Gait variability development in autistic individuals across childhood and adulthood

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Shafer, Jingying Wang, Hang Qu, Joelle P. Simpson, Matthew Terza, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9622252/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Atypical sensorimotor behaviors are prevalent in autism spectrum disorder (ASD), often emerging in infancy and persisting into early adulthood. Prior focus on qualitative metrics and developmentally specific motor skills has limited understanding of sensorimotor differences in ASD across the lifespan. The present study quantified gait kinematics in ASD across a wide age range, including analysis of 60 autistic individuals (ages 4-35 years) and 53 age-, sex-, and performance IQ-matched neurotypical (NT) controls. Autistic individuals differed from NT controls on seven gait variables reflecting within-subject variability of gait kinematics. These seven gait variability metrics were integrated into a canonical correlation analysis (CCA) with select demographic features (group, age, sex, IQ) to derive a composite gait variability score that accounts for the multicollinearity of gait variability and demographic metrics. The composite score revealed increased gait variability in autistic individuals relative to NT controls. Gait variability was negatively associated with age across the sample. Age-by-group interactions suggested that gait variability is more severely elevated in autistic individuals during adolescence and adulthood relative to childhood. Increased gait variability also was associated with more severe clinically rated ritualistic behaviors in autistic individuals. This study leverages a novel approach to define a multidimensional component score of gait variability in ASD from childhood to adulthood. Results indicate that gait variability is elevated in ASD, and that the severity of variability differences increases with age during adolescence and into adulthood. These findings suggest that autistic individuals show an attenuated development of sensorimotor feedback and motor planning processes that are involved in maintaining accuracy and stability of movements. Unconstrained walking canonical correlation analysis gait variability composite score sensorimotor development autism spectrum disorder Figures Figure 1 Figure 2 Figure 3 ABBREVIATED SUMMARY This study used multiple gait variability metrics and demographic factors to create a gait variability (instability) composite score for characterizing autistic and neurotypical gait development. We found that gait variability decreases with age in autistic and neurtypical individuals, but autistic individuals show slower change and persistently elevated gait variability. INTRODUCTION Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social-communication challenges, restricted interests and stereotypic behaviors 1 . Sensorimotor impairments are highly prevalent in ASD, affecting 35–87% of autistic individuals 2 – 5 . Sensorimotor differences are also closely associated with more severe autistics traits, poorer long-term outcomes, and reduced quality of life 6 – 9 . They commonly manifest early in infancy and persist into adulthood, impacting both fine and gross motor systems 10 – 15 . Most previous studies of sensorimotor impairments in ASD have relied on clinical assessments and ratings of motor behaviors that broadly categorize motor skills (e.g., fine and gross motor) 2 , 10 , 16 , 17 or on quantitative, objective measures that are constrained to developmentally specific actions (e.g., spoon feeding, handwriting) 18 , 19 . While clinical assessments offer valuable diagnostic insights, their broad scope preclude a mechanistic understanding of sensorimotor differences in ASD. Studies of developmentally constrained behaviors also are limited in their ability to clarify how sensorimotor differences vary across the lifespan in ASD. Measurement of movement features during natural walking (i.e., gait) provides an important framework for understanding sensorimotor behavior and development 20 . As a fundamental gross motor skill, gait supports mobility and enables individuals to explore and interact with their environment in ways that diversify sensorimotor, cognitive, and social experiences 21 – 23 . Gait also undergoes a prolonged and complex maturational time course extending from the first year of life through the third decade 24 – 28 . Typical gait development is characterized by rapid maturation of spatiotemporal and joint kinematic patterns in the first few years of walking (2–5 years of age) including transitioning to striking with heel first (e.g., instead of flat foot), introduction of reciprocating arm swing, and reduced coactivation of agonist-antagonist muscles 25 . Stride length increases across childhood into adolescence, while gait speed increases primarily during adolescence and early adulthood 24 , 26 . Variability of stride length decreases rapidly in early childhood, reaching adult levels by mid-adolescence 24 , 27 . Similarly, cadence, stride time and stride duration variability decrease rapidly throughout childhood, with a slower, steadier decrease during adolescence as these metrics approach adult levels 24 , 26 – 28 . These developmental changes are present even when controlling for differences in body size (e.g., height) 24 , 27 . Aging-related behavioral changes in gait also serve as critical and sensitive biomarkers for multiple health-related outcomes, including independence during aging and in clinical conditions, adaptive functioning, and well-being 29 – 32 . Findings on spatiotemporal and joint kinematics during gait in ASD have been mixed. For example, several studies have found decreased step and stride length 33 – 35 , reduced cadence 33 , 36 and velocity 33 , 36 , as well as increased double support time (i.e., time when both feet are in contact with the floor or other support surface) 33 in autistic compared to neurotypical (NT) individuals. In contrast, other studies have reported opposite patterns 37 , 38 or no significant between-group differences in these metrics 14 , 34 , 35 , 38 . Similarly, while some studies have noted differences in joint angles during gait, the specific joints for which differences are reported vary across studies 34 , 38 , 39 . Together, inconsistencies in both metric selection and reported outcomes limit our ability to identify gait profiles associated with ASD or to characterize how gait differences in ASD may vary across the lifespan 34 , 38 , 39 . Variability of findings on spatiotemporal and joint kinematic differences in ASD may reflect inherent heterogeneity of sensorimotor behavior among autistic individuals 33 , 39 that covaries with key demographic features, including age, sex, IQ, or other unidentified characteristics (e.g., etiology) 40 , 41 . In contrast to findings from studies examining mean values for select gait features, findings from studies analyzing variability (i.e., standard deviation) of gait metrics – both within a movement and across repeated movements – appear to show more consistent differences between autism and NT controls. Multiple studies have identified elevated gait variability in autistic children (ages 4–18 years) across several metrics, including stride length, stride width, stride time, velocity, and foot-shank coupling (i.e., ankle) angle 14 , 37 , 42 . In other conditions, such as aging and neurodegeneration, gait variability metrics have proven more sensitive for detecting atypical gait patterns than mean values of spatiotemporal metrics 43 . Moreover, changes in variability of gait dimensions with age reflect neural processes distinct from those underlying spatiotemporal metrics 43 , suggesting that studying gait variability may provide unique insights into neurodevelopmental mechanisms associated with ASD. Nonetheless, inconsistencies remain regarding which gait variability metrics are selected for analysis, which metrics are most robust to differences between autistic and NT individuals, and the extent to which variability relates to demographic factors (e.g., age, sex, IQ). These gaps underscore the need for approaches that integrate gait variability across spatiotemporal and joint kinematic metrics and compare them with demographic features to more comprehensively characterize gait development from childhood through adulthood in autistic individuals. The objectives of this study were to (1) optimize measurement of gait variability differences in ASD by deriving a composite gait variability metric that accounts for multicollinearity across individual gait metrics and demographic characteristics, and (2) characterize age-related changes (4–35 years) in gait variability in autistic and NT individuals. For the first objective, we applied a multi-dimensional analytic approach, canonical correlation analysis (CCA), to derive a composite gait variability score (CGVS) for each participant. The CCA model reflects relationships both between and within two multivariate datasets – gait variability (dataset 1) and demographic variables (dataset 2) – while accounting for multicollinearity among measures 44 – 46 . Consistent with prior literature, we hypothesized that autistic individuals would show elevated gait variability 14 , 37 , 42 , measured with the CGVS, relative to NT individuals, and that CGVS would decline with increasing age in both groups 14 , 24 , 43 , 47 – 49 . The literature is mixed on whether motor variability in autistic individuals converges with or diverges from NT individuals with increasing age 14 , 15 , 50 , 51 , so we did not have a priori hypotheses about the group-by-age interaction. Finally, to determine whether gait variability relates to select traits associated with ASD, we explored the relationship between CGVS and clinical autistic traits. METHODS Participants were examined at one of three sites: the University of Texas Southwestern Medical Center (UTSW), the University of Kansas (KU), or the University of Florida (UF). Diagnostic standards, testing procedures, and task instructions were consistent across sites. The study protocol was approved by the Institutional Review Boards (IRB) at UTSW and Children’s Hospital of Dallas (STU052013-4), KU Medical Center (STUDY00140269), and UF (201801378). Participants 18 years of age or older provided written consent. Minors or adults under legal guardianship provided assent in addition to guardians' written consent based on the Declaration of Helsinki. Participants Sixty autistic individuals ages 4–35 years and 53 NT controls matched on age, sex, anthropometry and performance IQ participated in this study. Table 1 shows the demographic and clinical characteristics of autistic individuals and NT controls (see Supplementary Table 1 for demographic and clinical feature breakdowns for UTSW, KU, and UF cohorts separately). Autistic individuals were recruited through community advertisements, clinical programs, institutional research registries, and SPARK Research Match ( https://www.sfari.org/resource/research-match/ ). Control participants were recruited through flyers and word of mouth. The UTSW cohort consisted of 23 autistic individuals and 23 NT controls; the KU cohort included 32 autistic individuals and 23 NT controls; the UF cohort consisted of 5 autistic adults and 7 NT controls. All study procedures were approved by the Internal Review Boards at UTSW, KU, and UF, and written informed consent was obtained from all participants (or their legal guardians) according to the Declaration of Helsinki. Table 1 Demographic and clinical characteristics of autistic individuals (ASD) and neurotypical controls (NT) Sample size (n) ASD (mean ± SD) NT (mean ± SD) t/χ 2 p 60 53 − − Age (years) 15.700 ± 6.071 18.170 ± 8.274 -1.788 0.077 Sex (M/F) a 43/17 34/19 0.732 0.392 Height (cm) 161.735 ± 16.913 159.135 ± 18.590 0.778 0.438 Weight (kg) 61.434 ± 23.690 59.578 ± 25.826 0.399 0.691 Full-scale IQ 103.800 ± 14.907 109.615 ± 9.547 -2.490 0.014 * Verbal IQ 101.000 ± 16.239 108.692 ± 11.290 -2.940 0.004 ** Performance IQ 106.100 ± 15.208 107.981 ± 10.584 -0.767 0.445 ADOS-2 b 6.321 ± 1.869 − − − RBS-R c 31.310 ± 19.395 2.865 ± 4.498 10.379 < 0.001 *** a Chi-square statistical results; b ADOS-2 calibrated severity score; c RBS-R total raw score. * p < 0.05, ** p < 0.01, *** p < 0.001 [Insert Table 1 here] ASD diagnosis was established following the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) 52 . Autistic individuals at UTSW and KU screened ≥ 15 on the Social Communication Questionnaire (SCQ) 53 , and participants in the UF cohort scored ≥ 32 on the Autism Spectrum Quotient (AQ-50) for adults 54 and ≥ 65 on the Social Responsiveness Scale-2 (SRS-2) adult self-report 55 , 56 . Eligible individuals were invited for a diagnostic evaluation and completed the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) 57 administered by a research-reliable study clinician. The UTSW and KU sites additionally administered the Autism Diagnostic Interview–Revised (ADI-R) 58 . In addition to standardized measures, expert clinical opinion was used to confirm ASD diagnosis at all sites. Across sites, all autistic individuals were required to have a full-scale IQ (fs-IQ) ≥ 70 to participate in this study. No autistic individuals had any known genetic or metabolic disorder associated with ASD (e.g., Fragile X syndrome, Tuberous Sclerosis, Phelan McDermid syndrome, Burnside-Butler syndrome) based on medical history reviews. NT controls were enrolled if they scored < 8 on the SCQ during initial screening at UTSW or KU. NT controls enrolled at UF had a score of < 22 on the AQ-50 or a score of < 60 on the SRS-2. Across sites, NT controls were excluded if they (a) had any major psychiatric (e.g., schizophrenia, bipolar affective disorder, and obsessive compulsive disorder) or neurological (e.g., epilepsy, cerebral palsy, spinal cord injury, and ataxia) disorders prior to or at the time of study participation; (b) had a family history of ASD or other forms of neurodevelopmental disorders in first- or -second-degree relatives; (c) had a family history of any major psychiatric disorders in first-degree relatives, or (d) had a fs-IQ < 80. No participant (autistic or NT control) had a history of birth asphyxia or non-febrile seizure. One autistic adult at UF reported a prior history of substance abuse. Thirteen UTSW (8 on antidepressants, 7 on stimulants, 1 on sedatives), six KU (4 on antidepressants and 2 on stimulants) and four UF (1 on antipsychotics, 3 on antidepressants, 2 on antihypertensives) participants reported being on medication within 48 hours of gait testing. All participants were able to ambulate independently without assistive devices. All participants completed the Repetitive Behavior Scale-Revised (RBS-R) 59 and had their IQ assessed using the Wechsler Abbreviated Scales of Intelligence, 2nd Edition (WASI-II) 60 . Apparatus and Procedures The apparatus and procedures were consistent across sites. Prior to testing, 16 passive reflective markers were attached to participants’ major joints based on the industry-standardized Plug-in-Gait Lower Body template (Vicon, Centennial, Co). Kinematic data were recorded using Nexus 3D motion capture systems. The motion capture systems at UTSW and KU included 10 Vicon Bonita 10 near-infrared cameras with a spatial error of 0.5 mm and a sampling rate of 250 Hz. The motion capture system at UF incorporated 12 VICON Vantage 5 cameras and a spatial error of 0.2 mm and sampling rate of 120 Hz. The walking distance was 5 meters at UTSW and KU and 7 meters for UF. Participants started each trial by standing upright with their feet shoulder width apart and arms by their sides. After receiving a verbal cue, participants began walking continuously at their self-selected speed toward the finish line. Each individual completed 3–5 trials. Practice trials were administered prior to data acquisition to ensure participants felt comfortable wearing reflective markers and understood task instructions. To minimize variability in the rates at which individuals increased or decreased their walking speed at the beginning and end of each trial, only kinematic data representing individuals’ “preferred speed” were selected and post-processed. Heel strikes and toe-offs of each leg were manually labeled on the raw kinematic data by trained scorers and cross-checked among scorers (JW, JPS, DJS, WSM, and ZW). Kinematic data were then exported, filtered, and analyzed using custom scripts in MATLAB 2023a (MathWorks, Inc., Natick, MA). A low-pass, zero-lag, 4th -order Butterworth filter was applied at a cutoff frequency of 7 Hz to filter the raw kinematic data. Spatiotemporal variables of gait consisted of the step width (i.e., mediolateral distance between heel markers of two consecutive heel strikes), step velocity (i.e., ratio of step length and step time), and cadence (i.e., number of steps per minute). Kinematic features of gait included relative angles of the ipsilateral hip (i.e., pelvis-thigh), knee (i.e., thigh-shank), and ankle (i.e., shank-foot) joints at heel-strikes and toe-offs within the sagittal plane. Within-individual standard deviation was calculated for each gait variable across all walking trials. These nine gait standard deviation measures were used as candidate variables to derive the composite gait variability score (CGVS). Clinical Assessments To test relationships between gait and clinical behaviors, associations with ADOS-2 57 and RBS-R 59 were examined for the autistic participants. The ADOS-2 is a semi-structured assessment of play, social abilities, communicative skills, and imaginative use of materials. ADOS-2 calibrated severity scores were calculated to allow comparisons across different modules and age ranges 61 , 62 . Higher scores reflect increased clinical severity of ASD. The RBS-R is a parent/self-report measure of the presence and severity of restricted, repetitive, and stereotyped behaviors. It contains six subscales, including stereotyped behavior, self-injurious behavior, compulsive behavior, ritualistic behavior, sameness behavior, and restricted behavior. Both subscale and total scores were calculated for each individual. Higher scores reflect more severe repetitive behaviors in autistic individuals. Statistical Analyses Demographic and clinical characteristics. Demographic (age, sex, fs-IQ, verbal IQ, and performance IQ), anthropomorphic (height and weight), and clinical characteristics (ADOS-2 calibrated severity scores and RBS-R total scores) were compared between autistic individuals and NT controls using independent-sample t-tests or Chi-square tests, as appropriate. Composite gait variability score (CGVS). Two procedural steps were implemented to derive the CGVS for each individual. Step 1 involved identifying gait standard deviation measures that significantly differentiated autistic individuals from NT controls. To this end, a multivariate analysis of covariance (MANCOVA) was conducted with group (ASD vs. NT) as a fixed factor, nine gait standard deviation measures as dependent variables, and height and weight as covariates. Height and weight were included in this model because anthropometry significantly affects gait characteristics, especially for studies with a broad developmental age range 63 – 65 . False discovery rate (FDR) correction for multiple comparisons was applied to control Type I error, and statistical significance was interpreted at p FDR < 0.05 66 . Canonical correlation analysis (CCA) was implemented as Step 2 using two datasets (Fig. 1 ). Dataset 1 included seven gait standard deviation measures determined to significantly differentiate autistic from NT individuals in the Step 1 analysis , and Dataset 2 consisted of four demographic variables including group, age, sex, and fs-IQ. Four canonical variates were derived from the canonical correlation model because the smaller dataset (i.e., Dataset 2) consisted of four variables. Canonical variates were calculated following the formula: Table 2 Descriptive statistics of gait standard deviation variables and MANCOVA results of autistic individuals (ASD) and neurotypical controls (NT) Gait standard deviation measurement ASD (mean ± sd) NT (mean ± sd) F p raw p FDR Cadence.sd (steps/min) 6.612 ± 4.548 5.237 ± 3.879 5.628 0.020 0.030 * StepWidth.sd (cm) 2.615 ± 1.078 2.106 ± 0.769 8.017 0.006 0.011 * StepVelocity.sd (cm/s) 9.935 ± 4.706 7.587 ± 4.632 10.240 0.002 0.009 ** HipAngle.HS.sd (deg) 2.173 ± 1.107 1.663 ± 0.788 8.551 0.004 0.012 * KneeAngle.HS.sd (deg) 2.062 ± 1.256 1.514 ± 0.867 8.484 0.004 0.010 * AnkleAngle.HS.sd (deg) 2.168 ± 1.196 1.684 ± 1.761 5.065 0.027 0.035 * HipAngle.TO.sd (deg) 2.439 ± 1.570 1.632 ± 0.781 13.208 < 0.001 0.004 ** KneeAngle.TO.sd (deg) 2.697 ± 1.541 2.375 ± 1.303 2.164 0.144 0.162 AnkleAngle.TO.sd (deg) 3.566 ± 2.391 3.184 ± 2.149 1.694 0.196 0.196 sd: standard deviation. HS: heel-strike; TO: toe-off. * p < 0.05, ** p < 0.01, *** p < 0.001 $$\:{\rho\:}_{i}=\:\frac{cov\left({U}_{i},\:{V}_{i}\right)}{\sigma\:\left({U}_{i}\left)\sigma\:\right({V}_{i}\right)}\:$$ 2 where ρ i represents the i th canonical variate, \(\:cov\left({U}_{i},\:{V}_{i}\right)\) represents the covariance matrix of the Datasets 1 and 2, and \(\:\sigma\:\left({U}_{i}\left)\:and\:\sigma\:\right({V}_{i}\right)\) stand for the standard deviation matrix of each individual dataset. Canonical variates were interpreted as significant when p raw < 0.05. CGVS was then derived based on the statistically significant canonical variate(s) following the formula: CGVS (i) = Cadence.coeff (i) \(\:\times\:\) Cadence.sd + StepWidth.coeff (i) \(\:\times\:\) StepWidth.sd + StepVelocity.coeff (i) \(\:\times\:\) StepVelocity.sd + HipAngle.HS.coeff (i) \(\:\times\:\) HipAngle.HS.sd + KneeAngle.HS.coeff (i) \(\:\times\:\) KneeAngle.HS.sd + AnkleAngle.HS.coeff (i) \(\:\times\:\) AnkleAngle.HS.sd + HipAngle.TO.coeff (i) \(\:\times\:\) HipAngle.TO.sd. (3) where CGVS (i) represents the i th canonical gait variability score whose canonical variate was statistically significant; coeff (i) stands for the unstandardized canonical coefficient of the i th linear combination of gait standard deviation measures included in Dataset 1 (U i ), sd stands for standard deviation, HS stands for heel strikes, and TO is toe-offs. Unstandardized canonical coefficients (i.e., weightings) represent the degree to which changes to one gait standard deviation measure in Dataset 1 correlate with changes in the corresponding canonical variate of Dataset 2, when all remaining gait standard deviation measures are held constant. Only the first canonical variate ( \(\:{\rho\:}_{1})\) was statistically significant (see Results), so only the first CGVS was derived for each individual. [Insert Fig. 1 ] Diagnostic sensitivity and specificity of CGVS relative to discrete gait standard deviation measures. Receiver Operating Characteristic (ROC) curve analysis 67 was performed as a post hoc analysis to examine the specificity and sensitivity of CGVS against discrete gait standard deviation measures for differentiating autistic and NT individuals. The area under the curve (AUC) was derived to identify the outcome measure with optimal specificity and sensitivity. Demographic and clinical correlations. Spearman’s rank-order correlation analyses were conducted to examine the relationship between CGVS and demographic variables in both groups and clinical ratings for autistic individuals, including age, fs-IQ, ADOS-2 calibrated severity score, and RBS-R subscale scores. The RBS-R self-injury subscale was omitted from these analyses due to restricted score variability in the sample. Correlations involving age, fs-IQ, and ADOS-2 scores were interpreted as significant at p raw < 0.05. To account for multiple comparisons across RBS-R subscales, FDR correction was applied 66 and correlations involving RBS-R subscale scores were interpreted as significant at p FDR < 0.05. Comparison of correlation coefficients between groups. Fisher's z transformation 68 , 69 was applied to statistically significant correlations to test whether the strength of correlations between CGVS and demographic measures differed significantly between the ASD and NT groups. RESULTS Composite gait variability score (CGVS). Table 2 summarizes MANCOVA results for gait variability comparisons across autistic individuals and NT controls ( Step 1 of deriving the CGVS). Seven of the nine gait variability measures that were analyzed significantly differentiated autistic from NT individuals after FDR correction. These seven measures included the standard deviations of cadence (Cadence.sd), step width (StepWidth.sd), step velocity (StepVelocity.sd), hip angle at heel strikes (HipAngle.HS.sd) and toe-offs (HipAngle.TO.sd), and knee (KneeAngle.HS.sd) and ankle angles at heel strikes (AnkleAngle.HS.sd). These gait variability measures comprised Dataset 1 of the canonical correlation model ( Step 2 of deriving the CGVS). [Insert Table 2 here] Only the first canonical variate ( \(\:{\rho\:}_{1}\) ) was significant (Table 3 ), so it was used to derive the CGVS for each individual using the formula below: Table 3 Canonical correlation analysis results Canonical variate Canonical correlation ρ Wilks’ Lambda F df 1 df 2 p 1 0.662 0.475 2.820 28 343.950 < 0.001 *** 2 0.286 0.845 0.929 18 272.014 0.543 3 0.274 0.920 0.829 10 194.000 0.601 4 0.077 0.994 0.145 4 98.000 0.965 * p < 0.05, ** p < 0.01, *** p < 0.001 CGVS = (0.048) \(\:\times\:\) Cadence.sd + (0.240) \(\:\times\:\) StepWidth.sd + (0.036) \(\:\times\:\) StepVelocity.sd + (0.296) \(\:\times\:\) HipAngle.HS.sd + (0.147) \(\:\times\:\) KneeAngle.HS.sd + (0.210) \(\:\times\:\) AnkleAngle.HS.sd + (0.096) \(\:\times\:\) HipAngle.TO.sd (1) The derived CGVS represents individuals' overall gait variability during natural walking, accounting for the multicollinearity of each individual gait variability measure included in Dataset 1 and the demographic variables included in Dataset 2. All unstandardized canonical coefficients were positive (1) suggesting a positive relationship between each gait standard deviation measure and CGVS. As such, higher CGVS scores indicated greater overall gait variability. [Insert Table 3 here] ROC analysis demonstrated that CGVS achieved fair discriminative ability in differentiating autistic from NT individuals (Fig. 2 and Table 4 ). CGVS yielded the greatest area under the curve (AUC) value among the tested measures (AUC CGVS = 0.729; 95% CI: 0.629–0.828). Table 4 ROC performance (AUC with 95% CI) of CGVS and discrete gait metrics for differentiating ASD from NT Measure AUC (95% CI) CGVS 0.729 (0.629–0.828) Cadence.sd (steps/min) 0.599 (0.491–0.706) StepWidth.sd (cm) 0.650 (0.545–0.756) StepVelocity.sd (cm/s) 0.679 (0.577–0.781) HipAngle.HS.sd (deg) 0.648 (0.544–0.752) KneeAngle.HS.sd (deg) 0.638 (0.533–0.743) AnkleAngle.HS.sd (deg) 0.697 (0.592–0.802) HipAngle.TO.sd (deg) 0.659 (0.555–0.762) AUC = area under the ROC curve; CI = 95% confidence interval; ASD coded as the positive class (1); HS = heel strike; TO = toe-off [Insert Table 4 and Fig. 2 here] Demographic and clinical correlations. Lower CGVS was associated with increased age for both autistic individuals (ρ = -0.377, p = 0.005) and NT controls (ρ = -0.788, p < 0.001, Fig. 3 A). Fisher's z transformation revealed that the relationship between CGVS and age differed significantly between groups ( z = -3.364, p FDR = 0.001). Specifically, NT individuals showed a strong negative correlation between CGVS and age, whereas autistic individuals demonstrated a weaker negative correlation. The CGVS was not associated with fs-IQ for autistic individuals (ρ = 0.058, p = 0.675) or NT control participants (ρ = − 0.169, p = 0.231; Fig. 3 B). The relationship between CGVS and fs- IQ also did not differ significantly between groups ( z = -1.143, p FDR = 0.253). Increased CGVS was associated with increased RBS-R ritualistic behavior subscale scores in autistic individuals (ρ = 0.370, p FDR = 0.050; Fig. 3 C). No other clinical associations with CGVS were identified (Supplementary Table 2). [Insert Fig. 3 here] DISCUSSION The present study used canonical correlation analyses to quantify gait variability differences in ASD. This approach allowed us to derive a composite metric of gait variability (CGVS) that accounts for the multicollinearity of spatiotemporal and joint kinematic variables, as well as demographic characteristics, and it provides a singular, powerful metric for comparing gait profiles in autistic and NT individuals from childhood through adulthood. Three key findings are reported: (1) autistic individuals show increased gait variability compared to chronological-age and sex-matched NT peers, suggesting that sensorimotor processes involved in modulating the variability of motor behavior are disrupted in autistic individuals; (2) the multidimensional CGVS derived here provided greater sensitivity to gait differences in ASD than any individual measurement of gait, suggesting it may offer important advantages for tracking gait development in ASD and identifying clinical and neurobiological correlates, and (3) gait