Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.

ABSTRACT: Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many approaches have been proposed, these typically focus on linear approaches like computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. This has the advantage of allowing our model to capture differences at multiple temporal scales. For the timestep-specific scale, which has higher temporal precision, we find significant differences between schizophrenia patients and control subje

SUBMITTER: Geenjaar E 

PROVIDER: S-EPMC11230350 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature