulos et al., 2012). Although no differences in movement times were reported, the ASD group had more variable endpoint error across repeated aiming attempts compared to the TD control group. The ASD group may have had difficulty using visual input of the target distances to predict sensory consequences (forward model) and select the appropriate motor commands

(inverse model) to be retrieved over multiple repetitions. It is possible that the children may rely more on visual feedback control, during the ongoing aiming task, resulting in more variable attempts. However, since movement times were similar between ASD and TD groups, the ASD group likely did not adjust motor output effectively using feedback control and may have repeated the inaccurate initial motor commands over multiple repetitions.

Impaired feedforward control has also been demonstrated in children with ASD during precision-grip-to-lift paradigms (David et al., 2009, 2012). In these studies, children with ASD showed temporal coordination deficits with increased grip to lift force onset latency and increased time to peak grip force. These deficits demonstrate impaired selection of initial motor commands by the inverse model even with previous experience with the task. If the forward model, used during this task is inaccurate or cannot be updated by teaching signals via climbing fibers from the inferior olive, it might be ignored, and slower feedback mechanisms may be used.

However, the deficits observed in children with ASD were also observed in children with general

developmental delay (DD) showing a lack of specificity of the findings to children with ASD

(David et al., 2012). In contrast to the experimental procedures of Wang et al. (2015) and

Mosconi et al. (2015), the precision-grip-to-lift has low visuomotor integration demands and higher demands on proprioception. This may have resulted in the lack of specificity of the findings from the precision-grip-to-lift to differentiate ASD from general DD. Individuals with

ASD show increased reliance on proprioceptive sensory input compared to visual input (Haswell et al., 2009; Izawa et al., 2012; Marko et al., 2015; Masterton & Biederman, 1983) and using tasks that assess visuomotor integration may better differentiate ASD from other NDDs.

1.3.2.2 Children with ASD form Internal Models using Proprioception Input over Vision

If vision is an inaccurate input to the forward model and/or predictions of the forward model are inaccurate using visual input, sensory reweighting may occur biasing other sensory forms (proprioception, auditory, and somatosensory). Masterton and Biederman (1983), demonstrated that children (7-15 years) with ASD showed increased reliance on proprioceptive feedback during online visual control of reaching that was not observed in children with intellectual disability and neurotypical children (Masterton & Biederman, 1983). The children first learned a coin and block placement task, and then performed a transfer of adaptation task with the contralateral hand and a prism-induced lateral shift by 60mm. The children in the ASD showed transfer of adaptation to the non-adapted hand in the transfer task, showing increased reliance on proprioceptive feedback compared to online visual control of reaching. Forming internal models in children with ASD may therefore rely more on proprioception compared to visual input. This may result in improved performance on transfer tasks where visual feedback is absent or inaccurate.

Using a similar experimental design to Masterton and Biederman, increased reliance on proprioceptive input to form internal models in children with ASD has been shown using a robotic arm target task (Haswell et al., 2009; Izawa et al., 2012; Marko et al., 2015). Earlier research from this research group, however, showed children with ASD (8-13 years) had normal adaptation rates to a visuomotor transformation task and a novel tool target task compared to a

TD group, demonstrating their ability to form internal models (Gidley Larson, Bastian, Donchin,

Shadmehr, & Mostofsky, 2008). However, the contribution of proprioception and vision to forming the internal model of the task was not examined. This gap within the literature was addressed in a series of experiments in which children were trained in a novel goal-directed

target task requiring formation of an internal model (Haswell et al., 2009; Izawa et al., 2012;

Marko et al., 2015). The children were provided visual feedback of the handle position via an

LED (projected from underneath the table surface occluding vision of the hand) and proprioception feedback through a force field applied perpendicular to the displayed visual target. Following a training period on the left-side of the workspace, generalization was assessed in the right-side of the workspace using two target locations and a force-measuring error clamp to assess force errors. In the ASD groups, significant generalization was performed when the target location allowed the same joint rotation (proprioception), however, this was not observed when the target allowed the same hand motion or path of the handle (vision) (Haswell et al.,

2009; Izawa et al., 2012; Marko et al., 2015). The findings from these studies show that internal models are formed biasing proprioception feedback over visual feedback. In the ASD group, proprioceptive generalization was significantly related to social function, motor skill function, and imitation impairment (Haswell et al., 2009; Izawa et al., 2012) showing that deficits in integrating visual input into internal models may explain deficits in the social and motor function in children with ASD. Furthermore, evidence from Izawa (2012) suggests proprioception generalization is greater in children with ASD compared to children with ADHD (Izawa et al.,

2012), providing some evidence that proprioception bias is specific to ASD diagnosis.

Findings of increased proprioception generalization have also been observed in adults with ASD, providing evidence that visuomotor integration deficits persist from childhood into adulthood (Sharer et al., 2016). In Sharer et al. (2016), participants were trained using a serial reaction time task on their right-hand in response to presented visual stimuli (sequence=digit 2- digit 5-digit 4). Following a training period, proprioception generalization was assessed using the contralateral hand with a sequence mirroring that of the original sequence so that order of fingers

pressing the keys was the same as the trained hand (digit 2-digit 5-digit 4). Visual-based generalization was also assessed with the contralateral hand using the same sequence as the trained hand (digit 5-digit 2-digit 3). In contrast to previous findings of proprioception bias in children with ASD (Haswell et al., 2009; Izawa et al., 2012; Marko et al., 2015), proprioception generalization was not observed in either group, however the TD group showed significant visual-based generalization that was not observed in the ASD group. These findings, including those previously discussed in children with ASD, provide evidence supporting deficits in integrating visual input into internal models in both children and adults with ASD. Whereas the current section has reviewed the role of vision in planning and learning motor tasks, the following section will overview studies examining the integration of visual input into ongoing motor tasks in individuals with ASD.

1.3.2.3 Visual Integration During Ongoing Motor Tasks

In individuals with ASD, motor deficits are observed during ongoing motor tasks that involve visuomotor integration. Reports of poor ball catching skills on standardized motor tests in children with ASD are common (Green et al., 2009; Pan, Tsai, & Chu, 2009; Whyatt & Craig,

2012), a finding that differentiates children with ASD from ADHD (Ament et al., 2015).

Furthermore, visually guided motor tasks are more variable and slower than TD controls

(Glazebrook et al. 2009; Sacrey et al. 2014 - Review). Visual feedback control of movement requires integrating visual feedback with other sensory modalities and comparing sensory afferent feedback to predicted sensory consequences (forward model) of the movement to modify or continue the motor commands (inverse model) (Desmurget & Grafton, 2000). It can be observed that feedforward and feedback control are highly interconnected with each dependent on the other for adequate control of movement. As discussed previously, children with ASD may

form internal models with an overreliance on proprioception over visual input. This may impair the prediction capabilities of the forward model during visual guided tasks. In the absence of internal models during visual-guided tasks, increased reliance of a feedback model results producing intermittent motor corrections to ongoing movements in the order of 250-300ms

(Miall, Weir, & Stein, 1986, 1987). It is proposed that a hybrid model comprising feedback and feedforward control are used to control fast reaching movements involving internal feedback loops that rely on intact cerebellum and PPC (Desmurget & Grafton, 2000). If a feedback model is used in isolation, movements are prolonged in duration to accommodate sensory feedback.

When reaching toward a target using visual feedback control movement time is prolonged, a finding observed in individuals with ASD (Glazebrook et al., 2009). The feedforward control of movement can produce corrections to reach trajectory, in response to visual perturbations, with a minimum latency of 30 ms (Cooke & Diggles, 1984; van Sonderen, Gielen, & van der Gon

Denier, 1989). Without forward modelling during rapid movements, continuous computation of error between predicted and actual sensory state information is not performed, resulting in overshoot or end-point errors. Proposed deficits in posterolateral cerebellum and IPL may prevent individuals from making rapid on-line corrections during movements, resulting in (1) slower feedback control mechanisms, (2) prolonged movements, and (3) reduced frequency of on-line corrections. In the current section, evidence of impaired visuomotor integration using during ongoing motor tasks during (1) goal-directed reaching, (2) precision-grip target tracking,

(3) postural control, and (4) gait will be discussed.

GOAL-DIRECTED REACHING TASKS

As discussed previously, deficits in visual processing may increase reliance on proprioception on tasks that permit proprioception as the predominant source of sensory input over vison. However, when vision is required to perform a task, movement times may increase to allow visual processing to correct the ongoing movement via feedback control, if there are deficits in visually guided feedforward-feedback internal control loops (Desmurget & Grafton,

2000). Prolonged movement times have been observed in individuals with ASD during goal- directed visually guided reaching tasks (Barbeau et al., 2015; Glazebrook et al., 2009;

Glazebrook et al., 2006; Stoit et al., 2013; Szatmari et al., 1990), that are not observed when vision is removed (Glazebrook et al., 2009). Within the literature, tests used to assess visual guided reaching include (1) timed peg board tests, (2) reaches with a planning component, (3) ballistic reaches to visual targets, and (4) voluntary-timed reaches to visual targets.

Timed pegboard tests assess both finger dexterity and sensorimotor integration during multiple goal directed reaching tasks. Norms are established on the total time to: (1) pick up each peg from a dish, (2) place each peg in a hole, and (3) remove each peg one-by-one placing them back in the dish. Findings show individuals with ASD take longer to complete pegboard tasks compared to TD children (Barbeau et al., 2015) and children with other psychiatric diagnoses, such as ADHD (Szatmari et al., 1990). Deficits in peg-board performance may also be related to cerebellum deficit. Previous findings showed that 1 Hz (inhibitory) repetitive TMS over the cerebellum in neurotypical individuals results in increased movement times on a 10-hole peg board task (Miall & Christensen, 2004). Furthermore, in cerebellar ataxia patients, excitatory

(anodal tDCS) stimulation of the cerebellum reduces peg-board times (Benussi et al., 2017;

Benussi, Koch, Cotelli, Padovani, & Borroni, 2015).

Using a different goal-directed reaching task with a more cognitive component, Stoit and colleagues (2013) reported similar prolonged movement times in children with ASD (~11 years) compared to age-matched TD controls (Stoit et al., 2013). Participants were presented with two cylindrical objects and received cues about whether to grasp an object with a power or a precision-grip. No differences were observed in reaction times (stimulus onset to movement) or accuracy of responses showing intact motor planning. However, movement times were significantly prolonged in the ASD group. If more automated feedforward control processes were impaired in individuals with ASD during visually guided reaching, reliance on slower feedback control could explain slower movement times observed. In contrast to the findings from

Stoit (2013), Glazebrook et al. (2006) found longer reaction times during goal-directed reaching

(Glazebrook et al., 2006). However, similarly prolonged movement times were observed, and kinematic analysis of the reaching movements showed higher spatial and temporal variability of the initial ballistic phase of the movement. Furthermore, the target size did not influence reaction time or peak velocity of the reach in the ASD group to the same extent as the TD group. These findings support inaccurate visuospatial state estimate computation input to the inverse model

(initial burst) and feedforward control impairments resulting in reliance on slow feedback control to correct initial errors and complete the task. Support for visual involvement in prolonged reaching times in individuals with ASD is provided by Glazebrook et al. (2009). In Glazebrook et al. (2009), participants performed eye and hand movements toward visual targets (Glazebrook et al., 2009). Their findings showed increased variability of eye and hand movement amplitudes and longer movement times. Interestingly, when visual feedback was removed immediately after reach initiation, movement time and spatial variability of reaches were significantly reduced compared to trials when visual feedback was available. Furthermore, the ASD group had higher

endpoint error compared to the TD group when vision was available, a finding not observed when vision was removed.

PRECISION-GRIP VISUOMOTOR FORCE TRACKING

In addition to visual-guided reaching, visuomotor integration deficits are also observed in children with ASD performing a precision-grip isometric force tracking (Mosconi et al., 2015;

Neely et al., 2016; Parma & de Marchena, 2016; Wang et al., 2015), a task shown to involve dorsal/ventral premotor cortex, IPL, and anterior cerebellum with activation in the posterolateral cerebellum (Crus I/II) specific to increased visuomotor processing demands (Moulton et al.,

2017; Soteropoulos, 2005; Vaillancourt et al., 2005). Findings show that visual feedback during an isometric precision-grip task can be incorporated into an ongoing motor command with a minimum delay of 83 ms (12 Hz), demonstrating the use of visual input into on-line feedback and feedforward internal control loops (Sosnoff & Newell, 2005). In a sample of individuals with

ASD, Wang et al. (2015) showed individuals with ASD are more variable and show greater force overshoot vs. TD controls suggesting force regulation and feedforward deficits (Wang et al.,

2015). Similarly, Mosconi (2015) found significantly increased force error, increased variability, and lower entropy (higher predictability) using the same precision-grip force tracking task in individuals with ASD vs. TD controls (5-35 years) (Mosconi et al., 2015). Visual gain was also manipulated at the target force of 15% of MVC with the ASD group showing more variability and less complexity at the largest visual gains where more rapid corrections and complexity would be expected (Mosconi et al., 2015). These results suggest that individuals with ASD show deficits integrating visual feedback into on-line corrective motor commands. Mosconi et al.

(2015) also reported lower entropy and higher frequency content in the low frequency domain

(0-4 Hz), consistent with higher visual feedback control.

POSTURAL CONTROL AND GAIT

Deficits in visuomotor integration have also been observed in individuals with ASD during static postural control tasks. Postural control tasks provide a valuable method to assess visuomotor integration during ongoing motor tasks in children with ASD since minimal instructions are required and responses are unprompted (Gepner et al., 1995; Gepner & Mestre,

2002; Minshew et al., 2004). Furthermore, postural control deficits are commonly reported in children with ASD (Doumas, McKenna, & Murphy, 2016; Goulème et al., 2017). Visuomotor integration deficits may contribute to postural deficits in individuals with ASD and increase weighing of proprioceptive sensory input over visual input during postural control tasks. Gepner and Mestre (2002), examined postural reactivity to visual stimuli in children with high- functioning autism (HFA), children with low-functioning autism (LFA), and TD controls

(Gepner & Mestre, 2002). The visual stimulus used was a “tunnel expanding in depth” at a frequency of 0.2 Hz. To examine the children’s sensitivity to the visual stimulus, frequency analysis was performed. It was observed that the frequency content of center-of-pressure (COP) sway in the 0.2 Hz domain was higher in the HFA and TD groups compared to the LFA group.

These results demonstrate less postural dependence on vision in children with LFA. In a much larger sample of individuals with ASD (79 children with HFA), abnormal weighting of somatosensory input has also been observed (Minshew et al., 2004). Minshew and colleagues

(2004) showed that postural control was most disrupted in individuals with ASD, when somatosensory information was inaccurate, compared to TD controls. These findings demonstrate abnormal proprioception weighing in individuals with ASD that may be a

compensation strategy for deficits in visuomotor integration. Greffou et al. (2012), also reported visual hyposensitivity of children (12-15 years) with ASD during a postural control task (Greffou et al., 2012). Their findings showed that children with ASD had significantly less postural sway at the highest frequency perturbation (0.5 Hz), a finding that was not observed in TD children.

Together these data show abnormal integration of vision to control posture in children with ASD.

In addition to postural control deficits, gait abnormalities are also commonly observed in children with ASD (Dufek, Eggleston, Harry, & Hickman, 2017; Nayate et al., 2012; Rinehart et al., 2006). Furthermore, children with ASD show impaired integration of visual targets into gait motor commands (Nayate et al., 2012). Impaired visual processing may therefore contribute

“clumsy” gait observed in children with ASD. Some evidence for a link between impaired visuomotor integration and gait abnormality was provided by Mosconi et al. (2015). Mosconi et al. (2015) compared entropy of force tracing at different force and visual gain levels within the

ASD group between individuals with vs. without a history of gait abnormality. Interestingly, individuals within the ASD group that had gait abnormality showed lower entropy in their force signal. Higher entropy of the force signal during precision-grip force tracking task is related to fast feedforward integration of visual input. Mosconi et al. (2015), therefore provides indirect evidence that visuomotor integration deficits may contribute to gait abnormalities in ASD. Using a more direct approach to examine visuomotor integration during gait, Nayate et al. (2012), examined the effect of equally spaced visual spatial cueing (20% greater than individual preferred stride length) on gait parameters in children with ASD and TD controls (7-18 years).

The ASD group had more variable stride length in the cued vs. un-cued conditions and a larger base of support compared to children with TD. These findings provide further support for

impaired integration of visual information that may contribute to reported “clumsy” gait in individuals with ASD.

1.3.3 Concluding Remarks

The current section has synthesized evidence supporting impaired integration of visual input in individuals with ASD to (1) plan movements, (2) form internal models, and (3) guide ongoing movements using feedforward control. Furthermore, these findings support structural and/or functional deficits in the IPL and its connections with the posterolateral cerebellum.

Together the IPL and posterolateral cerebellum provide integrate multimodal sensory inputs

(IPL) and calculate state estimations from efferent motor commands and sensory states

(cerebellum). These functions are necessary to generate accurate initial motor commands during the planning states of movement and provide fast computations of errors to automatically correct ongoing movement via internal feedforward and feedback loops (Desmurget & Grafton, 2000).

To evaluate the integrity of the IPL and posterolateral cerebellum, motor control paradigms that involve visuomotor integration may be used. Precision-grip force tracking tasks are well-studied paradigms that involves motor circuitry (PMv, M1, SMA, IPL, and Crus I) (Moulton et al., 2017;

Vaillancourt et al., 2005, 2006) overlapping with circuitry mediating social and communication deficits in ASD (IPL and Crus I) (Badura et al., 2018; Stoodley et al., 2018). Furthermore, left

IPL is crucial for visuomotor adaptation by storing visuomotor maps (Crottaz-herbette et al.,

2014; Leow, Marinovic, Riek, & Carroll, 2017; Mutha, Sainburg, & Haaland, 2011).

Furthermore, the posterolateral cerebellum participates in remapping visuomotor representations of the left IPL during visuomotor adaptation tasks (Donchin et al., 2012; Martin et al., 1996).

Therefore, visuomotor adaptation paradigms should provide a means to assess the integrity of left IPL-RCrusI functional connections that mediate social and communication deficits in ASD.

Visuomotor perturbations via prisms require recalibration of visuomotor maps located in the IPL via posterolateral cerebellar corrective input, that may overtime increase connectivity between left IPL and RCrusI (Leow et al., 2017; Rossetti et al., 1998). Prism adaptation may therefore be an effective intervention to improve visuospatial abilities and functional connectivity between left IPL and RCrusI in children with ASD (Riquelme et al., 2015). Neuromodulation devices, including transcranial direct current stimulation (tDCS), of the right posterolateral cerebellum has also been shown to modulate connectivity between left IPL and RCrusI (Stoodley et al.,

2018).

1.4 Force Control of Grasping: Implications for Studying Grasping Circuitry in Children with ASD

The previous section reviewed motor control studies showing that the visuomotor integration network is abnormal in individuals with ASD. Furthermore, neuroanatomical findings from individuals with ASD show connectivity and structural abnormalities within the visuomotor network (Stoodley et al., 2018). While integration of visual information with motor commands appears to be related to motor deficits observed in individuals with ASD, disruptions in other circuits, such as those involved in force control, may also contribute to motor deficits in ASD. In this section, we will review circuitry involved in force regulation, generation, and relaxation.

We will argue that specific structures and M1 intracortical inhibitory circuits involved in force control may also be disrupted in individuals with ASD.

1.4.1 fMRI Findings Suggests Different Circuitry is Involved in Isometric and Dynamic Force Control

Findings from Neely et al. (2013) show that different circuitry is involved in the production of isometric static force (force regulation) vs. isometric dynamic force output in

healthy right-handed participants (Table 9). Neely et al. (2013), showed unique activity during

static force holds observed in right-lateralized circuits involving right IPL, ventral premotor area

(PMv), and dorsolateral prefrontal cortex (DLPFC) with dynamic force production showing

unique activation of left-lateralized circuits involving supplementary motor area (SMA), superior

parietal lobule (SPL), fusiform gyrus (FG), V3, and cerebellar lobule VI (Neely, Coombes,

Planetta, & Vaillancourt, 2013). Lastly, non-unique but significant increases in activity of left

M1, left primary somatosensory cortex (S1), left dorsal premotor area (PMd), V5, left lateral

occipital (LO), right cerebellar lobule VI, and right Crus II were observed during dynamic vs.

static force production (Table 9). These findings show that circuitry involved in force control

during static and dynamic force tracking tasks activate parietal, parietal, frontal, and cerebellar

areas that have also been implicated in ASD.

Table 8: fMRI activation findings examining regions of activation specific to static and dynamic precision-grip force tasks in right-handed healthy participants (Neely et al., 2013).

Brain Activation Unique to Each Task Non-Unique Activation Isometric Static Force Isometric Dynamic Force Dynamic > Static Target Target x Right Inferior Parietal x Supplementary Motor x Left Primary Motor Cortex Lobule (IPL) Area (SMA) (M1) x Right Ventral Premotor x Left Superior Parietal x Left Primary Area (PMv) Lobule (SPL) Somatosensory Cortex (S1) x Right Dorsolateral x Left Fusiform Gyrus (FG) x Left Dorsal Premotor Area Prefrontal Cortex x Left V3 (PMd) (DLPFC) x Left Cerebellar Lobule VI x Left V5 x Left Lateral Occipital (LO) x Right Cerebellar Lobule VI x Right Crus II

1.4.2 fMRI Findings Suggests Different Circuitry is Involved in Force Generation vs. Force Relaxation

In addition to segregated circuits regulating static and dynamic isometric force production, distinct brain areas are implicated in controlled force-generation and force- relaxation. Spraker et al. (2009), evaluated circuitry regulating isometric precision-grip force- generation and force-relaxation using a 4-second controlled force generation ramp (0-15%

MVC) and controlled force-relaxation ramp (15-0% MVC) (Spraker, Corcos, & Vaillancourt,

2009). Brain activations, in right-handed participants, were significantly greater during force generation vs. relaxation in left M1 and bilateral caudate (Spraker et al., 2009). During force relaxation, significantly greater activation was observed in right DLPFC whereas significantly greater deactivation was observed in anterior cingulate cortex (ACC) compared to force generation (Spraker et al., 2009) (Table 10). The reduction of activation in left M1 during force generation vs. relaxation may be due to increased activation of M1 intracortical inhibitory circuits, a finding that is supported by Buccolieri et al. (2004) and discussed in the following section. Collectively, these findings suggest that different types of precision-grip isometric force tracking tasks produce unique brain activations. Therefore, the comparison of performance between dynamic vs. isometric and force generation vs. relaxation during isometric precision- grip force tracking tasks may be useful in identifying impaired circuitry in children with neurodevelopmental disorders, such as ASD. The following section will discuss intracortical inhibitory circuits regulating force control that may be impaired in individuals with ASD.

Table 9: fMRI activation findings from Spraker et al. (2009) on force generation and force relaxation using a precision-grip force tracking task in right-handed healthy participants (Spraker et al., 2009). These findings indicate specific force generation and relaxation networks.

Specific Brain Regions Activated in Each Task Regions Unique to Force Generation Regions Unique Force Relaxation x Left M1 x Right DLPFC x Bilateral Caudate (Basal Ganglia) x Bilateral Anterior Cingulate Cortex (ACC) – greater deactivation during force relaxation vs. generation

1.4.3 Intracortical Inhibition and Force Relaxation

In the previous section, circuitry contributing to force generation and force relaxation was

discussed. However, evidence suggests that M1 intracortical inhibitory circuits may also

contribute to force relaxation control. The integrity of GABAA receptor-mediated intracortical

inhibitory circuits can be evaluated through Short-Interval Intracortical Inhibition (SICI) induced

by a paired-pulse Transcranial Magnetic Stimulation (TMS) technique (Chen, 2004). Evidence

of reduced SICI has been shown in motor disorders affecting the basal ganglia, such as

Parkinson’s disease (Hallett, 2007) and dystonia (Beck et al., 2008). SICI can be elicited using a

conditioning pulse equal to 80% of active motor threshold (AMT) preceding a test pulse by 3 ms

(Buccolieri, Abbruzzese, & Rothwell, 2004). The test pulse is selected as the intensity required

to elicit a motor evoked potential (MEP) with an amplitude of 1mV. The result of SICI is a

reduction in the amplitude of the MEP compared to the MEP elicited by the unconditioned test

pulse. SICI is mediated by the GABAA receptor – specifically the α2 and α3 subunits of the

GABAA receptor (Di Lazzaro et al., 2006).

A recent meta-analysis also shows SICI reduction observed in individuals with ASD

(Masuda et al., 2019). In addition to SICIs role in surround inhibition in movement disorders

(Sandra Beck & Hallett, 2011), SICI is also involved in deactivating M1 prior to force-relaxation

(Buccolieri, Abbruzzese, et al., 2004). Buccolieri et al. (2004), reported an increase in SICI prior to EMG changes in a muscle relaxation task (Buccolieri, Abbruzzese, et al., 2004). They also observed a reduction in unconditioned MEP amplitudes prior to relaxation that coincided with an increase in SICI. Further evidence of the role of intracortical inhibition during force-relaxation has also been shown by Kimura et al. (2003) using a dynamic sinusoidal force tracking task. At the same percentage of maximal force output and background EMG levels, Kimura et al. (2003) found a significant reduction in MEP amplitude during force relaxation vs. generation (Kimura et al., 2003). Therefore, GABAergic M1 intracortical inhibitory interneurons may mediate the reduced corticomotor excitability observed during force-relaxation that is not observed during force generation or static force holds. These findings suggest that SICI may be important role for force relaxation and abnormal SICI in ASD may contribute to fine-motor deficits.

Although SICI has been suggested to be impaired in individuals with ASD, at least one study has examined force-relaxation in this population. Wang et al. (2015) examined force- relaxation times in children and adults with ASD using a precision-grip force tracking task of static targets equal to 15, 45, and 85% MVC. Participants with ASD showed prolonged relaxation time across all force levels compared to the neurotypical control group (Wang et al.,

2015). Findings of prolonged relaxation time have also been observed in patients with basal ganglia disorders that have impaired SICI such as Parkinson’s disease (Robichaud, Pfann,

Vaillancourt, Comella, & Corcos, 2005) and dystonia (Buccolieri, Avanzino, et al., 2004; Hallett

& Pisani, 2011). As discussed earlier in this literature review, basal ganglia abnormalities of the indirect (inhibitory) pathway (Wilkes & Lewis, 2018) and reduced SICI (Masuda et al., 2019) are also implicated in ASD. Therefore, basal ganglia abnormalities in ASD may mediate deficits

in M1 intracortical inhibitory pathways contributing to deficits in force control. To the best of our knowledge, no studies have examined force production during dynamic controlled force- relaxation in children with ASD. Deficits in relaxation force control may further support a role of

SICI in fine-motor deficits in children with ASD. Lastly, no studies have examined surround inhibition of the hand muscles in children with ASD. Findings from such studies may further support the contribution of the basal ganglia and SICI to fine-motor control deficits in individuals with ASD.

Abnormal cerebellar activity may also contribute to disrupted M1 intracortical inhibitory circuitry. In a study by Brighina et al. (2008), non-invasive cerebellar stimulation significantly increased intracortical facilitation (ICF) and significantly reduced SICI in healthy controls

(Brighina et al., 2009). Abnormally high cerebellar Purkinje cell activation may therefore contribute to the excitatory/inhibitory imbalance reported in individuals with ASD (Enticott et al., 2013). This excitatory/inhibitory imbalance may be mediated by abnormal cerebellar hyperactivation resulting in reduced activation of M1 intracortical inhibitory circuits ultimately leading to increased ICF (Brighina et al., 2009). However, findings suggest that the cerebellum in individuals with ASD has (1) a lower metabolism as measured by position emission topography (PET) (Sharma et al., 2018), (2) abnormal cortico-cerebellar connectivity as measured by fMRI (Ramos, Balardin, Sato, & Fujita, 2019), and (3) hypoactivation as measured by fMRI (D’Mello & Stoodley, 2015; Philip et al., 2011). In individuals with ASD, hypoactivation of the cerebellum may increase SICI and reduce ICF. A significant reduction in

ICF compared to controls has recently been observed in 30 adolescents with ASD and co- occurring ADHD, that was not observed in children with ASD-only and TD controls (Erickson et al., 2019). In children (7-12 years old) with ADHD, reductions in SICI are correlated with a

reduction in ADHD symptoms (Chen et al., 2014). Therefore, increased activity of M1 intracortical inhibitory interneurons may be a biomarker of ADHD that contributes to reduced

ICF in children with ASD+ADHD (Erickson et al., 2019). Erickson et al. (2019), did not observe any differences in SICI between the groups. Together, these findings show abnormal inhibition/excitation balance that may contribute to the behavioral phenotype and motor abnormalities in children with ASD. However, TMS measurements are highly variable

(Wiethoff, Hamada, & Rothwell, 2014), and there is a need for more studies examining the integrity of intracortical inhibitory and facilitatory circuits within the M1 of individuals with

ASD. Furthermore, the prevalence of ADHD and other comorbidities in ASD is high and this may also contribute to the variability in TMS measures in this population. Inclusion of more homogeneous groups of children with ASD should help identify what impaired intracortical inhibitory/facilitatory circuits are specific to ASD. Next steps would then be to track changes within the impaired inhibitory and/or facilitatory circuits using M1 intracortical paired-pulse

TMS following pharmacological intervention and/or non-invasive neuromodulation techniques.

1.4.4 Power Spectra of Frequencies of Force Fluctuations are Associated with Impaired Force Control

The proportion of power in low frequency bins (0-1 Hz) during isometric force tracking is another variable contributing to force control. Several studies have observed the proportion of power in the 0-1Hz frequency bin is significantly positively associated with force error during a precision-grip force tracking task (Baweja, Kennedy, Vu, Vaillancourt, & Christou, 2010;

Baweja, Patel, Martinkewiz, Vu, & Christou, 2009; Park, Kim, Yacoubi, & Christou, 2019;

Slifkin, Vaillancourt, & Newell, 2000). Altered visuomotor processing appears to be related to the proportion of power in the 0-1Hz bin as (1) increases in the frequency of visual feedback reduces power in the 0-1Hz bin (Slifkin et al., 2000), (2) increases in visual gain of feedback significantly reduces force oscillations in the 0-1Hz bin (Baweja et al., 2010), and (3) removal of visual feedback significantly increases power in the 0-1Hz bin (Baweja et al., 2009) when tracking static force targets. Furthermore, the proportion of power in the 0-1Hz bin can explain

~50% of the reduction of variability associated with increased visual feedback gain (Baweja et al., 2010). Low frequency oscillations (0-1Hz) are also significantly associated with reduced accuracy during isometric tracking of dynamic oscillating force targets (Park et al., 2019). Park et al. (2019), showed that ~57% of the RMSE could be explained by force oscillation < 1Hz

(Park et al., 2019). These findings may suggest that a reduction in power in the 0-1Hz force oscillations is associated with reduced visuomotor processing and ultimately reduced force control. We propose that examining force oscillations in the 0-1Hz bin during static and dynamic isometric precision grip-force tracking could help identify children with impaired visuomotor integration. This may also help determine whether force control deficits are associated with visuomotor processing in children with ASD as hypothesized by our group, or deficits in structures regulating static/dynamic force control and/or force generation/relaxation circuits.

1.5 Fetal Alcohol Spectrum Disorder: Neuroanatomical and Motor Control Perspectives on the Disorder

In this section, we will examine neuroanatomical and motor control deficits observed in children with Fetal Alcohol Spectrum Disorder (FASD). We will argue that brain regions affected by prenatal alcohol exposure differ from those implicated in ASD. Children with FASD also show motor deficits that have been observed in children with ASD. Therefore, including children with FASD in a cross-syndrome design with children with ASD may reveal ASD specific-deficits that are not observed in children with FASD. Furthermore, children with FASD have below average intellectual ability, neuroanatomical deficits, and complex psychiatric comorbidities – in similarity to children with ASD. Finally, the inclusion of children with FASD in cross-syndrome research may reveal specific motor deficits associated with prenatal alcohol exposure that may improve clinical diagnostic practices. In the following sections, (1) brain regions sensitive to prenatal alcohol exposure and (2) motor deficits observed in children with

FASD will be discussed.

1.5.1 Introduction

The following section focuses on affected brain regions and motor deficits observed in children with Fetal Alcohol Spectrum Disorder (FASD). FASD affects up to 5% of children in the US, although the prevalence may be as high as 9.8% (May et al., 2018). FASD is diagnosed based on the presence of (A) ≥2 facial anomalies (1. short palpebral fissures; 2. thin vermilion border; 3. smooth philtrum), (B) prenatal/postnatal growth deficiency, (C) abnormal brain growth/neurophysiology, (D) neurobehavioral impairment, and (E) documented prenatal alcohol exposure. Fetal Alcohol Syndrome (FAS) is diagnosed based on the presence of A-D features and is the least common FASD diagnosis with a US prevalence of 0 to 0.78% (May et al., 2018).

The more common FASD diagnoses are partial FAS (0.84 to 5.91%) and Alcohol-Related

Neurodevelopmental Disorder (ARND) (0.97 to 5.04%) (May et al., 2018). Partial FAS is diagnosed based on the presence of A (facial features) and D (neurobehavioral impairment) and

B (pre/postnatal growth deficiency) or C (abnormal CNS growth/function). ARND, the most common FASD diagnosis, is diagnosed based on the presence of D (neurobehavioral impairment) and E (documented prenatal alcohol exposure). The most difficult FASD diagnosis to determine is ARND, due to the requirement of documentation of prenatal alcohol exposure from a reliable source. Furthermore, most children with FASD (~70%) are adopted or are in foster care and establishing maternal alcohol use may be impossible (Burd, Cohen, Shah, &

Norris, 2011). Therefore, motor or neurophenotypes – if sensitive to prenatal alcohol exposure – may be useful in helping diagnose FASD in children without facial features or CNS abnormalities.

Adding to the difficulties diagnosing and treating children with FASD are comorbid inherited complex psychiatric disorders. Eighty percent of mothers who give birth to a child with

FASD have at least one psychiatric disorder (Singal et al., 2017). Furthermore, 50-70% of individuals with at least one psychiatric disorder abuse or are dependent on alcohol (Kessler et al., 1997) and 27.6% of US adolescent females have at least one psychiatric disorder with severe impairment (Merikangas et al., 2010). These findings show the strong link between psychiatric illness and alcohol consumption during pregnancy and provides an explanation for the high- prevalence of psychiatric disorders in children with FASD (Weyrauch, Schwartz, Hart, Klug, &

Burd, 2017). Children with FASD show a high-prevalence of ADHD (~50%), Intellectual

Disability (~23%), Learning Disorder (~20%), Depression (~14%), Psychotic Disorder (~12%),

Bipolar Disorder (~9%), Anxiety disorder (~7.8%), and Obsessive-compulsive disorder (4.9%)

(Weyrauch et al., 2017). Furthermore, children with FASD have highly prevalent comorbid conditions including abnormal function of peripheral nervous system and special senses (90.9%), conduct disorder (90.7%), receptive language disorder (81.8%), chronic serous otitis media

(77.3%), and expressive language disorder (76.2%) (Popova et al., 2016).

Motor deficits are also apparent in children and adults with FASD (Moore & Riley, 2015) that limit independence and increase the need for services (Clark, Lutke, Minnes, & Ouellette- kuntz, 2004; Domeij et al., 2018; Moore & Riley, 2015). One study showed that 80% of young adults with FASD (~25 years old) need assistance with daily living activities (Moore & Riley,

2015). Furthermore, a survey of caregivers of adults with FASD found that 81% of individuals with FASD require greater than minimal levels of care, although only 34% had an intellectual disability (IQ≤70) (Clark et al., 2004). Most individuals with FASD are not intellectually disabled (Moore & Riley, 2015), and it is currently unknown the degree to which motor deficits in this population contribute to reduced independence in performing daily living activities. The high prevalence of FASD combined with the need for services for daily living activities in adulthood (Clark et al., 2004; Moore & Riley, 2015) also poses a significant financial burden on families and society (Greenmyer, Klug, Kambeitz, Popova, & Burd, 2018). Addressing motor deficits of children with FASD, early in development, may have significant effects on increasing independence of individuals with FASD into adulthood – especially since motor deficits in individuals with FASD become more pronounced with increasing age (Tamana & Pei, 2014).

A focus on improving motor skills is most important in child-care systems where children are at a disproportionately higher-risk for FASD (16.9% prevalence of FASD) (Lange, Shield,

Rehm, & Popova, 2013). Furthermore, approximately 70% of children diagnosed with FASD are or have been in the foster care system (Burd et al., 2011) and ~80% of children with FASD in

foster or adoptive care are misdiagnosed (Chasnoff, Wells, & King, 2015). Brain development of children with FASD living in child-care institutions may also exacerbate the neurodevelopmental deficits caused from prenatal alcohol exposure. Reduced sensory, cognitive, and social stimulation in child-care institutions has been shown to cause excessive synaptic pruning during development (McLaughlin, Sheridan, & Nelson, 2017). Furthermore, children who experience early deprivation show impaired development in the left and right superior-posterior cerebellar lobes (Crus I and lobule VI) (Bauer, Hanson, Pierson, Davidson, & Pollak, 2009). However, recent findings suggest that postnatal neglect does not make developmental outcomes worse in children with FASD (Mukherjee, Cook, Norgate, & Price, 2019). Therefore, although most children diagnosed with FASD develop in low-stimulation environments where they experience neglect, evidence suggests that the prenatal alcohol exposure is what drives the developmental outcomes for these children.

The following section will review the effect of prenatal alcohol exposure on the developing brain. The brain damage in children with FASD produces a cascade of behavioral, neuropsychological, and motor deficits – the severity of which falls along a spectrum. The pattern of brain structural abnormalities in children with FASD produces a complex behavioral phenotype that differs from children with ASD (Derauf, Kekatpure, Neyzi, Lester, & Kosofsky,

2009; Mattson, Crocker, & Nguyen, 2011; Riccio & Sullivan, 2016). The behavioral and motor deficits associated with brain damage induced from prenatal alcohol exposure is complex and therefore a summary schematic is provided below (Figure 6) (Derauf et al., 2009; Mattson et al.,

2011; Riccio & Sullivan, 2016).

Figure 6: Review of neuroanatomical, neuropsychological, behavioral, and motor outcomes associated with brain damage resulting from prenatal alcohol exposure. FAS = Fetal Alcohol Syndrome; ARBD = Alcohol- Related Birth Defect; ARND = Alcohol-Related Neurodevelopmental Disorder. (Derauf et al., 2009; Mattson et al., 2011; Riccio & Sullivan, 2016).

1.5.2 Brain Motor Areas are Sensitive to Prenatal Alcohol Exposure

Motor areas of the brain – particularly the cerebellum – are sensitive to prenatal alcohol exposure (PAE) (Table 11). For the purposes of this review the effects of PAE on the cerebellum will be reviewed since it has shown to be sensitive to PAE and is also implicated in the core symptoms of both ASD and ADHD (Stoodley, 2014). Structural and functional abnormalities in the corpus callosum and basal ganglia will also briefly be discussed.

Table 10: Review of neuroanatomical structures sensitive to prenatal alcohol exposure. Focus on brain regions involved in motor control.

Neuroanatomical Structure(s) Findings Relative to Controls × Inferior parietal gray matter × Superior temporal lobe gray matter Gray/White Matter Density Ø Inferior parietal white matter (Moore, Migliorini, Infante, & Riley, 2014) – review Ø Bilateral parietal white matter (most prominent in (Derauf et al., 2009) – review left lobe) (Sowell et al., 2008) Ø Cerebral white matter Ø Cerebellar white matter Ø White matter density in lateral splenium of corpus callosum and posterior cingulate × Bilateral temporal × Inferior parietal lobe Cortical Thickness × Right frontal gyrus (Derauf et al., 2009) - review - Findings suggest abnormal synaptic pruning and myelination Corticospinal Tract × Density and percentage of corticospinal neurons in Rat model motor and somatosensory cortex (Miller, 1987) Ø Anterior and posterior CC volume Corpus Callosum (CC) × Inferior and anterior displacement (Moore et al., 2014) – review Ø Thickness of CC (Derauf et al., 2009) – review CC abnormalities (hypoplasia most common) in 42% (Boronat et al., 2017) of children with FASD Ø Gray and white matter of cerebellum Cerebellum Ø Volume of hemispheres and vermis (Moore et al., 2014) – review Ø Volume most significant in anterior vermis (Derauf et al., 2009) – review Anterior vermis anteriorly and superiorly displaced (Boronat et al., 2017) Cerebellum abnormalities in 24% of children with (Zhou et al., 2017) FASD Basal Ganglia Ø Gray matter in caudate, putamen, and pallidum (Moore et al., 2014) – review Ø Volume of caudate nucleus (Derauf et al., 2009) – review Ø Metabolic activity of caudate and putamen (Zhou et al., 2017)

Most studies examine PAE during the rodent postnatal period equal to the human 3rd trimester period. During the 3rd trimester rapid brain growth is occurring especially within the cerebellum (Wang, Kloth, & Badura, 2014b). In humans, the cerebellum continues to develop in the first postnatal year, however in the case of PAE, exposure to the teratogenic effects of alcohol stops after birth. Therefore, later developing cerebellar regions may continue to develop normally. Although the effects of 3rd trimester PAE on the cerebellum has been a focus of research, findings suggest that PAE in the embryonic period of development (3-8 weeks gestation) can cause excessive cell death of cerebellar progenitor cells in the rhombic lip that produces cerebellar hypoplasia of the developing fetus (Sulik, Zucker, Dunty, Dehart, & Chen,

2003). Therefore, although most women who report drinking during pregnancy – 30 to 58 % of women (Edwards & Werler, 2006; Ethen et al., 2008) – stop after the first month of pregnancy

(Ethen et al., 2008), malformations of the cerebellum may have already occurred. Therefore, motor deficits may be identifiable in children prenatally exposed to alcohol in the first month of pregnancy. One main distinction between cerebellum neuroanatomical findings in ASD and

FASD is that the anterior vermis – an early developing region of the cerebellum (O’Hare et al.,

2005) – is most sensitive to PAE with the posterior regions relatively spared (Derauf et al., 2009)

(Table 12). The following section will review the effects of PAE on the developing cerebellum.

1.5.3 The Effects of Ethanol on the Developing Cerebellum

Prenatal alcohol exposure (PAE) has devastating effects on the developing cerebellum at the structural, electrophysiological, and molecular level (Table 12). One of the most consistent neuroanatomical findings in PAE animal models and human imaging studies is a reduction in the size or hypoplasia of the cerebellum (Derauf et al., 2009). In Table 12, more detailed findings of the effects of PAE on the developing cerebellum and its cells are outlined. PAE animal models, with timing of exposure equivalent to the 3rd trimester of human gestation, show (1) reduced number of Purkinje Cells (PC) (Bäckman, West, Mahoney, & Palmer, 1998; Goodlett,

Marcussen, & West, 1990; Hamre & West, 1993; Nirgudkar, Taylor, Yanagawa, & Fernando

Valenzuela, 2016; Re, Tong, & De la Monte, 2016; Servais et al., 2007), (2) reduced number of granule cells (Hamre & West, 1993), (3) reduced number of cerebellar GABAergic interneurons

(Nirgudkar et al., 2016), (4) altered electrophysiological characteristics (Bäckman et al., 1998;

Servais et al., 2007), (5) altered synaptic protein expression (M. Carta, Mameli, & Valenzuela,

2006; Guo et al., 2011), and (6) altered climbing-fiber mediated Parallel Fiber-Purkinje Cell (PF-

PC) long-term depression (LTD) (Carta et al., 2006; Servais et al., 2007) (Table 12). Although most studies have focused on PAE effects on cerebellum development during the brain “growth spurt” period (3rd trimester), findings suggest that cerebellum development can be affected as early as 3 weeks of gestation in 1st trimester (Sulik et al., 2003) and in 2nd trimester mouse models (Nirgudkar et al., 2016). These findings suggest there is no safe timing of PAE on the developing cerebellum.

Table 11: Effects of PAE at the structural, electrophysiological, and molecular level of the developing cerebellum.

Main Findings Study PAE vs. Controls Ø Cerebellum weight Ø PC survival in early vs. late 3rd trimester (Hamre & West, 1993) exposure 3rd trimester rat model Ø PC survival in lobules I-V and IX with Effect of PAE timing early 3rd trimester exposure PCs from Lobules I-X Ø PC survival in lobules VII with late 3rd trimester exposure Ø Granule cells in concert with PCs (Bäckman et al., 1998) 3rd trimester rat model Ø Surviving PCs PC electrophysiology Ø Complex spike activity PCs from Lobules IX-X (Sulik et al., 2003) × Cell death in rhombic lip of developing 1st trimester mouse model embryo (contains cerebellar progenitor cells) (3-8 weeks human equivalent) No PAE (Carta et al., 2006) Ø Climbing Fiber LTD Rat model × Inhibition of mGluR1 receptor signaling Ethanol effects on CF-mediated PC LTD Ø CF-mediated LTD from inhibition of mGluR1 Ø Surviving PCs (20% loss) Ø Motor coordination (Servais et al., 2007) Ø Eye-blink conditioned responses PAE mouse model × PC simple spike firing (30% increase) PC electrophysiology × LTP at PC synapse (LTD converted to LTP) Ø Expression of PKC-J (important for CF- mediated LTD at PF-PC synapse) (Guo et al., 2011) Ø CREB-binding protein expression in 3rd trimester rat model developing cerebellum (50% decrease) PAE on CREB Binding Protein in Developing Ø Histone acetylation (AcH3) in cerebellum Cerebellum (Re et al., 2016) Ø Surviving PCs PAE rat model (Nirgudkar et al., 2016) Ø Golgi, stellate, and basket cells in lobule II 2nd and 3rd trimester PAE mouse model Ø PC number in lobules II, IV-V, and IX PAE on GABAergic interneurons and PCs of Ø Volume of lobules II, IV-V, VI-VII, IX and cerebellum X Lobules I-X

One of the most consistent findings of regional effects of PAE on the developing cerebellum in individuals with FASD is the anterior cerebellar vermis determined using MRI

(Table 13). These findings, summarized in Table 13, show a consistent reduction of area and volumetric reduction of the anterior vermis in children with FASD (Astley et al., 2009; Cardenas et al., 2014; O’Hare et al., 2005; Sowell et al., 1996) with a less consistent reduction in vermal lobules VIII-X (Cardenas et al., 2014; O’Hare et al., 2005). Lesions to the anterior cerebellar lobe are associated with poorer manual dexterity in stroke patients (Stoodley et al., 2016).

Furthermore, structural abnormalities in the anterior lobe are associated with increased postural sway in the anterior-posterior direction (Morton & Bastian, 2004).

In contrast to findings in children with FASD, children with Attention-Deficit

Hyperactivity Disorder (ADHD) – a neurodevelopmental disorder characterized by inattentive, hyperactive and/or impulsive behaviors – show consistent reductions in the posterior inferior vermis (VIII-X) (Table 14). ADHD is a disorder strongly related to dopaminergic function with several dopamine-related genes increasing susceptibility to ADHD (Gold, Blum, Oscar-Berman,

& Braverman, 2014). Furthermore, medications used to treat the hypodopaminergic state of

ADHD either stimulate dopamine release (Amphetamine) or inhibit re-uptake of dopamine from the synaptic cleft (Methylphenidate) (Gold et al., 2014). The use of stimulants may help to normalize cerebellar activity in children with ADHD. In children with ADHD, a single-dose of

Methylphenidate significantly increases resting-state activity of the posterior cerebellar vermis and prefrontal cortex (An et al., 2013) and significantly improves postural control in children with ADHD (Bucci, Stordeur, Acquaviva, Peyre, & Delorme, 2016) – a function mediated by the cerebellar vermis (Colnaghi, Honeine, Sozzi, & Schieppati, 2017). Interestingly, dopamine receptors are present in vermal lobules IX and X of the cerebellum (Hurley, Mash, & Jenner,

2003) and repetitive stimulation of the cerebellum in rats increases dopamine release in prefrontal cortex (Rogers et al., 2011) via cerebellar projections to the ventral tegmental area

(Carta, Chen, Schott, Dorizan, & Khodakhah, 2019). The interconnectedness of the cerebellum and basal ganglia may help explain anatomical deficits within the cerebellum of individuals with

ADHD possibly resulting from a hypodopaminergic state. The complexity of interactions between the dopaminergic system and the cerebellum in children with ADHD warrants an in- depth review that will not be provided here. The major take-away of this section is that the cerebellum appears to be differentially affected by various neurophysiological mechanisms or teratogenic effects from which distinct neurodevelopmental disorders originate.

Table 12: FASD-specific structural deficits in cerebellar vermis. MRI=Magnetic Resonance Imaging; FASD=Fetal Alcohol Spectrum Disorder; FAS=Fetal Alcohol Syndrome; FAE=fetal alcohol effects; ARND=alcohol-related neurobehavioral disorder; PAE=prenatal exposure to alcohol; SE/AE=static encephalopathy/alcohol exposed; NB/AE=neurobehavioral disorder/alcohol exposed. (Ù=no change from controls; Ø=decrease from controls).

FASD Cerebellar Vermis Study FASD-Specific Findings Volume or Area Difference Age Measurement Total VI- VIII- Original Studies Groups I-V (years) Tool Vermis VII X (Goodlett et al., Neonatal AE Stereological - Ø Ù Ø 1990)a Rats TD Assessmenta FAS (Sowell et al., 15 PAE MRI - Ø Ù Ù 1996) TD FAS (Autti-Ramo et FAE 12-14 MRI Ø - - - al., 2002) ARND TD (O’Hare et al., FAS/PAE 8-22 MRI - Ø Ù Ø 2005) TD FAS (Astley et al., SE/AE 12 MRI - Ø Ù Ù 2009) NB/AE TD (Cardenas et al., PAE 10-18 MRI - Ø Ù Ø 2014) TD

Table 13: ADHD-specific structural deficits in cerebellar vermis. MRI=Magnetic Resonance Imaging; ADHD=Attention-Deficit Hyperactivity Disorder. (×=increase from controls; Ù=no change from controls; Ø=decrease from controls; -=not measured).

