Fig 4. Network co-occurrence modeling: Sparse PCA network decomposition of ARCHI task maps and network-task assignment.

40 network components underlying 18 ARCHI tasks have been discovered by sparse PCA (Comp1-40 on the left). The ensuing network loadings from the second half of the ARCHI task data were submitted to classification of the psychological tasks based on the implication of brain networks (l2-penalized support vector machines, multi-class, one-versus-rest). l2-penalized support vector machines was employed to choose the most discriminative network variables by a preceding classical univariate test in a discrete fashion rather than by sparse variable selection based on l1 penalization (cf. methods section). This diagnostic analysis (right) revealed the most distinctive k = 1, 5, 10, and 20 network features (red cubes) for each experimental condition of the task battery (cf. Fig 3). The thus discretely selected network features per task were then fed into supervised multi-task classification as a feature space of activity-map-wise continuous activity values. The color intensity of the k cubes quantifies how often the corresponding brain network was selected as important for a task across cross-validation folds. This diagnostic test performed inference on a) the single most discriminative network for each task at k = 1, b) the network variables that are added step-by-step to the feature space of network implications with increasing k, and c) what network variables are unspecific (i.e., not selected) for a given task at k = 20. See tables for the corresponding descriptions of task 1–18. See S1–S3 Figs for analogous plots based on different matrix factorizations and datasets.