Scholarlink

<p>Deciphering the directionality of information flow in cortical circuits is essential for understanding brain dynamics, learning, and neuroplasticity after injury. However, current noninvasive methods cannot distinguish bottom-up from top-down signals across entire networks, including deep brain regions. Here, we present UltraFast Layer-Resolved Encoding (uFLARE) that combines ultrafast-fMRI with a Layer-based Connective Field (lCF) model to disentangle bottom-up from top-down signaling. Our findings reveal that lCF size, an indicator of information integration, differentiates bottom-up and top-down activity through distinct layer-specific connectivity patterns during spontaneous activity, challenging the previous suggestions that bottom-up signals are solely stimulus-driven. Bottom-up connectivity follows an inverted U-shape, peaking in layer IV, while top-down exhibits a U-shaped pattern, with peaks in layers I and VI. These profiles generalize across sensory pathways (visual, somatosensory, and motor) and reveal injury-induced network reorganization, such as LGN bypassing V1 to provide direct bottom-up input to higher visual areas.</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

UltraFast Layer-Resolved Encoding (uFLARE) functional MRI deciphers bidirectional signaling from spontaneous activity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月4日

查看全文
  • Joana Carvalho,
  • Francisca F. Fernandes,
  • Mafalda Valente,
  • Koen V. Haak,
  • Noam Shemesh

摘要

Deciphering the directionality of information flow in cortical circuits is essential for understanding brain dynamics, learning, and neuroplasticity after injury. However, current noninvasive methods cannot distinguish bottom-up from top-down signals across entire networks, including deep brain regions. Here, we present UltraFast Layer-Resolved Encoding (uFLARE) that combines ultrafast-fMRI with a Layer-based Connective Field (lCF) model to disentangle bottom-up from top-down signaling. Our findings reveal that lCF size, an indicator of information integration, differentiates bottom-up and top-down activity through distinct layer-specific connectivity patterns during spontaneous activity, challenging the previous suggestions that bottom-up signals are solely stimulus-driven. Bottom-up connectivity follows an inverted U-shape, peaking in layer IV, while top-down exhibits a U-shaped pattern, with peaks in layers I and VI. These profiles generalize across sensory pathways (visual, somatosensory, and motor) and reveal injury-induced network reorganization, such as LGN bypassing V1 to provide direct bottom-up input to higher visual areas.