Formal Models of the Network Co-occurrence Underlying Mental Operations
Fig 3
Network co-occurrence modeling: Predictive accuracy across network dictionary sizes.
40 ICA networks (upper row) and 40 sparse PCA networks (lower row) were discovered in HCP task data (left column) and ARCHI task data (right column) and used for feature engineering to facilitate classification of 18 psychological tasks (l2-penalized support vector machines, multi-class, one-versus-rest). One half of the task data (i.e., 4325 activity maps from HCP, 702 activity maps from ARCHI) were used for discovery of the ICA and sparse PCA networks. The network loadings of the previously unseen half of the task data (i.e., 4325 HCP maps, 702 ARCHI maps) were then submitted to an 18-task classification problem. The support vector machines were penalized by l2-regularization because classifier fitting was preceded by automatic selection of the k most relevant networks for each task (cf. methods section). We used a univariate feature selection procedure to evaluate the classification performance (y axis) as a function of k known network loadings per task (x axis). A two-step procedure therefore first subselected the k = 40, 20, 10, 5, and 1 most important network predictors for each task by univariate ANOVA tests and subsequent multivariate support vector machine fitting on the k most relevant network loadings per task. Note that each psychological task could therefore be associated with a different subselection of network loading features. To measure generalization performance, all task maps of one selected participant were left out in each cross-validation fold. See Fig 4 and S1–S3 Figs for the network topographies and the complete task-network assignments for each k.