variability, as represented by the CGVS, shows age-related reductions across childhood and into adulthood in autistic and NT individuals, but this age-related reduction is attenuated in autistic individuals. We also document that gait differences in ASD are independent of general cognitive abilities, indicating that sensorimotor differences in ASD occur across individuals with a range of developmental abilities within the non-intellecual disability range. Composite gait variability for understanding sensorimotor mechanisms of ASD. Our findings demonstrate that variability of gait can be represented in a single composite gait variability score (CGVS) that distinguishes gait patterns in ASD from those in NT individuals. Specifically, autistic individuals showed higher CGVS than NT individuals, indicating greater variability of multiple gait features, consistent with prior findings 14 , 37 , 42 . We also found that mean spatiotemporal and joint angle metrics of gait did not differentiate autistic and NT individuals (Supplementary Table 3), which could indicate that autistic individuals show specific increases in gait variability that may be critical for distinguishing sensorimotor behaviors across autistic and NT individuals. The lack of significant group differences on mean gait measures could also be attributed to nonlinear age-associated changes that are documented in several mean gait metrics 24 , 48 , such that differences between autistic and NT gait may only appear at certain points in development, and between group effects may not be detectable if averaged across samples with broad age ranges. These results reinforce findings showing that elevated gait variability in autistic individuals relative to NT controls 14 , 37 , 42 is more consistent across studies and ages than differences in mean spatiotemporal 33 – 38 or joint kinematic measures 34 , 38 , 39 . Measures of gait variability are often more robust to changes in gait control (e.g., during development, aging, or disease) than mean spatiotemporal or kinematic measures 70 , 71 . Variability can indicate differences in stability and consistency of gait control even when averaged parameters (e.g., cadence or step length) show no or smaller differences. The key mechanisms contributing to increases in gait variability in ASD are not yet fully elucidated, but consideration of gait variability differences in separate clinical conditions may provide important insights. Elevated gait variability is a characteristic feature of cerebellar ataxia 72 , and it is common in other populations that show motor or cognitive issues including Parkinson’s disease 43 , 72 , 73 , peripheral neuropathy 74 , and healthy aging 75 , 76 . In these populations, elevated gait variability is associated with deficits in feedforward control of intralimb movements 72 , disrupted sensorimotor feedback processing 73 , 77 , and reduced cognitive and attentional capacity 73 , 76 , suggesting that each of these processes or their combination may contribute to elevated gait variability in autistic individuals. This hypothesis is consistent with findings of elevated motor variability across multiple motor behaviors and effector systems in autistic individuals including visually guided saccades 78 , 79 , precision grip force control 13 , 80 – 82 , reaching 83 , and postural control 84 , 85 , which implicate atypical feedforward processing, sensorimotor feedback processing, and cognitive motor control processing. During studies of gait and postural control, autistic individuals show a greater increase in gait and postural variability than NT individuals under sensory conditions that put greater demand on sensory feedback processing (e.g., walking or standing on a pliable surface), while increasing cognitive demand only increases variability of gait and postural control in NT individuals but not in autistic individuals, suggesting that atypical integration of sensory feedback may contribute more to elevated gait variability in autism than cognitive motor control processes 86 , 87 . The CGVS described here showed greater than 80% sensitivity for detecting autism with specificity of up to around 60%, suggesting that multidimensional measurements of gait variability may provide unique power for tracking sensorimotor differences in ASD and establishing clinical and neurobiological correlates. Prior studies have assessed gait variability using only one or a small number of discrete metrics (e.g., standard deviation of stride length, time, or velocity), and results have been somewhat inconsistent in terms of which metrics appear to be most robust for differentiating autistic and NT individuals 14 , 37 , 42 , 88 , limiting the understanding of gait and broader sensorimotor differences in ASD. In contrast, the CGVS derived in the present study considers several discrete gait variability metrics, as well as demographic factors. This approach is particularly important for understanding sensorimotor differences across a clinically and neurobiologically heterogeneous condition such as ASD. Age-associated gait variability differences in ASD . In our sample of individuals aged 4–35 years old, we observed a decrease in gait variability with increased age in both autistic and NT individuals. However, NT individuals showed a stronger age-related reduction in gait variability than autistic individuals suggesting a slower (or otherwise atypical) development of gait stability in ASD. This atypical course of motor development appears to lead to persistent elevations in gait variability through childhood that become more pronounced in later adolescence and adulthood. These results suggest that neurotypical processes supporting reductions of movement variability during gait in adolescence and adulthood are either attenuated or deviant in ASD, contributing to persistent elevations in gait variability through childhood and into adulthood. This finding contrasts with a prior study of gait variability across ages (4–16 years) in ASD 14 , which found that elevated gait variability in ASD is most pronounced at younger ages and normalizes during later childhood and adolescence. These discrepant findings could be attributed to our use of the CGVS, which integrates multiple discrete measures of gait variability, while the prior study assessed a smaller number (N = 3) of discrete gait variability measures. Distinct gait variability metrics may follow separate developmental trajectories which could lead to inconsistent age-associations when comparing metrics, as has been observed in normative development. Specifically, in normative development, temporal gait variability metrics (e.g., stride time variability) decrease dramatically from the onset of walking until around 6–8 years of age 24 , 43 , 47 – 49 , but spatial variability metrics (e.g., variability of stride width or knee angle) show little to no change in early childhood 48 , 49 . However, since the CGVS integrates multiple discrete gait metrics, it provides a more comprehensive quantification of gait variability that may be more sensitive to age- or diagnosis-related differences than individual measures. Differences across studies also could reflect heterogeneity within the autistic population both in terms of the extent to which gait variability is elevated and the rate at which gate variability shows age-dependent changes within individuals. The prior study of age-related changes in gait variability in ASD specifically assessed autistic individuals with ‘infantile’ or ‘atypical’ autism, including individuals with known genetic syndromes associated with autism, and excluding individuals with a diagnosis of Asperger’s syndrome or pervasive developmental disorder – not otherwise specified 14 . This suggests that their autistic sample had more severe autistic traits than the present study, which did not include individuals with known genetic syndromes or fs-IQ below 70 but did include individuals with milder autistic traits, some of whom likely would have met DSM-IV criteria for Asperger’s or pervasive developmental disorder – not otherwise specified. Studies of age-associated changes (4–29 years 50 and 10–20 years 51 ) in precision grip force variability in autistic individuals have shown greater decreases in variability with increased age in ASD relative to NT, such that differences were most pronounced at younger ages, consistent with Manicolo et al.’s observations of gait variability in ASD. Other studies of trial-to-trial variability in saccadic eye-movement gain (ages 5–29 years) 50 and postural control (ages 8–20 years) 15 suggest that motor variability shows similar age-related reductions in autistic and NT individuals. Inconsistencies in findings of age-associated changes in motor variability in ASD highlight the need for future studies to examine gait variability at younger ages and in longitudinal samples to determine when gait variability differences first emerge and how they evolve across different individuals over time. Increased gait variability in autism implicates select brain networks. Variability and mean gait parameters appear to be controlled by separate, but overlapping neurophysiologic mechanisms 73 , 89 – 91 , suggesting that our finding showing specificity of gait variability in differentiating autistic individuals from NT individuals may be associated with atypical function of select motor control networks in ASD. Neuropathology, brain stimulation, and neuroimaging studies indicate that gait variability is associated with cerebellar-cortical 72 , 73 , 91 , 92 and cortico-cortical 73 , 89 , 93 – 95 brain networks involved in motor planning, sensorimotor feedback processing, and coordination. Elevated variability of gait is associated with pathology in the cerebellum including cerebellar lesions and atrophy 72 , 92 , as well as reduced metabolism 73 . Increases in gait variability can also be induced by inhibiting activity in the cerebellum or posterior parietal cortex using transcranial magnetic stimulation 91 . These findings are consistent with cerebellar involvement in somatosensory and vestibular integration, coordination across limbs, and postural control, as well as the role of posterior parietal cortex in integrating multimodal sensory feedback for correcting motor error. Additionally, elevated gait variability in healthy and pathologic aging is associated with reduced involvement of the dorsal attention network, increased involvement of the default mode network when measured at rest 89 , 93 and atypical functional connectivity within frontal-parietal networks 94 , 95 , consistent with involvement of cognitive and attentional control in attenuating inherent variability of movement. Each of these brain networks has been implicated in ASD. Task-free neuroimaging studies have reported that, relative to NT controls, autistic individuals show increased functional connectivity between cerebellum and posterior parietal cortex, as well as primary sensory and motor cortices 96 – 98 , suggesting heightened intrinsic connectivity within cerebellar-parietal networks that integrate and translate sensory feedback information into corrective motor commands. In contrast, autistic individuals show reduced functional connectivity between cerebellum and dorsolateral prefrontal cortex 98 , suggesting reduced connectivity within frontal-cerebellar networks that are involved in motor planning. During tests of visually guided precision grip control, autistic individuals show elevated variability of force output that is associated with atypical activation and functional connectivity within parietal-cerebellar brain networks 81 , 82 , 99 . During precision gripping, autistic individuals also show reduced functional connectivity between the anterior cingulate and motor (M1) cortices, as well as increased activation within posterior parietal cortex that are associated with increased grip force variability 82 . These findings indicate that elevated grip force variability in ASD is associated with reduced modulation of the same sensorimotor, cognitive and attentional networks that are associated with elevated gait variability in other clinical populations, suggesting that functional differences in these networks likely contribute to elevated variability of multiple motor behaviors in ASD including gait and manual motor control. Clinical correlations . We found that increased gait variability was moderately associated with clinical ratings of ritualistic behavior. This finding suggests that gait control differences may share important neurodevelopmental mechanisms with certain core autistic traits. While this correlation was marginally significant and should be interpreted with caution, it is consistent with findings from other studies demonstrating a relationship between motor variability and repetitive behaviors in autistic individuals 85 , 100 . Separate studies also have found associations between repetitive behavior and the structure and function of the cerebellum 99 , 101 , 102 . This includes a finding that repetitive behavior is associated with increased activation of cerebellar lobule VIIb during a precision gripping task for which autistic individuals show elevated motor variability 99 . Together these findings suggest that shared or overlapping cerebellar mechanisms may underlie both elevated motor variability and repetitive behavior in autistic individuals. LIMITATIONS The present study used a composite metric of gait variability, the CGVS, which differentiated autistic from NT individuals and demonstrated that gait variability is elevated across ages in ASD relative to NT. This is a critical step towards developing more quantitative, objective measures of atypical sensorimotor function in ASD that could be used for tracking changes in sensorimotor differences and their underlying mechanisms across the lifespan. While the CGVS appears to show benefit relative to discrete gait variability metrics in differentiating autistic and NT individuals (as evidenced by the relatively large AUC in the ROC curve), the present study was not appropriately powered to statistically compare the sensitivity and specificity of the CGVS relative to the discrete gait variability metrics. Future studies using large samples are needed to evaluate the robustness of the CGVS relative to discrete gait metrics for distinguishing autistic from NT and non-autistic clinical populations. Additionally, while the present study found persistently elevated gait variability in autistic individuals relative to NT individuals in a sample of individuals ages four