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

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. UFLARE is a fMRI method that dissociates bottom-up from top-down signals by tracking ultrafast information flow across cortical layers. Validated across brain systems and injury models, it enables studying brain communication, learning, and disease.