Distributed network flows generate localized category selectivity in human visual cortex
Fig 3
Mapped activations across all HCP conditions yielded response profiles for four functional complexes of interest.
(A) Causally confounding graph patterns in standard FC estimation methods (adapted with permission from [127]). CombinedFC incorporates both bivariate and multiple regression measures such that confounders, chains, and colliders are accounted for. (B) The cross-participant (n = 176) average resting-state connectivity matrix (estimated via combinedFC) of 360 MMP regions [56], ordered along each axis per the Cole Anticevic brain-wide network partition (CAB-NP [25]; color-coded on each axis to match panel C). This was the functional network organization utilized in the present study for activity flow mapping. Note that our implementation of combinedFC used multiple regression as the final step, and therefore FC estimates were given by beta coefficients (see Methods). (C) Cortical schematic of the CAB-NP and its 12 functional networks from Ji et al. [25], reproduced with permission. (D) Response profiles (across all 24 HCP conditions) of four complexes of interest to the present study (indicated along the y-axis; note that “r” stands for right hemisphere); mapped versus actual (left and right respectively; mean across participants depicted in each panel). Black boxes highlight the n-back conditions that maintained visual semantic category embeddings germane to a given functional complex (e.g., 0-back bodies and 2-back bodies for the right EBA and right FBA). Activity-flow-mapped response profiles were highly accurate, suggesting that mapped activation patterns of the functional complexes of interest were reliable across multiple cognitive domains.