Dynamic functional connectivity reveals abnormal variability and hyper-connected pattern in autism spectrum disorder
Abstract
Autism spectrum disorder (ASD) is a general neurodevelopmental disorder associated with altered brain connectivity. However, most connectivity analyses in ASD focus on static functional connectivity, largely neglecting brain activity dynamics that have been reported to provide deeper insight into the underlying mechanisms of brain functions. Therefore, we anticipate that the use of dynamic functional connectivity (DFC) with interaction of clustering measures could help characterize ASD severity and reveal more information. In this study, we applied the sliding-window and k-means clustering methods to perform DFC and clustering analyses in ASD and typically developing (TD) groups. Data from 62 ASD and 63 TD children were acquired from the open-access data set Autism Brain Imaging Data Exchange. Our findings revealed higher DFC variability between the posterior cingulate gyrus (PCC) and middle temporal pole (TPOmid) in subjects with ASD. The connection between the PCC and pars opercularis of inferior frontal gyrus (IFGoper) also presented greater variability in ASD, with the increase depending on ASD symptom severity. Furthermore, clustering analysis showed higher averaged dwell time and probability of transition for globally hyper-connected state in the ASD group, which could be related to connection variability between the PCC and IFGoper. Our results demonstrate that both the PCC and IFGoper play crucial roles in characterizing symptom severity and state configuration in ASD, and brain connectivity dynamics may serve as potential indicators of ASD in future studies. Autism Res 2020, 13: 230-243. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Dynamic functional connectivity (DFC) refers to functional connectivity that changes over a short time. This study found that DFC instability between the posterior cingulate gyrus and pars opercularis of inferior frontal gyrus is associated with abnormal brain pattern configurations and dysfunction of social cognitive processes in autism spectrum disorder (ASD). These findings could contribute to a deeper understanding of the neural mechanisms of ASD and help characterize ASD severity.
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Similar findings of hyper-variability of functional connections in ASD have been observed in studies focusing on the adolescent period.
“Similar findings of hyper-variability of functional connections in ASD have been observed in studies focusing on the adolescent period (119,120).”
From Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder (2021) · cites this paper as [120]
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Self-citation
- Authors
- Li, Y., Zhu, Y., Nguchu, B. A., Wang, Y., Wang, H., Qiu, B., & Wang, X.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Wang, Xiaoxiao
- ORCID
- Venue
- Autism Research
- Publisher
- Wiley
- Published
- 2019-10-15
- Volume
- 13
- Issue
- 2
- Pages
- 230-243
- ISSN
- 1939-3792 (ISSN-L)
- 1939-3806
- OpenAlex
- W2981270536
- Cited by
- 95
- References
- 83
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 19 · 2023: 18 · 2022: 21 · 2021: 8 · 2020: 2
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Autism Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81627806, 81701665, 91432301, 91859121)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- autism spectrum disorder, dynamic functional connectivity, ASD severity, posterior cingulate cortex, inferior frontal gyrus, K-means clustering, dwell time, transition probabilities, social cognition
- MeSH terms
- Autism Spectrum Disorder, Adolescent, Brain, Brain Mapping, Child, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Severity of Illness Index, Cluster Analysis
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