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Research Paper|Volume 16, Issue 11|pp 10004—10015

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

Rui Zhou1,2, Chenhao Sun3, Mingxiang Sun4, Yudi Ruan2, Weikai Li2,5, Xin Gao4
  • 1School of Zhang Jian, Nantong University, Nantong, China
  • 2College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
  • 3Department of Radiology, Rugao Jian’an Hospital, Nantong, China
  • 4Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
  • 5Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
* Equal contribution
Received: December 1, 2023Accepted: May 3, 2024Published: June 10, 2024
https://doi.org/10.18632/aging.205913

Copyright: © 2024 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Objective: A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored.

Methods: We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC.

Results: Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity.

Conclusions: ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.