ADHD vs Controls Cerebellar Vermis Study ADHD-Specific Findings Volume or Area Difference Age Measurement VI- VIII- Original Studies Groups I-V (years) Tool VII X (Berquin et al., ADHD 12 MRI Ù Ù Ø 1998) TD (Mostofsky, Reiss, Lockhart, ADHD 8-14 MRI Ù Ù Ø & Denckla, TD 1998) (Castellanos et ADHD 5-16 MRI - - Ø al., 2001) TD (Bussing, Grudnik, Mason, ADHD 8-12 MRI Ù Ø Ø Wasiak, & TD Leonard, 2002) ADHD (Hill et al., 2003) 10 MRI Ø Ù Ø TD (Bledsoe, Semrud- ADHD 12 MRI Ù Ù Ø Clikeman, & TD Pliszka, 2009) (Bledsoe, Semrud- ADHD 12 MRI Ù Ù Ø Clikeman, & TD Pliszka, 2011) Meta-analyses (Valera, Faraone, ADHD Murray, & 11-14 MRI - - Ø TD Seidman, 2006) ASD (Stoodley, 2014) 7-37 ADHD VBM - - Ø DYS

1.5.4 FASD Motor Deficits

This section will review findings of motor deficits in children with FASD. These findings support cerebellar dysfunction in children with FASD. Deficits in motor timing during rhythmic finger tapping (Du Plessis et al., 2015; Simmons, Levy, Riley, Madra, & Mattson, 2009), visuomotor force regulation (Nguyen, Ashrafi, Thomas, Riley, & Simmons, 2013; Nguyen,

Levy, Riley, Thomas, & Simmons, 2013; Simmons et al., 2012), gait (Taggart, Simmons,

Thomas, & Riley, 2017), cerebellar-mediated learning (Jacobson et al., 2011), visuomotor reaction time (Jacobson, Jacobson, & Sokol, 1994), and postural control (Kooistra et al., 2009) support cerebellar dysfunction in children with FASD (Table 15; Appendix 1). However, few studies have compared motor function in children with FASD to children with other neurodevelopmental disorders. This would be beneficial since there is clear overlap in motor deficits in children with FASD with deficits reported in children with ASD. For example, structural integrity of Crus I is crucial for eyeblink conditioning (Mccormick & Thompson,

1984), a region showing hypoactivation during rhythmic finger tapping in children with FASD

(Du Plessis et al., 2015). Furthermore, visuomotor static and dynamic isometric force tracking deficits implicate visuomotor integration circuitry such as Crus I/II, left IPL, and premotor areas

(Vaillancourt et al., 2005). Right Crus I is implicated as a mediating structure in core social and communication deficits of ASD (Stoodley et al., 2018), however, only 2.6% of children with

FASD present co-occurring ASD (same prevalence as general population) (Lange, Rehm,

Anagnostou, & Popova, 2017). It is unclear whether visuomotor integration circuitry is truly impaired or whether force tracking deficits are due to underlying circuitry involved in force control (ex. basal ganglia, M1, anterior cerebellum). Furthermore, there is overlap in postural control deficits between children with FASD and ADHD (Kooistra et al., 2009) and children

with ASD also show postural control deficits (Doumas et al., 2016; Lim et al., 2017). As we have outlined previously, PAE exerts its greatest influence on the development of the anterior cerebellum (Table 14), a region where sensorimotor function is topographically organized

(Schmahmann, MacMore, & Vangel, 2009; Stoodley et al., 2016). Furthermore, limb ataxia and ataxia of posture and gait are associated with anterior cerebellar vermal and paravermal damage

(lobules II-VI) (Schoch et al., 2006). These findings suggest cerebellar structural deficits within the anterior cerebellar vermis may contribute to force-control, postural control, and gait deficits reported in children with FASD (Table 15; Appendix 1).

In addition to cerebellum-mediated learning and motor deficits, corpus callosum (CC) abnormalities have also been identified in children with FASD (Boronat et al., 2017; Derauf et al., 2009) (Table 15; Appendix 1). A recent MRI study showed 26/62 patients (42%) with suspected FASD showed CC structural abnormalities with thinning of the CC (hypoplasia) the most common abnormality followed by partial agenesis (Boronat et al., 2017). Furthermore, this study showed CC abnormalities (42% of patients) were more common than cerebellar abnormalities (24% of patients) (Boronat et al., 2017). The CC is an important structure important for bimanual coordination, interhemispheric inhibition/facilitation, hand preference, and bimanual skill learning (Ciechanski, Zewdie, & Kirton, 2017; Cowell & Gurd, 2018;

Gooijers & Swinnen, 2014). A few studies have examined motor functions related to CC function in children with FASD, including bimanual coordination (Roebuck-Spencer, Mattson,

Marion, Brown, & Riley, 2004) and functional hand dominance (Domellöf, Rönnqvist, Titran,

Esseily, & Fagard, 2009; Janzen, Nanson, & Block, 1995) (Table 15; Appendix 1).

Considering the overlap in cerebellar structural deficits and motor skills impaired in both children with ASD and FASD, cross-syndrome studies may be useful in identifying specific

motor phenotypes between neurodevelopmental disorders. Furthermore, assessing bimanual coordination or functional hand dominance may show specific deficits in children with FASD when compared to children with ADHD and ASD resulting from corpus callosal structural abnormalities.

1.6 References

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CHAPTER 2

Isometric Force Regulation in Children with Autism Spectrum Disorder: A Cross- Syndrome Study

Daniel E. Lidstone

Co-authored By

Faria Z. Miah, Brach Poston, Julie F. Beasley, Janet S. Dufek

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Significance of the Chapter

The precision-grip isometric force tracking tasks represents one of the most well-studied motor control tasks. One reason is that both non-human primates and human research participants can manually adjust precision-grip force output to match a target force displayed on a monitor. Furthermore, grasping represents one of the most frequently performed tasks in daily life. The grasping circuit includes the inferior parietal lobule (IPL), ventral premotor (PMv), and hand area of primary motor cortex (M1). In addition to the grasping circuit, the subcortical basal ganglia and cerebellar regions are also activated. In children with ASD, structural deficits within the IPL, cerebellum, and basal ganglia have been observed. Therefore, deficits in the control of isometric grip-force may be impaired in children with ASD. In addition to eliciting widespread cortical activation, several features can be extracted from the static force signal including: (1) error; (2) variability; (3) complexity or the structure of variability; and (4) the frequency structure of the signal. A small number of studies have identified: (1) greater error; (2) greater variability; (3) lower complexity; and (4) increased proportion of low frequency power (0-4 Hz) in the isometric static force-signals produced by individuals with ASD compared to typically developing controls. However, the current study uses, for the first-time, a cross-syndrome approach to examine what isometric force signal features differentiate children with ASD from typically developing children and children with other neurodevelopmental disorders. The findings from this study may help: (1) determine the clinical utility of acquiring isometric grip- force data from children with ASD and (2) contribute knowledge towards understanding the neurological underpinnings of ASD.

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Abstract

Motor deficits are highly prevalent in children with Autism Spectrum Disorder (ASD) that may be associated with impaired regulation of force output. The purpose of this study was to examine force regulation performance in children with ASD by assessing the accuracy, variability, complexity, and frequency structure of force oscillations during a visuomotor precision-grip force-maintenance task. The force-signal features were compared between children with ASD, children with Fetal Alcohol Spectrum Disorder, children with Attention-

Deficit Hyperactivity Disorder, and typically developing (TD) children. We hypothesized that force oscillations from children with ASD would be less accurate, more variable, less complex, and have a higher proportion of low-frequency content compared to TD children. Eighty children

(7-17 years old) participated in this study. Following measurement of precision-grip maximal voluntary contraction (MVC), participants completed five trials maintaining a target force at

15% of MVC for 20-s. Results showed no differences in force accuracy, variability, complexity, and frequency structure between any group. However, low-frequency force oscillations were significantly associated with force accuracy, variability, and complexity in the ASD group only.

Therefore, the magnitude of low-frequency force oscillations may contribute to motor control deficits in children with ASD.

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2.1 INTRODUCTION

Children with Autism Spectrum Disorder (ASD) display a complex behavioral phenotype that includes social-interaction deficits, communication challenges, and restricted and repetitive behaviors or interests (American Psychiatric Association, 2013). Recent findings suggest ASD- specific social and communication deficits are mediated by structural deficits in the right posterolateral cerebellum (Badura et al., 2018; Stoodley et al., 2018). Furthermore, ASD-specific repetitive and stereotyped behaviors are mediated by the cerebellar posterior-superior vermis

(lobules VI-VII) (Badura et al., 2018). Posterior-superior vermis structural abnormalities are the most prevalent neuroanatomical finding in the cerebellar vermis of ASD patients (Stanfield et al.,

2008). The posterior cerebellum is also functionally connected to the indirect pathway of the basal ganglia (Bostan, Dum, & Strick, 2010; Hoshi, Tremblay, Féger, Carras, & Strick, 2005;

Milardi et al., 2016), with vermal lobule VII being the most prominent target of the subthalamic nucleus (STN) (Jwair, Coulon, & Ruigrok, 2017). ASD-specific repetitive and stereotyped behaviors are also mediated by the STN (Chang et al., 2016; Tanimura, King, Williams, &

Lewis, 2011; Tanimura, Vaziri, & Lewis, 2010). Therefore, structural abnormalities in specific neuroanatomical regions of the posterior cerebellum and basal ganglia may mediate the ASD- specific behavioral phenotype (Badura et al., 2018; Stoodley, 2014; Stoodley et al., 2018; Wilkes

& Lewis, 2018).

The posterolateral cerebellum (Crus I/II) overlaps with motor circuitry and is activated during the visuomotor isometric precision-grip force tracking task (Coombes, Corcos, Sprute, &

Vaillancourt, 2010; Moulton et al., 2017; Neely, Coombes, Planetta, & Vaillancourt, 2013;

Vaillancourt, Mayka, & Corcos, 2005). Therefore, if deficits in the posterolateral cerebellum are specific to children with ASD, visuomotor isometric force regulation performance should be

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more impaired in children with ASD compared to typically developing (TD) children. Previous studies have shown individuals with ASD show greater force variability and lower force complexity during visuomotor isometric precision-grip force regulation tasks (Mosconi et al.,

2015; Wang et al., 2015). The increased variability of force output in individuals with ASD may result from deficits in structures involved with visuomotor integration. Furthermore, Mosconi et al. (2015) found that individuals with ASD have greater low frequency power (0-4 Hz) compared to TD controls during an isometric force regulation task (Mosconi et al., 2015). In TD individuals, increased force variability, lower force complexity, and increased proportion of power in frequencies between ~0-1.95 Hz are produced when the frequency of visual feedback is decreased (Slifkin, Vaillancourt, & Newell, 2000). Therefore, the force output features found in individuals with ASD performing a precision-grip force regulation task are associated with impaired visuomotor integration.

During isometric precision-grip static force tracking, visuomotor processing is most related to the proportion of power in the 0-1 Hz bin since: (1) increases in the frequency of visual feedback reduces force oscillations between 0-1 Hz (Slifkin et al., 2000); (2) increases in visual gain of feedback significantly reduces force oscillations between 0-1 Hz (Baweja, Kennedy, Vu,

Vaillancourt, & Christou, 2010); and (3) removal of visual feedback significantly increases force oscillations between 0-1 Hz (Baweja, Patel, Martinkewiz, Vu, & Christou, 2009). Furthermore, several studies have observed that the proportion of power in the 0-1 Hz frequency bin is significantly associated with force error during an isometric precision-grip static force tracking task (Baweja et al., 2010, 2009; Park, Kim, Yacoubi, & Christou, 2019; Slifkin et al., 2000). The proportion of power in the 0-1 Hz bin can also explain ~50% of the reduction of variability associated with increased visual feedback gain (Baweja et al., 2010). These findings suggest that

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increased force oscillations between 0-1 Hz are associated with reduced visuomotor processing and ultimately reduced force control. Therefore, force oscillations between 0-1 Hz during precision-grip static force tracking may be greater in children with ASD.

The purpose of the current study is to examine force regulation performance in children with ASD by assessing the accuracy, variability, complexity, and frequency structure of force oscillations during a visuomotor precision-grip force regulation task. To examine what features are specifically impaired in children with ASD, clinical controls were included in the study in addition to TD controls. The clinical control groups consisted of a Fetal Alcohol Spectrum

Disorder (FASD) and an Attention-Deficit Hyperactivity Disorder (ADHD) group. Based on previous findings that visuomotor processing may be impaired in individuals with ASD, it was hypothesized that: (1) force accuracy and variability would be significantly greater in children with ASD vs. TD controls; (2) force complexity would be significantly lower in children with

ASD vs. TD controls; and (3) the proportion of 0-1 Hz force oscillations would be significantly greater in children with ASD vs. TD controls. Finally, it was hypothesized that children in the clinical control groups would not show any differences between ASD or TD groups on force output features.

2.2 METHODS

2.2.1 Participants

A total of eighty children were recruited to participate in the study (Table 16). Children with FASD were diagnosed through the University of Nevada/Las Vegas FAS clinics by a multi- disciplinary team. Children diagnosed with ASD or ADHD were recruited through the UNLV

Ackerman Autism Center and the Las Vegas community. Parents of children recruited in the Las

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Vegas community confirmed diagnoses by presenting a copy of the clinical diagnosis to the experimenter prior to data collection. Inclusion criteria for the study required the child to (1) be

7-17 years old, (2) have normal or corrected-to-normal vision, and (3) have a clinical diagnosis of ASD, ADHD, FASD, or be TD. Children were excluded from participation if the child had an

(1) intellectual disability (FSIQ-2 ≤ 70), (2) upper or lower extremity deformity, (3) current orthopedic injury, and (4) a known genetic disorder. Intellectual disability was ruled out prior to testing using two sub-tests of the WASI-II (vocabulary and matrix reasoning) to provide a Full-

Scale Intelligence Quotient (FSIQ-2). Children in the ASD group did not have a co-occurring

FASD diagnosis. TD children were excluded if they had a psychiatric disorder and/or a first- degree relative with a diagnosis of either ASD or ADHD. Parental consent and child assent were obtained prior to testing.

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Table 14: Demographic characteristics of participants in ASD, FASD, ADHD, and TD groups.

FASD (N=17) ASD (N=23) ARND (n=5) ADHD (N=18) TD (N=22) P value pFAS (n=6) FAS (n=3) N.S. (n=3) Sex [no. Males (%)] 19 (83%) 6 (35%) 12 (67%) 14 (64%) - Age (years) 12.4±2.8 12.5±3.0 10.4±2.1 11.7±2.7 0.08 (min-max) (7.3-17.9) (7.4-17.6) (7.3-14.5) (7.4-16.8) FSIQ-2 93±13 91±11 99±17 112±10*** <0.0005 (min-max) a (71-122) (72-118) (71-137) (94-129) Comorbid ADHD 12 (52%) 15 (88%) - - - [no. (%)] Prematurity 6 (26%) 4 (24%) 4 (22%) 2 (9%) - <37wks [no. (%)] Low Birth Weight 2 (8.7%) 4 (24%) 3 (17%) 2 (9%) - <2500 g [no. (%)] Prenatal Drug 3 (13%) 6 (35%) 7 (39%) 0 - Exposure [no. (%)] Vanderbilt

(no. ≥ 2) b ADHD a 8.3±5.1 8.3±5.4 11.1±4.3 1.1±3.0*** <0.0005 Oppositional 2.0±2.9 6.1±4.7 4.9±4.7 0.4±1.2** <0.0005 Defiant/Conduct a Anxiety/Dep.a 1.2±2.0 1.7±1.8 1.7±2.1 0.2±0.7** 0.008 Handedness (n/%) Right 19 (82%) 16 (94%) 15 (83%) 21 (95%) - Left 2 (9%) 1 (6%) 2 (11%) 1 (5%) - Bilateral 2 (9%) 0 1 (6%) 0 - Medication Use 12 (52%) 14 (82%) 11 (61%) 0 - [no. (%)] a = Group differences assessed using Kruskal-Wallis test. Bonferroni corrections for multiple comparisons. (α≤0.05) b = Mean number of symptoms scored as often (score=2) or very often (score=3) on Vanderbilt PARENT Informant. Symptom scores (≥2) were totaled for Attention-Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant/Conduct Disorder, and Anxiety/Depression. ARND = Alcohol Related Neurodevelopmental Disorder pFAS = Partial Fetal Alcohol Syndrome FAS = Fetal Alcohol Syndrome N.S. = Not specified *** = Significant difference between TD and all groups. ** = Significant difference between TD group and both ADHD and FASD groups.

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2.2.2 Procedures

Handedness Assessment

Following intellectual testing, handedness was assessed using the Almli Handedness

Assessment (Almli, Rivkin, & McKinstry, 2007; Knaus, Kamps, & Foundas, 2016). This handedness assessment has previously been used on children with ASD between the ages of 3-17

(Knaus et al., 2016). The participant was seated in front of the experimenter and asked to complete 10 tasks using their hands with items presented at midline. Handedness was scored from -3.16 (complete left handedness) and +3.16 (complete right-handedness) (Almli et al.,

2007; Knaus et al., 2016). If handedness was scored between -1 and +1, indicating no hand preference, the hand used for the writing task was selected as the dominant-hand.

Isometric Precision-Grip Force Regulation Task Children were seated in a height adjustable chair with their dominant forearm resting on a height adjustable table with their thumb and index fingers squeezing a precision-grip apparatus

(Figure 7, A). Desk height was adjusted to allow for a 90-degree flexed elbow position and participants were seated 1-meter from the center of a 47 in monitor (NEC E424, resolution =

1080p 60 Hz, IL, USA). The participant distance from the monitor, the target position, and the target/cursor size were held constant for each participant resulting in a visual gain (α) of α =

1.43. A custom-designed 3D printed precision-grip device with alignment shafts and a sliding block was used to house a calibrated load cell (Futek LB 350 - 25 lb capacity, Irvine, CA, USA) and allow measurement of the normal force component during precision gripping. The height of the grip device was adjusted so the thumb and index finger were parallel to the base of the apparatus. Amplification and A/D conversion was performed using a 24-bit resolution, 25mV/V

NI 9237 bridge module (National Instruments, Austin, TX, USA) and force output was sampled

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at 2000 Hz. Data was acquired, and visual stimuli presented using LabVIEW 2017 software

(National Instruments, Austin, TX, USA).

Figure 7: Experimental procedure. (A) precision-grip apparatus, (B) MVC visual stimulus, (C) force regulation target (green) and cursor (white), (D) sample data force output from a single participant, (E) force signal (D) decomposed into the frequency domain.

Prior to performing the force regulation task, the participants performed three maximum voluntary contractions (MVC). MVCs were performed in a similar manner to a previous study

(Poston, Christou, Enoka, & Enoka, 2010). The participants kept their forearm on the table and were told to press on the blocks using only their thumb and index finger to make a space ship go as high as possible (Figure 7, B). A self-selected rest period was provided between attempts. The best of three attempts was recorded as the MVC.

Following the MVC, participants completed five trials of a static force target tracking task at 15% of MVC (Figure 7, D). The start of each trial was signaled using an auditory tone.

The participants were instructed to, as quickly and accurately as possible, move the white block cursor inside the green target window and hold it as steady as possible until a second tone signaled the end of the trial (Mosconi et al., 2015; Wang et al., 2015). The position of the target

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was automatically adjusted for each participant so the 15% MVC static target was fixed at the center of the display at eye-level. A rest period of 20-s was provided after each trial (Figure 7,

C).

Signal Processing All signal processing was performed in MATLAB 2018a (MathWorks, Natick, MA). For all five trials, the force time-series data was down sampled from 2000 Hz to 200 Hz (Mosconi et al., 2015) and digitally filtered using a 4th order low-pass Butterworth filter with a 30 Hz cut-off

(Mosconi et al., 2015). The first 4-s and last 1-s of force data in the 15% MVC static trial were discarded to exclude initial adjustments in matching the target force and adjustments in force due to anticipation of the end of the trial (Slifkin et al., 2000; Sosnoff & Newell, 2005). The quality of each trial was defined by the ability to keep the cursor block within the target. The three most accurate trials out of the five recorded trials were analyzed for accuracy, variability, complexity, and frequency structure.

Accuracy: Relative Root Mean Square Error (rRMSE) To examine the accuracy of static tracking, the rRMSE was calculated (Kurillo, Zupan, &

Bajd, 2004) (Eq. 1). The rRMSE is defined as the square root of the ratio of the mean square value of the error signal to the maximum square value of the 15% MVC target – expressed as a percentage. A lower tracking error suggests better visuomotor tracking performance.

(Eq. 1)

where:

is the measured force output

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is the target force is the trial time Variability: Signal-to-Noise Ratio

To examine the variability of force output, the signal-to-noise ratio (SNR) (mean/SD) of the force signal was calculated (Nguyen, Levy, Riley, Thomas, & Simmons, 2013; Simmons et al., 2012). Although the reciprocal of the SNR is usually reported as the measure of relative variability of the force-signal, the SNR was used in this study to allow for comparison of SNR values from precision-grip isometric force output from children with and without neurodevelopmental disorders (Deutsch & Newell, 2004; Nguyen et al., 2013; Simmons et al.,

2012). Larger SNR values represent lower relative variability whereas smaller SNR values represent greater relative variability.

Complexity: Sample Entropy Sample entropy (SampEn) was defined as the negative natural logarithm of the conditional probabilities that two sequences at m distance apart remain similar at m+1 (Richman

& Randall Moorman, 2000). A series of vectors of m length were formed for the entire length of the detrended time-series (N) and vectors were considered alike if the tail and head of the m- length vector falls within a tolerance (r) defined as a proportion (R) of the standard deviation of the detrended force-signal (r = R X SD) (Yentes et al., 2013). The total number of vectors identified as matches were then divided by N-m+1 and defined as B. This process was subsequently repeated for m+1 and defined as A. The equation for SampEn is shown below (Eq.

2). The SampEn algorithm returns a value between 0 and 2, with ~0 reflecting a perfectly repeatable time series (ex. sine wave) and ~2 reflecting a perfectly random time series. It has been suggested that low entropy reflects high attentional demands, whereas higher entropy reflects greater automaticity and less attentional demands (Donker, Roerdink, Greven, & Beek,

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2007; Roerdink, Hlavackova, & Vuillerme, 2011). Lower SampEn have also been observed in the isometric force output of individuals diagnosed with multiple concussions, demonstrating its sensitivity to CNS function (Studenka & Raikes, 2017). The algorithm used for calculating sample entropy was provided by the 2017 University of Nebraska at Omaha (UNO) Nonlinear

Analysis Workshop. The algorithm parameters m (vector length) and R (proportion of the standard deviation to be used for matching) were defined as m = 2 and R = 0.2 (Mosconi et al.,

2015; Studenka & Raikes, 2017).

(Eq. 2)

Where: m = vector length (m = 2) r = tolerance (r = 0.2 x SD) N = dataset = number of matching patterns of length m + 1 = number of matching patterns of length m

Power Spectrum The power spectrum for each detrended force time-series was computed in MATLAB using Welch’s averaged periodogram method with a non-overlapping 1024-point Hanning window and a 0.1953 Hz bin width (Figure 7, E) (Deutsch & Newell, 2003; Mosconi et al.,

2015). The percent proportion of power (%) in four frequency bins 0-1 Hz (0.1953-0.9766 Hz),

0-4 Hz (0.1953-3.9063 Hz), 4-8 Hz (4.1016-7.8125 Hz), and 8-12 Hz (8.0078-11.914 Hz) was calculated by normalizing the integrated power (N2) in each frequency bin to the overall power spectrum between 0 and 12 Hz (Deutsch & Newell, 2003, 2004; Sosnoff & Newell, 2005).

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Increased power in the 0-4 Hz frequency range is associated with slow visual feedback processes whereas higher frequencies up to 12 Hz are associated with faster feedforward processes

(Sosnoff & Newell, 2005). Furthermore, changes in visuomotor processing are reflected in frequencies below 1 Hz (Baweja et al., 2010, 2009; Slifkin et al., 2000). Therefore, a separate 0-

1 Hz frequency bin was included. The dependent variable for the spectral analysis was the normalized power (%) in each frequency bin.

2.2.3 Statistical Procedures

A series of one-way ANCOVAs were conducted to examine between-group (ASD,

FASD, ADHD, TD) differences on force accuracy (rRMSE), variability (SNR), complexity

(SampEn), and proportion of power in each frequency band (0-1 Hz, 0-4 Hz, 4-8 Hz, 8-12 Hz).

Age was used as a covariate for all statistical tests. Post-hoc analyses were performed using the

Bonferroni adjustment and all tests were conducted with significance set at α = 0.05. Partial correlations – covarying for age – were also performed between the proportion of power in each frequency bin and motor performance measures. All statistical procedures were performed using

IBM SPSS Statistics Package 26.0 (IBM, Armonk, NY).

