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<p>Functional connectivity (FC) has become central to understanding human brain dynamics and a reliable pursuit for investigating neuropsychiatric disorders. The human brain operates as a modular system, with distinct regions forming functional modules critical for neuronal processing. High-resolution modeling of these modules provides essential insights into the structural and functional basis of neural processing. However, subject heterogeneity–arising from individual variability and diverse symptom profiles–often obscures fine-grained neural patterns, limiting current methods in resolving disease-related alterations. To address this, we propose BrainBiC–a deep biclustering framework that jointly stratifies subjects and features, enabling effective navigation of the data manifold and meaningful knowledge discovery. It leverages semantic locality to preserve coherence in subgrouped neural patterns and jointly optimizes sample and attribute assignment probability distributions for novel bicluster retrieval. Comprehensive experiments on multiple neuroimaging datasets demonstrate that BrainBiC outperforms state-of-the-art methods in identifying neurologically meaningful brain connectivity substructures. The extracted connectivity signatures are highly modular, show strong associations with cognitive and behavioral variables, and reveal disruptions in functional integration within subcortical, sensorimotor, and visual circuits. These findings position BrainBiC as a robust framework for modeling disease heterogeneity and associated connectivity alterations, advancing data-driven precision psychiatry.</p>

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Semantic locality-aware biclustering for brain functional network connectivity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Md Abdur Rahaman,
  • Rogers Silva,
  • Zening Fu,
  • Armin Iraji,
  • Vince Calhoun

摘要

Functional connectivity (FC) has become central to understanding human brain dynamics and a reliable pursuit for investigating neuropsychiatric disorders. The human brain operates as a modular system, with distinct regions forming functional modules critical for neuronal processing. High-resolution modeling of these modules provides essential insights into the structural and functional basis of neural processing. However, subject heterogeneity–arising from individual variability and diverse symptom profiles–often obscures fine-grained neural patterns, limiting current methods in resolving disease-related alterations. To address this, we propose BrainBiC–a deep biclustering framework that jointly stratifies subjects and features, enabling effective navigation of the data manifold and meaningful knowledge discovery. It leverages semantic locality to preserve coherence in subgrouped neural patterns and jointly optimizes sample and attribute assignment probability distributions for novel bicluster retrieval. Comprehensive experiments on multiple neuroimaging datasets demonstrate that BrainBiC outperforms state-of-the-art methods in identifying neurologically meaningful brain connectivity substructures. The extracted connectivity signatures are highly modular, show strong associations with cognitive and behavioral variables, and reveal disruptions in functional integration within subcortical, sensorimotor, and visual circuits. These findings position BrainBiC as a robust framework for modeling disease heterogeneity and associated connectivity alterations, advancing data-driven precision psychiatry.