IQ and Sensory Symptom Severity Best Predict Motor Ability in Children With and Without Autism Spectrum Disorder.
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ABSTRACT:
SUBMITTER: Surgent OJ
PROVIDER: S-EPMC7665981 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity.
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Project description:BackgroundSymptoms of autism spectrum disorder (ASD) emerge in the first years of life. Yet, little is known about the organization and development of functional brain networks in ASD proximally to the symptom onset. Further, the relationship between brain network connectivity and emerging ASD symptoms and overall functioning in early childhood is not well understood.MethodsResting-state fMRI data were acquired during natural sleep from 24 young children with ASD and 23 typically developing (TD) children, aged 17-45 months. Intrinsic functional connectivity (iFC) within and between resting-state functional networks was derived with independent component analysis (ICA).ResultsIncreased iFC between visual and sensorimotor networks was found in young children with ASD compared to TD participants. Within the ASD group, the degree of overconnectivity between visual and sensorimotor networks was associated with greater autism symptoms. Age-related weakening of the visual-auditory between-network connectivity was observed in the ASD but not the TD group.ConclusionsTaken together, these results provide evidence for disrupted functional network maturation and differentiation, particularly involving visual and sensorimotor networks, during the first years of life in ASD. The observed pattern of greater visual-sensorimotor between-network connectivity associated with poorer clinical outcomes suggests that disruptions in multisensory brain circuitry may play a critical role for early development of behavioral skills and autism symptomatology in young children with ASD.
| S-EPMC7688487 | biostudies-literature
Project description:A notable characteristic of autism spectrum disorder (ASD) is co-occurring deficits in low-level sensory processing and high-order social interaction. While there is evidence indicating detrimental cascading effects of sensory anomalies on the high-order cognitive functions in ASD, the exact pathological mechanism underlying their atypical functional interaction across the cortical hierarchy has not been systematically investigated. To address this gap, here we assessed the functional organisation of sensory and motor areas in ASD, and their relationship with subcortical and high-order trandmodal systems. In a resting-state fMRI data of 107 ASD and 113 neurotypical individuals, we applied advanced connectopic mapping to probe functional organization of primary sensory/motor areas, together with targeted seed-based intrinsic functional connectivity (iFC) analyses. In ASD, the connectopic mapping revealed topological anomalies (i.e., excessively more segregated iFC) in the motor and visual areas, the former of which patterns showed association with the symptom severity of restricted and repetitive behaviors. Moreover, the seed-based analysis found diverging patterns of ASD-related connectopathies: decreased iFCs within the sensory/motor areas but increased iFCs between sensory and subcortical structures. While decreased iFCs were also found within the higher-order functional systems, the overall proportion of this anomaly tends to increase along the level of cortical hierarchy, suggesting more dysconnectivity in the higher-order functional networks. Finally, we demonstrated that the association between low-level sensory/motor iFCs and clinical symptoms in ASD was mediated by the high-order transmodal systems, suggesting pathogenic functional interactions along the cortical hierarchy. Findings were largely replicated in the independent dataset. These results highlight that atypical integration of sensory-to-high-order systems contributes to the complex ASD symptomatology.
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Project description:The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression. To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures (Social Responsiveness Scale) were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p = 0.01, n = 5000 permutations; 10 surface area features, p = 0.006, n = 5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD. Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.
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Project description:Spinocerebellar ataxia type 6 (SCA6) is a genetic disease that causes degeneration of Purkinje cells, and recent evidence points to degeneration of Betz cells in the motor cortex. The relation between functional activity of motor cortex and symptom severity during a hand-grip motor control in vivo has not yet been investigated. This study explored both functional changes in the sensorimotor cortex and cerebellar regions and structural alterations in the cerebellum for SCA6 patients as compared to age-matched healthy controls using a multimodal imaging approach (task-based fMRI, task-based functional connectivity, and free-water diffusion MRI). Further, we tested their relation with the severity of ataxia symptoms. SCA6 patients had reduced functional activity in the sensorimotor cortex, supplementary motor area (SMA), cerebellar vermis, and cerebellar lobules I-VI (corrected P < 0.05). Reduced task-based functional connectivity between cortical motor regions (i.e., primary motor cortex and SMA) and cerebellar regions (i.e., vermis and lobules I-VI) was found in SCA6 (corrected P < 0.05). SCA6 had elevated free-water values throughout the cerebellum as compared with controls (corrected P < 0.05). Importantly, reduced functional activity in the sensorimotor cortex and SMA and increased free-water in the superior cerebellar peduncle and cerebellar lobule V were related to more severe symptoms in SCA6 (all pairs: R 2 ≥ 0.4 and corrected P < 0.05). Current results demonstrate that impaired functional activity in sensorimotor cortex and SMA and elevated free-water of lobule V and superior cerebellar peduncle are both related to symptom severity, and may provide candidate biomarkers for SCA6.
