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A major challenge in neuroimaging is understanding the mapping ofneurophysiological dynamics onto cognitive functions. Traditionally, these mapshave been constructed by examining changes in the activity magnitude of regionsrelated to task performance. Recently, network neuroscience has producedmethods to map connectivity patterns among many regions to certain cognitivefunctions by drawing on tools from network science and graph theory. However,these two different views are rarely addressed simultaneously, largely becausefew tools exist that account for patterns between nodes while simultaneouslyconsidering activation of nodes. We address this gap by solving the problem ofcommunity detection on weighted networks with continuous (non-integer)annotations by deriving a generative probabilistic model. This model generatescommunities whose members connect densely to nodes within their own community,and whose members share similar annotation values. We demonstrate the utilityof the model in the context of neuroimaging data gathered during a motorlearning paradigm, where edges are task-based functional connectivity andannotations to each node are beta weights from a general linear model thatencoded a linear decrease in blood-oxygen-level-dependent signal with practice.Interestingly, we observe that individuals who learn at a faster rate exhibitthe greatest dissimilarity between functional connectivity and activationmagnitudes, suggesting that activation and functional connectivity are distinctdimensions of neurophysiology that track behavioral change. More generally, thetool that we develop offers an explicit, mathematically principled link betweenfunctional activation and functional connectivity, and can readily be appliedto a other similar problems in which one set of imaging data offers networkdata, and a second offers a regional attribute.

Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation