Abnormal gray matter structure in children and adolescents with high-functioning autism spectrum disorder

Structural and functional whole brain changes in autism spectrum disorder at different age stages

Autism spectrum disorder (ASD) is a developmental disorder involving regional changes and local neural disturbances. However, few studies have investigated the dysfunctional phenomenon across different age stages. This study explores the structural and functional brain changes across different developmental stages in individuals with ASD, focusing on childhood (6-12 years), adolescence (12-18 years), and adulthood (18 + years). Using a comprehensive set of neuroimaging metrics, including modulated and non-modulated voxel-based morphometry (VBM), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF), we identified significant stage-specific alterations in both VBM and functional measurements. Our results reveal that ASD is associated with progressive and stage-specific abnormalities in brain structure and function, with distinct patterns emerging at each developmental stage. Specifically, we observed significant modulated VBM reductions in the precuneus, lentiform nucleus, and inferior parietal lobule, accompanied by increases in the midbrain and sub-gyral regions. Moreover, we observed unmodulated VBM increment in regions including lentiform nucleus and thalamus. Functionally, ReHo analyses demonstrated disrupted local synchronization in the medial frontal gyrus, while ALFF and fALFF metrics highlighted altered spontaneous brain activity in the sub-gyral and sub-lobar. Finally, correlation analyses revealed that stage-specific findings are closely linked to clinical social- and behavior-related scores, with VBM in the inferior parietal lobule and putamen as well as ReHo in supplemental motor area being significantly associated with restrictive repetitive behaviors in childhood. These findings underscore the importance of considering age-specific brain changes when studying ASD and suggest that targeted interventions may be necessary at different developmental stages.

Parsing the heterogeneity of brain structure and function in male children with autism spectrum disorder: a multimodal MRI study

Autism spectrum disorder (ASD) is a neurodevelopmental condition with high structural and functional heterogeneity. Multimodal fusion of structural and functional magnetic resonance imaging (MRI) allows better integration of ASD features from multiple perspectives. This study aimed to uncover the potential ASD subtypes by fusing the features of brain structure and function. An unsupervised learning method, similarity network fusion (SNF), was used. Resting-state functional MRI and structural MRI from the Autism Brain Imaging Data Exchange database of 207 male children were included in this study (105 ASD; 102 healthy controls (HC)). Gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF) were utilized to represent structural and functional features separately. Structural and functional distance networks were constructed and fused by SNF. Then spectral clustering was carried out on the fused network. At last, the multivariate support vector regression analysis was used to investigate the relationship between the multimodal alterations and symptom severity of ASD subtypes. Two ASD subtypes were identified. Compared to HC, the two ASD subtypes demonstrated opposite GMV changes and distinct ALFF alterations. Furthermore, the alterations of ALFF predicted the severity of social communication impairments in ASD subtype 1. However, no significant associations were found between the multimodal alterations and symptoms in ASD subtype 2. These findings demonstrate the existence of heterogeneity with distinct structural and functional patterns in ASD and highlight the crucial role of combining multimodal features in investigating the neural mechanism underlying ASD.

Dynamic and static brain functional abnormalities in autism patients at different developmental stages

To date, most studies on autism spectrum disorder (ASD) have focused on specific age ranges, while the mechanisms underlying the entire developmental process of autism patients remain unclear. The aim of this study was to investigate the alterations in brain function in autistic individuals at different developmental stages by resting-state functional MRI (rs-fMRI). We obtained rs-fMRI data from 173 ASD and 178 typical development (TD) individuals in Autism Brain Imaging Data Exchange, spanning child, adolescent, and adult groups. We characterized local brain activity using the amplitude of low-frequency fluctuations (ALFFs), regional homogeneity (ReHo), dynamic ALFF (dALFF), and dynamic ReHo (dReHo) metrics. Pearson correlation analyses were conducted on relationships between Autism Diagnostic Observation Schedule scores and activity measures in abnormal brain regions. We found abnormal ALFF values in the medial and lateral orbitofrontal gyrus and right insula cortex with ASD compared with the TD group. In addition, compared with adolescents with ASD, we found that adults with ASD exhibited an increase in dReHo values in the posterior lateral frontal lobe. We also found that changes in ALFF were associated with the severity of autism. We found abnormal activity in multiple brain regions in individuals with autism and correlated it with clinical characteristics. Our results may provide some help for further exploring the age-related neurobiological mechanisms of ASD patients.

Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder accompanied by structural and functional changes in the brain. However, the relationship between brain structure and function in children with ASD remains largely obscure. In the current study, parallel independent component analysis (pICA) was performed to identify inter-modality associations by drawing on information from different modalities. Structural and resting-state functional magnetic resonance imaging data from 105 children with ASD and 102 typically developing children (obtained from the open-access Autism Brain Imaging Data Exchange database) were combined through the pICA framework. Features of structural and functional modalities were represented by the voxel-based morphometry (VBM) and amplitude of low-frequency fluctuations (ALFF), respectively. The relationship between the structural and functional components derived from the pICA was investigated by Pearson's correlation analysis, and between-group differences in these components were analyzed through the two-sample t-test. Finally, multivariate support vector regression analysis was used to analyze the relationship between the structural/functional components and Autism Diagnostic Observation Schedule (ADOS) subscores in the ASD group. This study found a significant association between VBM and ALFF components in ASD. Significant between-group differences were detected in the loading coefficients of the VBM component. Furthermore, the ALFF component loading coefficients predicted the subscores of communication and repetitive stereotypic behaviors of the ADOS. Likewise, the VBM component loading coefficients predicted the ADOS communication subscore in ASD. These findings provide evidence of a link between brain function and structure, yielding new insights into the neural mechanisms of ASD.

Robust Autism Spectrum Disorder-Related Spatial Covariance Gray Matter Pattern Revealed With a Large-Scale Multi-Center Dataset

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and its underlying neuroanatomical mechanisms still remain unclear. The scaled subprofile model of principal component analysis (SSM-PCA) is a data-driven multivariate technique for capturing stable disease-related spatial covariance pattern. Here, SSM-PCA is innovatively applied to obtain robust ASD-related gray matter volume pattern associated with clinical symptoms. We utilized T1-weighted structural MRI images (sMRI) of 576 subjects (288 ASDs and 288 typically developing (TD) controls) aged 7-29 years from the Autism Brain Imaging Data Exchange II (ABIDE II) dataset. These images were analyzed with SSM-PCA to identify the ASD-related spatial covariance pattern. Subsequently, we investigated the relationship between the pattern and clinical symptoms and verified its robustness. Then, the applicability of the pattern under different age stages were further explored. The results revealed that the ASD-related pattern primarily involves the thalamus, putamen, parahippocampus, orbitofrontal cortex, and cerebellum. The expression of this pattern correlated with Social Response Scale and Social Communication Questionnaire scores. Moreover, the ASD-related pattern was robust for the ABIDE I dataset. Regarding the applicability of the pattern for different age stages, the effect sizes of its expression in ASD were medium in the children and adults, while small in adolescents. This study identified a robust ASD-related pattern based on gray matter volume that is associated with social deficits. Our findings provide new insights into the neuroanatomical mechanisms of ASD and may facilitate its future intervention.

Exploring the most discriminative brain structural abnormalities in ASD with multi-stage progressive feature refinement approach

Objective

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by increasing prevalence, diverse impairments, and unclear origins and mechanisms. To gain a better grasp of the origins of ASD, it is essential to identify the most distinctive structural brain abnormalities in individuals with ASD.

Methods

A Multi-Stage Progressive Feature Refinement Approach was employed to identify the most pivotal structural magnetic resonance imaging (MRI) features that distinguish individuals with ASD from typically developing (TD) individuals. The study included 175 individuals with ASD and 69 TD individuals, all aged between 7 and 18 years, matched in terms of age and gender. Both cortical and subcortical features were integrated, with a particular focus on hippocampal subfields.

Results

Out of 317 features, 9 had the most significant impact on distinguishing ASD from TD individuals. These structural features, which include a specific hippocampal subfield, are closely related to the brain areas associated with the reward system.

Conclusion

Structural irregularities in the reward system may play a crucial role in the pathophysiology of ASD, and specific hippocampal subfields may also contribute uniquely, warranting further investigation.

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.

Region-Specific Brain Volume Changes Emerge in Adolescence in the Valproic Acid Model of Autism and Parallel Human Findings

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits, cognitive dysfunction, and stereotyped repetitive behaviors. Regional volume changes are commonly observed in individuals with ASD. To examine volumetric dysregulation across adolescence, the valproic acid (VPA) model was used to induce ASD-like phenotypes in rats.

METHOD

Regional volumes were obtained via magnetic resonance imaging at either postnatal day 28 or postnatal day 40 (P40), which correspond to early and late adolescence, respectively.

RESULTS

Consistent with prior research, VPA animals had reduced total brain volume compared to control animals. A novel outcome was that VPA animals had overgrown right hippocampi at P40. Differences in the pattern of development of the anterior cingulate cortex were also observed in VPA animals. Differences for the posterior cingulate were only observed in males, but not females.

CONCLUSION

These results demonstrate differences in region-specific developmental trajectories between control and VPA animals and suggest that the VPA model may capture regional volume changes consistent with human ASD.

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits, cognitive dysfunction, and stereotyped repetitive behaviors. Regional volume changes are commonly observed in individuals with ASD. To examine volumetric dysregulation across adolescence, the valproic acid (VPA) model was used to induce ASD-like phenotypes in rats.

METHOD

Regional volumes were obtained via magnetic resonance imaging at either postnatal day 28 or postnatal day 40 (P40), which correspond to early and late adolescence, respectively.

RESULTS

Consistent with prior research, VPA animals had reduced total brain volume compared to control animals. A novel outcome was that VPA animals had overgrown right hippocampi at P40. Differences in the pattern of development of the anterior cingulate cortex were also observed in VPA animals. Differences for the posterior cingulate were only observed in males, but not females.

CONCLUSION

These results demonstrate differences in region-specific developmental trajectories between control and VPA animals and suggest that the VPA model may capture regional volume changes consistent with human ASD.

Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change

Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age-related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co-development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age-related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) (N = 434). We used structural MRI data from the largest high-quality cross-sectional sample (NYU) as well as two other large high-quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co-development features in ASD and TD exhibit contrasting patterns; specifically, some higher-order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.

Autism Spectrum Disorder: Brain Areas Involved, Neurobiological Mechanisms, Diagnoses and Therapies

Autism spectrum disorder (ASD), affecting over 2% of the pre-school children population, includes an important fraction of the conditions accounting for the heterogeneity of autism. The disease was discovered 75 years ago, and the present review, based on critical evaluations of the recognized ASD studies from the beginning of 1990, has been further developed by the comparative analyses of the research and clinical reports, which have grown progressively in recent years up to late 2023. The tools necessary for the identification of the ASD disease and its related clinical pathologies are genetic and epigenetic mutations affected by the specific interaction with transcription factors and chromatin remodeling processes occurring within specific complexes of brain neurons. Most often, the ensuing effects induce the inhibition/excitation of synaptic structures sustained primarily, at dendritic fibers, by alterations of flat and spine response sites. These effects are relevant because synapses, established by specific interactions of neurons with glial cells, operate as early and key targets of ASD. The pathology of children is often suspected by parents and communities and then confirmed by ensuing experiences. The final diagnoses of children and mature patients are then completed by the combination of neuropsychological (cognitive) tests and electro-/magneto-encephalography studies developed in specialized centers. ASD comorbidities, induced by processes such as anxieties, depressions, hyperactivities, and sleep defects, interact with and reinforce other brain diseases, especially schizophrenia. Advanced therapies, prescribed to children and adult patients for the control of ASD symptoms and disease, are based on the combination of well-known brain drugs with classical tools of neurologic and psychiatric practice. Overall, this review reports and discusses the advanced knowledge about the biological and medical properties of ASD.

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.

Margin-Maximized Norm-Mixed Representation Learning for Autism Spectrum Disorder Diagnosis With Multi-Level Flux Features

Early diagnosis and timely intervention are significantly beneficial to patients with autism spectrum disorder (ASD). Although structural magnetic resonance imaging (sMRI) has become an essential tool to facilitate the diagnosis of ASD, these sMRI-based approaches still have the following issues. The heterogeneity and subtle anatomical changes place higher demands for effective feature descriptors. Additionally, the original features are usually high-dimensional, while most existing methods prefer to select feature subsets in the original space, in which noises and outliers may hinder the discriminative ability of selected features. In this article, we propose a margin-maximized norm-mixed representation learning framework for ASD diagnosis with multi-level flux features extracted from sMRI. Specifically, a flux feature descriptor is devised to quantify comprehensive gradient information of brain structures on both local and global levels. For the multi-level flux features, we learn latent representations in an assumed low-dimensional space, in which a self-representation term is incorporated to characterize the relationships among features. We also introduce mixed norms to finely select original flux features for the construction of latent representations while preserving the low-rankness of latent representations. Furthermore, a margin maximization strategy is applied to enlarge the inter-class distance of samples, thereby increasing the discriminative ability of latent representations. The extensive experiments on several datasets show that our proposed method can achieve promising classification performance (the average area under curve, accuracy, specificity, and sensitivity on the studied ASD datasets are 0.907, 0.896, 0.892, and 0.908, respectively) and also find potential biomarkers for ASD diagnosis.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Relationship between gray matter structure and age in children and adolescents with high-functioning autism spectrum disorder

Objective

The present study used magnetic resonance imaging to investigate the difference in the relationship between gray matter structure and age in children and adolescents with autism spectrum disorder (ASD) and typically developing (TD) subjects.

Methods

After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 111 children and adolescents (7-18 years old) with high-functioning ASD and 151 TD subjects matched for age, sex and full IQ were included in the current study. By using the voxel-based morphological analysis method, gray matter volume/density (GMV/GMD) maps were obtained for each participant. Then, a multiple regression analysis was performed for ASD and TD groups, respectively to estimate the relationship between GMV/GMD and age with gender, education, site, and IQ scores as covariates. Furthermore, a z-test was used to compare such relationship difference between the groups.

Results

Results showed that compared with TD, the GMD of ASD showed stronger positive correlations with age in the prefrontal cortex, and a stronger negative correlation in the left inferior parietal lobule, and a weaker positive correlation in the right inferior parietal lobule. The GMV of ASD displayed stronger positive correlations with age in the prefrontal cortex and cerebellum.

Conclusion

These findings may provide evidence to support that the brain structure abnormalities underlying ASD during childhood and adolescence may differ from each other.