| S-EPMC6276122 | biostudies-literature
Project description:BackgroundAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity.MethodsThe developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n?=?247) and typically developing children (TDC) (n?=?220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity.ResultsMachine learning model trained on all five metrics accurately predicted age in the TDC (r?=?0.88) and the ASD (r?=?0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r?=?-?0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity.LimitationsThis work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable.ConclusionsFindings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.
| S-EPMC6907209 | biostudies-literature
Project description:Mounting evidence suggests that poorer motor skills may be related to more severe autism symptoms. This study investigated if atypical white matter microstructure in the brain mediated the relationship between motor skills and ASD symptom severity. Sixty-seven males with ASD and 42 males with typical development (5-33 years old) completed a diffusion tensor imaging scan and measures of grip strength, finger tapping, and autism symptom severity. Within the ASD group, weaker grip strength predicted more severe autism symptoms. Fractional anisotropy of the brainstem's corticospinal tract predicted both grip strength and autism symptom severity and mediated the relationship between the two. These findings suggest that brainstem white matter may contribute to autism symptoms and grip strength in ASD.
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Project description:BackgroundIndividuals with autism spectrum disorder (ASD) often have different social characteristics and particular sensory processing patterns, and these sensory behaviors may affect their social functioning. The objective of our study is to investigate the sensory profiles of children with ASD and their association with social behavior. Specifically, we aim to identify the predictive role of sensory processing in social functioning.MethodsThe Short Sensory Profile (SSP) was utilized to analyze sensory differences between ASD children and their peers. The Social Responsiveness Scale (SRS) and other clinical scales were employed to assess the social functioning of children with ASD. Additionally, the predictive ability of sensory perception on social performance was discussed using random forest and support vector machine (SVM) models.ResultsThe SSP scores of ASD children were lower than those of the control group, and there was a significant negative correlation between SSP scores and clinical scale scores (P < 0.05). The random forest and SVM models, using all the features, showed higher sensitivity, while the random forest model with 7-feature factors had the highest specificity. The area under the receiver operating characteristic (ROC) curve (AUC) for all the models was higher than 0.8.ConclusionAutistic children in our study have different patterns of sensory processing than their peers, which are significantly related to their patterns of social functioning. Sensory features can serve as a good predictor of social functioning in individuals with ASD.
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Project description:Rumination has a large direct effect on psychopathology but has received relatively little attention in autism spectrum disorder despite the propensity to perseverate in this population. This study provided initial evidence that adolescents with autism spectrum disorder self-report more anger-focused rumination than typically developing controls, though there was substantial within-group variability. Anger rumination was positively correlated with autism symptom severity with both groups combined. Future studies that include measures of perseveration on special interests are needed to understand whether anger rumination is a manifestation of a perseverative type of repetitive behavior or a distinct trait. Even when controlling for autism symptom severity, however, anger-focused rumination was associated with poorer functioning, including more depression symptoms and overall emotional and behavioral dysregulation. Therefore, further inquiry regarding anger rumination in autism spectrum disorder is clinically important, and the potential impact of rumination-focused interventions should be explored.
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Project description:Although growing evidence indicates atypical long-distance connectivity in autism spectrum disorder (ASD), much less is known about local connectivity, despite conjectures that local overconnectivity may be causally involved in the disorder. Using functional connectivity MRI and graph theory, we found that local functional connectivity was atypically increased in adolescents with ASD in temporo-occipital regions bilaterally. Posterior overconnectivity was found to be associated with higher ASD symptom severity, whereas an ASD subsample with low severity showed frontal underconnectivity. The findings suggest links between symptomatology and local connectivity, which vary within the autism spectrum.
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