Decoding directionality of information in cortical networks using layer-based connective field model

Motivation: To disentangle feedback and feedforward signals in cortical circuits. Goal(s): To unravel the intricate neural connections within cortical layers. Approach: We implemented a layer connective field (lCF) model and applied it to ultrafast RS data and RS dfMRI data. Results: 1.Intracortical lCF shows two lCF size profiles: feedforward with inverse U shape with the larger lCF sizes at layer 5 and feedback with U shape and larger CF sizes at superficial and deeper layers. 2.In the absence of visual input the functional connectivity reflects visuotopic organization. 3.lCF estimates obtained from dfMRI(ADC) are more layer specific than the ones estimated from BOLD. Impact: This study showcases the ability of high spatio-temporal resolution MRI techniques (ultrafast BOLD and dfMRI) when coupled with biologically grounded connectivity models (lCF) to unveil the intricacies of information directionality within topographically organized cortices.