to 35 years old, this is a cross-sectional study, and the 20–30 year age range is sparsely sampled. Future longitudinal studies of gait development across a broad age range are needed to determine developmental mechanisms leading to elevated gait variability across middle and later adulthood. CONCLUSIONS Our findings document a new robust measure of gait variability in ASD that offers promise for understanding key mechanisms of sensorimotor differences in ASD across the lifespan. Understanding these sensorimotor differences is important for both determining neurodevelopmental processes associated with ASD, as well as establishing novel biomarkers that may be sensitive to important changes related to development, interventions, or neurological or psychiatric conditions that often co-occur with ASD (e.g., seizures). Our results also suggest that sensorimotor behaviors show elevated variability in autistic individuals across childhood and adulthood, implicating persistent neurodevelopmental differences in brain networks supporting modulation of intrinsic variability of motor behavior. Declarations Data availability All data are available from the corresponding authors upon reasonable request. Acknowledgment We sincerely thank all autistic individuals and their families who participated in this study. Competing interests The authors have no competing interests to declare. Funding information The following funding awarded to Dr. Shafer supported this work: National Institutes of Health (K01 MH137518) and the University of Kansas – Life Span Institute: Stephen and Carolyn Schroeder Young Investigator Award for Research in Neurodevelopmental Disorders. The following funding awarded to Dr. Matthew Mosconi supported this work: NIH R01 MH112734, NIH K23 MH092696, NIH P50 HD090216, and Department of Defense AR100276. The following funding awarded to Dr. Zheng Wang supported this work: National Institutes of Health (R21AG 065621, R01NS121120, and R01AG086493) and the University of Florida APK Research Investment Grants Award. Ethics approval The study protocol received approval from the Institutional Review Boards (IRB) at the University of Texas Southwestern Medical Center and Children’s Hospital of Dallas (STU052013-4), the University of Kansas Medical Center (STUDY00140269), and the University of Florida (201801378). Consent for publication All participants provided their informed consent regarding data handling procedures. Authors’ contribution JW, MWM, and ZW conceptualized and designed the study. JPS, DJS, WSM, MWM and ZW collected kinematic data. JW, JPS, MT, DJS, WSM, and ZW performed data post-processing and derived gait variables. SW, AMO, MWM and RAR confirmed diagnosis for autistic individuals. JW, DJS, and WSM aggregated demographic, clinical severity, and 48-hour medication data. JW, HQ, JPS, and BK conducted statistical analyses. JW and HQ prepared graphic presentations and tables of the results. RLS, JW, HQ, ZW, and MWM interpreted the results. 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J Gerontol Biol Sci Med Sci 76(10):e328–e334. 10.1093/gerona/glab200 Nishida A, Shima A, Kambe D et al (2024) Frontoparietal-Striatal Network and Nucleus Basalis Modulation in Patients With Parkinson Disease and Gait Disturbance. Neurology 103(3):e209606. 10.1212/WNL.0000000000209606 Wang Y, Yu N, Lu J et al (2023) Increased Effective Connectivity of the Left Parietal Lobe During Walking Tasks in Parkinson’s Disease. J Parkinsons Dis 13(2):165–178. 10.3233/JPD-223564 Khan AJ, Nair A, Keown CL, Datko MC, Lincoln AJ, Müller RA (2015) Cerebro-cerebellar Resting-State Functional Connectivity in Children and Adolescents with Autism Spectrum Disorder. Biol Psychiatry 78(9):625–634. 10.1016/j.biopsych.2015.03.024 Oldehinkel M, Mennes M, Marquand A et al (2018) Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project. Biol Psychiatry: Cogn Neurosci Neuroimaging 4(3):260–270. 10.1016/j.bpsc.2018.11.010 Wang Z, Wang Y, Sweeney JA, Gong Q, Lui S, Mosconi MW (2019) Resting-State Brain Network Dysfunctions Associated With Visuomotor Impairments in Autism Spectrum Disorder. Front Integr Neurosci 13. 10.3389/fnint.2019.00017 Unruh KE, Martin LE, Magnon G, Vaillancourt DE, Sweeney JA, Mosconi MW (2019) Cortical and subcortical alterations associated with precision visuomotor behavior in individuals with autism spectrum disorder. J Neurophysiol 122(4):1330–1341. 10.1152/jn.00286.2019 Bojanek EK, Wang Z, White SP, Mosconi MW (2020) Postural control processes during standing and step initiation in autism spectrum disorder. J Neurodev Disord 12(1):1. 10.1186/s11689-019-9305-x Wolff JJ, Swanson MR, Elison JT et al (2017) Neural circuitry at age 6 months associated with later repetitive behavior and sensory responsiveness in autism. Mol Autism 8(1):8. 10.1186/s13229-017-0126-z McKinney WS, Kelly SE, Unruh KE et al (2022) Cerebellar Volumes and Sensorimotor Behavior in Autism Spectrum Disorder. Front Integr Neurosci 16:821109. 10.3389/fnint.2022.821109 Feaster DJ, Mikulich-Gilbertson S, Brincks AM (2011) Modeling site effects in the design and analysis of multi-site trials. Am J Drug Alcohol Abuse 37(5):383–391. 10.3109/00952990.2011.600386 Additional Declarations The authors declare no competing interests. Supplementary Files GA.png SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9622252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635021319,"identity":"84d6b0d4-306d-4802-a60b-2498a1b10d6f","order_by":0,"name":"Robin L. Shafer","email":"","orcid":"","institution":"University of Kansas","correspondingAuthor":false,"prefix":"","firstName":"Robin","middleName":"L.","lastName":"Shafer","suffix":""},{"id":635021320,"identity":"03f530e2-d916-4335-a288-3a8c158b1df0","order_by":1,"name":"Jingying Wang","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Jingying","middleName":"","lastName":"Wang","suffix":""},{"id":635021321,"identity":"f41d9f52-b38a-462a-a1c1-62adafc69fa9","order_by":2,"name":"Hang Qu","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Qu","suffix":""},{"id":635021322,"identity":"7d013325-c4d9-49a1-b975-75b9807584fb","order_by":3,"name":"Joelle P. Simpson","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Joelle","middleName":"P.","lastName":"Simpson","suffix":""},{"id":635021323,"identity":"f866d0d5-73d3-4814-b183-3cadbdf979ce","order_by":4,"name":"Matthew Terza","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Terza","suffix":""},{"id":635021324,"identity":"7f1fb8a2-c93d-49d0-b6d0-842aa13943af","order_by":5,"name":"Desirae J. Shirley","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Desirae","middleName":"J.","lastName":"Shirley","suffix":""},{"id":635021325,"identity":"2b3a1c3a-f27a-4528-8c4b-0d320232d582","order_by":6,"name":"Walker S. McKinney","email":"","orcid":"","institution":"University of Kansas","correspondingAuthor":false,"prefix":"","firstName":"Walker","middleName":"S.","lastName":"McKinney","suffix":""},{"id":635021326,"identity":"20cdcc94-f487-47fd-853c-af31a62996a2","order_by":7,"name":"Stormi Pulver","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Stormi","middleName":"","lastName":"Pulver","suffix":""},{"id":635021327,"identity":"ff9d9b60-cd31-4e82-8de5-df26b87ea7dd","order_by":8,"name":"Ann-Marie Orlando","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Ann-Marie","middleName":"","lastName":"Orlando","suffix":""},{"id":635021328,"identity":"bc56a3b6-6918-4721-8aa6-86986d16975c","order_by":9,"name":"Regilda A. Romero","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Regilda","middleName":"A.","lastName":"Romero","suffix":""},{"id":635021329,"identity":"3ea6c8a3-d327-4247-9512-3e2771cfc00c","order_by":10,"name":"Bikram Karmakar","email":"","orcid":"","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Bikram","middleName":"","lastName":"Karmakar","suffix":""},{"id":635021330,"identity":"b069cab5-aa01-41d1-9281-b0cd46200aaf","order_by":11,"name":"Matthew W. Mosconi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYPACCTkJEMVDihZjkrUwJM4gWotue/vDDx93WKTPnJHA+OBtGxFazM6cMZaceUYid7ZEArPhXKK03MhhY+Ztk8idJ5HAJs1LnJb0ZyAt6XISCey/idSSYAbSkiANtIWZOC1gv7RJGM7sedgsOeccMVqOg0KsrU5e4njywQ9vyojQggQYG0hTPwpGwSgYBaMANwAAXVYxJJx7bpMAAAAASUVORK5CYII=","orcid":"","institution":"University of Kansas","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"W.","lastName":"Mosconi","suffix":""},{"id":635021331,"identity":"240db5cc-bf66-4109-b789-b3e9daef0170","order_by":12,"name":"Zheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCTiL+QBDRQGQPkC8FrYEhjMGpGnhMSBOi/zs5mMPvzAcljfnX/NN4oABgxzfjQT8WhjnHEs3lmE4bLhzxtttIC3GkoS0MEvkmElLMNxm3HDj7DbpDwYMiRsIaWGTyP8G0mK/4caZZyBb6glq4ZHIYZP8wHA7ccP5HjaQlgQDQlokJNLMpBkM/idvuMFmbHHAQMJw5pkH+LXIz0h+JvmjIs12w/nDD28cqLCR5ztOwBYQYOYBRYcEWKUEfqUwwPgDRPIfIE71KBgFo2AUjDwAAFsCR2/cQ8cnAAAAAElFTkSuQmCC","orcid":"","institution":"University of Florida","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-05-05 19:40:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9622252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9622252/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108677327,"identity":"394149b2-5d85-489a-94f2-baa1af446162","added_by":"auto","created_at":"2026-05-07 08:46:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162542,"visible":true,"origin":"","legend":"\u003cp\u003eDerivation of CGVS using CCA.\u003cstrong\u003e \u003c/strong\u003eDataset 1 consisted of seven gait standard deviation measures that significantly differentiated autistic individuals from NT controls (Table 2). Dataset 2 included four demographic variables critically affecting gait variability in autistic individuals and NT controls. sd = standard deviation; HS = heel strike; TO = toe-off.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9622252/v1/85b5d61036cf3603d8753e44.png"},{"id":108677329,"identity":"63ff7390-4e9b-4de7-a375-bb72ce590f27","added_by":"auto","created_at":"2026-05-07 08:46:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222896,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic sensitivity and specificity of the CGVS and discrete gait metrics. Receiver operating characteristic (ROC) curves for the CGVS and each discrete gait metric. CGVS = composite gait variability score; sd = standard deviation; HS = heel strike; TO = toe-off.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9622252/v1/3d95b5b9e694144815dbce4b.png"},{"id":108806518,"identity":"b18a5cb5-9aa2-4c83-a73e-6408fcc22735","added_by":"auto","created_at":"2026-05-08 15:28:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368920,"visible":true,"origin":"","legend":"\u003cp\u003eClinical and demographic correlations with CGVS.\u003cstrong\u003e \u003c/strong\u003eCorrelation analysis shows the relationship of CGVS with age (A), fs-IQ (B), and ritualistic behavior subscale score of RBS-R (C). Shaded areas show 95% confidence intervals of corresponding measurements for each group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9622252/v1/9728395a16e72412c99133a1.png"},{"id":108810133,"identity":"7fbd5356-41ca-4c8d-9b2f-5aa7956c5615","added_by":"auto","created_at":"2026-05-08 15:57:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9622252/v1/94ed0fac-4c5e-4471-aba0-2be5182b6b6a.pdf"},{"id":108677326,"identity":"22df4f7e-88af-4179-a9f3-128552da6eb8","added_by":"auto","created_at":"2026-05-07 08:46:01","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":113617,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-9622252/v1/120dcafd8fd7d0ea7148cb9c.png"},{"id":108806505,"identity":"82fa8e29-4e8d-4100-ac6c-620eb046f14d","added_by":"auto","created_at":"2026-05-08 15:28:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26414,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9622252/v1/1ddd06795fd95681bf3b848c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGait variability development in autistic individuals across childhood and adulthood\u003c/p\u003e","fulltext":[{"header":"ABBREVIATED SUMMARY","content":"\u003cp\u003eThis study used multiple gait variability metrics and demographic factors to create a gait variability (instability) composite score for characterizing autistic and neurotypical gait development. We found that gait variability decreases with age in autistic and neurtypical individuals, but autistic individuals show slower change and persistently elevated gait variability.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social-communication challenges, restricted interests and stereotypic behaviors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Sensorimotor impairments are highly prevalent in ASD, affecting 35\u0026ndash;87% of autistic individuals\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Sensorimotor differences are also closely associated with more severe autistics traits, poorer long-term outcomes, and reduced quality of life \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. They commonly manifest early in infancy and persist into adulthood, impacting both fine and gross motor systems\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Most previous studies of sensorimotor impairments in ASD have relied on clinical assessments and ratings of motor behaviors that broadly categorize motor skills (e.g., fine and gross motor)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e or on quantitative, objective measures that are constrained to developmentally specific actions (e.g., spoon feeding, handwriting)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. While clinical assessments offer valuable diagnostic insights, their broad scope preclude a mechanistic understanding of sensorimotor differences in ASD. Studies of developmentally constrained behaviors also are limited in their ability to clarify how sensorimotor differences vary across the lifespan in ASD.