Topological data analysis for revealing dynamic brain reconfiguration in MEG data

• Published in: PeerJ (San Francisco, CA) Vol. 11; p. e15721
• Main Authors: Duman, Ali Nabi, Tatar, Ahmet E.
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 20.07.2023; PeerJ Inc
• Subjects: Brain; Cognition; Computational linguistics; Data Analysis; Data Mining and Machine Learning; Data Science; Dynamic functional connectivity; Electroencephalography; Forecasts and trends; Humans; Information management; Language processing; Magnetoencephalography; Mapper; MEG; Natural language interfaces; Neuroscience; Sensory motor; Topological data analysis; Working memory; Mapper; Dynamic functional connectivity; Sensory motor; Working memory; Topological data analysis; MEG
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.15721

In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.