Scholarlink

<p>The brain is a dynamic system where complex behaviours emerge from interactions across distributed regions. Accurately linking brain function to cognition requires methods sensitive to these interactions. We introduce Feature Similarity (FS), which integrates a broad set of interpretable time-series features—such as covariance, temporal dependencies, and entropy —to move beyond traditional single-metric approaches. FS captured functional brain organization: regions within the same network showed greater similarity than those in different networks, and FS identified the principal gradient from unimodal to transmodal cortices. Compared with Pearson correlation-based functional connectivity (FC) and 46 out of 49 statistical pairwise interaction metrics (SPIs), FS demonstrated greater sensitivity to task modulation. Critically, FS revealed a task-dependent double dissociation in the Dorsal Attention Network, interacting more strongly with the Visual network during working memory but with the default mode network during long-term memory. FS thus provides a powerful tool for uncovering task-specific brain network interactions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Feature similarity, a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

查看全文
  • Xiuyi Wang,
  • Baihan Lyu,
  • Katya Krieger-Redwood,
  • Nicholas E. Souter,
  • Golia Shafiei,
  • Nan Lin,
  • Jonathan Smallwood,
  • Elizabeth Jefferies,
  • Yi Du

摘要

The brain is a dynamic system where complex behaviours emerge from interactions across distributed regions. Accurately linking brain function to cognition requires methods sensitive to these interactions. We introduce Feature Similarity (FS), which integrates a broad set of interpretable time-series features—such as covariance, temporal dependencies, and entropy —to move beyond traditional single-metric approaches. FS captured functional brain organization: regions within the same network showed greater similarity than those in different networks, and FS identified the principal gradient from unimodal to transmodal cortices. Compared with Pearson correlation-based functional connectivity (FC) and 46 out of 49 statistical pairwise interaction metrics (SPIs), FS demonstrated greater sensitivity to task modulation. Critically, FS revealed a task-dependent double dissociation in the Dorsal Attention Network, interacting more strongly with the Visual network during working memory but with the default mode network during long-term memory. FS thus provides a powerful tool for uncovering task-specific brain network interactions.