\u003c/p\u003e \u003cp\u003eMeasurement of movement features during natural walking (i.e., gait) provides an important framework for understanding sensorimotor behavior and development\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. As a fundamental gross motor skill, gait supports mobility and enables individuals to explore and interact with their environment in ways that diversify sensorimotor, cognitive, and social experiences\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Gait also undergoes a prolonged and complex maturational time course extending from the first year of life through the third decade\u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Typical gait development is characterized by rapid maturation of spatiotemporal and joint kinematic patterns in the first few years of walking (2\u0026ndash;5 years of age) including transitioning to striking with heel first (e.g., instead of flat foot), introduction of reciprocating arm swing, and reduced coactivation of agonist-antagonist muscles\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Stride length increases across childhood into adolescence, while gait speed increases primarily during adolescence and early adulthood\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Variability of stride length decreases rapidly in early childhood, reaching adult levels by mid-adolescence\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Similarly, cadence, stride time and stride duration variability decrease rapidly throughout childhood, with a slower, steadier decrease during adolescence as these metrics approach adult levels\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These developmental changes are present even when controlling for differences in body size (e.g., height)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Aging-related behavioral changes in gait also serve as critical and sensitive biomarkers for multiple health-related outcomes, including independence during aging and in clinical conditions, adaptive functioning, and well-being\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFindings on spatiotemporal and joint kinematics during gait in ASD have been mixed. For example, several studies have found decreased step and stride length\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, reduced cadence \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and velocity\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, as well as increased double support time (i.e., time when both feet are in contact with the floor or other support surface)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e in autistic compared to neurotypical (NT) individuals. In contrast, other studies have reported opposite patterns\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e or no significant between-group differences in these metrics \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Similarly, while some studies have noted differences in joint angles during gait, the specific joints for which differences are reported vary across studies\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Together, inconsistencies in both metric selection and reported outcomes limit our ability to identify gait profiles associated with ASD or to characterize how gait differences in ASD may vary across the lifespan\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVariability of findings on spatiotemporal and joint kinematic differences in ASD may reflect inherent heterogeneity of sensorimotor behavior among autistic individuals\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e that covaries with key demographic features, including age, sex, IQ, or other unidentified characteristics (e.g., etiology)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In contrast to findings from studies examining mean values for select gait features, findings from studies analyzing variability (i.e., standard deviation) of gait metrics \u0026ndash; both within a movement and across repeated movements \u0026ndash; appear to show more consistent differences between autism and NT controls. Multiple studies have identified elevated gait variability in autistic children (ages 4\u0026ndash;18 years) across several metrics, including stride length, stride width, stride time, velocity, and foot-shank coupling (i.e., ankle) angle\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In other conditions, such as aging and neurodegeneration, gait variability metrics have proven more sensitive for detecting atypical gait patterns than mean values of spatiotemporal metrics\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Moreover, changes in variability of gait dimensions with age reflect neural processes distinct from those underlying spatiotemporal metrics\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, suggesting that studying gait variability may provide unique insights into neurodevelopmental mechanisms associated with ASD. Nonetheless, inconsistencies remain regarding which gait variability metrics are selected for analysis, which metrics are most robust to differences between autistic and NT individuals, and the extent to which variability relates to demographic factors (e.g., age, sex, IQ). These gaps underscore the need for approaches that integrate gait variability across spatiotemporal and joint kinematic metrics and compare them with demographic features to more comprehensively characterize gait development from childhood through adulthood in autistic individuals.\u003c/p\u003e \u003cp\u003eThe objectives of this study were to (1) optimize measurement of gait variability differences in ASD by deriving a composite gait variability metric that accounts for multicollinearity across individual gait metrics and demographic characteristics, and (2) characterize age-related changes (4\u0026ndash;35 years) in gait variability in autistic and NT individuals. For the first objective, we applied a multi-dimensional analytic approach, canonical correlation analysis (CCA), to derive a composite gait variability score (CGVS) for each participant. The CCA model reflects relationships both between and within two multivariate datasets \u0026ndash; gait variability (dataset 1) and demographic variables (dataset 2) \u0026ndash; while accounting for multicollinearity among measures\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Consistent with prior literature, we hypothesized that autistic individuals would show elevated gait variability\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, measured with the CGVS, relative to NT individuals, and that CGVS would decline with increasing age in both groups\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The literature is mixed on whether motor variability in autistic individuals converges with or diverges from NT individuals with increasing age\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, so we did not have \u003cem\u003ea priori\u003c/em\u003e hypotheses about the group-by-age interaction. Finally, to determine whether gait variability relates to select traits associated with ASD, we explored the relationship between CGVS and clinical autistic traits.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e Participants were examined at one of three sites: the University of Texas Southwestern Medical Center (UTSW), the University of Kansas (KU), or the University of Florida (UF). Diagnostic standards, testing procedures, and task instructions were consistent across sites. The study protocol was approved by the Institutional Review Boards (IRB) at UTSW and Children\u0026rsquo;s Hospital of Dallas (STU052013-4), KU Medical Center (STUDY00140269), and UF (201801378). Participants 18 years of age or older provided written consent. Minors or adults under legal guardianship provided assent in addition to guardians' written consent based on the Declaration of Helsinki.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eSixty autistic individuals ages 4\u0026ndash;35 years and 53 NT controls matched on age, sex, anthropometry and performance IQ participated in this study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic and clinical characteristics of autistic individuals and NT controls (see Supplementary Table\u0026nbsp;1 for demographic and clinical feature breakdowns for UTSW, KU, and UF cohorts separately). Autistic individuals were recruited through community advertisements, clinical programs, institutional research registries, and SPARK Research Match (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sfari.org/resource/research-match/\u003c/span\u003e\u003cspan address=\"https://www.sfari.org/resource/research-match/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Control participants were recruited through flyers and word of mouth. The UTSW cohort consisted of 23 autistic individuals and 23 NT controls; the KU cohort included 32 autistic individuals and 23 NT controls; the UF cohort consisted of 5 autistic adults and 7 NT controls. All study procedures were approved by the Internal Review Boards at UTSW, KU, and UF, and written informed consent was obtained from all participants (or their legal guardians) according to the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of autistic individuals (ASD) and neurotypical controls (NT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.700\u0026thinsp;\u0026plusmn;\u0026thinsp;6.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.170\u0026thinsp;\u0026plusmn;\u0026thinsp;8.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.735\u0026thinsp;\u0026plusmn;\u0026thinsp;16.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.135\u0026thinsp;\u0026plusmn;\u0026thinsp;18.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.434\u0026thinsp;\u0026plusmn;\u0026thinsp;23.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.578\u0026thinsp;\u0026plusmn;\u0026thinsp;25.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-scale IQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.800\u0026thinsp;\u0026plusmn;\u0026thinsp;14.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.615\u0026thinsp;\u0026plusmn;\u0026thinsp;9.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerbal IQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.000\u0026thinsp;\u0026plusmn;\u0026thinsp;16.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.692\u0026thinsp;\u0026plusmn;\u0026thinsp;11.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance IQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.100\u0026thinsp;\u0026plusmn;\u0026thinsp;15.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.981\u0026thinsp;\u0026plusmn;\u0026thinsp;10.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.321\u0026thinsp;\u0026plusmn;\u0026thinsp;1.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBS-R\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.310\u0026thinsp;\u0026plusmn;\u0026thinsp;19.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.865\u0026thinsp;\u0026plusmn;\u0026thinsp;4.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Chi-square statistical results; \u003csup\u003eb\u003c/sup\u003e ADOS-2 calibrated severity score; \u003csup\u003ec\u003c/sup\u003e RBS-R total raw score.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eASD diagnosis was established following the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5)\u003csup\u003e52\u003c/sup\u003e. Autistic individuals at UTSW and KU screened\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;15 on the Social Communication Questionnaire (SCQ)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, and participants in the UF cohort scored\u0026thinsp;\u0026ge;\u0026thinsp;32 on the Autism Spectrum Quotient (AQ-50) for adults\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and \u0026ge;\u0026thinsp;65 on the Social Responsiveness Scale-2 (SRS-2) adult self-report\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Eligible individuals were invited for a diagnostic evaluation and completed the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2)\u003csup\u003e57\u003c/sup\u003e administered by a research-reliable study clinician. The UTSW and KU sites additionally administered the Autism Diagnostic Interview\u0026ndash;Revised (ADI-R)\u003csup\u003e58\u003c/sup\u003e. In addition to standardized measures, expert clinical opinion was used to confirm ASD diagnosis at all sites. Across sites, all autistic individuals were required to have a full-scale IQ (fs-IQ)\u0026thinsp;\u0026ge;\u0026thinsp;70 to participate in this study. No autistic individuals had any known genetic or metabolic disorder associated with ASD (e.g., Fragile X syndrome, Tuberous Sclerosis, Phelan McDermid syndrome, Burnside-Butler syndrome) based on medical history reviews.\u003c/p\u003e \u003cp\u003eNT controls were enrolled if they scored\u0026thinsp;\u0026lt;\u0026thinsp;8 on the SCQ during initial screening at UTSW or KU. NT controls enrolled at UF had a score of \u0026lt;\u0026thinsp;22 on the AQ-50 or a score of \u0026lt;\u0026thinsp;60 on the SRS-2. Across sites, NT controls were excluded if they (a) had any major psychiatric (e.g., schizophrenia, bipolar affective disorder, and obsessive compulsive disorder) or neurological (e.g., epilepsy, cerebral palsy, spinal cord injury, and ataxia) disorders prior to or at the time of study participation; (b) had a family history of ASD or other forms of neurodevelopmental disorders in first- or -second-degree relatives; (c) had a family history of any major psychiatric disorders in first-degree relatives, or (d) had a fs-IQ\u0026thinsp;\u0026lt;\u0026thinsp;80.\u003c/p\u003e \u003cp\u003eNo participant (autistic or NT control) had a history of birth asphyxia or non-febrile seizure. One autistic adult at UF reported a prior history of substance abuse. Thirteen UTSW (8 on antidepressants, 7 on stimulants, 1 on sedatives), six KU (4 on antidepressants and 2 on stimulants) and four UF (1 on antipsychotics, 3 on antidepressants, 2 on antihypertensives) participants reported being on medication within 48 hours of gait testing. All participants were able to ambulate independently without assistive devices. All participants completed the Repetitive Behavior Scale-Revised (RBS-R)\u003csup\u003e59\u003c/sup\u003e and had their IQ assessed using the Wechsler Abbreviated Scales of Intelligence, 2nd Edition (WASI-II)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eApparatus and Procedures\u003c/h3\u003e\n\u003cp\u003eThe apparatus and procedures were consistent across sites. Prior to testing, 16 passive reflective markers were attached to participants\u0026rsquo; major joints based on the industry-standardized Plug-in-Gait Lower Body template (Vicon, Centennial, Co). Kinematic data were recorded using Nexus 3D motion capture systems. The motion capture systems at UTSW and KU included 10 Vicon Bonita 10 near-infrared cameras with a spatial error of 0.5 mm and a sampling rate of 250 Hz. The motion capture system at UF incorporated 12 VICON Vantage 5 cameras and a spatial error of 0.2 mm and sampling rate of 120 Hz. The walking distance was 5 meters at UTSW and KU and 7 meters for UF.