Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits.

ABSTRACT: Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamental mechanisms underlying time-varying functional networks are still not well understood. Here, we develop a novel nonlinear feature extraction framework called local space-contrastive learning (LSCL), which extracts distinctive nonlinear temporal structure hidden in time series, by training a deep temporal convolutional neural network in an unsupervised, data-driven manner. We demonstrate that LSCL identifies certain distinctive local temporal structures, referred to as temporal primitives, which repeatedly appear at different time points and sp

SUBMITTER: Morioka H 

PROVIDER: S-EPMC7759729 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature