Topological data analysis for revealing dynamic brain reconfiguration in MEG data

PeerJ

article

Ali Nabi Duman¹  Ahmet E. Tatar² 

[1] Department of Mathematics, King Fahd University of Petroleum and Minerals;Center for Information Technology, University of Groningen

关键词: Topological data analysis;    MEG;    Dynamic functional connectivity;    Working memory;    Sensory motor;    Mapper;   

DOI  :  10.7717/peerj.15721

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY