Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.

ABSTRACT: Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining cons

SUBMITTER: Gomez C 

PROVIDER: S-EPMC11686193 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature