Research Paper Volume 16, Issue 11 pp 10004—10015
Altered intra- and inter-network connectivity in autism spectrum disorder
- 1 School of Zhang Jian, Nantong University, Nantong, China
- 2 College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
- 3 Department of Radiology, Rugao Jian’an Hospital, Nantong, China
- 4 Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
- 5 Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
Received: December 1, 2023 Accepted: May 3, 2024 Published: June 10, 2024
https://doi.org/10.18632/aging.205913How to Cite
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.