\u003c/p\u003e \u003cp\u003eParticipants started each trial by standing upright with their feet shoulder width apart and arms by their sides. After receiving a verbal cue, participants began walking continuously at their self-selected speed toward the finish line. Each individual completed 3\u0026ndash;5 trials. Practice trials were administered prior to data acquisition to ensure participants felt comfortable wearing reflective markers and understood task instructions. To minimize variability in the rates at which individuals increased or decreased their walking speed at the beginning and end of each trial, only kinematic data representing individuals\u0026rsquo; \u0026ldquo;preferred speed\u0026rdquo; were selected and post-processed. Heel strikes and toe-offs of each leg were manually labeled on the raw kinematic data by trained scorers and cross-checked among scorers (JW, JPS, DJS, WSM, and ZW). Kinematic data were then exported, filtered, and analyzed using custom scripts in MATLAB 2023a (MathWorks, Inc., Natick, MA). A low-pass, zero-lag, 4th -order Butterworth filter was applied at a cutoff frequency of 7 Hz to filter the raw kinematic data.\u003c/p\u003e \u003cp\u003eSpatiotemporal variables of gait consisted of the step width (i.e., mediolateral distance between heel markers of two consecutive heel strikes), step velocity (i.e., ratio of step length and step time), and cadence (i.e., number of steps per minute). Kinematic features of gait included relative angles of the ipsilateral hip (i.e., pelvis-thigh), knee (i.e., thigh-shank), and ankle (i.e., shank-foot) joints at heel-strikes and toe-offs within the sagittal plane. Within-individual standard deviation was calculated for each gait variable across all walking trials. These nine gait standard deviation measures were used as candidate variables to derive the composite gait variability score (CGVS).\u003c/p\u003e\n\u003ch3\u003eClinical Assessments\u003c/h3\u003e\n\u003cp\u003eTo test relationships between gait and clinical behaviors, associations with ADOS-2\u003csup\u003e57\u003c/sup\u003e and RBS-R\u003csup\u003e59\u003c/sup\u003e were examined for the autistic participants. The ADOS-2 is a semi-structured assessment of play, social abilities, communicative skills, and imaginative use of materials. ADOS-2 calibrated severity scores were calculated to allow comparisons across different modules and age ranges \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Higher scores reflect increased clinical severity of ASD. The RBS-R is a parent/self-report measure of the presence and severity of restricted, repetitive, and stereotyped behaviors. It contains six subscales, including stereotyped behavior, self-injurious behavior, compulsive behavior, ritualistic behavior, sameness behavior, and restricted behavior. Both subscale and total scores were calculated for each individual. Higher scores reflect more severe repetitive behaviors in autistic individuals.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eDemographic and clinical characteristics.\u003c/em\u003e Demographic (age, sex, fs-IQ, verbal IQ, and performance IQ), anthropomorphic (height and weight), and clinical characteristics (ADOS-2 calibrated severity scores and RBS-R total scores) were compared between autistic individuals and NT controls using independent-sample t-tests or Chi-square tests, as appropriate.\u003c/p\u003e \u003cp\u003e \u003cem\u003eComposite gait variability score (CGVS).\u003c/em\u003e Two procedural steps were implemented to derive the CGVS for each individual. \u003cem\u003eStep 1\u003c/em\u003e involved identifying gait standard deviation measures that significantly differentiated autistic individuals from NT controls. To this end, a multivariate analysis of covariance (MANCOVA) was conducted with group (ASD vs. NT) as a fixed factor, nine gait standard deviation measures as dependent variables, and height and weight as covariates. Height and weight were included in this model because anthropometry significantly affects gait characteristics, especially for studies with a broad developmental age range\u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. False discovery rate (FDR) correction for multiple comparisons was applied to control Type \u003cem\u003eI\u003c/em\u003e error, and statistical significance was interpreted at \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt; 0.05\u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCanonical correlation analysis (CCA) was implemented as \u003cem\u003eStep 2\u003c/em\u003e using two datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Dataset 1 included seven gait standard deviation measures determined to significantly differentiate autistic from NT individuals in the \u003cem\u003eStep 1 analysis\u003c/em\u003e, and Dataset 2 consisted of four demographic variables including group, age, sex, and fs-IQ. Four canonical variates were derived from the canonical correlation model because the smaller dataset (i.e., Dataset 2) consisted of four variables. Canonical variates were calculated following the formula:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of gait standard deviation variables and MANCOVA results of autistic individuals (ASD) and neurotypical controls (NT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait standard deviation measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eraw\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence.sd (steps/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.612\u0026thinsp;\u0026plusmn;\u0026thinsp;4.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.237\u0026thinsp;\u0026plusmn;\u0026thinsp;3.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStepWidth.sd (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.615\u0026thinsp;\u0026plusmn;\u0026thinsp;1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStepVelocity.sd (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.935\u0026thinsp;\u0026plusmn;\u0026thinsp;4.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.587\u0026thinsp;\u0026plusmn;\u0026thinsp;4.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHipAngle.HS.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.173\u0026thinsp;\u0026plusmn;\u0026thinsp;1.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.663\u0026thinsp;\u0026plusmn;\u0026thinsp;0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKneeAngle.HS.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.062\u0026thinsp;\u0026plusmn;\u0026thinsp;1.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.514\u0026thinsp;\u0026plusmn;\u0026thinsp;0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkleAngle.HS.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.168\u0026thinsp;\u0026plusmn;\u0026thinsp;1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.684\u0026thinsp;\u0026plusmn;\u0026thinsp;1.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHipAngle.TO.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.439\u0026thinsp;\u0026plusmn;\u0026thinsp;1.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.632\u0026thinsp;\u0026plusmn;\u0026thinsp;0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKneeAngle.TO.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.697\u0026thinsp;\u0026plusmn;\u0026thinsp;1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.375\u0026thinsp;\u0026plusmn;\u0026thinsp;1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkleAngle.TO.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.566\u0026thinsp;\u0026plusmn;\u0026thinsp;2.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.184\u0026thinsp;\u0026plusmn;\u0026thinsp;2.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003esd: standard deviation. HS: heel-strike; TO: toe-off.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\rho\\:}_{i}=\\:\\frac{cov\\left({U}_{i},\\:{V}_{i}\\right)}{\\sigma\\:\\left({U}_{i}\\left)\\sigma\\:\\right({V}_{i}\\right)}\\:$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere ρ\u003csub\u003ei\u003c/sub\u003e represents the i\u003csup\u003eth\u003c/sup\u003e canonical variate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:cov\\left({U}_{i},\\:{V}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the covariance matrix of the Datasets 1 and 2, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\left({U}_{i}\\left)\\:and\\:\\sigma\\:\\right({V}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e stand for the standard deviation matrix of each individual dataset. Canonical variates were interpreted as significant when \u003cem\u003ep\u003c/em\u003e\u003csub\u003eraw\u003c/sub\u003e \u0026lt; 0.05. CGVS was then derived based on the statistically significant canonical variate(s) following the formula:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCGVS (i) = Cadence.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e Cadence.sd\u0026thinsp;+\u0026thinsp;StepWidth.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e StepWidth.sd\u0026thinsp;+\u0026thinsp;StepVelocity.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e StepVelocity.sd\u0026thinsp;+\u0026thinsp;HipAngle.HS.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e HipAngle.HS.sd\u0026thinsp;+\u0026thinsp;KneeAngle.HS.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e KneeAngle.HS.sd\u0026thinsp;+\u0026thinsp;AnkleAngle.HS.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e AnkleAngle.HS.sd\u0026thinsp;+\u0026thinsp;HipAngle.TO.coeff (i) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e HipAngle.TO.sd. (3)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere CGVS (i) represents the i\u003csup\u003eth\u003c/sup\u003e canonical gait variability score whose canonical variate was statistically significant; coeff (i) stands for the unstandardized canonical coefficient of the i\u003csup\u003eth\u003c/sup\u003e linear combination of gait standard deviation measures included in Dataset 1 (U\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e), sd stands for standard deviation, HS stands for heel strikes, and TO is toe-offs. Unstandardized canonical coefficients (i.e., weightings) represent the degree to which changes to one gait standard deviation measure in Dataset 1 correlate with changes in the corresponding canonical variate of Dataset 2, when all remaining gait standard deviation measures are held constant. Only the first canonical variate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{1})\\)\u003c/span\u003e\u003c/span\u003e was statistically significant (see Results), so only the first CGVS was derived for each individual.\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cem\u003eDiagnostic sensitivity and specificity of CGVS relative to discrete gait standard deviation measures.\u003c/em\u003e Receiver Operating Characteristic (ROC) curve analysis\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e was performed as a \u003cem\u003epost hoc\u003c/em\u003e analysis to examine the specificity and sensitivity of CGVS against discrete gait standard deviation measures for differentiating autistic and NT individuals. The area under the curve (AUC) was derived to identify the outcome measure with optimal specificity and sensitivity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDemographic and clinical correlations.\u003c/em\u003e Spearman\u0026rsquo;s rank-order correlation analyses were conducted to examine the relationship between CGVS and demographic variables in both groups and clinical ratings for autistic individuals, including age, fs-IQ, ADOS-2 calibrated severity score, and RBS-R subscale scores. The RBS-R self-injury subscale was omitted from these analyses due to restricted score variability in the sample. Correlations involving age, fs-IQ, and ADOS-2 scores were interpreted as significant at \u003cem\u003ep\u003c/em\u003e\u003csub\u003eraw\u003c/sub\u003e \u0026lt; 0.05. To account for multiple comparisons across RBS-R subscales, FDR correction was applied\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and correlations involving RBS-R subscale scores were interpreted as significant at \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt; 0.05.\u003c/p\u003e \u003cp\u003e \u003cem\u003eComparison of correlation coefficients between groups.\u003c/em\u003e Fisher's \u003cem\u003ez\u003c/em\u003e transformation\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e was applied to statistically significant correlations to test whether the strength of correlations between CGVS and demographic measures differed significantly between the ASD and NT groups.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cem\u003eComposite gait variability score (CGVS).\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes MANCOVA results for gait variability comparisons across autistic individuals and NT controls (\u003cem\u003eStep 1\u003c/em\u003e of deriving the CGVS). Seven of the nine gait variability measures that were analyzed significantly differentiated autistic from NT individuals after FDR correction. These seven measures included the standard deviations of cadence (Cadence.sd), step width (StepWidth.sd), step velocity (StepVelocity.sd), hip angle at heel strikes (HipAngle.HS.sd) and toe-offs (HipAngle.TO.sd), and knee (KneeAngle.HS.sd) and ankle angles at heel strikes (AnkleAngle.HS.sd). These gait variability measures comprised Dataset 1 of the canonical correlation model (\u003cem\u003eStep 2\u003c/em\u003e of deriving the CGVS).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eOnly the first canonical variate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e) was significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), so it was used to derive the CGVS for each individual using the formula below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCanonical correlation analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanonical variate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanonical correlation ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWilks\u0026rsquo; Lambda\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.820\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e343.950\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e194.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eCGVS\u003c/b\u003e = (0.048) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e Cadence.sd + (0.240) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e StepWidth.sd + (0.036) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e StepVelocity.sd + (0.296) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e HipAngle.HS.sd + (0.147) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e KneeAngle.HS.sd + (0.210) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e AnkleAngle.HS.sd + (0.096) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e HipAngle.TO.sd (1)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe derived CGVS represents individuals' overall gait variability during natural walking, accounting for the multicollinearity of each individual gait variability measure included in Dataset 1 and the demographic variables included in Dataset 2. All unstandardized canonical coefficients were positive (1) suggesting a positive relationship between each gait standard deviation measure and CGVS. As such, higher CGVS scores indicated greater overall gait variability.