2.3 RESULTS

2.3.1 Motor Performance

2 A significant group effect was observed for MVC (F(3,75) = 3.837, p = 0.013, η = 0.133) and the post-hoc revealed that children with ASD had significantly lower MVC compared to children with FASD (p = 0.038) and TD (p = 0.025) children (Figure 8). For relative force

2 accuracy (rRMSE) (F(3,75) = 0.605, p = 0.614, η = 0.024), relative variability (SNR) (F(3,75) =

2 2 1.015, p = 0.391, η = 0.039), and force complexity (SampEn) (F(3,75) = 1.863, p = 0.143, η =

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0.069) there were no significant differences observed (Figure 8). However, for SampEn the effect size was moderate (η2 = 0.069) (Cohen, 1988) and the largest pairwise effect size was observed between the ASD and FASD groups (d = 0.155) (Cohen, 1988). As an exploratory analysis, an ANCOVA – with age as the covariate – was performed between the ASD and FASD groups only. The resulting ANCOVA showed a significant difference between ASD and FASD

2 for SampEn (F(1,37) = 4.451, p = 0.042, η = 0.107).

Individual trial data for the best three trials – three trials out of five with the lowest rRMSE – are shown in Figure 9.

Figure 8: Mean and standard errors of motor performance data for MVC, rRMSE, SNR, and SampEn. *p < 0.05. 128

Figure 9: Best three trials (lowest rRMSE) for each participant in ASD, FASD, ADHD, and TD groups. Green = best trial for each participant, Blue = 2nd best trial for each participant, Red = 3rd best trial for each participant.

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Frequency Structure

No significant group differences were observed for proportion of power in the 0-1 Hz

2 2 (F(3,75) = 1.441, p = 0.238, η = 0.054), 0-4 Hz (F(3,75) = 1.377, p = 0.256, η = 0.052), 4-8 Hz

2 2 (F(3,75) = 1.258, p = 0.295, η = 0.048), or 8-12 Hz (F(3,75) = 1.032, p = 0.384, η = 0.040) frequency bands (Figure 10).

Figure 10: Proportion of power in each frequency band (0-1 Hz, 0-4 Hz, 4-8 Hz, 8-12 Hz) normalized to total power between 0-12 Hz.

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2.3.2 Partial Correlations

Partial correlations between 0-1 Hz and force performance characteristics (rRMSE, SNR,

SampEn) were only significant for the ASD group (|r| > 0.42, p ≤ 0.05) (Table 17, Figure 11). In the TD group, 0-1 Hz oscillations were not associated with any performance measure (|r| < 0.06, p > 0.8) (Table 17). Collectively, increased 0-1 Hz oscillations were associated with greater error, greater variability, and lower complexity of force output only in the clinical groups (Table

17).

Partial correlations between proportion of 0-4 Hz power and rRMSE was significant for the ASD, ADHD, and TD groups (r > 0.4, p < 0.05). SNR was significantly associated with proportion of 0-4 Hz power in the FASD and TD groups (r > 0.4, p < 0.05). The proportion of 0-

4 Hz power and SampEn were significant for all groups (r > -0.58, p ≤ 0.005). Overall, increased

0-4 Hz oscillations were associated with greater error, greater variability, and lower complexity of force output (Table 17).

Partial correlations between proportion of 4-8 Hz power and rRMSE were significant for the ADHD group only (r = -0.54, p = 0.02). SNR was significantly associated with the proportion of 4-8 Hz power in the FASD group only (r = -0.58, p = 0.01). The proportion of 4-8

Hz power and SampEn were significant for both the FASD and ADHD groups (r ≥ 0.65, p ≤

0.005). Overall, increased 4-8 Hz oscillations were associated with lower error, lower variability, and greater complexity of force output (Table 17).

Partial correlations between the proportion of 8-12 Hz power and rRMSE, SNR, and

SampEn were significant for each group (|r| ≥ 0.42, p ≤ 0.04) (Table 17). Overall, increased 8-12

Hz oscillations were associated with lower error, lower variability, and greater complexity of force output (Table 17).

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Table 15: Partial correlations (covarying for age) between normalized power in each frequency band and performance variables (rRMSE, SNR, and SampEn). P values are shown in brackets and significant correlations are in bold (p ≤ 0.05).

Normalized Power (%) 0-1 Hz 0-4 Hz 4-8 Hz 8-12 Hz

ASD r = 0.45 (0.03) ASD r = 0.44 (0.04) ASD r = -0.34 (0.11) ASD r = -0.42 (0.04)

FASD r = 0.43 (0.09) FASD r = 0.48 (0.055) FASD r = -0.46 (0.07) FASD r = -0.50 (0.04) rRMSE ADHD r = 0.38 (0.12) ADHD r = 0.55 (0.02) ADHD r = -0.54 (0.02) ADHD r = -0.59 (0.01)

TD r = 0.03 (0.87) TD r = 0.46 (0.03) TD r = -0.30 (0.18) TD r = -0.50 (0.01)

ASD r = -0.42 (0.04) ASD r = -0.36 (0.10) ASD r = 0.22 (0.32) ASD r = 0.48 (0.02)

FASD r = -0.30 (0.2) FASD r = -0.61 (0.01) FASD r = 0.58 (0.01) FASD r = 0.65 (0.006) SNR ADHD r = -0.12 (0.62) ADHD r = -0.44 (0.07) ADHD r = 0.40 (0.11) ADHD r = 0.52 (0.03)

TD: r = -0.053 (0.81) TD r = -0.48 (0.02) TD r = 0.35 (0.11) TD r = 0.49 (0.02)

ASD r = -0.42 (0.050) ASD r = -0.59 (0.004) ASD r = 0.39 (0.06) ASD r = 0.67 (0.001)

FASD r = -0.24 (0.36) FASD r = -0.69 (0.003) FASD r = 0.66 (0.005) FASD r = 0.72 (0.002) SampEn ADHD r = -0.32 (0.20) ADHD r = -0.66 (0.004) ADHD r = 0.65 (0.004) ADHD r = 0.65 (0.004)

TD r = -0.03 (0.89) TD r = -0.59 (0.005) TD r = 0.34 (0.12) TD r = 0.75 (<0.0005)

Figure 11: Partial correlations in the ASD group (covarying for age) between 0 to 1 Hz normalized power and (A) rRMSE (r = 0.45, p = 0.03), (B) SNR (r = -0.42, p = 0.04), and (C) SampEn (r = -0.42, p = 0.050).

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2.4 DISCUSSION

The purpose of the current study was to examine visuomotor performance in children with ASD by assessing the accuracy, variability, complexity, and frequency structure of force oscillations during a visuomotor precision-grip force regulation task. In this study, isometric precision-grip force regulation was examined in children with ASD, ADHD, FASD, and TD controls. This was the first study to use a cross-syndrome approach to examine force output features (relative accuracy, variability, complexity, and frequency structure) specifically impaired in children with ASD. There were three main findings. First, although MVC was significantly lower in the ASD group compared to the FASD and TD groups, no other group differences were observed for any other motor performance or frequency structure features.

Second, the proportion of power in the 0-1 Hz frequency band was associated with force accuracy, variability, and complexity in children in each clinical group, a finding that was not observed in TD children. Third, only significant partial correlations between the proportion of 0-

1 Hz power and force accuracy, variability, and complexity were observed in children with ASD.

The fact that all three motor performance measures were correlated to 0-1 Hz power in the ASD group represents a novel and potentially important finding. The magnitude of 0-1 Hz force oscillations may be associated with visuomotor processing and possibly contribute to more general motor control deficits in children with ASD.

Motor Performance

Previous studies have shown contradictory findings relating to maximal grip-strength in individuals with ASD. Although, this study remains the first to compare motor performance between children with ASD and FASD, the literature supports the finding of reduced grip strength in children with ASD compared to TD children (Travers et al., 2015). Travers et al.

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(2015), provided evidence in a large sample of children and adults with ASD (N=67, 5-33 years old) that brainstem white matter integrity is significantly associated with grip strength in children with ASD (Travers et al., 2015). In contrast, other studies have reported no differences in grip- strength between individuals with ASD and TD controls (Duffield et al., 2013; Mosconi et al.,

2015; Neely et al., 2016; Wang et al., 2015). In the current study, children with ASD had significantly lower precision-grip MVC compared to children with FASD and TD children.

Further studies are required to determine whether grip-strength deficit is a motor phenotype of

ASD.

Force accuracy, relative variability, and complexity were measured in this study using the relative root-mean square error (rRMSE), signal-to-noise ratio (SNR), and sample entropy

(SampEn), respectively. The results revealed that there were no group differences in all three performance measures. In support of the findings from this study, Neely et al. (2016) and Wang et al. (2019) also showed no significant differences in relative variability between individuals with ASD and TD controls during a grip-force regulation task (Neely et al., 2016; Wang et al.,

2019). However, three other studies support greater relative variability (Mosconi et al., 2015;

Wang et al., 2017, 2015) and lower complexity (Mosconi et al., 2015) during a force regulation task in children with ASD compared to TD controls. Furthermore, children with FASD demonstrated greater rRMSE, lower SNR, and lower SampEn compared to TD children

(Simmons et al., 2012). Our findings of no significant differences in rRMSE, SNR, and SampEn between any of the clinical groups and TD children could be due to a lack of task difficulty.

Furthermore, previous studies showing differences between clinical and TD groups during force regulation tasks manipulated visual gain (Mosconi et al., 2015) and the frequency of visual feedback (Simmons et al., 2012). Mosconi et al. (2015) also found progressive increases in force

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variability and decreases in complexity with increasing target force in individuals with ASD compared to TD controls (Mosconi et al., 2015). Therefore, visual perturbations and target force modulation may be necessary to examine force regulation deficits between children with ASD and TD children. In the current study, no such visual perturbations or variations in target forces were used since the purpose of this study was to examine force regulation performance and not to assess visuomotor processing per se or the integrity of circuitry involved in scaling force output. However, in one of the experiments presented in Mosconi et al. (2015), individuals with

ASD were significantly more variable and showed lower force complexity than TD controls, across several visual gains, while maintaining a 15% MVC grip-force. Wang et al. (2015), also provided evidence of significantly greater relative variability maintaining a 15% MVC grip-force in children with ASD (5-15 years old) without any visual gain modification (Wang et al., 2015).

Therefore, our findings of preserved isometric force regulation performance at 15% MVC in children with ASD contrasts with findings presented in Mosconi et al. (2015) and Wang et al.

(2015). Collectively, the current study provides evidence that isometric force regulation is intact in children with ASD, FASD, and ADHD in the absence of any sensory or force-output manipulation.

Frequency Structure

Findings from previous studies show that the proportion of spectral power is concentrated below 4 Hz in children (Deutsch & Newell, 2003, 2004). However, in individuals with ASD, increased low frequency (0-4 Hz) power during a visuomotor isometric force regulation task compared to TD controls has been observed that may be associated with increased utilization of slow visual feedback mechanisms (Mosconi et al., 2015). Furthermore, Mosconi et al. (2015) did not observe any differences in 4-8 Hz and 8-12 Hz power between ASD and TD groups in

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support of our findings. In addition to 0-4 Hz power, the 0-1 Hz band is also associated with visuomotor processing (Baweja et al., 2010, 2009; Lodha & Christou, 2017), and has not been previously examined in children with ASD. The findings in the current study did not support any group differences in the proportion of 0-1 Hz oscillations. Increased 0-1 Hz oscillations were significantly associated with lower force accuracy, greater force variability, and lower force complexity only in children with ASD. Increased force oscillations below 1 Hz are associated with reduced visual feedback, reduced visual gain, and increased voluntary cortical drive

(Baweja et al., 2010, 2009; Lodha & Christou, 2017; Moon et al., 2014; Park, Casamento-Moran,

Yacoubi, & Christou, 2017). Future studies should examine the neurophysiological basis of 0-1

Hz force oscillations and the association of 0-1 Hz force oscillations with the severity of core

ASD behaviors including the severity of motor deficits in children with ASD.

A robust finding in children with ASD is impaired integration of visual input into motor commands. Visuomotor integration deficits have been described in children with ASD in the planning stages of movement (Cattaneo et al., 2007; Dowd, McGinley, Taffe, & Rinehart, 2012;

Fabbri-Destro, Cattaneo, Boria, & Rizzolatti, 2009; Hughes, 1996; Papadopoulos et al., 2012;

Schmitz, Martineau, Barthélémy, & Assaiante, 2003), during movement (Glazebrook, Gonzalez,

Hansen, & Elliott, 2009; Greffou et al., 2012; Mosconi et al., 2015; Stoit, Van Schie, Slaats-

Willemse, & Buitelaar, 2013), and during learning of new motor tasks (Haswell, Izawa, R

Dowell, H Mostofsky, & Shadmehr, 2009; Izawa et al., 2012; Marko et al., 2015; Masterton &

Biederman, 1983; Sharer, Mostofsky, Pascual-Leone, & Oberman, 2016). Oculomotor smooth pursuit accuracy is also impaired in children with ASD (Takarae, Minshew, Luna, Krisky, &

Sweeney, 2004) that may explain why vision a sensory modality that is weighted less than somatosensory input to plan, control, and learn movements (Haswell et al., 2009; Izawa et al.,

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2012) . Furthermore, in children with ASD, structural impairments within the visuomotor network, including the posterolateral cerebellum (D’Mello, Crocetti, Mostofsky, & Stoodley,

2015; Stoodley et al., 2018), posterior superior vermis (Stanfield et al., 2008), and left inferior parietal lobule (Mahajan, Dirlikov, Crocetti, & Mostofsky, 2016), may mediate visuomotor integration deficits in children with ASD. However, despite widely reported deficits within the visuomotor network in children with ASD, no performance deficits in this study were observed.

The force regulation task used in the current study may not have taxed the visuomotor network since once the target force is reached, somatosensory integration – if intact – can maintain a constant motor output even in the absence of vision (Nowak, Glasauer, & Hermsdörfer, 2003).

Through an exploratory analysis, increased force complexity in the ASD group compared to the FASD group was observed. Considering children with ASD weight somatosensory input greater than visual input (Haswell et al., 2009; Izawa et al., 2012; Sharer et al., 2016), higher force complexity in children with ASD may reflect greater automaticity of motor output (Donker et al., 2007; Roerdink et al., 2011) and fewer regular visual-guided corrections during ongoing force output. The reverse may be true in children with FASD, where lower force complexity may reflect more regular visually guided corrections to sustain ongoing force output (Simmons et al.,

2012). Previous studies support an increased reliance on vision to sustain motor output in children with FASD compared to TD children (Nguyen et al., 2013; Simmons et al., 2012).

However, this study is the first to show that children with ASD have more complex force output compared to children with FASD.

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2.5 CONCLUSION

In conclusion, our results show isometric force regulation performance in children with

ASD is intact. No differences in force accuracy, variability, complexity, and frequency structure were observed between any of the clinical groups (ASD, ADHD, FASD) and TD controls.

However, in the ASD group, 0-1 Hz force oscillations were significantly associated with force accuracy, variability, and complexity. Future studies should examine the neurophysiological basis of 0-1 Hz oscillations in children with ASD and the relationship of low frequency force oscillations to the severity of core ASD behaviors. Finally, the authors suggest that future studies examining motor control in children with ASD include clinical controls in addition to TD controls. This methodological shift may help to differentiate motor features specific to children with ASD from those associated with more general neurobehavioral and intellectual impairments.

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CHAPTER 3

Children with Autism Spectrum Disorder Show Force-Generation and Force-Relaxation Deficits: A Cross-Syndrome Study

Daniel E. Lidstone

Co-authored By

Faria Z. Miah, Brach Poston, Julie F. Beasley, Janet S. Dufek

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Significance of the Chapter

In the previous chapter, force signal features from an isometric precision-grip static force-tracking task were examined. Results showed no features that were significantly different in the ASD group. We discuss these results relative to the relatively low visuomotor integration demands of the static force tracking task. Furthermore, studies show tracking a dynamic target results in significant increases in activation of brain areas involved in visuomotor integration.

Therefore, in this chapter, force-signal features are extracted from an isometric precision-grip dynamic force tracking task. Participants in this study tracked a ramp-up and ramp-down target that increased in amplitude (0-25% of MVC over 10 seconds) followed by a decrease in amplitude (25-0% of MVC over 10 seconds). In addition to evaluating overall task performance, as measured by relative error, force features during ramp-up and ramp-down are examined separately. This approach allowed for examination of the integrity of circuitry involved in controlled force-generation vs. force-relaxation in children with ASD. Circuits involved in the control of force-generation differ from those involved in force-relaxation. Dynamic precision- grip force tracking has not previously been examined in children with ASD. Prior to reading this chapter, the reader is directed to Section 1.4 (Chapter 1) for a detailed review of brain regions involved in the control of grip-force.

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Abstract

Brain regions implicated in Autism Spectrum Disorder (ASD) are involved in controlling grip-force output during force-generation (FG) and force-relaxation (FR). The current study examined whether FG and FR control is specifically impaired in children with ASD by comparing FG and FR performance to children with similar neurobehavioral and intellectual impairments – using a cross-syndrome design – and typically-developing (TD) controls.

Seventy-nine children (7-17 years old) participated in this study. Maximal voluntary contractions

(MVC) were measured and children performed five trials (20-s duration) of a ramp-up (0-25% of

MVC) and ramp-down (25-0% of MVC) precision-grip force tracking task. Relative force accuracy and entropy of the force-signal during ramp-up and ramp-down were examined.

Results showed that children with ASD had larger errors during both ramp-up and ramp-down compared to the TD group (p < 0.05). Ramp-down error was most impaired in children with

ASD when transitioning between ramp-up and ramp-down (p = 0.050). Entropy was significantly lower during ramp-up in children with ASD vs. TD children (p < 0.05). In conclusion, our findings suggest that FG and FR control are impaired in children with ASD.

Circuitry mediating both FG and FR may be impaired in children with ASD. This is the first study to describe controlled FG and FR deficits in children with ASD.

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3.1 INTRODUCTION

ASD have motor impairments 3. Motor deficits observed in children with ASD may result from specific neuroanatomical and neurophysiological deficits that are important for specific aspects of motor control. Investigating what specific aspects of motor control are impaired in children with ASD may improve our understanding of the neurological basis of ASD.

Neuroanatomical findings from individuals with ASD show connectivity and structural abnormalities within the visuomotor network that mediate the core social and communication deficits in ASD 4–6. The right posterolateral cerebellum (Crus I/II) is a region of the cerebellum that is part of the visuomotor network 7–10 and reduced gray matter volume within this region is associated with the severity of (1) social communication 11–13 and (2) repetitive and stereotyped behaviors 14 in individuals with ASD. Evidence also suggests that structural deficits within vermal lobule VII – a cerebellar region part of the oculomotor vermis and smooth pursuit visual target tracking 15 – mediates repetitive and stereotyped behaviors 5. Saccade/smooth pursuit deficits have been observed in children with ASD 16,17 – implicating vermal lobules VI-VII.

Reduced area and volume of the posterior superior vermis (vermal lobules VI-VII) is one of the most consistent neuroanatomical findings in ASD 18. Cerebellar vermal lobule VII is also the most prominent target of the subthalamic nucleus (STN) 19 – a structure shown to be involved in incremental force-generation 20. The caudate nucleus is another basal ganglia structure that shows structural deficits in children with ASD 21,22 and incremental force-generation specific

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activation during precision-gripping 23. Cerebellar Crus I/II and vermis VI are also structures implicated in ASD whose activity scales with increases in force (Spraker et al., 2012).

Supporting deficits in scaling force output in individuals with ASD, Mosconi et al. (2015) demonstrated significantly greater force variability with increasing target force 25. Together, these findings suggest that posterior cerebellar and basal ganglia regions implicated in ASD overlap with both visuomotor and force control networks. Children with ASD may therefore demonstrate impaired scaling of force-generation output during visuomotor force tracking.

In addition to structural deficits within the visuomotor and the force-generation network, deficits within force-relaxation circuitry has also been observed in individuals with ASD –

26–28 specifically in GABAA receptor-mediated short-interval intracortical inhibition (SICI) .

Evidence of reduced SICI has also been shown in motor disorders characterized by basal ganglia dysfunction such as Parkinson’s disease 29 and dystonia 30. Intracortical inhibitory circuits mediating SICI are involved in muscle relaxation 31 and in patients with reduced SICI, prolonged relaxation times are observed 32–34. Prolonged grip-force relaxation times have also been observed in individuals with ASD 35. In addition to SICI, force relaxation is also facilitated by increased activation of ipsilateral dorsolateral prefrontal cortex (DLPFC) and reduced activation of bilateral anterior cingulate cortex 23. Further evidence suggesting that circuitry involved in force-generation differs from force-relaxation is that force output during relaxation is more variable than force-generation 36. Findings from cortical electrophysiological measurements have shown that primary motor cortex is less excitable during force-relaxation vs. force-generation 36 and somatosensory evoked potentials are greater during controlled force-relaxation compared to force-generation 37. In children with ASD, structural abnormalities have been observed within the DLPFC 38 and the somatosensory cortex 39. Therefore, brain regions implicated in force-

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relaxation control may be impaired in children with ASD resulting in deficits performing controlled force-relaxation. Together, these findings suggest force-relaxation deficits may also be observed in children with ASD, in addition to force-generation deficits. To date, no study has examined controlled force-generation and force-relaxation control in children with ASD.

The purpose of this study was to examine force-generation control and force-relaxation control in children with ASD compared to both clinical controls and TD controls using a dynamic-target isometric precision-grip force-tracking task. We hypothesized that children with

ASD would show (1) significantly increased force tracking error for both force ramp-up and ramp-down phases of the task compared to clinical (FASD/ADHD) and TD control groups and

(2) significantly reduced force-signal entropy compared to clinical (FASD/ADHD) and TD control groups. Lastly, we hypothesized that all three clinical groups (ASD/FASD/ADHD) would show significant deficits on all dependent variables compared to the TD control group.

In the current study, we examined force accuracy and complexity measures during a dynamic-target isometric precision-grip force-tracking task. Furthermore, we examined the integrity of force-generation and force-relaxation circuitry through separately using a ramp-up and ramp-down force tracking task. Performance within the ramp-up and ramp-down phases were examined by measuring the relative accuracy and complexity of the force signal. Finally, we compared the performance of children with ASD to two separate clinical control groups and a

TD control group. The clinical control groups include (1) children with Fetal Alcohol Spectrum

Disorder (FASD) and (2) children with Attention-Deficit Hyperactivity Disorder (ADHD).

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3.2 METHODS

3.2.1 Participants

A total of seventy-nine children were recruited to participate in the study (Table 18).

Children with FASD were diagnosed through the University of Nevada/Las Vegas FAS clinics by a multi-disciplinary team. Children in the ASD and ADHD clinical control groups were recruited through the UNLV Ackerman Autism Center and the Las Vegas community. Parents of children recruited in the Las Vegas community confirmed diagnoses by presenting a copy of the clinical diagnosis to the experimenter prior to data collection. Inclusion criteria for the study required the child to (1) be 7-17 years old, (2) have normal to corrected-to-normal vision, and (3) have a clinical diagnosis of ASD, ADHD, FASD, or be TD. Children were excluded from participation if the child had an (1) intellectual disability (FSIQ-2 ≤ 70), (2) upper or lower extremity deformity, (3) current orthopedic injury, and (4) a known genetic disorder. Intellectual disability was ruled out prior to testing using two sub-tests of the WASI-II (vocabulary and matrix reasoning) to provide a Full-Scale Intelligence Quotient (FSIQ-2). Children in the ASD group did not have a co-occurring FASD diagnosis. Furthermore, TD children were excluded if they had a psychiatric disorder and/or a first-degree relative with a diagnosis of either ASD or

ADHD. Parental consent and child assent were obtained prior to testing.

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Table 16: Demographic characteristics of participants in ASD, FASD, ADHD, and TD groups.

FASD (N=17) ARND (n=5) ASD (N=22) pFAS (n=6) ADHD (N=18) TD (N=22) P value FAS (n=3) N.S. (n=3) Sex [no. Males (%)] 18 (82%) 6 (35%) 12 (67%) 14 (64%) - Age (years) 12.29±2.88 12.5±3.0 10.4±2.1 11.7±2.7 0.09 (min-max) (7.38-17.95) (7.4-17.6) (7.3-14.5) (7.4-16.8) FSIQ-2 94.6±13.3 91±11 99±17 112±10*** <0.0005 (min-max) a (71-122) (72-118) (71-137) (94-129) Comorbid ADHD 11 (50%) 15 (88%) - - - [no. (%)] Prematurity 6 (27%) 4 (24%) 4 (22%) 2 (9%) - <37wks [no. (%)] Low Birth Weight 2 (9%) 4 (24%) 3 (17%) 2 (9%) - <2500 g [no. (%)] Prenatal Drug 3 (14%) 6 (35%) 7 (39%) 0 - Exposure [no. (%)] Vanderbilt

(no. ≥ 2) b ADHD a 8.4±5.2 8.3±5.4 11.1±4.3 1.1±3.0*** <0.0005 Oppositional 2.1±2.9 6.1±4.7 4.9±4.7 0.4±1.2** <0.0005 Defiant/Conduct a Anxiety/Dep.a 1.2±2.0 1.7±1.8 1.7±2.1 0.2±0.7** 0.009 Handedness (n/%) Right 18 (82%) 16 (94%) 15 (83%) 21 (95%) - Left 2 (8%) 1 (6%) 2 (11%) 1 (5%) - Bilateral 2 (8%) 0 1 (6%) 0 - Medication Use 12 (55%) 14 (82%) 11 (61%) 0 - [no. (%)] a = Group differences assessed using Kruskal-Wallis test. Bonferroni corrections for multiple comparisons. (α≤0.05) b = Mean number of symptoms scored as often (score=2) or very often (score=3) on Vanderbilt PARENT Informant. Symptom scores (≥2) were totaled for Attention-Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant/Conduct Disorder, and Anxiety/Depression. ARND = Alcohol Related Neurodevelopmental Disorder pFAS = Partial Fetal Alcohol Syndrome FAS = Fetal Alcohol Syndrome N.S. = Not specified *** = Significant difference between TD and all groups. ** = Significant difference between TD group and both ADHD and FASD groups.

3.2.2 Experimental Procedures

Intellectual and Handedness Assessment All data were collected in a single session. First, intellectual disability was ruled out using two sub-tests of the WASI-II (vocabulary and matrix reasoning) to provide a Full-Scale

Intelligence Quotient (FSIQ-2). Children scoring 70 and below on the WASI-II were excluded

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from further participation. Following intellectual testing, handedness was assessed using the

Almli Handedness Assessment that scores handedness from -3.16 (complete left handedness) and

+3.16 (complete right-handedness) 40,41. If handedness was scored between -1 and +1, indicating no hand preference, the hand used for writing was selected as the dominant-hand.

Apparatus: Isometric Precision-Grip Dynamic Force Tracking Task Children were seated in a height adjustable chair with their dominant forearm resting on a height adjustable table with their thumb and index fingers squeezing a precision-grip apparatus.

Desk height was adjusted to allow for a 90-degree flexed elbow position and participants were seated 1m from the center of a 47 in monitor (NEC E424, resolution = 1080p 60 Hz, IL, USA).

The participant distance from the monitor, the target position, and the target/cursor size were held constant for each participant resulting in a visual gain (α) of α = 1.43. The testing room had adjustable lighting, so it could be darkened. A custom-designed 3D printed precision-grip device with alignment shafts and a sliding block was used to house a calibrated load cell (Futek LB 350

- 25 lb capacity, Irvine, CA, USA) and allow measurement of the normal force component during precision gripping. The height of the grip device was adjusted so the thumb and index finger were parallel to the base of the apparatus (Figure 12, A). Amplification and A/D conversion was performed using a 24-bit resolution, 25mV/V NI 9237 bridge module (National

Instruments, Austin, TX, USA) and force output was sampled at 2000 Hz. Data was acquired, and visual stimuli presented using LabVIEW 2017 software (National Instruments, Austin, TX,

USA). The precision-grip force tracking task was used in the current study because (1) it is a simple task that activates grasping and visuomotor circuitry that have been implicated in ASD

9,23,42,43, (2) it has previously been used to examine motor control deficits in children with ASD

25,35,44, and (3) pincer-grasping is a fine-motor task that children perform in daily life 45.

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Prior to performing the static force target tracking task, the participants performed three maximum voluntary contractions (MVC) 35. The participants were told to press on the blocks using only their thumb and index finger to make a space ship go as high as possible (Figure 12,

B). A self-selected rest period was provided between attempts. The best of three attempts was recorded as the MVC.

Following the MVC, participants completed five trials of a dynamic force target tracking task that were 20-s in duration. The start of each trial was signaled using an auditory tone after which the target began to move. The participants were instructed to keep the white block cursor inside the green target window for the duration of the trial (Figure 12, C). A rest period of 20-s was provided after each trial. The position of the target was automatically adjusted for each participant, so the target moved the same distance on the monitor from 0-25% of MVC. The visual angle (α) of the moving target was α = 17.9 degrees (Figure 13).

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Figure 13: Visual angle for force tracking task.

Where:

D = 100 cm W1 = 15.75 cm

Signal Processing All signal processing was performed in MATLAB 2018a (MathWorks, Natick, MA).

Each force time-series data was down sampled from 2000 Hz to 200 Hz 25 and digitally filtered using a 4th order low-pass Butterworth filter with a 30 Hz cut-off 25. The first and last 2-s of force data in the were discarded so that force accuracy was examined from 2-18-s or from 5-25%

MVC. The three most accurate trials out of the five recorded trials were analyzed separately for tracking accuracy and force-signal entropy. These measures are described in detail in the following sections.

Accuracy: Relative Root Mean Square Error (rRMSE) To examine the accuracy of dynamic tracking, the total rRMSE was calculated 46 (Eq. 1).

The rRMSE measure is defined as the square root of the ratio of the mean square value of the error signal to the maximum square value of the maximum target force – expressed as a

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percentage. A lower rRMSE suggests greater tracking accuracy. The ramp was separated into ramp-up ( ; 5-25% MVC; Eq. 2) and ramp-down ( 25-5%

MVC; Eq. 3) phase for separate analyses. The ramp-down portion of the force signal was separated into a transition ( 25-20% MVC; Eq. 4) and post-transition

( 20-5% MVC; Eq. 5) phase based on the observation of large initial unloading errors in participants during a ~2-second period following transitioning between ramp-up and ramp-down phases. The maximum percent error during the transition period was also examined as a measure of the magnitude of the target undershoot error between ramp-up and ramp-down phases (Figure 12, D).

(Eq. 1)

(Eq. 2)

(Eq. 3)

(Eq. 4)

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(Eq. 5)

Where:

is the measured force output is the target force is the trial time

Complexity: Sample Entropy Sample entropy (SampEn) was defined as the negative natural logarithm of the conditional probabilities that two sequences at m distance apart remain similar at m+1 47. A series of vectors of m length were formed for the entire length of the detrended time-series (N) and vectors were considered alike if the tail and head of the m-length vector falls within a tolerance

(r) defined as a proportion (R) of the standard deviation of the detrended force-signal (r = R X

SD) 48. The total number of vectors identified as matches were then divided by N-m+1 and defined as B. This process was subsequently repeated for m+1 and defined as A. The equation for

SampEn is shown below (Eq. 6). The SampEn algorithm returns a value between 0 and 2, with

~0 reflecting a perfectly repeatable time series (ex. sine wave) and ~2 reflecting a perfectly random time series. It has been suggested that low entropy reflects high attentional demands, whereas higher entropy reflects greater automaticity and less attentional demands 49,50. Lower

SampEn have also been observed in the isometric force output of individuals diagnosed with multiple concussions, demonstrating its sensitivity to CNS function 51. The algorithm used for calculating sample entropy was provided by the 2017 University of Nebraska at Omaha (UNO)

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Nonlinear Analysis Workshop. The algorithm parameters m (vector length) and R (proportion of the standard deviation to be used for matching) were defined as m = 2 and R = 0.2 25,51.

(Eq. 6)

Where: m = vector length (m = 2) r = tolerance (r = 0.2 x SD) N = dataset = number of matching patterns of length m + 1 = number of matching patterns of length m

3.2.3 Statistical Procedures

A series of two-way ANCOVAs were conducted to examine GROUP (ASD, FASD,

ADHD, TD) and DIRECTION-specific differences on force accuracy (rRMSEramp-up vs. rRMSEramp-down) and force-signal complexity (SampEnramp-up vs. SampEnramp-down). To examine the effect of GROUP (ASD, FASD, ADHD, TD) on accuracy within each DOWN-PHASE

(rRMSEtrans vs. rRMSEpost-trans), a two-way ANCOVA was performed. This analysis was performed to examine deficits associated with the transition (rRMSEtrans) from ramp-up to ramp- down. Age was used as a covariate for all statistical tests. Post hoc analyses were performed using the Bonferroni adjustment and all tests were conducted with significance set at α=0.05. All statistical procedures were performed using IBM SPSS Statistics Package 26.0 (IBM, Armonk,

NY).

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3.3 RESULTS

Figure 14: Each participants trial with the lowest total error (rRMSE) shown for each group (ASD, FASD, ADHD, and TD).

3.3.1 Ramp-Up vs. Ramp-Down

Relative Error: rRMSE

There was no significant interaction between GROUP and DIRECTION on rRMSE,

2 while controlling for age (F(3,149) = 0.471, p = 0.703, η = 0.009) (Table 19). However, there were

2 significant main effects for GROUP (F(3,149) = 5.861, p = 0.001, η = 0.106, ASD > TD) and

-6 2 DIRECTION (F(1,149) = 23.01, p =10 , η = 0.134, DOWN > UP). For the main effect of

GROUP, post-hoc tests revealed that the ASD group had significantly greater relative error compared to the TD group (p = 0.0003). Furthermore, a significant effect was found for ramp-up

2 2 (F(3,149) = 3.43 , p = 0.019, η = 0.065) and ramp-down (F(3,149) = 2.892, p = 0.037, η = 0.055)

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with post-hoc tests showing that, compared to the TD group, children in the ASD group had significantly greater error in both ramp-up (p = 0.011) and ramp-down (p = 0.049) directions.

Finally, pairwise tests showed significantly greater relative error in the ramp-down vs. the ramp-

2 up portion of the task in the ADHD (F(1,149) = 10.25, p =0.002, η = 0.064) and TD (F(1,149) = 5.32,

2 2 p =0.022, η = 0.034), FASD (F(1,149) = 5.021, p =0.027, η = 0.033) groups, but not in the ASD

2 group (F(1,149) = 3.24, p =0.074, η = 0.021) (Figure 15).

Figure 15: Relative accuracy (rRMSE) mean ± standard errors shown for ramp-up and ramp-down. * p < 0.05; ** p = 0.002.

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Table 17: Mean (M) and standard deviation (SD) for relative error (rRMSE) during ramp- up and ramp-down. Age-adjusted mean (Madj) and standard errors (SE) are also shown.

rRMSE (%) - Ramp-Up rRMSE (%) - Ramp-Down ASD FASD ADHD TD ASD FASD ADHD TD M 8.021 5.973 7.061 4.717 10.07 8.885 11.10 7.351 (SD) 4.968 2.657 2.981 1.811 5.366 0.107 5.638 3.716 Madj 8.375 6.481 6.135 4.73 10.430 9.393 10.178 7.364 (SE) 0.810 0.923 0.906 0.808 0.810 0.923 0.906 0.808

Complexity: SampEn There was no significant interaction between GROUP and DIRECTION for SampEn

2 (F(3,149) = 1.069, p =0.364, η = 0.021) (Table 20). However, there were significant main effects

2 for GROUP (F(3,149) = 5.384, p = 0.002, η = 0.098, TD > ASD/FASD/ADHD) and DIRECTION

-10 2 (F(1,149) = 45.17, p =10 , η = 0.233, UP > DOWN). The TD group had significantly higher

SampEn than the ASD (p=0.005), FASD (p=0.019), and ADHD (p = 0.013) groups, and SampEn was higher for the ramp-up vs. ramp-down (p = 10-10) portion of the tracking task. Furthermore,

2 a significant effect was found for ramp-up (F(3,149) = 5.225 , p = 0.002, η = 0.095) and post-hoc tests revealed children in the TD group had significantly higher SampEn during ramp-up compared to the ASD (p = 0.002) and ADHD (p = 0.017) groups, with no group differences

2 observed for ramp-down (F(3,149) = 1.229, p = 0.301, η = 0.024). Finally, pairwise tests showed significantly greater SampEn in the ramp-up vs. the ramp-down portion of the task in the ASD

2 (F(1,149) = 5.793, p = 0.017, η2 = 0.037), FASD (F(1,149) = 12.48, p = 0.001, η = 0.077), ADHD

2 -6 2 (F(1,149) = 7.691, p = 0.006, η = 0.049), and TD (F(1,149) = 22.95, p = 10 , η = 0.133) groups

(Figure 16).

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Figure 16: Sample entropy (SampEn) mean ± standard errors shown for ramp-up and ramp-down. * p < 0.02; ** p ≤ 0.006

Table 18: Mean (M) and standard deviation (SD) for sample entropy (SampEn) during ramp-up and ramp-down. Age-adjusted mean (Madj) and standard errors (SE) are also shown.

SampEn - Ramp-Up SampEn - Ramp-Down ASD FASD ADHD TD ASD FASD ADHD TD M 0.150 0.164 0.1494 0.200 0.116 0.107 0.105 0.131 (SD) 0.059 0.065 0.046 0.039 0.045 0.036 0.042 0.043 Madj 0.148 0.162 0.154 0.200 0.114 0.105 0.111 0.132 (SE) 0.010 0.011 0.011 0.010 0.010 0.011 0.011 0.010

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3.3.2 Transition vs. Post-Transition Period

Relative error during transition and post-transition period was also examined between groups by a two-way ANCOVA. There was no statistically significant interaction between

2 GROUP and DOWN-PHASE (F(3,149) = 0.973, p = 0.407, η = 0.019), while controlling for age

(Table 21). However, there were statistically significant main effects for GROUP (F(3,149) =

2 - 3.099, p = 0.029, η = 0.059, ASD > TD, p =0.031) and DOWN-PHASE (F(1,149) = 32.593, p =10

8, η2 = 0.179, TRANSITION > POST-TRANSITON). Furthermore, a significant effect was found

2 for transition (F(3,149) = 3.199, p = 0.025, η = 0.064) with post-hoc tests showing a difference between the ASD and TD group (p = 0.050). The post-transition test was not significant (F(3,149)

= 0.855, p = 0.466, η2 = 0.017). Finally, pairwise tests showed significantly greater relative error in the transition vs. the post-transition phase of the task in the ASD (F(1,149) = 13.09, p

2 2 =0.0004, η = 0.081), ADHD (F(1,149) = 14.67, p =0.0001, η = 0.090) and TD (F(1,149) = 5.25, p

2 2 =0.023, η = 0.034) groups, but not in the FASD group (F(1,149) = 3.052, p =0.083, η = 0.020)

(Figure 17).

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Figure 17: Relative error (rRMSE) mean ± standard errors shown for transition and post-transition phases. * p < 0.05; ** p < 0.0005.

Table 19: Mean (M) and standard deviation (SD) for relative error (rRMSE) during transition and post-transition phases. Age-adjusted mean (Madj) and standard errors (SE) are also shown.

rRMSE (%) - Transition rRMSE (%) – Post-Transition ASD FASD ADHD TD ASD FASD ADHD TD M 15.302 12.300 16.870 10.688 8.506 8.567 8.915 6.383 (SD) 9.093 6.501 8.995 6.127 4.878 5.547 6.191 3.992 Madj 15.732 12.918 15.742 10.704 8.937 9.186 7.786 6.399 (SE) 1.332 1.517 1.490 1.328 1.332 1.517 1.490 1.328

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3.4 DISCUSSION

The purpose of this study was to examine force-generation control and force-relaxation control in children with ASD compared to both clinical controls and TD children. The current study presents three novel findings. First, relative error was significantly greater in children with

ASD compared to TD children for both the ramp-up and ramp-down phases of the tracking task.

Second, force complexity was significantly greater in the TD compared to all groups, with the ramp-up phase revealing significantly lower complexity in the ASD and ADHD groups compared to the TD group. Third, children with ASD showed significantly greater relative error in the transition phase of the tracking task compared to TD children. Collectively, the findings from this study reveal significant force-generation and force-relaxation deficits in children with

ASD. This is the first study to describe such impairments in children with ASD and, by employing a cross-syndrome design, we provide evidence of specific impairments in children with ASD. We will focus the discussion on findings observed in the ASD group to align with the overall purpose of this study.

Force Accuracy

In this study, relative force accuracy was examined is several phases (ramp-up, ramp- down, transition, post-transition) of the isometric precision-grip dynamic force tracking task.

Although, precision-grip force tracking tasks have been used in previous studies to examine force-output characteristics in children with ASD 25,35, to the best of our knowledge, no study has examined dynamic force tracking performance in children with ASD. However, by using multiple target forces, Mosconi et al. (2015) found that individuals with ASD demonstrated significantly greater force variability with increasing target forces 25. Mosconi et al. (2015),

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concluded that brain regions that scale their activity with increases in force amplitude may be impaired in children with ASD 25. Furthermore, using the same precision-grip static force tracking task as Mosconi et al. (2015), Wang et al. (2015) observed reduced rates of force decrease when children with ASD were asked to release their grip as quickly as possible 35. The force-relaxation deficits described in Wang et al. (2015) may implicate impairments in circuitry mediating SICI 32–34 such as the basal ganglia 29,30 and DLPFC deficits 23. In this study, children with ASD demonstrated deficits in tracking the dynamic target by appropriately scaling their force output with the ramp-up target. Furthermore, the ASD group also showed deficits with deactivating M1 23 to scale force-output with the ramp-down target. Therefore, our findings suggest that circuitry involved in controlled scaling of force-generation and force-relaxation may be impaired in children with ASD. Findings from this current study also support neuroimaging findings in children with ASD 4,14,18,21,22,38 that implicate brain regions that scale activation with increases in grip-force output such as M1, Crus I/II, vermis VI, STN, and caudate

20,23,52 and decreases in grip-force output such as DLPFC 23. Collectively, our findings suggest deficits in controlled force-generation and force-relaxation may be a motor phenotype specific to children with ASD. Our findings are further supported by previous neuroimaging research in children with ASD showing deficits in brain regions that mediate the control of grip force- generation and force-relaxation.

In this study, we also performed an exploratory analysis that revealed impairments in force-relaxation in children with ASD are specific to a phase, 2-s post ramp-up, that we termed transition. Error in the ASD group during the transition period of ramp-down was significantly greater than the TD group, a finding that was also specific to the ASD group. Therefore, in children with ASD, transitioning between ramp-up and ramp-down contributed most to the

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deficits observed during ramp-down. One explanation for this finding, that aligns with previous studies in individuals with ASD, is a deficit in feedforward control 25,35. In Wang et al. (2015) and Mosconi et al. (2015), large initial isometric force overshoot of static targets were observed in individuals with ASD. Large initial primary pulse overshoot errors implicate feedforward deficits in selecting appropriate motor commands to achieve a desired sensory state in the absence of influences from feedback control. In the current study, we observed large undershoot errors when transitioning between ramp-up and ramp-down that were significantly greater than that observed in TD children. The cerebellum participates in forward model computations and may mediate the large undershoot errors observed in the current study 53–55. Considering the strong evidence supporting cerebellar deficits in children with ASD 4,14,56, future studies should examine the association of dynamic grip-force transition error with cerebellar anatomical characteristics in children with ASD including the severity of ASD symptoms.

Force Complexity

In this study, complexity of the force signal was examined as a measure of automaticity of force-output during force-generation and force-relaxation. Generally, higher entropy is associated with tasks with reduced attentional demands and lower entropy is associated with tasks that involve greater attentional demands 49,50. Previous studies have shown that children with ASD have lower force complexity during a static force-tracking task compared to TD children 25. In this study, force complexity (SampEn) was significantly higher in the TD group compared to all clinical groups, demonstrating greater automaticity of force output in the TD group. Furthermore, when examining force complexity (SampEn) during ramp-up, both children with ASD and ADHD had significantly lower complexity compared to TD children. In this study, 50% of the children in the ASD group had co-occurring ADHD and the lower

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automaticity of force-generation output observed in both the ASD and ADHD groups may be a finding specific to ADHD. Inclusion of an ASD-only group in future studies may help determine whether low entropy of force-generation is specific to children with ADHD and ASD+ADHD.

No significant group differences were observed for ramp-down. Together with the findings demonstrating lower force accuracy during ramp-up, the findings of lower complexity of force output during ramp-up may provide further evidence of disrupted circuitry involved with scaling force output in children with ASD.

A limitation of the current study was that the severity of ASD symptoms was not examined. Inclusion of these metrics should allow a more detailed examination of whether dynamic force control deficits in children with ASD is a motor phenotype of ASD. Finally, we propose that a cross-syndrome approach to identifying motor phenotypes of ASD is valuable not only to determining the diagnosis-specificity of motor deficits of ASD, but also of other neurodevelopmental disorders with overlapping symptomology. Studying motor control in children with neurodevelopmental disorders may improve understanding of the neurological basis of various disorders and lead to improvements in current intervention strategies and the development of new intervention techniques. Furthermore, evaluating ASD-specific motor- phenotypes using biomechanics techniques in combination with various interventions (ex. behavioral, pharmacological, non-invasive brain stimulation techniques) may provide a quantitative way to examine the effectiveness of interventions and ultimately personalize treatment for children with ASD.

3.5 CONCLUSIONS

In summary, children with ASD demonstrated deficits in controlled scaling of force- generation and controlled force-relaxation. Furthermore, we observed large errors when

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transitioning between ramp-up and ramp-down phases of the dynamic force tracking task that were specific to children with ASD. None of the analyzed features of the force signals were specifically impaired in any of the clinical control groups (ADHD and FASD). To the best of our knowledge, this is the first study to examine dynamic force control in children with ASD. Future studies should consider cross-syndrome approaches to evaluate motor control deficits in children with ASD.

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CHAPTER 4

Examining the Specificity of Postural Control Deficits in Children with Autism Spectrum Disorder Using A Cross-Syndrome Approach

Daniel E. Lidstone

Co-authored By

Faria Z. Miah, Brach Poston, Julie F. Beasley, Janet S. Dufek

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Significance of the Chapter

The previous chapters detail deficits in the manual control of force output. Grip-force control is highly relevant to performing activities of daily living, however, the control of one’s posture is crucial to the performance of all motor tasks. Previous studies show that children with

ASD have significantly greater postural sway magnitude compared to typically developing children. Furthermore, the magnitude of postural sway is significantly associated with the performance of locomotor and object control skills in children with ASD. Although, hyposensitivity to visual input during quiet stance is hypothesized to contribute to abnormal postural sway in children with ASD, postural sway features specifically impaired in children with ASD are not well understood. Studies show that children with other neurodevelopmental disorders, such as FASD and ADHD, also have greater postural sway and balance impairments compared to typically developing children. Therefore, increased postural sway may be associated with intellectual and behavioral impairments commonly reported in children with neurodevelopmental disorders. The purpose of this chapter is to examine what postural sway features are specifically impaired in children with ASD. Furthermore, the association between sway measures and unipedal stance time is examined between groups to examine whether larger sway magnitude has some functional relevance in children with ASD. This is the first use of a cross-syndrome approach to the study of postural control in children with ASD.

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Abstract

Background: Postural control deficits are commonly reported in children with Autism Spectrum

Disorder (ASD). However, identification of specific postural sway features that differentiate

ASD from other neurodevelopmental disorders has not been examined. The current study employs a cross-syndrome approach by comparing postural sway area and direction-specific features of sway magnitude, sway velocity, and sway complexity between children with ASD,

Fetal Alcohol Spectrum Disorder (FASD), Attention-Deficit Hyperactivity Disorder (ADHD), and typically developing (TD) controls.

Method: Eighty children (7-17 years old) participated in this study. Postural sway was measured on a force plate during 30-s of bilateral quiet stance and balance was assessed using a timed unipedal stance test.

Results: Results showed that (1) postural sway area and mediolateral (ML) sway magnitude were significantly greater in children with ASD vs. all groups (p < 0.05); (2) Anteroposterior

(AP) sway magnitude and velocity were significantly greater in children with ASD vs. TD controls (p = 0.01); (3) complexity of ML sway was significantly lower in the ASD group vs. the

FASD group only (p = 0.01); and (4) sway area predicted unipedal stance time only in the ASD group (r2 = 0.20).

Conclusions: ASD-specific postural sway features were identified using a cross-syndrome design. Identifying ASD-specific motor impairments can be useful to understanding the neurological underpinnings of ASD.

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4.1 INTRODUCTION

Autism Spectrum Disorder is a neurodevelopmental disorder affecting approximately

2.5% of children in the US (Kogan et al., 2018) and is characterized by persistent deficits in social communication and social interaction accompanied by restricted, repetitive patterns of behaviors, interests, or activities (American Psychiatric Association, 2013). In addition to the core features of ASD, fine and gross motor deficits are highly prevalent in children with ASD

(Green et al., 2009), indicating that neurological deficits associated with ASD overlap with circuitry involved in the control of movement.

Recent findings suggest that specific regions of the posterior cerebellum mediate social deficits and repetitive behaviors in ASD-mouse models (Badura et al., 2018; Stoodley et al.,

2018). Furthermore, structural deficits within motor areas such as primary motor cortex, cerebellum, basal ganglia, left inferior parietal lobule, and primary somatosensory cortex are observed in children with ASD (D’Mello, Crocetti, Mostofsky, & Stoodley, 2015; Estes et al.,

2011; Mahajan, Dirlikov, Crocetti, & Mostofsky, 2016). Therefore, studying motor function in children with ASD provides another means by which to study the neurological underpinnings of the disorder.

Postural control deficits are frequently reported in children with ASD (Doumas,

McKenna, & Murphy, 2016; Goulème et al., 2017; Selma Greffou et al., 2012; Lim et al., 2018b;

Mache & Todd, 2016; Memari et al., 2013). Postural control during quiet stance is defined as the ability to maintain the line of gravity (LoG) within the base of support (BoS) (Pollock, Durward,

Rowe, & Paul, 2000). Increased postural sway reflects difficulties maintaining the LoG within the BoS. During quiet stance, without sensory manipulation, children with ASD display larger sway areas compared to typically developing (TD) children (Lim, Partridge, Girdler, & Morris,

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2017), a finding that supports impaired sensory integration to control balance (Doumas et al.,

2016). In addition to increased sway magnitude, recent findings also suggest lower entropy or complexity of mediolateral (ML) (Fournier, Amano, Radonovich, Bleser, & Hass, 2014; Li,

Mache, & Todd, 2019; Lim et al., 2018b) and anteroposterior (AP) sway (Fournier et al., 2014) in children with ASD. It has been suggested that low entropy or complexity of sway reflects high attentional demands during quiet stance, whereas higher entropy reflects greater automaticity of postural control and reduced attentional demands (Donker, Roerdink, Greven, & Beek, 2007;

Roerdink, Hlavackova, & Vuillerme, 2011). Furthermore, it has been suggested that AP sway is more sensitive to somatosensory input whereas ML sway is more sensitive to visual input

(Warren, Kay, & Yilmaz, 1996).

Deficits in integrating visual input during quiet stance may mediate postural control impairments in children with ASD. Individuals with ASD demonstrate hypo-reactivity to dynamic visual stimuli (Gepner & Mestre, 2002; Selma Greffou et al., 2012; Haworth,

Kyvelidou, Fisher, & Stergiou, 2016) and following vision removal (Lim et al., 2018b) during quiet stance. Collectively, these studies highlight that TD individuals rely more upon vision during quiet stance, whereas individuals with ASD place greater weight on somatosensory input over visual input (Minshew, Sung, Jones, & Furman, 2004). Supporting altered sensory weighting in children with ASD during quiet stance, Minshew et al. (2004) showed that disrupting somatosensory input impairs postural control more in children with ASD vs. TD controls (Minshew et al., 2004). Together, these findings support a reduced reliance on vision and increased reliance on somatosensory input for balance in children with ASD. Reduced reliance on vision for postural control is likely to impair normal development of postural control

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– considering that vision is the most strongly relied upon sensory modality in young children for balance (Greffou, Bertone, Hanssens, & Faubert, 2008).

Although evidence of postural control deficits in children with ASD is growing, these deficits are not unique to children with ASD. Individuals with Attention-Deficit Hyperactivity

Disorder (ADHD) (Bucci, Stordeur, Acquaviva, Peyre, & Delorme, 2016; Hove et al., 2015;

Sarafpour, Shirazi, Member, Shirazi, & Ghazaei, 2018) and Fetal Alcohol Spectrum Disorder

(FASD) (Connor, Sampson, Streissguth, Bookstein, & Barr, 2006; Jirikowic et al., 2013;

Kooistra et al., 2009) also show impaired postural control compared to TD individuals.

Therefore, to study the specificity of postural control deficits in children with ASD, the current study employs a cross-syndrome approach to determine whether specific features of postural control are more impaired in children with ASD compared to children with other neurodevelopmental disorders and TD controls.

The purpose of this study was to examine what features of postural control were specifically impaired in children with ASD. We hypothesized that children with ASD would show (1) significantly greater postural sway area, (2) significantly greater AP and ML sway magnitude, (3) significantly greater AP and ML sway velocity, and (4) significantly lower ML, but not AP, sway complexity compared to Clinical and TD controls. We also examined unipedal stance time between groups to examine whether balance was more impaired in children with

ASD vs. Clinical and TD controls. We hypothesized that unipedal stance time would be significantly lower in children with ASD compared to Clinical and TD controls.

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4.2. METHODS

4.2.1 Participants

A total of eighty children were recruited to participate in the study (Table 22). Children with Fetal Alcohol Spectrum Disorder (FASD) were diagnosed through the University of

Nevada/Las Vegas FAS clinics by a multi-disciplinary team. Children in the ASD and ADHD clinical control groups were recruited through the UNLV Medicine Ackerman Autism Center and the Las Vegas community. Parents of children recruited in the Las Vegas community confirmed diagnoses by presenting a copy of the clinical diagnosis to the experimenter prior to data collection.

Inclusion criteria for the study required the child to (1) be 7-17 years old, (2) have normal to corrected-to-normal vision, and (3) have a clinical diagnosis of ASD, ADHD, FASD, or have no psychiatric diagnoses (TD). Children were excluded from participation if the child had an (1) intellectual disability (FSIQ-2 ≤ 70), (2) upper or lower extremity deformity, (3) current orthopedic injury, and (4) a known genetic disorder. Intellectual disability was ruled out prior to testing using two sub-tests of the WASI-II (vocabulary and matrix reasoning) to provide a Full-

Scale Intelligence Quotient (FSIQ-2). Intellectual disability was ruled out using two sub-tests of the WASI-II (vocabulary and matrix reasoning) to provide a Full-Scale Intelligence Quotient-2

(FSIQ-2) score. Children in the ASD group did not have a co-occurring FASD diagnosis.

Children scoring ≤70 on the WASI-II were excluded from further participation. TD children were excluded if they had a psychiatric disorder and/or a first-degree relative with a diagnosis of either ASD or ADHD. Parental consent and child assent were obtained prior to testing.

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Table 20: Demographic characteristics of participants in ASD, FASD, ADHD, and TD groups.

FASD (N=17) ARND (n=5) ASD (N=23) pFAS (n=6) ADHD (N=18) TD (N=22) P value FAS (n=3) N.S. (n=3) Sex [no. Males (%)] 19 (83%) 6 (35%) 12 (67%) 14 (64%) - Age (years) 12.4±2.8 12.5±3.0 10.4±2.1 11.7±2.7 0.08 (min-max) (7.3-17.9) (7.4-17.6) (7.3-14.5) (7.4-16.8) Height (m) 1.5±0.1 1.5±0.1 1.4±0.1 1.4±0.1 0.15 Body Mass (kg) 53.4±22.5 53.7±24.6 43.1±17.2 47.6±17.0 0.33 FSIQ-2 93±13 91±11 99±17 112±10*** <0.0005 (min-max) a (71-122) (72-118) (71-137) (94-129) Comorbid ADHD 12 (52%) 15 (88%) - - - [no. (%)] Prematurity 6 (26%) 4 (24%) 4 (22%) 2 (9%) - <37wks [no. (%)] Low Birth Weight 2 (8.7%) 4 (24%) 3 (17%) 2 (9%) - <2500 g [no. (%)] Prenatal Drug 3 (13%) 6 (35%) 7 (39%) 0 - Exposure [no. (%)] Vanderbilt

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4.2.2 Experimental Procedures

Following IQ testing, the participants performed three trials each of a timed unipedal and bilateral quiet stance test without any sensory manipulation. Participants were randomly assigned to perform the unilateral or bilateral tests first.

Timed Unipedal Quiet Stance

To examine unipedal postural control, participants stood barefoot on a self-selected limb.

The participants were instructed to: (1) cross their arms over the chest; (2) keep their eyes open and focused straight ahead; (3) raise one leg off the ground; and (4) to maintain their balance for as long as possible. The experimenter started a stopwatch when the participant raised their foot off the floor. The test was ended if the participant: (1) uncrossed the arms; (2) used their raised foot to touch the floor or the stance leg; (3) moved the weight-bearing foot; or (4) a maximum

45-s elapsed (Springer, Marin, Cyhan, Roberts, & Gill, 2007). Self-selected rest time was provided between trials; however, a minimum rest time between trials was set as the total time of the previous trial. A total of three trials were completed with the highest time recorded.

Bilateral Quiet Stance

Postural sway was assessed with the participant standing barefoot on a portable force platform (model BP5050). Trail length was 30-s and participants were instructed to keep their eyes open and focused straight ahead, to keep their arms relaxed at their side, and to remain as still as possible (“freeze/stand like a statue”) for the duration of the trial (Kooistra et al., 2009).

Foot placement was established by having the child march in place for 10 steps and foot placement was marked with stickers, similar to a previous study (Kooistra et al., 2009). Between trials a self-selected period to move around the testing room was provided after which the child

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returned to the marked foot position on the force platform. A total of three 30 second trials were collected. The trial with the lowest sway area was selected for analysis, and the four dependent variables described in the following paragraphs were extracted from this trial.

Raw center of pressure (COP) data, representing the point location of the GRF vector in

X and Y coordinates, were sampled at 1000 Hz using Bertec Acquire 4 software (Thomas,

Vanlunen, & Morrison, 2013). All post-processing procedures were performed in MATLAB

2018a (MathWorks, Natick, MA). Prior to data analysis, the raw COP data was down-sampled from 1000 Hz to 100 Hz and filtered using a 2nd order low-pass Butterworth filter with a 30 Hz cut-off (Lim et al., 2018b; Thomas et al., 2013). To measure the COP sway area, the area of a

95% confidence ellipse, enclosing approximately 95% of the data points on the COP trajectory, was calculated using the equation by Prieto et al. (1996) (Prieto, Myklebust, Hoffmann, Lovett,

& Myklebust, 1996; Thomas et al., 2013). To examine direction specificity, postural sway magnitude in the anteroposterior (AP) and mediolateral (ML) directions were measured. The

RMS of COP position data in the AP and ML directions were computed:

(1)

(2)

Where:

data point of interest

total number of data points

and = AP and ML time series data, respectively.

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The mean velocity (MV) was calculated by dividing the total distance travelled in the ML and AP directions to the time of each trial (Memari et al., 2013).

(3)

(4)

Where: data point of interest

total number of data points

and = AP and ML time series data, respectively

trial duration

Sample Entropy

Sample entropy (SampEn) was defined as the negative natural logarithm of the conditional probabilities that two sequences at m distance apart remain similar at m+1 (Richman

& Randall Moorman, 2000). A series of vectors of m length were formed for the entire length of the detrended time-series (N) and vectors were considered alike if the tail and head of the m- length vector fell within a tolerance (r) defined as a proportion (R) of the standard deviation of the detrended force-signal (r = R X SD) (Yentes et al., 2013). The total number of vectors identified as matches were then divided by N-m+1 and defined as B. This process was subsequently repeated for m+1 and defined as A. The equation for SampEn is shown below (Eq.

5). The SampEn algorithm returns a value between 0 and 2, with ~0 reflecting a perfectly repeatable time series (ex. sine wave) and ~2 reflecting a perfectly random time series. It has

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been suggested that low entropy reflects high attentional demands, whereas higher entropy reflects greater automaticity and less attentional demands (Donker et al., 2007; Roerdink et al.,

2011). Lower SampEn have also been observed in the isometric force output of individuals diagnosed with multiple concussions, demonstrating its sensitivity to CNS function (Studenka &

Raikes, 2017). The algorithm used for calculating sample entropy was provided from the 2017

University of Nebraska at Omaha Nonlinear Analysis Workshop. The algorithm parameters m

(vector length) and R (proportion of the standard deviation to be used for matching) were defined as m = 2 and R = 0.2, as stated in a previous study (Lim et al., 2018b).

(Eq. 5)

Where: m = vector length (m = 2) r = tolerance (r = 0.2 x SD) N = dataset = number of matching patterns of length m + 1 = number of matching patterns of length m

4.2.3 Statistical Procedures

The 95% confidence ellipse area (cm2) and unipedal stance time (s) were examined between groups using a one-way analysis of covariance (ANCOVA) with age as the covariate.

Group differences in (1) RMS sway magnitude (cm), (2) mean sway velocity (cm/s), and (3)

SampEn were examined among groups were assessed using a two-way analysis of covariance

(ANCOVA) (GROUP [ASD/FASD/ADHD/TD] x DIRECTION [ML/AP]) that were controlled for age. The statistics were computed using SPSS (version 26.0, Armonk, NY: IBM Corp).

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4.3 RESULTS

Sway Area

2 There was a significant GROUP effect for sway area (F(3,75) = 5.335, p = 0.002, η =

0.176). Post-hoc analysis revealed children with ASD had significantly greater sway area compared to FASD (p = 0.012), ADHD (p = 0.04), and TD (p = 0.006) groups (Figure 18).

Figure 18: Postural sway area between groups. *p = 0.04, **p = 0.012, ***p=0.006.

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Unipedal Stance Time Ceiling effects were observed for several participants during the timed 45-s unipedal stance time test. A non-parametric Kruskal-Wallis H test was run to determine if there were differences in unipedal stance time between the ASD, FASD, ADHD, and TD groups. Distributions of unipedal times were not similar for all groups, as assessed by visual inspection of a boxplot. The distributions of unipedal stance scores were significantly different between groups, χ2(3) =

18.262, p = 0.0003. The post-hoc analysis revealed that unipedal stance times were significantly higher between the TD group (mean rank = 56.50) and the ASD (mean rank = 30.28) (p =

0.0002), FASD (mean rank = 37.74) (p = 0.041), and ADHD (mean rank = 36.61) (p = 0.022) groups (Figure 19).

Figure 19: Non-parametric Kruskal-Wallis H test performed on unipedal stance times. Reference line indicates 45-s test cut-off. *p = 0.041, **p = 0.022, ***p=0.0002.

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To examine whether children with ASD had lower unipedal stance times compared to children in clinical controls without ASD (FASD/ADHD), two balance-impaired groups were identified [ASD (N=16) & Clinical Controls (N=20)] that had unipedal stance times below 45-s.

Findings from ANCOVA showed no significant difference between unipedal stance times

2 between the balance-impaired ASD and Clinical Control groups (F(1,33) = 1.272, p = 0.268, η =

0.037) (Figure 20, A). However, sway area between the balance-impaired groups was significant

2 (F(1,33) = 5.450, p = 0.026, η = 0.142) (Figure 20, B). Furthermore, unipedal stance time was associated with increased sway area in the balance-impaired ASD group (r = -0.452, p = 0.078), a finding not observed in the Clinical Control group (r = -0.008, p = 0.975) (Figure 20, C).

Figure 20: Balance-impaired sub-group analysis. (A) unipedal stance time; (B) postural sway area, (C) correlation between postural sway area and unipedal stance time between ASD (solid circles, dashed regression line) and Clinical Controls (white diamond, solid regression line) (ASD: r = -0.452, p = 0.078; Clinical Controls: r = -0.008, p = 0.975).

ML and AP Sway Magnitude (RMS) No significant GROUP x DIRECTION interaction was observed for RMS sway

2 magnitude (F(3,151) = 0.232, p = 0.874, η = 0.005). However, significant main effects were

2 observed for GROUP (F(3,151) = 8.867, p = 0.00001, η = 0.15) and DIRECTION (F(3,151) =

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113.446, p = 10-20, η2 = 0.429, AP > ML). For the main effect of GROUP, post-hoc analysis revealed significantly greater RMS sway between the ASD group and the FASD (p = 0.001),

ADHD (p = 0.002), and TD (p = 0.0006) groups. Furthermore, a significant effect was found for

2 2 AP (F(3,151) = 3.824, p = 0.011, η = 0.071) and ML (F(3,151) = 5.339, p = 0.002, η = 0.096) directions. Post-hoc analysis revealed AP sway magnitude was significantly greater in the ASD group vs. the TD group (p = 0.012) and ML sway was significantly greater in the ASD group vs. the FASD (p = 0.02), ADHD (p = 0.01), and TD (p = 0.006) groups (Figure 21). Finally, pairwise tests showed significantly greater AP vs. ML sway magnitude for all groups (p < 10-6).

Figure 21: Anteroposterior (AP) and mediolateral (ML) RMS sway magnitude between groups. *p = 0.02, **p = 0.01, ***p=0.006.

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ML and AP Mean Velocity No significant GROUP x DIRECTION interaction was observed for mean velocity

2 (F(3,151) = 0.440, p = 0.725, η = 0.009), however, there were significant main effects of GROUP

2 -16 2 (F(3,151) = 5.782, p = 0.001, η = 0.103) and DIRECTION (F(3,151) = 87.515, p = 10 , η = 0.367,

AP > ML). For the main effect of GROUP, children in the ASD group had significantly greater sway velocity compared to the FASD (p = 0.01), ADHD (p = 0.041), and TD (p = 0.002) groups.

2 Significant effects for AP F(3,151) = 3.992, p = 0.009, η = 0.073, with post-hoc analysis revealing significantly greater AP sway velocity between the ASD and the FASD (p = 0.034) and TD (p =

0.015) groups, but not the ADHD group (p = 0.46). No significant effect was observed for ML

2 velocity (F(3,151) = 2.255, p = 0.084, η = 0.043) (Figure 22). Finally, pairwise tests showed significantly greater AP vs. ML sway velocity for all groups (all p < 0.0002).

Figure 22: Anteroposterior (AP) and mediolateral (ML) mean velocity of sway between groups. *p = 0.03, **p = 0.01.

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ML and AP Sample Entropy (SampEn)

No significant GROUP x DIRECTION interaction was observed for SampEn (F(3,151) =

2 2.062, p = 0.108, η = 0.039). There was no significant main effect for GROUP (F(3,151) = 1.236,

2 p = 0.299, η = 0.024), however, there was a significant main effect for DIRECTION (F(3,151) =

5.558, p = 0.020, η2 = 0.039, ML > AP). Furthermore, a significant effect was found for ML

2 SampEn (F(3,151) = 3.198, p = 0.025, η = 0.060), but not for AP SampEn (F(3,151) = 0.097, p =

0.961, η2 = 0.002). Post-hoc analysis revealed that ML SampEn was significantly greater in the

FASD group vs. the ASD group (p = 0.015), that was not observed in the ADHD (p = 0.412) or

TD (p = 0.236) groups (Figure 23). Finally, pairwise tests revealed a significantly greater ML vs.

2 AP SampEn only for the FASD group (F(1,151) = 9.400, p = 0.003, η = 0.059).

Figure 23: Anteroposterior (AP) and mediolateral (ML) sample entropy (SampEn) between groups using m = 2 and tolerance r = 0.2. *p = 0.01, **p = 0.003.

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4.4 DISCUSSION The purpose of the current study was to examine what features of postural control were specifically impaired in children with ASD. Several important findings were observed using the cross-syndrome approach employed in this study. In acceptance of the original hypotheses, sway area and ML sway magnitude were significantly greater in children with ASD compared to

FASD, ADHD, and TD groups – demonstrating a robust finding for the ASD group. However, in rejection of the original hypothesis, ML sway velocity did not differ between the ASD group and any other group. AP sway magnitude and AP sway velocity were only impaired in children with

ASD, however in partial acceptance of the stated hypothesis, these features were not significantly different between all groups. AP sway magnitude was only significantly different between the ASD group and the TD group, whereas AP sway velocity was significantly greater between the ASD group and the FASD and TD groups. ML complexity was significantly lower in the ASD group compared to the FASD group only, in partial acceptance of the stated hypothesis. Unipedal stance times were significantly lower in the ASD, ADHD, and FASD groups compared to the TD group, demonstrating non-ASD specific impairments in balance.

Exploratory analysis showed that a sub-group of balance-impaired children with ASD did not demonstrate significantly lower unipedal stance times compared to balance-impaired Clinical

Controls. However, sway area was significantly greater in the balance-impaired ASD sub-group vs. the balance-impaired Clinical Control sub-group, with sway area associated with unipedal stance times in the balance-impaired ASD sub-group only. Therefore, our exploratory analysis suggests that increased postural sway area observed in children with ASD may have functional relevance.

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Sway Area Previous studies have shown that postural sway area is significantly greater in individuals with ASD compared to TD controls without sensory manipulation and standing on a stable surface (Fournier et al., 2010; Lim et al., 2018b, 2017; Mache & Todd, 2016; Somogyi et al.,

2016; Travers, Mason, Gruben, Dean, & Mclaughlin, 2018). Furthermore, Mache and Todd

(2016) found that, in children with ASD, postural sway area was significantly associated with a test of gross motor development (TGMD-3) supporting a functional relevance of postural sway area in the motor development of children with ASD (Mache & Todd, 2016). The findings from the current study show that postural sway area is specifically impaired in children with ASD.

Furthermore, postural sway area in balance-impaired children with ASD was associated with unipedal stance time, a finding not observed in the balance-impaired Clinical Controls sub- group. Therefore, postural stability may have a functional relevance in children with ASD.

Increased sway observed in this study provides evidence of impaired multi-modal sensory integration (Doumas et al., 2016), since sensory manipulation was not performed in this study.

However, previous findings suggest that children with ASD are hypo-responsive to visual stimuli during postural control tasks (Gepner & Mestre, 2002; Selma Greffou et al., 2012; Haworth et al., 2016; Lim et al., 2018b). Therefore, increased postural instability may be mediated by deficits utilizing visual input for postural control (Greffou et al., 2008) and increased weighting of somatosensory input to control movement (Haswell, Izawa, Dowell, Mostofsky, & Shadmehr,

2009; Izawa et al., 2012). Furthermore, Minshew et al. (2004) found that reducing the accuracy of somatosensory input disrupts postural control to a greater extent in children with ASD than in

TD controls (Minshew et al., 2004). Increased prioritization of somatosensory input may result from ASD-specific overgrowth of the somatosensory cortex (Mahajan et al., 2016). Future

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studies should employ cross-syndrome designs to examine whether hypo-reactivity of vision is specific to children with ASD.

Direction-Specific Effects on Postural Control

Previous studies show that individuals with ASD demonstrate greater sway magnitude in the mediolateral (ML) direction (Fournier et al., 2010; Lim et al., 2018b). Paulus et al. (1984), showed that the central area of the visual field vs. the peripheral visual field, outside the point of fixation, dominates postural control and has the greatest influence on ML RMS sway magnitude

(Paulus, Straube, & Brandt, 1984). Lim et al. (2018a), using central visual field and peripheral visual field optical flow showed that individuals with ASD display abnormal weighting of peripheral visual field information over central visual field information (Lim et al., 2018a).

Therefore, collectively these findings suggest that abnormal central visual field processing may result in increased ML RMS sway magnitude and increased attentional demands to control ML sway in individuals with ASD. In Lim et al. (2018b), evidence showed increased ML RMS sway magnitude in individuals with ASD compared to TD individuals and increased attentional demands to control ML sway – as shown through sample entropy. These findings were specific to ML sway and not observed for AP RMS sway magnitude. Furthermore, increased attentional demands to control AP sway was not observed (Lim et al., 2018b). In contrast to findings from

Lim et al. (2018b), Memari et al. (2013) and Fournier et al. (2010) showed AP RMS sway magnitude was also significantly greater compared to TD controls in addition to ML RMS sway magnitude. However, Memari et al. (2013) did show direction-specificity for ML mean velocity that was not observed for AP mean velocity (Memari et al., 2013). Furthermore, in support of direction-specificity of sway magnitude, Fournier et al. (2010) found much larger ML sway vs.

AP sway in children with ASD (Fournier et al., 2010).

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In support of findings from previous studies, this study found an ASD-specific and robust finding of significantly greater ML RMS sway magnitude (Lim et al., 2018b; Memari et al.,

2013). Furthermore, in support of findings presented by Memari et al. (2013), AP RMS sway magnitude was significantly greater between the ASD and TD groups. However, in contrast to

Memari et al. (2013) we did not observe direction-specificity for ML mean velocity, and AP mean velocity was significantly greater between the ASD group vs. TD controls. However, our finding of a predominant AP vs. ML sway pattern in all groups, is supported by previous findings (Lim et al., 2018b).

In the current study, attentional demands to control ML sway were significantly greater in the ASD compared to the FASD group that was not observed for AP sway demonstrating directional-specificity. This represents a novel finding and suggests different control mechanisms of ML sway in children with ASD and FASD. One interpretation, that considers the role of vision in the control of ML sway, is that children with FASD have an increased reliance on visual input for postural control (Brink et al., 2018). In contrast, evidence suggests that children with ASD are hypo-sensitive to visual input (Gepner & Mestre, 2002; Selma Greffou et al.,

2012; Haworth et al., 2016; Lim et al., 2018b) resulting in greater attentional demands required to control ML sway. Findings presented in Brink et al. (2018), show that, in young girls with

FASD, balance is significantly more impaired when vision is removed compared to TD controls suggesting increased reliance of vision to maintain balance. Considering the relatively large number of studies substantiating claims of hypo-reactivity to vision in ASD, more studies are needed to examine the role of vision in postural control of children with FASD. The use of motor testing to differentiate the diagnosis of FASD from ASD could be valuable for clinicians considering the high rate of misdiagnosis of FASD (Chasnoff, Wells, & King, 2015) and

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difficulty diagnosing non-dysmorphic FASD in the absence of maternal alcohol use information

(Burd, Cohen, Shah, & Norris, 2011). Studying motor control in children using cross-syndrome approaches may help to identify more motor-features that are specific to various common neurodevelopmental disorders such as ASD, FASD, and ADHD.

4.5 CONCLUSION

In summary, children with ASD demonstrate specific postural deficits not observed in children with other neurodevelopmental disorders with similar intellectual and neurobehavioral impairments. Findings of greater postural sway area and ML sway in children with ASD compared to all Clinical Controls and TD controls demonstrate robust and potentially important findings. The findings presented in this study add to current knowledge by demonstrating that postural sway: (1) impacts balance performance in children with ASD; and (2) is significantly greater than children with other common neurodevelopmental disorders. Future studies should examine the influence of pharmacological, exercise-based, and non-invasive brain stimulation interventions on postural control. The finding of specificity of postural control deficits suggest that brain circuitry implicated in ASD such as the cerebellum, basal ganglia, inferior parietal lobule, and somatosensory cortex (D’Mello et al., 2015; Estes et al., 2011; Mahajan et al., 2016) may mediate postural control deficits and be potential targets for non-invasive stimulation.

Furthermore, exercise-based interventions may improve functioning of impaired brain circuitry in children with ASD that may reduce the severity of the core symptoms of the disorder. Finally, the current study highlights the value of using a cross-syndrome approach to studying motor control in children with neurodevelopmental disorders. We advocate for future studies to consider employing cross-syndrome approaches to studying motor control in children with

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neurodevelopmental disorders to improve understanding of the neurological underpinnings of

ASD and other neurodevelopmental disorders.

