Figure 1. Activity flow mapping over resting-state FC networks allows prediction of held-out task activations.

A) We developed a prediction-based approach that links resting-state FC to task activations to assess the relevance of FC to cognitive task activations. The prediction of a single region’s activation amplitude for a single task is depicted. Importantly, the to-be-predicted region’s activation amplitude is held out from the prediction calculation. B) This approach was validated using a simple computational model of large-scale neural activity. Whole-brain predicted-to-actual Pearson correlations (r-values) for distinct model parameters are shown. The success of activity flow mapping depended on the relative degree of local (within-region/recurrent) versus distributed (across-region) processing. This demonstrates that the success of activity flow mapping with empirical fMRI data would be non-trivial. C) Three structural connectivity graph communities (blocks along diagonal) were created, with the first split into two communities via synaptic strength modifications. D) Resting-state FC (Pearson correlation) was computed based on simulated time series using the computational model, revealing a strong correspondence with the underlying synaptic strengths. Note that other factors not modeled here (e.g., concentrations of neuromodulatory neurotransmitters) likely also influence resting-state FC. The global coupling and local processing parameters were set to 1.0 for this example. E) Simulated task-evoked activations were produced by stimulating groups of 5 nearby units in 6 separate “tasks”. The activity flow mapping procedure produced above-chance recovery (mean across-task Pearson correlation r=0.56, t(298)=11.7, p<0.00001), of the actual activations using the resting-state FC matrix shown in panel D.