Altered intra- and inter-network connectivity in autism spectrum disorder

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Computational Neuroscience

Background:

• Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social interaction impairments.
• Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies in ASD have shown mixed results regarding brain connectivity patterns.
• Specific patterns of intra- and inter-network connectivity in ASD remain underexplored.

Purpose of the Study:

• To investigate specific patterns of brain network connectivity within and between intrinsic networks in individuals with ASD.
• To identify potential neuroimaging biomarkers for differentiating ASD from healthy controls (HC).

Main Methods:

• Utilized a subset of high-quality rs-fMRI data from 45 individuals with ASD and 47 HCs from the ABIDE dataset.
• Analyzed pre-processed rs-fMRI signals partitioned into 90 regions of interest, focusing on eight intrinsic connectivity networks.
• Performed intra- and inter-network connectivity analyses and employed support vector machine (SVM) for classification.

Main Results:

• ASD exhibited decreased intra-network connectivity in the default mode and dorsal attention networks.
• Increased connectivity was observed between the limbic and subcortical networks, while decreased connectivity was found between the default mode and limbic networks.
• Multivariate pattern analysis using altered connectivity patterns achieved 71.74% accuracy, 70.21% specificity, and 75.56% sensitivity in classifying ASD from HC at 10% sparsity.

Conclusions:

• ASD is associated with characteristic reorganization of brain functional networks.
• These findings offer new insights into the functional connectome dysfunction underlying ASD.
• Altered intra- and inter-network connectivity patterns may serve as potential biomarkers for ASD.