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CHAPTER 5

Manual Dexterity in Children with Autism Spectrum Disorder: A Cross Syndrome Approach

Daniel E. Lidstone

Co-authored By

Faria Z. Miah, Brach Poston, Julie F. Beasley, Janet S. Dufek

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Significance of the Chapter

The previous chapters have identified several features associated with grip-force control and postural control in children with ASD using a cross-syndrome approach. In the current chapter, the lateralization of motor deficits is examined. In typically developing individuals, manual dexterity is lateralized the left-hemisphere. Previous studies show left-hemisphere dysfunction in children with ASD that may result in ASD-specific deficits in dominant and non- dominant hand manual dexterity performance. Cross-syndrome approaches have not been used to examine manual dexterity in children with ASD or other neurodevelopmental disorders. In addition to examining the lateralization of manual dexterity deficits in children with ASD, the current chapter exploits the cross-syndrome approach to examine a potential motor phenotype of

FASD. In children with FASD, one of the most common neuroanatomical findings is corpus callosum (CC) hypoplasia. Previous studies have associated CC hypoplasia in children with

FASD with findings of reduced lateralization of sensory and motor functions. Therefore, a secondary aim of this chapter was to examine whether hand performance asymmetry could differentiate children with FASD from children without FASD. Using the cross-syndrome approach, we were able to both test our hypothesis of dominant and non-dominant hand manual dexterity deficits in children with ASD and examine motor phenotypes that may be specific to other neurodevelopmental disorders.

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Abstract

Objective: Manual dexterity (MD) is important for performing most activities of daily living, and children with ASD show MD deficits. However, the specificity of MD deficits in children with ASD has not previously been examined. Furthermore, children with Fetal Alcohol Spectrum

Disorder (FASD) and Attention-Deficit Hyperactivity Disorder (ADHD) also show MD deficits.

The purpose of this study was to examine the specificity MD deficits in children with ASD using a cross-syndrome design. We examined dominant (D) and non-dominant (ND) hand performance using a relatively large sample of children with ASD, FASD, ADHD, and typically developing

(TD) children.

Method: Seventy-two right-handed children (7-17 years old) participated in this study. To examine MD, the 9-hole pegboard test was completed on the D and ND-hands. The fastest time of three attempts was recorded. HPA was defined as the percent difference between D and ND- hand times.

Results: D-hand MD was significantly worse in children with ASD vs. typically developing

(TD) children (p < 0.05). ND-hand dexterity was significantly worse in children with ASD vs.

FASD (p = 0.01) and TD groups (p < 0.0005). Hand performance asymmetry (HPA) was significantly lower in the FASD group vs the ASD and ADHD groups (p < 0.05).

Conclusion: These results show that children with ASD show specific deficits in MD not observed in children with FASD or ADHD. Furthermore, HPA was found to be a sensitive measure to prenatal alcohol exposure. Neurobiological mechanisms of ASD and FASD are discussed.

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5.1 INTRODUCTION

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication and social interaction accompanied by restricted, repetitive patterns of behaviors, interests, or activities (American Psychiatric Association, 2013).

Evidence suggests that cortical structural deficits implicated in ASD are left-lateralized and include regions such as left inferior parietal lobule (IPL) and left primary somatosensory cortex

(S1) (Mahajan, Dirlikov, Crocetti, & Mostofsky, 2016). Furthermore, deficits within subcortical regions such as the basal ganglia and the right posterior cerebellum are have also been observed in children with ASD (D’Mello, Crocetti, Mostofsky, & Stoodley, 2015; Estes et al., 2011;

Stoodley et al., 2018). Cortical and subcortical brain regions implicated in ASD participate in the control of gross and fine motor movements contributing to the high prevalence of motor deficits in children with ASD (Green et al., 2009). Fine motor deficits in children with ASD can be detrimental to normal development and also may contribute to low academic achievement, since approximately 60% of time in the classroom is spent on fine motor activities requiring manual dexterity (McHale & Cermak, 1992). Problems with manual dexterity are recognized as the most frequently occurring motor impairment in children with ASD (Hirata et al., 2014) with deficits observed across the lifespan (Riquelme, Hatem, & Montoya, 2016; Thompson et al., 2017).

Manual dexterity refers to the ability to perform coordinated finger movements and manipulate objects in a timely manner (Wang et al., 2011). Manual dexterity is often evaluated using pegboard tasks that involve picking up small pegs and placing them into holes. The total time to insert and remove all the pegs or the total number of pegs inserted within a set time limit, provides an objective measure of manual dexterity. Manual dexterity has been shown to be impaired in right-handed individuals with ASD with dexterity performance associated with: (1)

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structural deficits in the white matter tracts connecting the S1-M1 hand regions (Thompson et al.,

2017); (2) disrupted somatosensory processing (Riquelme et al., 2016); and (3) gray matter overgrowth of S1 (Mahajan et al., 2016). In addition to disrupted somatosensory processing, several cortical regions that are implicated in ASD are left-lateralized (Mahajan et al., 2016;

Peterson, Mahajan, Crocetti, Mejia, & Mostofsky, 2015; Thompson et al., 2017). Performing complex sequential and dexterous tasks, such as a pegboard task, are lateralized to the left- hemisphere – irrespective of the hand used (Haaland, Elsinger, Mayer, Durgerian, & Rao, 2004;

Hanna-Pladdy, Mendoza, Apostolos, & Heilman, 2002; Thompson et al., 2017). Furthermore, left-hemisphere damage results in both ipsilesional and contralesional deficits on tasks requiring manual dexterity (Hanna-Pladdy et al., 2002; Heilman, Meador, & Loring, 2000). Therefore, left-hemisphere dysfunction in children with ASD may result in both dominant and non- dominant hand deficits on manual dexterity tasks.

In addition to observed manual dexterity deficits in children with ASD, similar deficits have been observed children with other neurodevelopmental disorders such as Fetal Alcohol

Spectrum Disorder (FASD) and Attention-Deficit Hyperactivity Disorder (ADHD) (Barr,

Streissguth, Darby, & Sampson, 1990; Chiodo, Janisse, Delaney-Black, Sokol, & Hannigan,

2009; Hotham, Haberfield, Hillier, White, & Todd, 2018). In children with FASD, Corpus

Callosum (CC) hypoplasia is one of the most common neuroanatomical findings in children with

FASD (Boronat et al., 2017), and may mediate reduced lateralization of hand motor skill that has been observed in this population (Domellöf, Rönnqvist, Titran, Esseily, & Fagard, 2009; Janzen,

Nanson, & Block, 1995; Moreland, La Grange, & Montoya, 2002; Zimmerberg & Riley, 1988).

The purpose of this study was to examine the specificity of dominant and non-dominant hand manual dexterity deficits in children with ASD. A secondary aim of this study was to

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examine whether hand performance asymmetry differentiated children with FASD from children without FASD. In the current study, this was accomplished by using a cross-syndrome approach that compares children with ASD to children with FASD, ADHD, and typical development. It was hypothesized that: (1) children with ASD would demonstrate dominant and non-dominant hand deficits in manual dexterity compared to typically developing (TD) children and clinical controls. and (2) children with FASD would have significantly lower hand asymmetry than children without FASD.

5.2 METHODS

5.2.1 Participants A total of seventy-two right-handed children were recruited to participate in the study

(Table 23). Children with FASD were diagnosed through the University of Nevada/Las Vegas

FAS clinics by a multi-disciplinary clinical team. Children in the ASD and ADHD clinical control groups were recruited through the UNLV Ackerman Autism Center and the Las Vegas community. Parents of children recruited in the Las Vegas community confirmed diagnoses by presenting a copy of the clinical diagnosis to the experimenter prior to data collection. Inclusion criteria for the study required the child to: (1) be 7-17 years old; (2) have normal to corrected-to- normal vision; and (3) have a clinical diagnosis of ASD, ADHD, FASD, or be TD. Children were excluded from participation if the child had an: (1) intellectual disability (FSIQ-2 ≤ 70); (2) upper or lower extremity deformity; (3) current orthopedic injury; and (4) a known genetic disorder. Intellectual disability was ruled out prior to testing using two sub-tests of the WASI-II

(vocabulary and matrix reasoning) to provide a Full-Scale Intelligence Quotient (FSIQ-2).

Children in the ASD group did not have a co-occurring FASD diagnosis. TD children were

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excluded if they had a psychiatric disorder and/or a first-degree relative with a diagnosis of either

ASD or ADHD. Parental consent and child assent were obtained prior to testing.

Table 21: Participant Demographics.

FASD (N=16) ARND (N=4) ASD (N=20) pFAS (N=6) ADHD (N=15) TD (N=21) p value FAS (N=3) N.S. (N=3) Sex [no. Males (%)] 15 (75%) 5 (31%) 9 (60%) 13 (60%) - Age (years) 12.7±2.7 12.4±3.1 10.3±1.9 11.9±2.7 0.073 (min-max) (8.5±17.9) (7.4-17.6) (7.3-14.5) (7.4-16.8) FSIQ-2 91.3±13.4 91±11.2 102±17 111±10*** <0.0005 (min-max) a (71-120) (72-118) (78-137) (94-129) Laterality 2.67±0.57 2.76±0.60 2.56±0.64 2.89±0.37 0.341 Comorbid ADHD 9 (45%) 14 (88%) - - - [no. (%)] Prematurity 5 (25%) 3 (19%) 4 (27%) 1 (5%) - <37wks [no. (%)] Low Birth Weight 2 (10%) 3 (19%) 2 (13%) 1 (5%) - <2500 g [no. (%)] Prenatal Drug 2 (10%) 6 (38%) 6 (40%) 0 - Exposure [no. (%)] Vanderbilt

(no. ≥ 2) b ADHD a 8.3±5.0 8.3±5.6 10.6±4.5 1.1±3.1**** <0.0005 Oppositional 1.9±3.0 6.1±4.9 4.4±4.8 0.4±1.2** 0.01 Defiant/Conduct a Anxiety/Dep.a 1.2±2.1 1.7±1.9 1.8±2.2 0.2±0.7* <0.0005 Medication Use 9 (45%) 13 (81%) 8 (53%) 0 - [no. (%)] a = Group differences assessed using Kruskal-Wallis test. Bonferroni corrections for multiple comparisons. (α≤0.05) b = Mean number of symptoms scored as often (score=2) or very often (score=3) on Vanderbilt PARENT Informant. Symptom scores (≥2) were totaled for Attention-Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant/Conduct Disorder, and Anxiety/Depression. ARND = Alcohol Related Neurodevelopmental Disorder pFAS = Partial Fetal Alcohol Syndrome FAS = Fetal Alcohol Syndrome N.S. = Not specified **** = Significant difference between TD and all groups. *** = Significant difference between TD group and both ASD and FASD groups ** = Significant difference between TD group and both ADHD and FASD groups. * = Significant difference between TD group and FASD group only.

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5.2.2 Procedures Handedness Assessment Following intellectual testing, handedness was assessed using the Almli Handedness

Assessment (Almli, Rivkin, & McKinstry, 2007; Knaus, Kamps, & Foundas, 2016). This handedness assessment has previously been used on children with ASD between the ages of 3-17

(Knaus et al., 2016). The participant was seated in front of the experimenter and asked to complete 10 tasks using their hands with items presented at midline (writing, drawing, throw a ball, cut paper with scissors, hammer peg, eat with spoon, place puzzle piece, place Lego piece on tower, unscrew jar, place ring on rod) (Figure 24, A). This assessment was selected since it requires minimal instructions and allows direct observation of hand preference. Handedness was scored from -3.16 (complete left handedness) and +3.16 (complete right-handedness) (Almli et al., 2007; Knaus et al., 2016). If handedness was scored between -1 and +1, indicating no hand preference, the hand used for writing was selected as the dominant-hand.

Figure 24: Experimental set-up for (A) handedness assessment and (B) 9-hole pegboard test.

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9-Hole Pegboard Test The timed Rolyan® 9-Hole Peg Test was used to assess manual dexterity (Wang,

Bohannon, Kapellusch, Garg, & Gershon, 2015). The 9-hole pegboard was selected over the grooved pegboard test due to its simplicity, reduced administration time, and its inclusion in the motor battery of the NIH Toolbox (Wang et al., 2011). The pegboard was positioned at mid-line on non-slip netting with the dish placed next to the working hand (Figure 24, B). The height of the table was adjusted so that the table-top was approximately at mid-torso level. A red pad was placed contralateral to the working hand to help the participants remember not to move that hand during the test. Likewise, a green pad was placed under the hand of the working hand (closest to the peg dish) to signal that it would be the working hand. The participant was instructed to: (1) use the hand on the green pad to remove pegs from the dish one at a time as quickly as possible;

(2) place the pegs in the holes in any order; and (3) remove each peg as quickly as possible and return to the dish. The experimenter demonstrated one trial for the participant and one familiarization trial was allowed for each hand. Three recorded trials were completed for both the dominant and the non-dominant hands and the shortest time for each hand was recorded as the performance score. The order of hand used was randomized.

Hand Performance Asymmetry

Hand performance asymmetry was calculated as the percent difference between non- dominant (ND) and dominant (D) hand pegboard times [((ND – D)/ D)100] (Hanna-Pladdy et al.,

2002).

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5.2.3 Statistical Procedures

A two-way ANCOVA was conducted to examine GROUP (ASD, FASD, ADHD, TD) and HAND (dominant vs. non-dominant) specific differences on 9-hole pegboard times. Age was used as a covariate for the two-way ANCOVA. Post-hoc analyses were performed using the

Bonferroni adjustment and all tests were conducted with significance set at α=0.05. Effect size d

(UM / SDpooled) was also examined for some statistically non-significant comparisons and identified as small (d = 0.20), medium (d = 0.50), or large (d = 0.80) (Cohen, 1988).

To test the specificity of hand performance asymmetry as a motor phenotype of FASD, we compared the FASD group to each group separately since we were not interested in any other group comparisons. Homogeneity of slopes between age and hand performance asymmetry was violated, therefore, independent t-tests were run between each group (ASD, ADHD, and TD) and the FASD group.

5.3 RESULTS

There was no significant GROUP x HAND interaction for pegboard time while

2 controlling for age (F(1,135) = 0.872, p = 0.457, η = 0.019). However, there were significant main

2 effects for GROUP (F(1,135) = 8.873, p = 0.00002, η = 0.165, ASD > TD/FASD) and HAND

2 (F(1,135) = 12.763, p = 0.0004, η = 0.086, dominant < non-dominant). For the main effect of

GROUP, pegboard time was significantly greater for children in the ASD compared to children in the FASD (p = 0.024) and TD (p = 10-7) groups. Furthermore, significant GROUP effects

2 were detected for both the dominant (F(3,135) = 3.215, p = 0.025, η = 0.067) and non-dominant

2 (F(3,135) = 6.570, p = 0.0003, η = 0.127) hands. For the dominant hand, pegboard time was significantly greater in the ASD vs. the TD group (p = 0.017) with all other p values > 0.3. For

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the non-dominant hand, pegboard time was significantly greater in the ASD group vs. both the

FASD (p = 0.01) and TD (p = 0.0002) groups, but not between the ASD and ADHD group (p =

0.221). Finally, pairwise tests between the dominant and non-dominant pegboard times were significantly different for the ASD group (p = 0.003), but not for the FASD (p = 0.591), ADHD

(p = 0.066), or TD (p = 0.06) groups (Figure 25). However, pairwise Cohen’s d effect sizes for non-dominant vs. dominant were medium for the ADHD (d = 0.48) and large for the TD group

(d = 0.80), whereas effect size was small for the FASD group (d = 0.27) 27 (Table 24).

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Figure 25: 9-hole pegboard time mean ± standard errors for dominant and non-dominant hands. *p < 0.05, ** p = 0.01, *** p < 0.0005

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Table 22: 9-Hole Pegboard test times in seconds.

ASD FASD ADHD TD (N = 20) (N = 16) (N = 15) (N = 21) D ND D ND D ND D ND M 20.80 24.19 19.78 20.46 20.25 22.66 17.75 19.84 SD 2.91 5.73 2.60 2.26 4.29 5.58 1.83 3.19 Madj 21.15 24.55 20.03 20.71 19.51 21.92 17.75 19.83 SE 0.80 0.80 0.89 0.89 0.93 0.93 0.77 0.77 Mean (M) and standard deviation (SD) for dominant (D) and non-dominant (ND) 9-Hole Pegboard Test times. Age- adjusted mean (Madj) and standard errors (SE) are also shown.

Hand Performance Asymmetry Hand performance asymmetry differentiated the FASD group from the ASD (p = 0.035) and ADHD (p = 0.042) groups, but not from the TD group (p = 0.055). However, effect size d was medium-large between the FASD and TD group (d = 0.67) (Figure 26).

Figure 26: Hand performance asymmetry between children with FASD and ASD, ADHD, and TD groups.

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5.4 DISCUSSION

The purpose of this study was to examine the specificity of manual dexterity deficits in children with ASD by using a cross-syndrome approach. The main ASD-specific findings of this study were: (1) dominant-hand pegboard time was significantly greater in children with ASD vs.

TD controls; (2) non-dominant pegboard time was significantly greater in children with ASD vs.

FASD and TD controls; and (3) pegboard time was significantly greater in the non-dominant vs. the dominant hand only in children with ASD. The findings of this study support the original hypotheses of dominant and non-dominant hand manual dexterity deficits in children with ASD.

Furthermore, hand performance asymmetry differentiated children with FASD from the ASD and ADHD groups. This study represents the first cross-syndrome approach to studying manual dexterity in children with ASD, ADHD, and FASD.

Previous studies have shown that manual dexterity is impaired in children with ASD compared to TD controls (Duffield et al., 2013; Green et al., 2002, 2009; Hirata et al., 2014;

Provost, Lopez, & Heimerl, 2007; Riquelme et al., 2016; Staples & Reid, 2010; Thompson et al.,

2017; Whyatt & Craig, 2012) and structural deficits within the left-hemisphere in children with

ASD may mediate manual dexterity deficits (Mahajan et al., 2016; Thompson et al., 2017).

Motor areas of the left-hemisphere – such as sensorimotor, premotor, and parietal cortices – and the subcortical cerebellum are dominant for performing complex repetitive hand-motor sequences in TD individuals (Haaland et al., 2004; Miall & Christensen, 2004; Thompson et al.,

2017). Left-hemisphere dysfunction (Mahajan et al., 2016; Peterson et al., 2015; Thompson et al., 2017) and cerebellar deficits (D’Mello & Stoodley, 2015) in children with ASD are well documented in the literature. Furthermore, supporting left-hemisphere dysfunction, abnormal shifting from left-to-rightward lateralization of motor function has been observed in children

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with ASD (Cardinale, Shih, Fishman, Ford, & Müller, 2013; Floris et al., 2016; Thompson et al.,

2017). Additional evidence supporting left-rightward lateralization of motor function in ASD is that right-hemisphere white tract organization is negatively associated with pegboard performance, whereas in TD individuals the opposite is true with left-hemisphere white tract organization negatively associated with pegboard performance (Thompson et al., 2017). In the current study, the finding of both dominant and non-dominant manual dexterity deficits in children with ASD may therefore result from left-hemispheric dysfunction and structural abnormalities within the subcortical cerebellum.

The findings from the current study support previous studies comparing manual dexterity performance between children with ASD and TD controls (Duffield et al., 2013; Green et al.,

2002, 2009; Hirata et al., 2014; Provost et al., 2007; Riquelme et al., 2016; Staples & Reid, 2010;

Thompson et al., 2017; Whyatt & Craig, 2012). However, cross-syndrome designs – such as the one used in this study – have seldom been used to examine motor function in children with ASD.

In one study, Provost et al. (2007) found no difference in fine-motor skills between children with

ASD to children with developmental delay (without ASD) and motor delay using a standardized motor assessment (Provost et al., 2007). However, the timed 9-hole pegboard task used in the current study may be better suited to detect smaller differences in manual dexterity performance between groups than the less quantitative standardized motor tests (Wilson, McCracken,

Rinehart, & Jeste, 2018). In addition to the ASD-specific deficits observed for dominant and non-dominant hand dexterity, we did not observe any deficits in manual dexterity in the FASD and ADHD groups compared to TD controls in contrast to previous studies (Barr et al., 1990;

Chiodo et al., 2009; Hotham et al., 2018). However, the finding of lower manual dexterity performance compared to TD controls is mixed in individuals with FASD (Doney et al., 2014,

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2017) and ADHD (Pitcher, Piek, & Hay, 2003). Furthermore, in contrast to evidence supporting left-hemisphere dysfunction in ASD, findings in individuals with ADHD support right- hemisphere dysfunction (Almeida Montes et al., 2013; Segal, Shalev, & Mashal, 2017;

Stefanatos & Wasserstein, 2006). Therefore, this may partially account for the finding of sparing of manual dexterity deficits in children with ADHD in this study.

Previous studies show that performance on the 9-hole pegboard test across the lifespan is faster on the dominant vs. the non-dominant hand. In the current study, dominant hand performance was significantly faster than the non-dominant hand. At the group level, significantly better performance of the dominant vs. non-dominant hands was only significant for the ASD group, although effect sizes were moderate and large for the ADHD and TD groups, respectively. Effect size between non-dominant vs. dominant hand was smallest for the FASD group. Subsequent analysis of hand performance asymmetry showed that children with FASD had significantly lower asymmetry than children in the ASD and ADHD groups. Abnormal hand asymmetry has previously been observed in children with FASD (Domellöf et al., 2009; Janzen et al., 1995) and in animal models of prenatal alcohol exposure (Moreland et al., 2002;

Zimmerberg & Riley, 1988). Reduced lateralization of hand motor function is associated with abnormal corpus callosum (CC) development (Moreland et al., 2002). In children suspected of prenatal alcohol exposure, CC hypoplasia is one of the most common neuroanatomical findings

(Boronat et al., 2017). Therefore, our findings suggest that low hand performance asymmetry may be a motor phenotype of prenatal alcohol exposure that may have the potential to differentiate FASD from ASD and ADHD. The implementation of motor testing to assist in the diagnosis of FASD could be valuable for clinicians considering the high rate of missed diagnosis and misdiagnosis of FASD (Chasnoff, Wells, & King, 2015). Furthermore, the most common

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FASD sub-type, Alcohol-Related Neurodevelopmental Disorder (ARND), does not require the presence of facial features of FAS, and diagnosing ARND in the absence of maternal alcohol use information is currently impossible (Burd, Cohen, Shah, & Norris, 2011). Approximately only

25% of children with FASD exhibit any physical features of FAS (Streissguth et al., 2004) and ~

70% of children with FASD are in the foster care system (Burd et al., 2011), making the confirmation of maternal alcohol intake very challenging. Improving our understanding of motor phenotypes of children with FASD may, if combined with other risk-factors for FASD such as co-occurring ADHD (Chasnoff et al., 2015; Weyrauch, Schwartz, Hart, Klug, & Burd, 2017) and maternal psychiatric diagnosis (Singal et al., 2017), may lead to improved identification of children that may have been exposed prenatally to alcohol. Clinicians and adoptive/foster parents may benefit from this knowledge if confirmed maternal alcohol abuse use cannot be obtained.

This represents an important area of future research considering the prevalence of FASD is nearly twice that of ASD in the US (May et al., 2018). Inclusion of children with FASD in future studies, using cross-syndrome designs, may help to identify motor features sensitive to prenatal alcohol exposure.

5.5 CONCLUSION

In summary, dominant and non-dominant hand manual dexterity deficits are specifically impaired in children with ASD. These findings highlight manual dexterity deficits as a core feature of ASD that may negatively impact performance of daily living skills and academic success. Furthermore, using a cross-syndrome analysis, we examined significantly lower hand performance asymmetry in children with FASD compared to children in the ASD and ADHD groups. Collectively these findings support the use of cross-syndrome designs to assess the specificity of motor deficits in children with neurodevelopmental disorders. The use of cross-

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syndrome designs can improve our understanding of the neurological underpinnings of neurodevelopmental disorders.

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Appendix 1

Table 8: Summary of studies demonstrating ASD deficits to integrate visual input into motor commands. pIFG = posterior inferior frontal gyrus, PMd = dorsal pre-motor area, PMv = ventral pre-motor area, IPL = inferior parietal lobule, pSTS = posterior superior temporal sulcus.

Findings in ASD Studies Possible Neural Substrates Contributing to Motor Deficits Face processing deficits correlated with social deficits and empathy (Rigby et al., 2018)

Imitation deficits reduced when biological motion speed reduced (Lainé, Rauzy, Tardif, & Gepner, Biological motion processing: 2011) Crus I, pSTS, and IPL (Claeys,

Biological Motion Lindsey, De Schutter, & Orban, Reduced activation of cerebellum during Processing 2003; Grèzes et al., 2001; Sokolov biological motion perception correlated with social et al., 2012; Thompson, 2005) deficits (Jack et al., 2017)

Reduced activation of mirror network during action-observation correlated with social deficits (Enticott et al., 2012; Oberman et al., 2005; Oberman, Ramachandran, & Pineda, 2008) Impaired praxis and imitation correlated with Imitation: pIFG, IPL, and PM social and communication deficits (Dziuk et al., (Buccino et al., 2004; Decety, Imitation 2007; Gizzonio et al., 2015; Kaur, M. Srinivasan, & Chaminade, Grèzes, & Meltzoff, N. Bhat, 2018) 2002) Overreliance of proprioception vs. vision during Proprioceptive arm-reaching to visual target (Haswell, Izawa, R Proprioceptive bias: Anterior bias over vision Dowell, H Mostofsky, & Shadmehr, 2009; Izawa et cerebellum (extending into lobule in Internal Model al., 2012; Marko et al., 2015; Masterton & VI and VIII) (Marko et al., 2015) Formation Biederman, 1983; Sharer, Mostofsky, Pascual-Leone, & Oberman, 2016) Goal-directed reach planning deficit using visual Sensory-motor transformation of cues/target (Dowd, McGinley, Taffe, & Rinehart, target and initial effector 2012; Fabbri-Destro, Cattaneo, Boria, & Rizzolatti, position to eye-centered common 2009; Hughes, 1996; Papadopoulos et al., 2012) reference frame: IPL (Cohen & Andersen, 2002) Prolonged movement preparation with unimpaired accuracy (Dowd et al., 2012; Sachse et Goal-action coupling: PM and Goal-Directed al., 2013) IPL (Fogassi & Luppino, 2005 - Reach Planning Review) Delayed muscle-activity prior to goal-directed action (Cattaneo et al., 2007; Schmitz, Martineau, Early reach planning: IPL (Koch Barthélémy, & Assaiante, 2003) et al., 2008)

Impaired grip-lift force onset latency with Forward prediction of sensory previous experience (David et al., 2009; David, feedback prior to reaching: Crus

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Baranek, Wiesen, Miao, & Thorpe, 2012) I/II, HIV, HV, and HVI (Coltz et al., 1999; Norris, Greger, Hathaway, & Thach, 2004; Pasalar et al., 2006; Roitman, 2005)

Prolonged movement time during visual-guided reaching (Barbeau, Meilleur, Zeffiro, & Mottron, 2015; Glazebrook, Gonzalez, Hansen, & Elliott, Prolonged movement time 2009; Glazebrook, Elliott, & Lyons, 2006; Stoit, Van during visual-guided reaching: Schie, Slaats-Willemse, & Buitelaar, 2013; Szatmari, On-Line PMd and IPL (Archambault, Tuff, Finlayson, & Bartolucci, 1990) Reaching Ferrari-Toniolo, Caminiti, &

Battaglia-Mayer, 2015; Buiatti, Prolonged movement time during visual-guided Skrap, & Shallice, 2013) vs. non-visual-guided reaching (Glazebrook et al., 2009; Sacrey, Germani, Bryson, & Zwaigenbaum, 2014) Saccades: cerebellar oculomotor vermis (VI-VII), Crus I/II, and IPL (Collins & Jacquet, 2017; Ro, Rorden, Driver, & Rafal, 2001; Reduced saccade accuracy (Schmitt et al., 2014) Takagi, Zee, & Tamargo, 2000; Saccades and Voogd et al., 2012) Smooth Pursuit Reduced smooth eye pursuit accuracy (Takarae et

al., 2004) Smooth Pursuit: oculomotor vermis (VI-VII), Crus I/II, IPL (Lynch & Tian, 2005; Voogd et al., 2012) Postural under-reactivity to visual stimuli (Gepner, Mestre, Masson, & de Schonen, 1995; Gepner & Mestre, 2002; Greffou et al., 2012; Minshew, Sung, Postural Control Jones, & Furman, 2004) -

Increased postural instability with vs. without vision (Lim, Partridge, Girdler, & Morris, 2017) Precision-grip force tracking: IPL, lobule HVI, Crus I, PMv, PMd (Moulton et al., 2017; Vaillancourt, Mayka, & Corcos, 2005) Initial force-overshoot error related to social and communication deficits (Mosconi et al., 2015; Wang Visual target location in eye- et al., 2015) centered reference frame: IPL Visual-Force (Cohen & Andersen, 2002) Target Matching Forward prediction of visual Impaired storage/retrieval of motor memories target location: Crus I/II, HIV, (Neely et al., 2016) HV, and HVI (Coltz et al., 1999; Norris et al., 2004; Pasalar et al., 2006; Roitman, 2005)

PG force tracking motor memory retrieval: dorsolateral prefrontal cortex, ventral prefrontal

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cortex, and anterior cingulate (Vaillancourt, Thulborn, & Corcos, 2006)

Increased variability of spatiotemporal Gait parameters of gait with vs. without visual cueing - (Nayate et al., 2012)

Table 15: Summary of findings of motor deficits in children with Fetal Alcohol Spectrum Disorder (FASD) vs. controls (CON).

Main Findings Study Task Groups FASD vs. Controls × Inter-tap variability (only in children 7 to Rhythmic 18 FASD (Simmons et al., 11 yrs) Finger tapping 22 CON 2009) × Motor delay (only to Auditory Cue (7 to 17 years) in children 7 to 11 yrs) Ø Activation in Motor Timing cerebellar right Crus I (cerebellum-mediated) and vermal lobule IV- 17 FAS V Rhythmic 17 Heavy Exposure (Du Plessis et al., Ø Activation in Finger tapping (HE) 2015) cerebellar right Crus I, to Auditory Cue 16 CON vermis IV-VI, right (10 years) lobule VI associated with higher levels of PAE Isometric Static Force Tracking × nRMSE at 20% - 5+20% MVC 25 FASD (Simmons et al., MVC - Altered visual 18 CON 2012) Ø Entropy at each feedback (50, (7.25 to 16.5 years) feedback frequency Force Regulation & 3.15, and 1.35 Visuomotor Integration Hz) (cerebellum + basal Isometric × nRMSE at 20% ganglia-mediated) Dynamic Force MVC Tracking 24 FASD × nRMSE at low (Nguyen, Levy, et - 5-20% MVC 22 CON feedback frequency al., 2013) - Altered visual (7 to 17 years) Ø Entropy at each feedback (50, feedback frequency 3.15, and 1.35 and force condition Hz)

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Ø Mean frequency at 5 + 20% MVC Ø Mean frequency at Isometric Static all visual feedback Force Tracking conditions - 5+20% MVC Ø Spectral variability 25 FASD at each force and (Nguyen, Ashrafi, - Altered visual visual feedback level Continued 25 CON et al., 2013) feedback × Peak power (7 to 17 years) - Time-domain (amplitude) at each analysis of force and visual force-time feedback level signal Ø Frequency at which peak power occurred at each force and visual feedback level

× Time to complete paths with visual Computerized 21 FASD feedback (slowed Bimanual Coordination (Roebuck- bimanual motor speed and VMI Spencer et al., 17 CON (corpus callosum- coordination deficit) mediated) 2004) test (10 to 19 years) Ø Accuracy on bimanual coordination task

10 FASD No clear hand (Janzen et al., Grooved 10 CON dominance on 1995) Pegboard Functional Hand grooved pegboard Dominance (3.5 to 5 years) (corpus callosum- 11 FASD mediated) (Domellöf et al., Ø Right-hand and Handedness 10 CON 2009) preference (3 to 17 years)

× Foot angle 18 FASD × Step width Walking on Atypical Gait (Taggart et al., instrumented 26 CON 2017) × Intrasubject (cerebellum-mediated) walkway variability in gait (7 to 17 years) velocity and step width

Reaction Time Dose-Response (Jacobson et al., (RT): shifting 103 Infants with Reaction Time (no controls) 1994) gaze to visual PAE target Ø RT in infants exposed to 0.5 oz

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absolute alcohol/day

Manual dexterity Ø dexterity, finger 117 children and tapping speed, and Age-related declines in (Tamana & Pei, (grooved with FASD grip-strength in older motor skills 2014) pegboard), finger tapping, (5-17 years) vs. younger children with FASD grip strength

× COMPS Postural Sway 47 ADHD (cerebellum test) Postural Control (Kooistra et al., & COMPS 30 FASD score in ADHD group score (cerebellum-mediated) 2009) 39 CON Ø Postural stability in (cerebellum FASD and ADHD function test) (7-10 years) groups vs. CON

34 FASD Cerebellar-Mediated (Jacobson et al., Eyeblink Ø Number of 29 CON Learning 2011) Conditioning conditioned responses (8 to 12 years)

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Appendix 2

Chapter 2 Article Copyright The article in Chapter 2 titled Isometric Force Regulation in Children with Autism Spectrum

Disorder: A Cross-Syndrome Study has been submitted for publication consideration in Human

Movement Science. The publisher, Elsevier, allows for pre-printed manuscripts to be included in theses and dissertations; therefore, no copyright approval is required.

(https://service.elsevier.com/app/answers/detail/a_id/565/)

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Appendix 3

Chapter 3 Article Copyright The article in Chapter 3 titled Children with Autism Spectrum Disorder Show Force-Generation and Force-Relaxation Deficits: A Cross-Syndrome Study has been submitted for publication consideration in the European Journal of Paediatric Neurology. The publisher, Elsevier, allows for pre-printed manuscripts to be included in theses and dissertations; therefore, no copyright approval is required.

(https://service.elsevier.com/app/answers/detail/a_id/565/)

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Appendix 4

Chapter 4 Article Copyright The article in Chapter 4 titled Examining the Specificity of Postural Control Deficits in Children with Autism Spectrum Disorder Using A Cross-Syndrome Approach has been submitted for publication consideration in Research in Autism Spectrum Disorders. The publisher, Elsevier, allows for pre-printed manuscripts to be included in theses and dissertations; therefore, no copyright approval is required.

(https://service.elsevier.com/app/answers/detail/a_id/565/)

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Appendix 5

Chapter 5 Article Copyright The article in Chapter 5 titled Manual Dexterity in Children with Autism Spectrum Disorder: A

Cross-Syndrome Approach has been submitted for publication consideration in Research in

Autism Spectrum Disorders. The publisher, Elsevier, allows for pre-printed manuscripts to be included in theses and dissertations; therefore, no copyright approval is required.

(https://service.elsevier.com/app/answers/detail/a_id/565/)

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CURRICULUM VITAE

Daniel E. Lidstone, Ph.D. Department of Kinesiology and Nutrition Sciences Email: [email protected]

EDUCATION Ph.D. University of Nevada, Las Vegas, NV, 2019 Ph.D. – Interdisciplinary Health Science Dissertation: Motor deficits in Autism Spectrum Disorder

M.S. Appalachian State University, Boone, NC, 2015 MS – Exercise Science Thesis: Muscle-tendon mechanics during fatiguing single-joint hopping exercise

B.A. Wilfrid Laurier University, Waterloo, ON, CAN, 2013 Honors BA – Kinesiology & Physical Education

PROFESSIONAL EXPERIENCE 2015-2016 Adjunct Instructor Appalachian State University, Boone, NC, USA

RESEARCH EXPERIENCE 2019 Top Tier Doctoral Graduate Research Assistant UNLV Ackerman Autism Center University of Nevada, Las Vegas Faculty Mentors: Dr. Janet Dufek and Dr. Julie Beasley

2016-2019 Graduate Research Assistantship: Diabetic Foot Research Group University of Nevada, Las Vegas Faculty Mentors: Dr. Janet Dufek and Dr. Mohamed Trabia

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2018-2019 Human Neurophysiology Lab University of Nevada, Las Vegas Faculty Mentor: Dr. Brach Poston

2015-2017 Neuromuscular and Biomechanics Lab Appalachian State University Faculty Mentors: Dr. Herman van Werkhoven and Dr. Jeffrey McBride

PUBLICATIONS Refereed Journal Articles Lidstone, DE., DeBaradinis, J., Dufek, J., Trabia, M. (2019). Electronic Measurement of Plantar Contact Area during Walking using an Adaptive Thresholding Method for Medilogic® Pressure- Measuring Insoles. The Foot, 39,1-10.

Lidstone, DE., Procher, LM., DeBaradinis, J., Dufek, J., Trabia, M. (2018). Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area during Walking. Journal of the American Podiatric Medical Association. In Press.

Harry, JR., Eggleston, JD., Lidstone, DE., Dufek, JS. (2019). Investigating Weighted Vest Effects on Gait Smoothness in Children with Autism Spectrum Disorder. Translational Journal of the American College of Sports Medicine. 4(10), 64-73.

Lidstone, DE., van Werkhoven, H., Needle, AR., Rice, PE., & McBride, JM. (2018). Gastrocnemius Fascicle and Achilles Tendon Length at the End of the Eccentric Phase in a Single and Multiple Countermovement Hop. Journal of Electromyography and Kinesiology, 38, 175-181.

DeBaradinis, J., Dufek, J., Trabia, M., Lidstone, DE. (2017). Assessing the Validity of Pressure- Measuring Insoles in Quantifying Gait Variables. RATE Journal. Journal of Rehabilitation and Assistive Technologies Engineering, 5, 2055668317752088.

Lidstone, DE., Stewart, JA., Gurchiek, R., Needle, AR., van Werkhoven, H., & McBride, JM. (2017). Physiological and Biomechanical Responses to Prolonged Heavy Load Carriage during Level Treadmill Walking in Females. Journal of Applied Biomechanics, 33(4), 248-255.

Lidstone, DE., van Werkhoven, H., Stewart, JA., Gurchiek, R., Burris, M., Rice, P., & McBride, JM. (2016). Medial Gastrocnemius Muscle-Tendon Interaction and Architecture Change during Exhaustive Hopping Exercise. Journal of Electromyography and Kinesiology, 30, 89-97.

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Manuscripts in Submission DeBerardinis, J., Neilsen, C., Lidstone, DE., Dufek, JS., Trabia, MB. The Validity of Two Techniques for Center of Pressure Measurements. Journal of Rehabilitation and Assistive Technologies Engineering.

Conference Podium Presentations Lidstone, DE., DeBaradinis, J., Porcher, LM., Dufek, JS., Trabia, MB. (2017). Concurrent Validity of an Automatic Technique to Calculate Plantar Contact Area at Mid-Stance During Gait. 41st American Society of Biomechanics Annual Conference, Boulder, CO, USA. Conference Proceedings Lidstone, DE., Miah, FZ., Beasley, JF., Dufek. JS. (2019). Hand performance asymmetry as a motor phenotype of children with fetal alcohol spectrum disorder. 49th Annual Meeting of the Society for Neuroscience. Chicago, IL. Poster.

Lidstone, DE., Munoz, I., Albuquerque L., Pantovic, M., Aynlender DG., Poston, B., Dufek, JS. (2019). The Effects of Cerebellum Transcranial Direct Current Stimulation on Online and Offline Learning of a Complex Multi-Joint Throwing Task. XXVII Congress of the International Society of Biomechanics. Calgary, AB, CAN. Poster

Dufek, JS., Trabia, MB., Varre, MS., Lidstone, DE., DeBaradinis, J., Izuora, KE. (2019). Pressure Time Integral as a Discriminant for Group Membership in the Progression of Diabetes. XXVII Congress of the International Society of Biomechanics. Calgary, AB, CAN. Poster

Albuquerque L., Munoz, I., Lidstone, DE., Kreamer-Hope, S., Pomerantz, A., Pantovic, M., Zurowski, M., Petitt, M., Guadagnoli, M., Riley, Z., Poston, B. (2019). Transcranial Direct Current Stimulation of Motor Cortex over Multiple Days Enhances Motor Learning in a Complex Overhand Throwing Task. 3rd International Brain Stimulation Conference, Vancouver, BC, CAN. Poster

Lidstone, DE., Eggleston, JD., Dufek, J. (2018). Effect of Visual Rhythmic Cueing and Visual Feedback on Motor Control in Children with Autism. 42nd American Society of Biomechanics Annual Conference, Rochester, MN, USA. Poster

DeBaradinis, J., Neilsen, C., Lidstone, DE., Dufek, JS., Trabia, MB. (2018). Validity of Pressure-Measuring Insoles in Quantifying Center of Pressure. 42nd American Society of Biomechanics Annual Conference, Rochester, MN, USA. Poster

DeBaradinis, J., Lidstone, DE., Dufek, JS., Trabia, MB. (2018). Elliptical Estimation of Plantar Contact Areas During Walking. 42nd American Society of Biomechanics Annual Conference, Rochester, MN, USA. Poster

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Varre, MS., DeBaradinis, J., Lidstone, DE., Trotter, A., Trabia, MB. Dufek, JS. (2018). Estimating Walking Speed Using a Single Camera in the Plane of Progression. 42nd American Society of Biomechanics Annual Conference, Rochester, MN, USA. Poster

Izuora, KE., Trabia, MB., DeBaradinis, J., Lidstone, DE., Varre, MS, Trotter, A., Dufek, JS. (2018). Comparison of Peak Plantar Pressure and Peak Pressure Gradient among Patients with Prediabetes and Diabetes. American Diabetes Association’s 78th Scientific Sessions, Orlando, FL, USA. Poster

Lidstone, DE., DeBaradinis, J., Porcher, LM., Dufek, JS., Trabia, MB. (2017). Concurrent Validity of an Automatic Technique to Calculate Plantar Contact Area at Mid-Stance During Gait. 41st American Society of Biomechanics Annual Conference, Boulder, CO, USA.

Lidstone, DE., DeBaradinis, J., Ghanem, A., Trotter, A., Varre, MS., Trabia, MB. Dufek, JS. (2017). Characterization of Plantar Contact Area Error from Pressure-Measuring Insoles is Reduced Using an Adaptive Sensor Threshold Method. 37th Annual SWACSM Meeting. Poster

Varre, MS., Lidstone, DE., DeBaradinis, J., Trotter, A., Trabia, MB., Dufek, JS. (2017). Evaluation of Plantar Pressure Distribution in Prediabetic and Diabetic Individuals. 37th Annual SWACSM Meeting. Poster

Ghanem, A., DeBaradinis, J., Trabia, M., Dufek, JS., Lidstone, DE. (2017). Identification of Hysteresis Behavior of Pressure-Measuring Insoles. Summer Biomechanics, Bioengineering, and Biotransport Conference, Tucson, AZ, USA. Poster

Eggleston, JD., Flores, L., Mamauag, M., Lidstone, DE., Harry, J., Dufek, JS. (2017). Influence of a Weighted Backpack and Weighted Vest on Gait Kinematics in Children with Autism Spectrum Disorder. 13th Annual Northwest Biomechanics Symposium, Eugene, OR, USA. Poster

Mamauag, M., Eggleston, JD., Flores, L., Lidstone, DE., Dufek, JS. (2017) Examining the Influence of Backpack Weight of Stride Kinematics Among Children with Autism Spectrum Disorder. 13th Annual Northwest Biomechanics Symposium, Eugene, OR, USA. Poster

Lidstone, DE., DeBaradinis, J., Porcher, LM., Dufek, JS., Trabia, MB. (2017). Calculating the Area of Compressed Plantar Tissue. GCMAS Annual Conference, Salt Lake City, UT, USA. Poster

DeBaradinis, J., Lidstone, DE., Dufek, JS., Trabia, MB. (2017). Determining Gait Symmetry Using Pressure-Measuring Insoles. GCMAS Annual Conference, Salt Lake City, UT, USA. Poster

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Lidstone, DE., van Werkhoven, H., McBride, JM. (2016). Performance of Countermovement at Natural Frequency is Related to Increased Jump Height in a Maximal Single Joint Hop. 40th American Society of Biomechanics Annual Conference, Raleigh, NC, USA. Poster van Werkhoven, H., Lidstone, DE., McBride, JM. (2015). Shorter Heels are Associated with Stiffer Plantarflexor Tendons. 39th American Society of Biomechanics Annual Conference, Columbus, OH, USA. Poster

PROFESSIONAL ORGANIZATIONS 2019-current Society for Neuroscience (SfN) 2015-current American Society of Biomechanics (ASB) 2014-current National Strength and Conditioning Association (NSCA) Certified Strength and Conditioning Specialist (CSCS) [7247895626]

PROGRAMMING/INSTRUMENTATION SKILLS Programming – MATLAB, LabVIEW, Microsoft EXCEL, SPSS, Signal

Instrumentation – NI 9237 (National Instruments), load button LLB330 (Futek), Chattanooga Ionto/Neuroconn (tDCS), TMS (Magstim), 3D motion capture/analysis (Vicon Motion Capture & Visual 3D), wireless EMG (Delsys Trigno System), force plates (AMTI, Kistler, Bertec), muscle ultrasound imaging (L9.0/60/128Z, Telemed Echo Blaster 128), instrumented treadmill (Bertec), metabolic cart (Parvo Medics Metabolic Cart, TrueOne 2400), blood lactate (Lactate Plus, Nova Biomedical), pressure-measuring insoles (Medilogic)

PROFESSIONAL WORKSHOPS 2019 Motor Control Summer School XVI Antiochian Village, PA

2018-2019 Graduate College Mentorship Certification (GCMC) University of Nevada, Las Vegas

2017 UNO Nonlinear Analysis Workshop University of Nebraska, Omaha

2017 Responsible Conduct of Research (RCR) Training University of Nevada, Las Vegas

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STUDENT MENTORSHIP 2018-2019 Rebel Research and Mentorship Program – University of Nevada, Las Vegas Undergraduate mentee: Irwin Munoz (first-generation college student) Faculty supervisors: Dr. Janet Dufek and Dr. Brach Poston Project title: Effect of Multi-Day Online Cerebellar Transcranial Direct Current Stimulation on Corticomotor Excitability and Learning of a Throwing Task. Awards: 2nd place poster at 2019 UNLV Undergraduate Research Forum

2017 Nevada INBRE Research Mentor – University of Nevada, Las Vegas Undergraduate mentee: Phillipp Yu ($5,500 in research funds received) Faculty supervisor: Dr. Janet Dufek Project title: Frontal Plane Knee Kinetics and Kinematics in Collegiate Male and Female Soccer Athletes during a Single Leg Landing Task.

2015-2016 Honors Thesis Advisor – Appalachian State University Undergraduate mentee: Maddison Burris Project title: The Effects of Muscle-Tendon Length Change During a Fatiguing Hopping Protocol on Time to Exhaustion.

AWARDS AND HONORS 2017 Rebel Grad Slam: 3-Minute Thesis Competition (semifinalist) University of Nevada, Las Vegas

2016 Rebel Grad Slam: 3-Minute Thesis Competition (semifinalist) University of Nevada, Las Vegas

GRANTS AND FELLOWSHIPS Funded Proposals

2019 Lidstone, DE. Summer Doctoral Research Fellowship, UNLV Graduate College. University of Nevada, Las Vegas, $7,000. Role: Principle Investigator.

2018 Lidstone, DE. Effect of Cerebellar tDCS on Precision-Grip Isometric Force Control in Children with Autism Spectrum Disorder. Grant-In-Aid, American Society of Biomechanics, $1,000. Role: Principle Investigator.

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2018 Lidstone, DE. Doctoral Summer Session Scholarship, UNLV Graduate College, University of Nevada, Las Vegas. $2,000. Role: Principle Investigator.

2018 Lidstone, DE. Research Grant, Graduate and Professional Student Association, University of Nevada, Las Vegas. $1,150. Role: Principle Investigator.

2017 Lidstone, DE. Travel Award, Education Council of the Gait and Clinical Movement Analysis Society. $500. Role: Presenter.

Not Funded Grant Proposals

2019 Lidstone, DE., Dufek, JS. The Effects of Cerebellar Transcranial Direct Current Stimulation on Online Motor Learning and Transfer of Learning in Children with ASD. Organization for Autism Research, Graduate Research Grant Program. $2,000. Role: Principal Investigator.

2017 Poston, B., Dufek, JS., Freedman Silvernail, JA., Harry, JR., Eggleston, JD., Lidstone, DE. Noninvasive brain stimulation to improve movement coordination in children with Autism. Clinical Translational Research Infrastructure Network, National Institutes of General Medical Sciences (U54 GM104944), Health Disparities Pilot Grant. $60,000. Role: Co- Investigator.

TEACHING EXPERIENCE 2017-2019 Graduate Teaching Assistant University of Nevada, Las Vegas, NV Courses taught: Biomechanics Laboratory, Anatomy and Physiology Laboratory

2015-2016 Adjunct Instructor Appalachian State University, Boone, NC, USA Courses taught: Biomechanics Laboratory, Strength and Conditioning Practicum (instructor on record), Intro. to Physiological Assessment (instructor on record)

2015 Graduate Teaching Assistant Appalachian State University, Boone, NC, USA Courses taught: Anatomy, Intro. to Physiological Assessment

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DEPARTMENTAL/UNIVERSITY SERVICE 2018-2019 Graduate College Rebel Research and Mentorship Program (RAMP) 2018-2019 Graduate student representative on UNLV Top Tier Postdoc Committee 2017-2019 National Biomechanics Day (NBD) organizer and team lead 2017-2019 Dawson-UNLV Honors College-Bound Program (UNLV)

REFERENCES Dr. Janet Dufek, Ph.D. University of Nevada, Las Vegas, Dept. of Kinesiology and Nutrition Sciences Associate Dean, School of Allied Health Sciences [email protected]

Dr. Julie Beasley, Ph.D. University of Nevada, Las Vegas, School of Medicine Child Neuropsychologist and Director of UNLV Ackerman Autism Center [email protected]

Dr. Brach Poston, Ph.D. University of Nevada, Las Vegas, Dept. of Kinesiology and Nutrition Sciences Assistant Professor, School of Allied Health Sciences [email protected]

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