\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eROC analysis demonstrated that CGVS achieved fair discriminative ability in differentiating autistic from NT individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). CGVS yielded the greatest area under the curve (AUC) value among the tested measures (AUC\u003csub\u003eCGVS\u003c/sub\u003e = 0.729; 95% CI: 0.629\u0026ndash;0.828).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC performance (AUC with 95% CI) of CGVS and discrete gait metrics for differentiating ASD from NT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.729 (0.629\u0026ndash;0.828)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence.sd (steps/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.599 (0.491\u0026ndash;0.706)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStepWidth.sd (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650 (0.545\u0026ndash;0.756)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStepVelocity.sd (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.679 (0.577\u0026ndash;0.781)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHipAngle.HS.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648 (0.544\u0026ndash;0.752)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKneeAngle.HS.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.638 (0.533\u0026ndash;0.743)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkleAngle.HS.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.697 (0.592\u0026ndash;0.802)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHipAngle.TO.sd (deg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.659 (0.555\u0026ndash;0.762)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAUC\u0026thinsp;=\u0026thinsp;area under the ROC curve; CI\u0026thinsp;=\u0026thinsp;95% confidence interval; ASD coded as the positive class (1); HS\u0026thinsp;=\u0026thinsp;heel strike; TO\u0026thinsp;=\u0026thinsp;toe-off\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cem\u003eDemographic and clinical correlations.\u003c/em\u003e Lower CGVS was associated with increased age for both autistic individuals (ρ = -0.377, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and NT controls (ρ = -0.788, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Fisher's \u003cem\u003ez\u003c/em\u003e transformation revealed that the relationship between CGVS and age differed significantly between groups (\u003cem\u003ez\u003c/em\u003e = -3.364, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.001). Specifically, NT individuals showed a strong negative correlation between CGVS and age, whereas autistic individuals demonstrated a weaker negative correlation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CGVS was not associated with fs-IQ for autistic individuals (ρ\u0026thinsp;=\u0026thinsp;0.058, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.675) or NT control participants (ρ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.169, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.231; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The relationship between CGVS and fs- IQ also did not differ significantly between groups (\u003cem\u003ez\u003c/em\u003e = -1.143, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.253).\u003c/p\u003e \u003cp\u003eIncreased CGVS was associated with increased RBS-R ritualistic behavior subscale scores in autistic individuals (ρ\u0026thinsp;=\u0026thinsp;0.370, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.050; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). No other clinical associations with CGVS were identified (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study used canonical correlation analyses to quantify gait variability differences in ASD. This approach allowed us to derive a composite metric of gait variability (CGVS) that accounts for the multicollinearity of spatiotemporal and joint kinematic variables, as well as demographic characteristics, and it provides a singular, powerful metric for comparing gait profiles in autistic and NT individuals from childhood through adulthood. Three key findings are reported: (1) autistic individuals show increased gait variability compared to chronological-age and sex-matched NT peers, suggesting that sensorimotor processes involved in modulating the variability of motor behavior are disrupted in autistic individuals; (2) the multidimensional CGVS derived here provided greater sensitivity to gait differences in ASD than any individual measurement of gait, suggesting it may offer important advantages for tracking gait development in ASD and identifying clinical and neurobiological correlates, and (3) gait variability, as represented by the CGVS, shows age-related reductions across childhood and into adulthood in autistic and NT individuals, but this age-related reduction is attenuated in autistic individuals. We also document that gait differences in ASD are independent of general cognitive abilities, indicating that sensorimotor differences in ASD occur across individuals with a range of developmental abilities within the non-intellecual disability range.\u003c/p\u003e \u003cp\u003e \u003cem\u003eComposite gait variability for understanding sensorimotor mechanisms of ASD.\u003c/em\u003e Our findings demonstrate that variability of gait can be represented in a single composite gait variability score (CGVS) that distinguishes gait patterns in ASD from those in NT individuals. Specifically, autistic individuals showed higher CGVS than NT individuals, indicating greater variability of multiple gait features, consistent with prior findings\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. We also found that mean spatiotemporal and joint angle metrics of gait did not differentiate autistic and NT individuals (Supplementary Table\u0026nbsp;3), which could indicate that autistic individuals show specific increases in gait variability that may be critical for distinguishing sensorimotor behaviors across autistic and NT individuals. The lack of significant group differences on mean gait measures could also be attributed to nonlinear age-associated changes that are documented in several mean gait metrics\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, such that differences between autistic and NT gait may only appear at certain points in development, and between group effects may not be detectable if averaged across samples with broad age ranges. These results reinforce findings showing that elevated gait variability in autistic individuals relative to NT controls\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e is more consistent across studies and ages than differences in mean spatiotemporal\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e or joint kinematic measures\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Measures of gait variability are often more robust to changes in gait control (e.g., during development, aging, or disease) than mean spatiotemporal or kinematic measures\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Variability can indicate differences in stability and consistency of gait control even when averaged parameters (e.g., cadence or step length) show no or smaller differences.\u003c/p\u003e \u003cp\u003eThe key mechanisms contributing to increases in gait variability in ASD are not yet fully elucidated, but consideration of gait variability differences in separate clinical conditions may provide important insights. Elevated gait variability is a characteristic feature of cerebellar ataxia\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, and it is common in other populations that show motor or cognitive issues including Parkinson\u0026rsquo;s disease\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, peripheral neuropathy\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, and healthy aging \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. In these populations, elevated gait variability is associated with deficits in feedforward control of intralimb movements\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, disrupted sensorimotor feedback processing\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, and reduced cognitive and attentional capacity\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, suggesting that each of these processes or their combination may contribute to elevated gait variability in autistic individuals. This hypothesis is consistent with findings of elevated motor variability across multiple motor behaviors and effector systems in autistic individuals including visually guided saccades\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, precision grip force control\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, reaching\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, and postural control\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, which implicate atypical feedforward processing, sensorimotor feedback processing, and cognitive motor control processing. During studies of gait and postural control, autistic individuals show a greater increase in gait and postural variability than NT individuals under sensory conditions that put greater demand on sensory feedback processing (e.g., walking or standing on a pliable surface), while increasing cognitive demand only increases variability of gait and postural control in NT individuals but not in autistic individuals, suggesting that atypical integration of sensory feedback may contribute more to elevated gait variability in autism than cognitive motor control processes\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe CGVS described here showed greater than 80% sensitivity for detecting autism with specificity of up to around 60%, suggesting that multidimensional measurements of gait variability may provide unique power for tracking sensorimotor differences in ASD and establishing clinical and neurobiological correlates. Prior studies have assessed gait variability using only one or a small number of discrete metrics (e.g., standard deviation of stride length, time, or velocity), and results have been somewhat inconsistent in terms of which metrics appear to be most robust for differentiating autistic and NT individuals\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, limiting the understanding of gait and broader sensorimotor differences in ASD. In contrast, the CGVS derived in the present study considers several discrete gait variability metrics, as well as demographic factors. This approach is particularly important for understanding sensorimotor differences across a clinically and neurobiologically heterogeneous condition such as ASD.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAge-associated gait variability differences in ASD\u003c/em\u003e. In our sample of individuals aged 4\u0026ndash;35 years old, we observed a decrease in gait variability with increased age in both autistic and NT individuals. However, NT individuals showed a stronger age-related reduction in gait variability than autistic individuals suggesting a slower (or otherwise atypical) development of gait stability in ASD. This atypical course of motor development appears to lead to persistent elevations in gait variability through childhood that become more pronounced in later adolescence and adulthood. These results suggest that neurotypical processes supporting reductions of movement variability during gait in adolescence and adulthood are either attenuated or deviant in ASD, contributing to persistent elevations in gait variability through childhood and into adulthood. This finding contrasts with a prior study of gait variability across ages (4\u0026ndash;16 years) in ASD\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, which found that elevated gait variability in ASD is most pronounced at younger ages and normalizes during later childhood and adolescence. These discrepant findings could be attributed to our use of the CGVS, which integrates multiple discrete measures of gait variability, while the prior study assessed a smaller number (N\u0026thinsp;=\u0026thinsp;3) of discrete gait variability measures. Distinct gait variability metrics may follow separate developmental trajectories which could lead to inconsistent age-associations when comparing metrics, as has been observed in normative development. Specifically, in normative development, temporal gait variability metrics (e.g., stride time variability) decrease dramatically from the onset of walking until around 6\u0026ndash;8 years of age\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, but spatial variability metrics (e.g., variability of stride width or knee angle) show little to no change in early childhood\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. However, since the CGVS integrates multiple discrete gait metrics, it provides a more comprehensive quantification of gait variability that may be more sensitive to age- or diagnosis-related differences than individual measures. Differences across studies also could reflect heterogeneity within the autistic population both in terms of the extent to which gait variability is elevated and the rate at which gate variability shows age-dependent changes within individuals. The prior study of age-related changes in gait variability in ASD specifically assessed autistic individuals with \u0026lsquo;infantile\u0026rsquo; or \u0026lsquo;atypical\u0026rsquo; autism, including individuals with known genetic syndromes associated with autism, and excluding individuals with a diagnosis of Asperger\u0026rsquo;s syndrome or pervasive developmental disorder \u0026ndash; not otherwise specified\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This suggests that their autistic sample had more severe autistic traits than the present study, which did not include individuals with known genetic syndromes or fs-IQ below 70 but did include individuals with milder autistic traits, some of whom likely would have met DSM-IV criteria for Asperger\u0026rsquo;s or pervasive developmental disorder \u0026ndash; not otherwise specified.