A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

2021 ◽  
pp. 155005942110262
Author(s):  
Bo Chen
Keyword(s):  
Autism Spectrum Disorder ◽  
Activity Patterns ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Eigenvector Centrality ◽  
Information Process ◽  
Whole Brain ◽  
Functional Connections ◽  
Cortical Regions ◽  
Prefrontal Lobe

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

scholarly journals Neural correlates of polygenic risk score for autism spectrum disorders in general population

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Budhachandra Khundrakpam ◽  
Uku Vainik ◽  
Jinnan Gong ◽  
Noor Al-Sharif ◽  
Neha Bhutani ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
White Matter ◽  
General Population ◽  
Cortical Thickness ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Cortical Regions ◽  
White Matter Connectivity

Abstract Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.

Friendship characteristics and activity patterns of adolescents with an autism spectrum disorder

Autism ◽  
2011 ◽  
Vol 17 (4) ◽  
pp. 481-500 ◽  
Author(s):  
Melissa H. Kuo ◽  
Gael I. Orsmond ◽  
Ellen S. Cohn ◽  
Wendy J. Coster
Keyword(s):  
Autism Spectrum Disorder ◽  
Activity Patterns ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

2018 ◽  
Author(s):  
Bun Yamagata ◽  
Takashi Itahashi ◽  
Junya Fujino ◽  
Haruhisa Ohta ◽  
Motoaki Nakamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Connectivity Pattern ◽  
Learning Approach ◽  
Functional Connections ◽  
Communication Impairment ◽  
Machine Learning Approach

AbstractEndophenotype refers to a measurable and heritable component between genetics and diagnosis and exists in both individuals with a diagnosis and their unaffected siblings. We aimed to identify a pattern of endophenotype consisted of multiple connections. We enrolled adult male individuals with autism spectrum disorder (ASD) endophenotype (i.e., individuals with ASD and their unaffected siblings) and individuals without ASD endophenotype (i.e., pairs of typical development (TD) siblings) and utilized a machine learning approach to classify people with and without endophenotypes, based on resting-state functional connections (FCs). A sparse logistic regression successfully classified people as to the endophenotype (area under the curve=0.78, classification accuracy=75%), suggesting the existence of endophenotype pattern. A binomial test identified that nine FCs were consistently selected as inputs for the classifier. The least absolute shrinkage and selection operator with these nine FCs predicted severity of communication impairment among individuals with ASD (r=0.68, p=0.021). In addition, two of the nine FCs were statistically significantly correlated with the severity of communication impairment (r=0.81, p=0.0026 and r=-0.60, p=0.049). The current findings suggest that an ASD endophenotype pattern exists in FCs with a multivariate manner and is associated with clinical ASD phenotype.

scholarly journals Physical Literacy for Children Labeled With Autism Spectrum Disorder: Mothers’ Experiences of Ableism, Exclusion, and Trauma

2021 ◽  
pp. 1-21
Author(s):  
Kyle Pushkarenko ◽  
Janice Causgrove Dunn ◽  
Donna L. Goodwin
Keyword(s):  
Physical Activity ◽  
Autism Spectrum Disorder ◽  
Activity Patterns ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Physical Literacy ◽  
Research Attention ◽  
Semistructured Interviews ◽  
Interview Guide ◽  
Physical Activity Programs

Countering the declining physical activity patterns of children labeled with autism spectrum disorder (ASD) has gained considerable research attention given its impact on health and quality of life. The purpose of this study was to explore how parents of children labeled with ASD understand the concept of physical literacy, based on their children’s participation in community-based physical activity programs. Using interpretive phenomenological analysis, six mothers of children labeled with ASD participated in one-on-one semistructured interviews. The conceptual framework of ecological systems theory supported the rationale for the study purpose, provided structure for the interview guide, and offered a reflexive context for interpretation. Four themes were generated from the thematic analysis: From embodied movement to normative skill expectations, Be flexible, not rigid, Systematic exclusion, and Valuable? . . . Absolutely! Despite experiences of marginalization, exclusion, and trauma within physical activity programs, mothers valued physical literacy development for their children given the positive outcomes of increasing family connections, engagement with peers, and enhanced wellness.

