报告内容
The human brain is populated by specialized regions that are organized into networks. Using resting-state fMRI data from 15 intensively sampled participants (each scanned 8 or more times), we recently applied a multi-session hierarchical Bayesian model to delineate 15 distinct networks. Analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that linked to distant regions. Third-order networks possessed regions distributed widely throughout the association cortex. Regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated across multiple cortical zones. We refer to these as supra-areal association megaclusters (SAAMs). We demonstrate that these fine-grained spatial details are stable features of an individual’s brain, reproducible using only task-based functional connectivity. Further, we show that networks estimated from task-based functional connectivity can effectively predict functional specializations across multiple higher-order cognitive domains in independent task datasets, and that the same task data can simultaneously provide both within-individual network estimates and region-level functional response quantification. A complete set of atlases based on this 15-network model in both surface and volume-based formats is publicly available at https://url.scnu.edu.cn/record/view/index.html?key=ee32f7f5bd69d75efd91362139691640.