What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

Journal article

The development of functional magnetic resonance imaging (fMRI) in quiescent brain imaging has revealed that even at rest, brain activity is highly structured, with voxel-to-voxel comparisons consistently demonstrating a suite of resting-state networks (RSNs). Since its initial use, resting-state fMRI (RS-fMRI) has undergone a renaissance in methodological and interpretive advances that have expanded this functional connectivity understanding of brain RSNs. RS-fMRI has benefitted from the technical developments in MRI such as parallel imaging, high-strength magnetic fields, and big data handling capacity, which have enhanced data acquisition speed, spatial resolution, and whole-brain data retrieval, respectively. It has also benefitted from analytical approaches that have yielded insight into RSN causal connectivity and topological features, now being applied to normal and disease states. Increasingly, these new interpretive methods seek to advance understanding of dynamic network changes that give rise to whole brain states and behavior. This review explores the technical outgrowth of RS-fMRI from fMRI and the use of these technical advances to underwrite the current analytical evolution directed toward understanding the role of RSN dynamics in brain functioning

Authors: Soler-Toscano, F; Galadí, JA; Escrichs, A; Perl, YS; López-González, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Computational Biology
• Volume: 18
• Issue: 9
• Pages: e1010412-e1010412
• Publication date: 2022-09-06
• DOI: 10.1371/journal.pcbi.1010412
• EISSN: 1553-7358
• ISSN: 1553-734X
• Language: English
• Keywords: Complex dynamics; Causal structure; Brain activity and meditation; Statistical physics; Neuroimaging; Machine learning; Neuroscience; Topology (electrical circuits); Cognitive psychology; Attractor; Resting state fMRI; Wakefulness; Cognitive science; Nonlinear system; Dynamics (music); Psychology; Physics; Mathematics; Artificial intelligence; Electroencephalography; Computer science