scholarly journals Building Functional Connectivity Neuromarkers of Behavioral Self-Regulation in Children with and without Autism Spectrum Disorder

2019 ◽  
Author(s):  
Christiane S. Rohr ◽  
Shanty Kamal ◽  
Signe Bray
Keyword(s):  
Autism Spectrum Disorder ◽  
Cross Validation ◽  
Functional Neuroimaging ◽  
Individualized Medicine ◽  
Self Regulation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Behavioral Regulation ◽  
Whole Brain ◽  
Increased Risk

ABSTRACTChildren with Autism Spectrum Disorder (ASD) are known to struggle with behavioral self-regulation, which associates with greater daily-life challenges and an increased risk for psychiatric comorbidities. Despite these negative outcomes, little is known about the neural expression of behavioral regulation in children with and without ASD. Here, we examined whole-brain linear associations between brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM), a data-driven protocol for developing predictive models of brain–behavior relationships from data, assessing ‘neuromarkers’ using cross-validation. Using data from two sites of the ABIDE II dataset comprising 276 children with and without ASD (8-13 years), we identified functional brain networks whose FC predicted individual differences in two, of three, behavioral regulation subdomains. These distributed network models predicted novel individuals’ inhibition and shifting from resting-state FC data both in a leave-one-out, as well as split halves, cross-validation. We observed commonalities and differences in the functional networks associating with these subdomains, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN. Our findings present a substantial addition to our knowledge on the neural expressions of inhibition and shifting across the spectrum of children with and without ASD, demonstrating the utility of this trans-diagnostic modelling approach. Given the numerous cognitive and behavioral issues that can be quantified dimensionally in neurodevelopmental disorders, further refinement of whole-brain neuromarker techniques may thus pave a way for functional neuroimaging to meaningfully contribute to individualized medicine.

scholarly journals A network clustering based feature selection strategy for classifying autism spectrum disorder

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Lingkai Tang ◽  
Sakib Mostafa ◽  
Bo Liao ◽  
Fang-Xiang Wu
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Selection Strategy ◽  
Functional Networks ◽  
Network Clustering ◽  
Whole Brain ◽  
Default Mode ◽  
Brain Functional Networks

Abstract Background Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. Methods In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. Results The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. Conclusion It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

scholarly journals Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder

2018 ◽  
Author(s):  
Vatika Harlalka ◽  
Raju S. Bapi ◽  
P.K. Vinod ◽  
Dipanjan Roy
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Symptom Severity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Modular Organization ◽  
Strong Positive Correlation ◽  
Connection Strength ◽  
Whole Brain

AbstractResting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the age, disease phenotype and their interaction in the whole brain transient connectivity patterns. Contrasting with most extant literature in the present study, we investigated precisely how age and disease phenotypes factors into dynamic changes in functional connectivity of TD and ASD using resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7–11 years) and adolescents (12–17 years), and adults (18+) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures: flexibility, cohesion strength and disjointness were explored for each subject to characterize the differences between ASD and TD.In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hypervariability relates to the symptom severity in ASD. We found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with the flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing the high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.

scholarly journals The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

2019 ◽  
Author(s):  
Amirali Kazeminejad ◽  
Roberto C Sotero
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Networks ◽  
Model Performance ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Network Graph ◽  
Functional Connections ◽  
Functional Brain Networks

AbstractIn recent years, there has been a significant growth in the number of applications of machine learning (ML) techniques to the study and identification of neurological disorders. These methods rely heavily on what features are made available to the ML algorithm. Features such as graph theoretical metrics of resting-state fMRI-based brain networks have proven useful. However, the computation of functional brain networks relies on making an arbitrary choice about whether the obtained anti-correlations, representing the strengths of functional connections in the brain, should be discarded or not. In this study, we examine how this choice affects the performance of a support vector machine (SVM) model for classifying autism spectrum disorder. We extracted graph theoretical features using three different pipelines for constructing the functional network graph. These pipelines primarily used positive weights, negative weights (anti-correlations) and only the absolute value of weights of the correlation matrix derived from fMRI time-series. Our results suggest that in the presence of Global Signal Regression (GSR) the features extracted from anti-correlations play a major role in improving model performance. However, this does not undermine the importance of features from other pipelines.

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