\u003c/p\u003e \u003cp\u003eStudies of age-associated changes (4\u0026ndash;29 years\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and 10\u0026ndash;20 years\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e) in precision grip force variability in autistic individuals have shown greater decreases in variability with increased age in ASD relative to NT, such that differences were most pronounced at younger ages, consistent with Manicolo et al.\u0026rsquo;s observations of gait variability in ASD. Other studies of trial-to-trial variability in saccadic eye-movement gain (ages 5\u0026ndash;29 years)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and postural control (ages 8\u0026ndash;20 years)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e suggest that motor variability shows similar age-related reductions in autistic and NT individuals. Inconsistencies in findings of age-associated changes in motor variability in ASD highlight the need for future studies to examine gait variability at younger ages and in longitudinal samples to determine when gait variability differences first emerge and how they evolve across different individuals over time.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIncreased gait variability in autism implicates select brain networks.\u003c/em\u003e Variability and mean gait parameters appear to be controlled by separate, but overlapping neurophysiologic mechanisms\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan additionalcitationids=\"CR90\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e, suggesting that our finding showing specificity of gait variability in differentiating autistic individuals from NT individuals may be associated with atypical function of select motor control networks in ASD. Neuropathology, brain stimulation, and neuroimaging studies indicate that gait variability is associated with cerebellar-cortical\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e and cortico-cortical\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e brain networks involved in motor planning, sensorimotor feedback processing, and coordination. Elevated variability of gait is associated with pathology in the cerebellum including cerebellar lesions and atrophy\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, as well as reduced metabolism\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Increases in gait variability can also be induced by inhibiting activity in the cerebellum or posterior parietal cortex using transcranial magnetic stimulation\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. These findings are consistent with cerebellar involvement in somatosensory and vestibular integration, coordination across limbs, and postural control, as well as the role of posterior parietal cortex in integrating multimodal sensory feedback for correcting motor error. Additionally, elevated gait variability in healthy and pathologic aging is associated with reduced involvement of the dorsal attention network, increased involvement of the default mode network when measured at rest\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e and atypical functional connectivity within frontal-parietal networks\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e, consistent with involvement of cognitive and attentional control in attenuating inherent variability of movement.\u003c/p\u003e \u003cp\u003eEach of these brain networks has been implicated in ASD. Task-free neuroimaging studies have reported that, relative to NT controls, autistic individuals show increased functional connectivity between cerebellum and posterior parietal cortex, as well as primary sensory and motor cortices\u003csup\u003e\u003cspan additionalcitationids=\"CR97\" citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e, suggesting heightened intrinsic connectivity within cerebellar-parietal networks that integrate and translate sensory feedback information into corrective motor commands. In contrast, autistic individuals show reduced functional connectivity between cerebellum and dorsolateral prefrontal cortex\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e, suggesting reduced connectivity within frontal-cerebellar networks that are involved in motor planning. During tests of visually guided precision grip control, autistic individuals show elevated variability of force output that is associated with atypical activation and functional connectivity within parietal-cerebellar brain networks\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. During precision gripping, autistic individuals also show reduced functional connectivity between the anterior cingulate and motor (M1) cortices, as well as increased activation within posterior parietal cortex that are associated with increased grip force variability\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. These findings indicate that elevated grip force variability in ASD is associated with reduced modulation of the same sensorimotor, cognitive and attentional networks that are associated with elevated gait variability in other clinical populations, suggesting that functional differences in these networks likely contribute to elevated variability of multiple motor behaviors in ASD including gait and manual motor control.\u003c/p\u003e \u003cp\u003e \u003cem\u003eClinical correlations\u003c/em\u003e. We found that increased gait variability was moderately associated with clinical ratings of ritualistic behavior. This finding suggests that gait control differences may share important neurodevelopmental mechanisms with certain core autistic traits. While this correlation was marginally significant and should be interpreted with caution, it is consistent with findings from other studies demonstrating a relationship between motor variability and repetitive behaviors in autistic individuals\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Separate studies also have found associations between repetitive behavior and the structure and function of the cerebellum\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e,\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. This includes a finding that repetitive behavior is associated with increased activation of cerebellar lobule VIIb during a precision gripping task for which autistic individuals show elevated motor variability\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. Together these findings suggest that shared or overlapping cerebellar mechanisms may underlie both elevated motor variability and repetitive behavior in autistic individuals.\u003c/p\u003e"},{"header":"LIMITATIONS","content":"\u003cp\u003eThe present study used a composite metric of gait variability, the CGVS, which differentiated autistic from NT individuals and demonstrated that gait variability is elevated across ages in ASD relative to NT. This is a critical step towards developing more quantitative, objective measures of atypical sensorimotor function in ASD that could be used for tracking changes in sensorimotor differences and their underlying mechanisms across the lifespan. While the CGVS appears to show benefit relative to discrete gait variability metrics in differentiating autistic and NT individuals (as evidenced by the relatively large AUC in the ROC curve), the present study was not appropriately powered to statistically compare the sensitivity and specificity of the CGVS relative to the discrete gait variability metrics. Future studies using large samples are needed to evaluate the robustness of the CGVS relative to discrete gait metrics for distinguishing autistic from NT and non-autistic clinical populations.\u003c/p\u003e \u003cp\u003e Additionally, while the present study found persistently elevated gait variability in autistic individuals relative to NT individuals in a sample of individuals ages four to 35 years old, this is a cross-sectional study, and the 20\u0026ndash;30 year age range is sparsely sampled. Future longitudinal studies of gait development across a broad age range are needed to determine developmental mechanisms leading to elevated gait variability across middle and later adulthood.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur findings document a new robust measure of gait variability in ASD that offers promise for understanding key mechanisms of sensorimotor differences in ASD across the lifespan. Understanding these sensorimotor differences is important for both determining neurodevelopmental processes associated with ASD, as well as establishing novel biomarkers that may be sensitive to important changes related to development, interventions, or neurological or psychiatric conditions that often co-occur with ASD (e.g., seizures). Our results also suggest that sensorimotor behaviors show elevated variability in autistic individuals across childhood and adulthood, implicating persistent neurodevelopmental differences in brain networks supporting modulation of intrinsic variability of motor behavior.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available from the corresponding authors upon reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all autistic individuals and their families who participated in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following funding awarded to Dr. Shafer supported this work: National Institutes of Health (K01 MH137518) and the University of Kansas – Life Span Institute: Stephen and Carolyn Schroeder Young Investigator Award for Research in Neurodevelopmental Disorders. The following funding awarded to Dr. Matthew Mosconi supported this work: NIH R01 MH112734, NIH K23 MH092696, NIH P50 HD090216, and Department of Defense AR100276. The following funding awarded to Dr. Zheng Wang supported this work: National Institutes of Health (R21AG 065621, R01NS121120, and R01AG086493) and the University of Florida APK Research Investment Grants Award.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received approval from the Institutional Review Boards (IRB) at the University of Texas Southwestern Medical Center and Children’s Hospital of Dallas (STU052013-4), the University of Kansas Medical Center (STUDY00140269), and the University of Florida (201801378).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided their informed consent regarding data handling procedures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJW, MWM, and ZW conceptualized and designed the study. JPS, DJS, WSM, MWM and ZW collected kinematic data. JW, JPS, MT, DJS, WSM, and ZW performed data post-processing and derived gait variables. SW, AMO, MWM and RAR confirmed diagnosis for autistic individuals. JW, DJS, and WSM aggregated demographic, clinical severity, and 48-hour medication data. JW, HQ, JPS, and BK conducted statistical analyses. JW and HQ prepared graphic presentations and tables of the results. RLS, JW, HQ, ZW, and MWM interpreted the results. RLS, JW, HQ, JPS, MWM and ZW drafted the manuscript. RLS, MWM and ZW finalized the manuscript. All authors approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association (2022) \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/em\u003e. 5th, text revision ed\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat AN (2021) Motor Impairment Increases in Children With Autism Spectrum Disorder as a Function of Social Communication, Cognitive and Functional Impairment, Repetitive Behavior Severity, and Comorbid Diagnoses: A SPARK Study Report. 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Am J Drug Alcohol Abuse 37(5):383\u0026ndash;391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/00952990.2011.600386\u003c/span\u003e\u003cspan address=\"10.3109/00952990.2011.600386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Unconstrained walking, canonical correlation analysis, gait variability, composite score, sensorimotor development, autism spectrum disorder","lastPublishedDoi":"10.21203/rs.3.rs-9622252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9622252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAtypical sensorimotor behaviors are prevalent in autism spectrum disorder (ASD), often emerging in infancy and persisting into early adulthood. Prior focus on qualitative metrics and developmentally specific motor skills has limited understanding of sensorimotor differences in ASD across the lifespan. The present study quantified gait kinematics in ASD across a wide age range, including analysis of 60 autistic individuals (ages 4-35 years) and 53 age-, sex-, and performance IQ-matched neurotypical (NT) controls. Autistic individuals differed from NT controls on seven gait variables reflecting within-subject variability of gait kinematics. These seven gait variability metrics were integrated into a canonical correlation analysis (CCA) with select demographic features (group, age, sex, IQ) to derive a composite gait variability score that accounts for the multicollinearity of gait variability and demographic metrics. The composite score revealed increased gait variability in autistic individuals relative to NT controls. Gait variability was negatively associated with age across the sample. Age-by-group interactions suggested that gait variability is more severely elevated in autistic individuals during adolescence and adulthood relative to childhood. Increased gait variability also was associated with more severe clinically rated ritualistic behaviors in autistic individuals. This study leverages a novel approach to define a multidimensional component score of gait variability in ASD from childhood to adulthood. Results indicate that gait variability is elevated in ASD, and that the severity of variability differences increases with age during adolescence and into adulthood. These findings suggest that autistic individuals show an attenuated development of sensorimotor feedback and motor planning processes that are involved in maintaining accuracy and stability of movements.\u003c/p\u003e","manuscriptTitle":"Gait variability development in autistic individuals across childhood and adulthood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 08:45:45","doi":"10.21203/rs.3.rs-9622252/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ece508d7-98f9-41df-b8c1-3e545e8f2a14","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T08:45:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 08:45:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9622252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9622252","identity":"rs-9622252","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}