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Temporal and spatial variability of large-scale dynamic brain networks in ASD.

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Title: Temporal and spatial variability of large-scale dynamic brain networks in ASD.
Authors: Yin, Shunjie, Sun, Shan, Li, Jia, Feng, Yu, Zheng, Liqin, Chen, Kai, Ma, Jiwang, Xu, Fen, Yao, Dezhong, Xu, Peng, Liang, X. San, Zhang, Tao
Source: European Child & Adolescent Psychiatry. Aug2025, Vol. 34 Issue 8, p2555-2569. 15p.
Subjects: Diagnosis of autism, Research funding, Functional connectivity, T-test (Statistics), Amygdaloid body, Magnetic resonance imaging, Chi-squared test, Thalamus, Large-scale brain networks, Case-control method, Asperger's syndrome, Intelligence tests, Comparative studies
Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant impairments in social-cognitive functioning. Prior studies have identified abnormal brain functional connectivity (FC) patterns in individuals with ASD, which are associated with core symptoms and serve as potential biomarkers for diagnosis. However, the patterns of temporal and spatial variability in dynamic functional connectivity networks (dFCNs) in ASD and their relationship with ASD behaviors remain underexplored. This study uses fuzzy entropy to analyze the temporal variability and spatial variability of dFCNs, aiming to reveal distinctive FC patterns in ASD and identify new biomarkers. We conducted a comparative analysis between ASD and healthy controls (HCs), examining the association with clinical symptoms. Our findings indicate increased FC temporal variability in sensorimotor, subcortical, and cerebellar networks in ASD compared to HCs. Additionally, increased spatial variability was observed primarily in visual, limbic, subcortical, and cerebellar networks. Notably, these variability patterns correlated with symptom severity in ASD. Utilizing these spatiotemporal variability features, we developed multi-site classification models that achieved high accuracy (81.25%) in identifying ASD. These results provide novel insights into the neural mechanisms and clinical characteristics of ASD, suggesting that integrated spatiotemporal dFCN features may enhance diagnostic accuracy. [ABSTRACT FROM AUTHOR]
Copyright of European Child & Adolescent Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Temporal and spatial variability of large-scale dynamic brain networks in ASD.
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  Data: <searchLink fieldCode="AR" term="%22Yin%2C+Shunjie%22">Yin, Shunjie</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Shan%22">Sun, Shan</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Jia%22">Li, Jia</searchLink><br /><searchLink fieldCode="AR" term="%22Feng%2C+Yu%22">Feng, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Liqin%22">Zheng, Liqin</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Kai%22">Chen, Kai</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Jiwang%22">Ma, Jiwang</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Fen%22">Xu, Fen</searchLink><br /><searchLink fieldCode="AR" term="%22Yao%2C+Dezhong%22">Yao, Dezhong</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Peng%22">Xu, Peng</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+X%2E+San%22">Liang, X. San</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tao%22">Zhang, Tao</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22European+Child+%26+Adolescent+Psychiatry%22">European Child & Adolescent Psychiatry</searchLink>. Aug2025, Vol. 34 Issue 8, p2555-2569. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Diagnosis+of+autism%22">Diagnosis of autism</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+connectivity%22">Functional connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22T-test+%28Statistics%29%22">T-test (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Amygdaloid+body%22">Amygdaloid body</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Thalamus%22">Thalamus</searchLink><br /><searchLink fieldCode="DE" term="%22Large-scale+brain+networks%22">Large-scale brain networks</searchLink><br /><searchLink fieldCode="DE" term="%22Case-control+method%22">Case-control method</searchLink><br /><searchLink fieldCode="DE" term="%22Asperger's+syndrome%22">Asperger's syndrome</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence+tests%22">Intelligence tests</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink>
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  Data: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant impairments in social-cognitive functioning. Prior studies have identified abnormal brain functional connectivity (FC) patterns in individuals with ASD, which are associated with core symptoms and serve as potential biomarkers for diagnosis. However, the patterns of temporal and spatial variability in dynamic functional connectivity networks (dFCNs) in ASD and their relationship with ASD behaviors remain underexplored. This study uses fuzzy entropy to analyze the temporal variability and spatial variability of dFCNs, aiming to reveal distinctive FC patterns in ASD and identify new biomarkers. We conducted a comparative analysis between ASD and healthy controls (HCs), examining the association with clinical symptoms. Our findings indicate increased FC temporal variability in sensorimotor, subcortical, and cerebellar networks in ASD compared to HCs. Additionally, increased spatial variability was observed primarily in visual, limbic, subcortical, and cerebellar networks. Notably, these variability patterns correlated with symptom severity in ASD. Utilizing these spatiotemporal variability features, we developed multi-site classification models that achieved high accuracy (81.25%) in identifying ASD. These results provide novel insights into the neural mechanisms and clinical characteristics of ASD, suggesting that integrated spatiotemporal dFCN features may enhance diagnostic accuracy. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Child & Adolescent Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00787-025-02679-9
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        Text: English
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      – SubjectFull: Research funding
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      – SubjectFull: Functional connectivity
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      – SubjectFull: Thalamus
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