Abnormalities of Gray Matter Volume and Its Correlation with Clinical Symptoms in Adolescents with High-Functioning Autism Spectrum Disorder

Comparison of autism spectrum disorder subtypes based on functional and structural factors

Autism spectrum disorder (ASD) is a series of neurodevelopmental disorders that may affect a patient's social, behavioral, and communication abilities. As a typical mental illness, ASD is not a single disorder. ASD is often divided into subtypes, such as autism, Asperger's, and pervasive developmental disorder-not otherwise specified (PDD-NOS). Studying the differences among brain networks of the subtypes has great significance for the diagnosis and treatment of ASD. To date, many studies have analyzed the brain activity of ASD as a single mental disorder, whereas few have focused on its subtypes. To address this problem, we explored whether indices derived from functional and structural magnetic resonance imaging (MRI) data exhibited significant dissimilarities between subtypes. Utilizing a brain pattern feature extraction method from fMRI based on tensor decomposition, amplitude of low-frequency fluctuation and its fractional values of fMRI, and gray matter volume derived from MRI, impairments of function in the subcortical network and default mode network of autism were found to lead to major differences from the other two subtypes. Our results provide a systematic comparison of the three common ASD subtypes, which may provide evidence for the discrimination between ASD subtypes.

Sex differences in brain functional specialization and interhemispheric cooperation among children with autism spectrum disorders

The prevalence of autism spectrum disorders (ASDs) differs substantially between males and females, suggesting that sex-related neurodevelopmental factors are central to ASD pathogenesis. Numerous studies have suggested that abnormal brain specialization patterns and poor regional cooperation contribute to ASD pathogenesis, but relatively little is known about the related sex differences. Therefore, this study examined sex differences in brain functional specialization and cooperation among children with ASD. The autonomy index (AI) and connectivity between functionally homotopic voxels (CFH) derived from resting-state functional magnetic resonance imaging (rs-fMRI) were compared between 58 male and 13 female children with ASD. In addition, correlations were examined between regional CFH values showing significant sex differences and symptom scores on the autism behavior checklist (ABC) and childhood autism rating scale (CARS). Male children with ASD demonstrated significantly greater CFH in the left fusiform gyrus (FG) and right opercular part of the inferior frontal gyrus (IFGoperc) than female children with ASD. In addition, the CFH value of the left FG in male children with ASD was negatively correlated with total ABC score and subscale scores for sensory and social abilities. In contrast, no sex differences were detected in brain specialization. These regional abnormalities in interhemispheric cooperation among male children with ASD may provide clues to the neural mechanisms underlying sex differences in ASD symptomatology and prevalence. Autism spectrum disorders, sex, resting-state functional magnetic resonance imaging, cerebral specialization, interhemispheric cooperation.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Brain functional gradient and structure features in adolescent and adult autism spectrum disorders

Understanding how function and structure are organized and their coupling with clinical traits in individuals with autism spectrum disorder (ASD) is a primary goal in network neuroscience research for ASD. Atypical brain functional networks and structures in individuals with ASD have been reported, but whether these associations show heterogeneous hierarchy modeling in adolescents and adults with ASD remains to be clarified. In this study, 176 adolescent and 74 adult participants with ASD without medication or comorbidities and sex, age matched healthy controls (HCs) from 19 research groups from the openly shared Autism Brain Imaging Data Exchange II database were included. To investigate the relationship between the functional gradient, structural changes, and clinical symptoms of brain networks in adolescents and adults with ASD, functional gradient and voxel-based morphometry (VBM) analyses based on 1000 parcels defined by Schaefer mapped to Yeo's seven-network atlas were performed. Pearson's correlation was calculated between the gradient scores, gray volume and density, and clinical traits. The subsystem-level analysis showed that the second gradient scores of the default mode networks and frontoparietal network in patients with ASD were relatively compressed compared to adolescent HCs. Adult patients with ASD showed an overall compression gradient of 1 in the ventral attention networks. In addition, the gray density and volumes of the subnetworks showed no significant differences between the ASD and HC groups at the adolescent stage. However, adults with ASD showed decreased gray density in the limbic network. Moreover, numerous functional gradient parameters, but not VBM parameters, in adolescents with ASD were considerably correlated with clinical traits in contrast to those in adults with ASD. Our findings proved that the atypical changes in adolescent ASD mainly involve the brain functional network, while in adult ASD, the changes are more related to brain structure, including gray density and volume. These changes in functional gradients or structures are markedly correlated with clinical traits in patients with ASD. Our study provides a novel understanding of the pathophysiology of the structure-function hierarchy in ASD.

Autism-associated brain differences can be observed in utero using MRI

Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.

Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method

PURPOSE

This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods.

METHODS

Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results.

RESULTS

Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model.

CONCLUSION

Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.

Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?

Objectives

Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods.

Materials & Methods

Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups.

Results

Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods.

Conclusion

The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.

Cortical thickness abnormalities in autism spectrum disorder

The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD.

Reduction in Retinal Ganglion Cell Layer, Inner Plexiform Layer, and Choroidal Thickness in Children With Autism Spectrum Disorder

PURPOSES

The aim of this study was to evaluate the retinal nerve fiber layer (RNFL), choroidal layer, inner plexiform layer (IPL), and ganglion cell layer (GCL) in patients with autism spectrum disorder (ASD).

METHODS

In this study, we measured the thickness of the RNFL, GCL, IPL, and choroidal thickness using a spectral optical coherence tomography (OCT) device and we compared the results between the children diagnosed with ASD and healthy controls. Correlation between the Childhood Autism Rating Scale (CARS) and the OCT data was evaluated.

RESULTS

Both ASD and control group consisted of 40 subjects (30 males and 10 females). Of the children in the ASD group, 29 had normal intelligence and 11 had mild intellectual disability (MID). The mean age of patients in the ASD group and control groups were 9.77 ± 3.37 years and 9.85 ± 3.97 years (p = 0.928). There was a statistically significant difference between the ASD group and the control group in the nasal and nasal-superior sectors of the RNFL layers in the left eye when all the lower layers of RNFL were assessed. In both eyes, the children with ASD had considerably lower mean choroidal thicknesses than the controls. When compared to the controls, the GCL and IPL volumes in the individuals with ASD were considerably lower in both eyes. Compared to the MID group, the left GCL volume of the nasal-inferior group was noticeably higher. A significant correlation was found between CARS scores and left GCL left IPL.

CONCLUSIONS

In contrast to RNFL in the ASD group, significant reductions in IPL, GCL, and choroidal thickness were observed in both eyes. It is thought that GCL may be a much more important biomarker than RNFL in terms of representing the structural deterioration in the brain. In addition, these results may form the basis for a new perspective on the use of OCT for the diagnosis and clinical course of autism.

Relationship between Overweight/Obesity and Social Communication in Autism Spectrum Disorder Children: Mediating Effect of Gray Matter Volume

With advances in medical diagnostic technology, the healthy development of children with autism spectrum disorder (ASD) is receiving more and more attention. In this article, the mediating effect of brain gray matter volume (GMV) between overweight/obesity and social communication (SC) was investigated through the analysis of the relationship between overweight/obesity and SC in autism spectrum disorder children. In total, 101 children with ASD aged 3-12 years were recruited from three special educational centers (Yangzhou, China). Overweight/obesity in children with ASD was indicated by their body mass index (BMI); the Social Responsiveness Scale, Second Edition (SRS-2) was used to assess their social interaction ability, and structural Magnetic Resonance Imaging (sMRI) was used to measure GMV. A mediation model was constructed using the Process plug-in to analyze the mediating effect of GMV between overweight/obesity and SC in children with ASD. The results revealed that: overweight/obesity positively correlated with SRS-2 total points (p = 0.01); gray matter volume in the left dorsolateral superior frontal gyrus (Frontal_Sup_L GMV) negatively correlated with SRS-2 total points (p = 0.001); and overweight/obesity negatively correlated with Frontal_Sup_L GMV (p = 0.001). The Frontal_Sup_L GMV played a partial mediating role in the relationship between overweight/obesity and SC, accounting for 36.6% of total effect values. These findings indicate the significant positive correlation between overweight/obesity and SC; GMV in the left dorsolateral superior frontal gyrus plays a mediating role in the relationship between overweight/obesity and SC. The study may provide new evidence toward comprehensively revealing the overweight/obesity and SC relationship.