scholarly journals Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 32
Author(s):  
Lluis Borràs-Ferrís ◽  
Úrsula Pérez-Ramírez ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Statistical Dependence ◽  
Middle Temporal Gyrus ◽  
Middle Temporal ◽  
Brain Functions

Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals Non-replication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies

2019 ◽  
Author(s):  
Ye He ◽  
Lisa Byrge ◽  
Daniel P Kennedy
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Extensive Literature ◽  
Clinical Biomarkers ◽  
Cohort Differences

AbstractA rapidly growing number of studies on autism spectrum disorder (ASD) have used resting-state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there is therefore a pressing need to determine the primary factors influencing replicability. Here, we used resting-state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group-average functional connectomes and group-level differences (ASD vs. control) across 33 denoising pipelines and four independently-acquired datasets. The group-average connectomes were highly consistent across pipelines (r = 0.92±0.06) and sites (r = 0.88±0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76±0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07±0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across-site similarity remained low even when considering the data at a large-scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.

scholarly journals Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

2015 ◽  
Vol 72 (8) ◽  
pp. 767 ◽  
Author(s):  
Leonardo Cerliani ◽  
Maarten Mennes ◽  
Rajat M. Thomas ◽  
Adriana Di Martino ◽  
Marc Thioux ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Networks

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

scholarly journals Hypo- and hyper-connectivity in default mode network related to social impairment in tweens with autism spectrum disorder

2016 ◽  
Author(s):  
Wilma Matthysen ◽  
Daniele Marinazzo ◽  
Roma Siugzdaite
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Social Communication ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Connectivity Pattern ◽  
Resting State Networks ◽  
Default Mode

Background. Autism spectrum disorder is a neurodevelopmental disorder, marked by impairment in social communication and restricted, repetitive patterns of behavior, interests, or activities. Accumulating data suggests that alterations in functional connectivity might contribute to these deficits. Whereas functional connectivity in resting state fMRI is expressed by several resting-state networks, for this study we examined several of them, but our particular interest was in the default mode network (DMN), given its age dependent alteration of functional connectivity and its relation to social communication. Methods. Since the studies investigating young children (6-8 years) with autism have found hypo-connectivity in DMN and studies on adolescents (12-16 years old) with autism have found hyper-connectivity in the DMN, we were interested in connectivity pattern during the age of 8 to 12, so we investigated the role of altered intrinsic connectivity in 16 children (mean age 9.75 ±1.6 years) with autism spectrum disorder compared to 16 typically developing controls in the DMN and other resting-state networks. Results. Results show that, compared to controls, the group with autism spectrum disorder showed signs of both hypo- and hyper-connectivity in different regions of the resting-state networks related to social communication. Conclusion. That suggests that transition period from childhood to adolescence carries the complexity of functional connectivity from both age groups. Regions that showed differences in functional connectivity were discussed in relation to social communication difficulties.

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