Network co-occurrence modeling: Workflow.

<p>The three steps of the proposed analysis approach are outlined. (<i>1</i>) In two large neuroimaging datasets (HCP with n = 500, ARCHI with n = 81), the spatial patterns of neural activity dominant across time series were discovered by data-driven decomposition of neural activity maps (first half of the data). The repertoire of major networks in the human brain was hence derived without access to what experimental task each activity map belongs. (<i>2</i>) This dictionary of explicit network definitions allowed reducing the remaining task activity maps (second half of the data) underlying traditional psychological concepts into 40 component loadings per neural activity map. Statistical learning based on these biologically motivated features found a linear model to distinguish 18 tasks by leave-one-participant-out cross-validation. A characteristic configuration of network engagements was thus automatically derived for each of 18 experimental tasks. (<i>3</i>) As face-validity criterion, task activity maps were generated from the weights of the trained classification models. These allowed quantifying the recovery performance of a given statistical model as a measure of biological meaningfulness of the learned model parameters (cf. methods section).