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.
Carvalho et al. (Mon,) studied this question.