Atypical diffusion tensor hemispheric asymmetry in autism (original) (raw)
Related papers
Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging
Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using Johns Hopkins WM areas' atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder. INDEX TERMS Autism Spectrum disorder, connectivity, diffusion, DTI, dwMRI, gray matter and White matter.
Autistic spectrum disorder (ASD) is accompanied by subtle and spatially distributed differences in brain anatomy that are difficult to detect using conventional mass-univariate methods (e.g., VBM). These require correction for multiple comparisons and hence need relatively large samples to attain sufficient statistical power. Reports of neuroanatomical differences from relatively small studies are thus highly variable. Also, VBM does not provide predictive value, limiting its diagnostic value. Here, we examined neuroanatomical networks implicated in ASD using a whole-brain classification approach employing a support vector machine (SVM) and investigated the predictive value of structural MRI scans in adults with ASD. Subsequently, results were compared between SVM and VBM. We included 44 male adults; 22 diagnosed with ASD using " gold-standard " research interviews and 22 healthy matched controls. SVM identified spatially distributed networks discriminating between ASD and controls. These included the limbic, frontal-striatal, fronto-temporal, fronto-parietal and cerebellar systems. SVM applied to gray matter scans correctly classified ASD individuals at a specificity of 86.0% and a sensitivity of 88.0%. Cases (68.0%) were correctly classified using white matter anatomy. The distance from the separating hyperplane (i.e., the test margin) was significantly related to current symptom severity. In contrast, VBM revealed few significant between-group differences at conventional levels of statistical stringency. We therefore suggest that SVM can detect subtle and spatially distributed differences in brain networks between adults with ASD and controls. Also, these differences provide significant predictive power for group membership, which is related to symptom severity.
Brain and Development, 2007
The temporal lobe is thought to be abnormal in autism, yet standard volumetric analyses are often unrevealing when age, sex, IQ, and head size are controlled. Quantification of temporal lobe structures were obtained in male subjects with autism and controls, where subjects with head circumference (HC) defined macrocephaly were excluded, so that volume differences were not just related to the higher prevalence of macrocephaly in autism. Various statistical methods were applied to the analysis including a classification and regression tree (CART) method, a non-parametric technique that helps define patterns of relationships that may be meaningful in distinguishing temporal lobe differences between subjects with autism and age and IQ matched controls. Subjects with autism were also compared to a separate control group with reading disorder (RD), with the prediction that the temporal lobe morphometric analysis of the reading disorder controls would be more similar to that of the autism group. The CART method yielded a high specificity in classifying autism subjects from controls based on the relationship between the volume of the left fusiform gyrus (LFG) gray and white matter, the right temporal stem (RTS) and the right inferior temporal gyrus gray matter (RITG-GM). Reading disordered individuals were more similar to subjects with autism. Simple size differences did not distinguish the groups. These findings demonstrate different relationships within temporal lobe structures that distinguish subjects with autism from controls. Results are discussed in terms of pathological connectivity within the temporal lobe as it relates to autism.
Atypical Brain Asymmetry in Autism—A Candidate for Clinically Meaningful Stratification
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2020
BACKGROUND: Autism spectrum disorder ("autism") is a highly heterogeneous neurodevelopmental condition with few effective treatments for core and associated features. To make progress we need to both identify and validate neural markers that help to parse heterogeneity to tailor therapies to specific neurobiological profiles. Atypical hemispheric lateralization is a stable feature across studies in autism, but its potential as a neural stratification marker has not been widely examined. METHODS: In order to dissect heterogeneity in lateralization in autism, we used the large EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project dataset comprising 352 individuals with autism and 233 neurotypical control subjects as well as a replication dataset from ABIDE (Autism Brain Imaging Data Exchange) (513 individuals with autism, 691 neurotypical subjects) using a promising approach that moves beyond mean group comparisons. We derived gray matter voxelwise laterality values for each subject and modeled individual deviations from the normative pattern of brain laterality across age using normative modeling. RESULTS: Individuals with autism had highly individualized patterns of both extreme right-and leftward deviations, particularly in language, motor, and visuospatial regions, associated with symptom severity. Language delay explained most variance in extreme rightward patterns, whereas core autism symptom severity explained most variance in extreme leftward patterns. Follow-up analyses showed that a stepwise pattern emerged, with individuals with autism with language delay showing more pronounced rightward deviations than individuals with autism without language delay. CONCLUSIONS: Our analyses corroborate the need for novel (dimensional) approaches to delineate the heterogeneous neuroanatomy in autism and indicate that atypical lateralization may constitute a neurophenotype for clinically meaningful stratification in autism.
Autism Is Associated With Interindividual Variations of Gray and White Matter Morphology
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2023
BACKGROUND: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group. METHODS: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6-30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions-A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants' GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles. RESULTS: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group. CONCLUSIONS: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.
European Journal of Neuroscience
Autistic spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interactions, communication and stereotyped behaviour. Recent evidence from neuroimaging supports the hypothesis that ASD deficits in adults may be related to abnormalities in a specific frontal-temporal network [Autism-specific Structural Network (ASN)]. To see whether these results extend to younger children and to better characterize these abnormalities, we applied three morphometric methods on brain grey matter (GM) of children with and without ASD. We selected 39 sMRI images of male children with ASD and 42 typically developing (TD) from the Autism Brain Imaging Data Exchange database. We used source-based morphometry (SoBM), a whole-brain multivariate approach to identify GM networks, voxel-based morphometry (VBM), a voxel-wise comparison of the local GM concentration and surface-based morphometry (SuBM) for the estimation of the cortical parameters. SoBM showed a bilateral frontal-parietal-temporal network different between groups, including the inferior-middle temporal gyrus, the inferior parietal lobule and the postcentral gyrus; VBM returned differences only in the right temporal lobe; SuBM returned a thinning in the right inferior temporal lobe thinner in ASD, a higher gyrification in the right superior parietal lobule in TD and in the middle frontal gyrus in ASD. For the first time, we investigated the brain abnormalities in children with ASD using three morphometric techniques. The results were relatively consistent between methods, stressing the role of an Autism-specific Structural Network in ASD individuals. We also make methodological speculations on the relevance of using multivariate and whole-brain neuroimaging analysis to capture ASD complexity.
Basic and Clinical Neuroscience Journal, 2022
Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD). Method: We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144). Result: The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD. Conclusion: This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.
Journal of Neuroimaging, 2015
Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI). METHODS: The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation. RESULTS: The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70. CONCLUSIONS: Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.
Examination of Local Functional Homogeneity in Autism
Increasing neuroimaging evidence suggests that autism patients exhibit abnormal brain structure and function. We used the Autism Brain Imaging Data Exchange (ABIDE) sample to analyze locally focal (∼8 mm) functional connectivity of 223 autism patients and 285 normal controls from 15 international sites using a recently developed surface-based approach. We observed enhanced local connectivity in the middle frontal cortex, left precuneus, and right superior temporal sulcus, and reduced local connectivity in the right insular cortex. The local connectivity in the right middle frontal gyrus was positively correlated with the total score of the autism diagnostic observation schedule whereas the local connectivity within the right superior temporal sulcus was positively correlated with total subscores of both the communication and the stereotyped behaviors and restricted interests of the schedule. Finally, significant interactions between age and clinical diagnosis were detected in the left precuneus. These findings replicated previous observations that used a volume-based approach and suggested possible neuropathological impairments of local information processing in the frontal, temporal, parietal, and insular cortices. Novel site-variability analysis demonstrated high reproducibility of our findings across the 15 international sites. The age-disease interaction provides a potential target region for future studies to further elucidate the neurodevelopmental mechanisms of autism.