Gray matter microstructure differences in autistic males: A gray matter based spatial statistics study.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition. Understanding the brain's microstructure and its relationship to clinical characteristics is important to advance our understanding of the neural supports underlying ASD. In the current work, we implemented Gray-Matter Based Spatial Statistics (GBSS) to examine and characterize cortical microstructure and assess differences between typically developing (TD) and autistic males. METHODS: A multi-shell diffusion MRI (dMRI) protocol was acquired from 83 TD and 70 autistic males (5-to-21-years) and fit to the DTI and NODDI models. GBSS was performed for voxelwise analysis of cortical gray matter (GM). General linear models were used to investigate group differences, while age-by-group interactions assessed age-related differences between groups. Within the ASD group, relationships between cortical microstructure and measures of autistic symptoms were investigated. RESULTS: All dMRI measures were significantly associated with age across the GM skeleton. Group differences and age-by-group interactions are reported. Group-wise increases in neurite density in autistic individuals were observed across frontal, temporal, and occipital regions of the right hemisphere. Significant age-by-group interactions of neurite density were observed within the middle frontal gyrus, precentral gyrus, and frontal pole. Negative relationships between neurite dispersion and the ADOS-2 Calibrated Severity Scores (CSS) were observed within the ASD group. DISCUSSION: Findings demonstrate group and age-related differences between groups in neurite density in ASD across right-hemisphere brain regions supporting cognitive processes. Results provide evidence of altered neurodevelopmental processes affecting GM microstructure in autistic males with implications for the role of cortical microstructure in the level of autistic symptoms. CONCLUSION: Using dMRI and GBSS, our findings provide new insights into group and age-related differences of the GM microstructure in autistic males. Defining where and when these cortical GM differences arise will contribute to our understanding of brain-behavior relationships of ASD and may aid in the development and monitoring of targeted and individualized interventions.

IQ and Sensory Symptom Severity Best Predict Motor Ability in Children With and Without Autism Spectrum Disorder.

Motor challenges are commonly reported in autism spectrum disorder (ASD). Yet, there is substantial heterogeneity in motor ability within ASD, and it is unknown what behavioral characteristics best explain individual differences in motor ability in ASD and related conditions. This observational study examined motor ability as a function of sensory features, attention deficit/hyperactivity symptoms, ASD symptoms, and IQ in 110 children with ASD, typical development, or an intermediate behavioral profile. While motor challenges were more prevalent in the ASD group compared to other groups, sensory symptom severity and IQ across all individuals best predicted motor performance above-and-beyond group status. Therefore, motor challenges may be best characterized by individual variation in sensory features and cognitive abilities rather than diagnostic group.

Whole-Body Movement during Videogame Play Distinguishes Youth with Autism from Youth with Typical Development.

Individuals with autism spectrum disorder struggle with motor difficulties throughout the life span, and these motor difficulties may affect independent living skills and quality of life. Yet, we know little about how whole-body movement may distinguish individuals with autism spectrum disorder from individuals with typical development. In this study, kinematic and postural sway data were collected during multiple sessions of videogame play in 39 youth with autism spectrum disorder and 23 age-matched youth with typical development (ages 7-17 years). The youth on the autism spectrum exhibited more variability and more entropy in their movements. Machine learning analysis of the youths' motor patterns distinguished between the autism spectrum and typically developing groups with high aggregate accuracy (up to 89%), with no single region of the body seeming to drive group differences. Moreover, the machine learning results corresponded to individual differences in performance on standardized motor tasks and measures of autism symptom severity. The machine learning algorithm was also sensitive to age, suggesting that motor challenges in autism may be best characterized as a developmental motor delay rather than an autism-distinct motor profile. Overall, these results reveal that whole-body movement is a distinguishing feature in autism spectrum disorder and that movement atypicalities in autism are present across the body.

Balance and the brain: A review of structural brain correlates of postural balance and balance training in humans.

BACKGROUND: Balance challenges are associated with not only the aging process but also a wide variety of psychiatric and neurological disorders. However, relatively little is known regarding the neural basis of balance and the effects of balance interventions on the brain. RESEARCH QUESTION: This review synthesizes the existing literature to answer the question: What are the key brain structures associated with balance? METHODS: This review examined 37 studies that assessed brain structures in relation to balance assessment or intervention. These studies provided 234 findings implicating 71 brain structures. The frequency of implication for each structure was examined based upon specific methodological parameters, including study design (assessment/intervention), type of balance measured (static/dynamic), population (clinical/non-clinical), and imaging analysis technique (region of interest [ROI]/voxel-based morphometry [VBM]). RESULTS: Although a number of structures were associated with balance across the brain, the most frequently implicated structures included the cerebellum, basal ganglia, thalamus, hippocampus, inferior parietal cortex, and frontal lobe regions. Findings in the cerebellum and brainstem were most common in studies with clinical populations, studies that used an ROI approach, and studies that measured dynamic balance. Findings in the frontal, occipital, and parietal regions were also more common in studies that measured dynamic compared to static balance. SIGNIFICANCE: While balance appears to be a whole-brain phenomenon, a subset of structures appear to play a key role in balance and are likely implicated in balance disorders. Some of these structures (i.e., the cerebellum, basal ganglia and thalamus) have a well-appreciated role in balance, whereas other regions (i.e., hippocampus and inferior parietal cortex) are not commonly thought to be associated with balance and therefore may provide alternative explanations for the neural basis of balance. Key avenues for future research include understanding the roles of all regions involved in balance across the lifespan and in different clinical populations.