A Functional Cartography of Cognitive Systems
Fig 1
(A) A functional connectivity matrix is derived for each task condition as the Pearson correlation between the time series of every pair of nodes. This results in a set of weighted graphs of the same size, one for each task condition. For simplicity, the network is represented here as a binary network, with nodes identified by an index and by an association to a putative system (e.g. A1, A2, …). To identify the community structure of this multislice network, the identity of each node is imposed by adding interslice connections (dashed lines) between identical nodes across slices. (B) Using a dynamic network clustering approach known as multislice community detection (see Methods), we extract the community structure of the network for each individual task. Each community is represented by a different color. (C) The module allegiance matrix conveniently summarizes the community structure of the network across tasks. Each entry i, j of the matrix corresponds to the percentage of tasks in which regions i and j belong to the same community, describing how regions (A1, A2, …, D4) and large-scale systems (SA, SB, SC, SD) are dynamically engaged during the task battery. Nodes that tend to co-occur in the same communities are represented by brighter colors than nodes that tend to operate in isolation. (D) By comparing the recruitment and integration of each system with a null-model (see Methods), we can extract 9 system-independent ‘network roles’, yielding a cartographic representation of cognitive systems. The systems depicted in the previous insets (SA, SB, SC, SD) represent examples that could occupy each of the four corners.