Semantic locality-aware biclustering for brain functional network connectivity
8.5
来源：
Nature
关键字：
deep learning brain science
发布时间：
2025-09-30 07:46
摘要：
BrainBiC is a novel deep biclustering framework that enhances the analysis of brain functional connectivity, particularly in schizophrenia. It addresses the challenges of subject heterogeneity by jointly stratifying subjects and features, enabling the discovery of meaningful connectivity patterns. The framework demonstrates superior performance in identifying distinct brain connectivity subgraphs that correlate with cognitive functions and clinical symptoms, thus advancing precision psychiatry. Extensive experiments across multiple neuroimaging datasets validate its effectiveness in capturing the complexities of brain dynamics.
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0.5
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1.5
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1.5
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2.5
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关键证据
BrainBiC outperforms state-of-the-art methods in identifying neurologically meaningful brain connectivity substructures.
The extracted connectivity signatures show strong associations with cognitive and behavioral variables.
BrainBiC facilitates data-driven disease subtyping, capturing the broad spectrum of heterogeneity within schizophrenia.
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AI评分总结
BrainBiC is a novel deep biclustering framework that enhances the analysis of brain functional connectivity, particularly in schizophrenia. It addresses the challenges of subject heterogeneity by jointly stratifying subjects and features, enabling the discovery of meaningful connectivity patterns. The framework demonstrates superior performance in identifying distinct brain connectivity subgraphs that correlate with cognitive functions and clinical symptoms, thus advancing precision psychiatry. Extensive experiments across multiple neuroimaging datasets validate its effectiveness in capturing the complexities of brain dynamics.