Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis

Do it the transformer way: A comprehensive review of brain and vision transformers for autism spectrum disorder diagnosis and classification

Within the light of the DSM-5, the current study examined (1) how many and which children with a DSM-IV classification of autism spectrum disorder (ASD) fulfill the DSM-5 symptom-criteria, and (2) whether children who did and did not meet DSM-5 symptom-criteria and children with social anxiety disorder (SAD) can be differentiated from each other based on ASD symptomatology. In total, 90 referred children with a DSM-IV classification of high-functioning ASD, and 21 referred children with SAD participated (age range 7–17 years). ASD-symptoms were examined with the Autism Diagnostic Interview-Revised and the Children’s Social Behavior Questionnaire. It was found that 30 % of the ASD sample did not meet DSM-5 symptom-criteria for ASD, mainly because they failed to meet the DSM-5 criteria of the repetitive domain. Children with ASD who did and did not meet DSM-5 symptom-criteria differed on the repetitive domain, while children with ASD (according to DSM-IV and DSM-5 symptom criteria) had higher scores on the social-communication domain than children with SAD. Findings suggest a continuum of ASD-symptoms in the DSM-5 for children with SAD, social communication disorder and ASD. More research is needed to examine how these three disorders differ with respect to their etiology, neuropsychological profiles and clinical characteristics.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012

A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity

The resting-state functional magnetic resonance imaging (rs-fMRI) faithfully reflects the brain activities and thus provides a promising tool for autism spectrum disorder (ASD) classification. Up to now, graph convolutional networks (GCNs) have been successfully applied in rs-fMRI based ASD classification. However, most of these methods were developed based on functional connectivities (FCs) that only reflect low-level correlation between brain regions, without integrating both high-level discriminative knowledge and phenotypic information into classification. Besides, they suffered from the overfitting problem caused by insufficient training samples. To this end, we propose a novel contrastive multi-view composite GCN (CMV-CGCN) for ASD classification using both FCs and HOFCs. Specifically, a pair of graphs are constructed based on the FC and HOFC features of the subjects, respectively, and they share the phenotypic information in the graph edges. A novel contrastive multi-view learning method is proposed based on the consistent representation of both views. A contribution learning mechanism is further incorporated, encouraging the FC and HOFC features of different subjects to have various contribution in the contrastive multi-view learning. The proposed CMV-CGCN is evaluated on 613 subjects (including 286 ASD patients and 327 NCs) from the Autism Brain Imaging Data Exchange (ABIDE). We demonstrate the performance of the method for ASD classification, which yields an accuracy of 75.20% and an area under the curve (AUC) of 0.7338. Experimental results show that our proposed method outperforms state-of-the-art methods on the ABIDE database.

Disparities exist in the recognition of autism spectrum disorder (ASD) and intellectual disability (ID) in racial/ethnic minorities in the United States. This study examined whether rurality, race/ethnicity, and low resource availability are associated with disparities in primary educational classifications of ASD and ID in North Carolina (NC). Descriptive maps were created. Multilevel logistic regression models examined two separate outcomes (mild ID vs. ASD; moderate/severe ID vs. ASD). For the interaction term included in the model (race/ethnicity and residence), predicted probabilities were estimated and plotted. The effects of other covariates were also estimated. Rural counties had fewer students with ASD and a greater number of students with ID compared to urban counties. The majority of students with ASD were non-Hispanic Whites, while the majority of students with ID were non-Hispanic Blacks. Compared to non-Hispanic White students, non-Hispanic Black students were overrepresented in the ID classification and underrepresented in the ASD classification across urban and rural areas. Indicators of low resource availability were also associated with higher probabilities of ID vs. ASD classification. Differences in primary educational classification based on urban-rural divide, race/ethnicity, and resource availability are important to understand as they may point to disparities that could have significant policy and service implications. Because disparities manifest through complex interactions between environmental, socioeconomic and system-level factors, reduction in these disparities will require broader approaches that address structural determinants. Future research should utilize disparity frameworks to understand differences in primary educational classifications of ASD and ID in the context of race/ethnicity and rurality. LAY SUMMARY: Rural counties in North Carolina had fewer students with ASD and a greater number of students with ID compared to urban counties. Compared to non-Hispanic White students, non-Hispanic Black students were over-represented in the ID educational classification and underrepresented in the ASD classification. Differences in classification of ASD and ID based on urban-rural divide, race/ethnicity, and resource availability may point to disparities that could have significant policy and service implications. Autism Res 2021, 14: 1046-1060. © 2021 International Society for Autism Research, Wiley Periodicals LLC.

The Highs and Lows of Counting Autism

ObjectiveThe etiology of autism spectrum disorder (ASD) remains unclear, due to genetic heterogeneity and heterogeneity in symptoms across individuals. This study compares ASD symptomatology between monogenetic syndromes with a high ASD prevalence, in order to reveal syndrome specific vulnerabilities and to clarify how genetic variations affect ASD symptom presentation.MethodsWe assessed ASD symptom severity in children and young adults (aged 0-28 years) with Fragile X Syndrome (FXS, n = 60), Angelman Syndrome (AS, n = 91), Neurofibromatosis Type 1 (NF1, n = 279) and Tuberous Sclerosis Complex (TSC, n = 110), using the Autism Diagnostic Observation Schedule and Social Responsiveness Scale. Assessments were part of routine clinical care at the ENCORE expertise center in Rotterdam, the Netherlands. First, we compared the syndrome groups on the ASD classification prevalence and ASD severity scores. Then, we compared individuals in our syndrome groups with an ASD classification to a non-syndromic ASD group (nsASD, n = 335), on both ASD severity scores and ASD symptom profiles. Severity scores were compared using MANCOVAs with IQ and gender as covariates.ResultsOverall, ASD severity scores were highest for the FXS group and lowest for the NF1 group. Compared to nsASD, individuals with an ASD classification in our syndrome groups showed less problems on the instruments' social domains. We found a relative strength in the AS group on the social cognition, communication and motivation domains and a relative challenge in creativity; a relative strength of the NF1 group on the restricted interests and repetitive behavior scale; and a relative challenge in the FXS and TSC groups on the restricted interests and repetitive behavior domain.ConclusionThe syndrome-specific strengths and challenges we found provide a frame of reference to evaluate an individual's symptoms relative to the larger syndromic population and to guide treatment decisions. Our findings support the need for personalized care and a dimensional, symptom-based diagnostic approach, in contrast to a dichotomous ASD diagnosis used as a prerequisite for access to healthcare services. Similarities in ASD symptom profiles between AS and FXS, and between NF1 and TSC may reflect similarities in their neurobiology. Deep phenotyping studies are required to link neurobiological markers to ASD symptomatology.

An optimized Kernel Extreme Learning Machine for the classification of the autism spectrum disorder by using gaze tracking images

Autism spectrum disorder (ASD) is a neurodevelopmental condition for which early identification and intervention is crucial for optimum prognosis. Our previous work showed gut Immunoglobulin A (IgA) to be significantly elevated in the gut lumen of children with ASD compared to typically developing (TD) children. Gut microbiota variations have been reported in ASD, yet not much is known about virulence factor-related gut microbiota (VFGM) genes. Upon determining the VFGM genes distinguishing ASD from TD, this study is the first to utilize VFGM genes and IgA levels for a machine learning-based classification of ASD. Sequence comparisons were performed of metagenome datasets from children with ASD (n = 43) and TD children (n = 31) against genes in the virulence factor database. VFGM gene composition was associated with ASD phenotype. VFGM gene diversity was higher in children with ASD and positively correlated with IgA content. As Group B streptococcus (GBS) genes account for the highest proportion of 24 different VFGMs between ASD and TD and positively correlate with gut IgA, GBS genes were used in combination with IgA and VFGMs diversity to distinguish ASD from TD. Given that VFGM diversity, increases in IgA, and ASD-enriched VFGM genes were independent of sex and gastrointestinal symptoms, a classification method utilizing them will not pertain only to a specific subgroup of ASD. By introducing the classification value of VFGM genes and considering that VFs can be isolated in pregnant women and newborns, these findings provide a novel machine learning-based early risk identification method for ASD.

P.7.a.003 - Subcortical brain volume development over age in autism spectrum disorder: results from the ENIGMA-ASD working group

Autism is a neurodevelopmental disorder characterized by deficits in social communication and interaction, impaired language development, and stereotyped repetitive behaviors. The broader category of autism spectrum disorders (ASDs) includes individuals with subsets of these symptoms ([Geschwind and

Classification of autism spectrum disorder based on fluctuation entropy of spontaneous hemodynamic fluctuations

High-functioning autism spectrum disorder: phenotypic subgroups, diagnostic instruments, and predictors of behavioural and emotional functioning