Reduced causal connectivity in multiple resting-state networks in autism spectrum disorder detected by convergent cross mapping
- 1Department of Child Healthcare, Xi’an Children’s Hospital
- 2Department of Child Healthcare, Xi’an Maternity and Child Healthcare Hospital
- 3Department of Integrated traditional Chinese and western medicine, Xi’an Children’s Hospital
Abstract
Background: This study investigates alterations in causal connectivity within resting-state brain functional networks in patients with Autism Spectrum Disorder (ASD) using the Convergent Cross Mapping (CCM) method.
Methods: Data were obtained from the Autism Brain Imaging Data Exchange I (ABIDE I) public database. To minimize variability, resting-state functional magnetic resonance imaging (rs-fMRI) data were restricted to the NYU site, comprising 52 ASD patients and 52 matched typically developing controls. Based on the AAL template, time series were extracted from five resting-state networks: attention network, default mode network, sensorimotor network, visual network, and subcortical network. The CCM method was employed to analyze causal connectivity among these brain regions. Additionally, a support vector machine (SVM) classifier was utilized to assess the discriminative potential of the identified causal connectivity patterns.
Results: Significant differences in causal connections were observed, particularly in the default mode network, which exhibited the most pronounced alterations. Overall, ASD patients showed a general decrease in causal connectivity across networks, accompanied by some localized increases. The SVM classifier accurately differentiated individuals with ASD from controls based on the causal connectivity patterns derived from resting-state brain networks.
Conclusion: These findings elucidate dynamical network mechanisms underlying ASD pathophysiology and highlight candidate neurobiological markers with potential translational relevance.
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BibTeX
@article{zhang2026reduced,
title = {{Reduced causal connectivity in multiple resting-state networks in autism spectrum disorder detected by convergent cross mapping}},
author = {Haiqing Zhang and Li He and Duan Wang and Yingyu Yan and Yuying He and Yi Zhang and Yanping Shi and Yanni Chen},
journal = {Frontiers in Psychiatry},
year = {2026},
month = oct,
doi = {10.3389/fpsyt.2026.1828923},
url = {https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1828923/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Reduced causal connectivity in multiple resting-state networks in autism spectrum disorder detected by convergent cross mapping
AU - Haiqing Zhang
AU - Li He
AU - Duan Wang
AU - Yingyu Yan
AU - Yuying He
AU - Yi Zhang
AU - Yanping Shi
AU - Yanni Chen
PY - 2026
DA - 2026/10/13
JO - Frontiers in Psychiatry
DO - 10.3389/fpsyt.2026.1828923
UR - https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1828923/full
AB - Background: This study investigates alterations in causal connectivity within resting-state brain functional networks in patients with Autism Spectrum Disorder (ASD) using the Convergent Cross Mapping (CCM) method. Methods: Data were obtained from the Autism Brain Imaging Data Exchange I (ABIDE I) public database. To minimize variability, resting-state functional magnetic resonance imaging (rs-fMRI) data were restricted to the NYU site, comprising 52 ASD patients and 52 matched typically developing controls. Based on the AAL template, time series were extracted from five resting-state networks: attention network, default mode network, sensorimotor network, visual network, and subcortical network. The CCM method was employed to analyze causal connectivity among these brain regions. Additionally, a support vector machine (SVM) classifier was utilized to assess the discriminative potential of the identified causal connectivity patterns. Results: Significant differences in causal connections were observed, particularly in the default mode network, which exhibited the most pronounced alterations. Overall, ASD patients showed a general decrease in causal connectivity across networks, accompanied by some localized increases. The SVM classifier accurately differentiated individuals with ASD from controls based on the causal connectivity patterns derived from resting-state brain networks. Conclusion: These findings elucidate dynamical network mechanisms underlying ASD pathophysiology and highlight candidate neurobiological markers with potential translational relevance.
SN - 1664-0640
ER -