Distributed network flows generate localized category selectivity in human visual cortex
Fig 2
Activity flow procedure to map task activity in held out brain regions.
(A) Activity flow mapping toy diagram and formula. Task activity for held-out region j (purple) is mapped as the sum of task activity of all other cortical regions, i (coral) (n = total number of regions), weighted by their connectivity estimates with j (gray). (B) Activity flow simulation results (reproduced with permission from [27]) demonstrating that activity flow mapping is most successful when distributed processing mechanisms are high and localized processing mechanisms are low. (C) Example of activity flow mapping with empirical data (steps 1–6) (reproduced with permission from [28]). For a given target region j, estimates of intrinsic (e.g., resting-state) connectivity between j and all other source regions (step 1) are multiplied by all other regions’ actual task activations (step 2). This yields an activity flows map quantifying the contribution of all other regions’ activity flow upon the held-out region, j, for the task of interest (step 3). These are summed to equal the mapped task activity of j (step 4). This procedure is iterated over all regions to generate activity-flow-mapped task activations across the whole cortex (step 5). This is compared with the actual whole-cortex map of task activations via Pearson’s r, MAE, and R2 to estimate accuracy (step 6). Importantly, this approach is flexible to different estimates of connectivity. Source vertices not included in the analyses (10 mm from the target region j; see Methods) are depicted in green.