Print Email Facebook Twitter Predicting time-resolved electrophysiological brain networks from structural eigenmodes Title Predicting time-resolved electrophysiological brain networks from structural eigenmodes Author Tewarie, Prejaas (University of Nottingham) Prasse, B. (TU Delft Network Architectures and Services) Meier, Jil (Freie Universität Berlin; Humboldt-Universitat zu Berlin; Berlin Institute of Health) Mandke, Kanad (University of Cambridge) Warrington, Shaun (University of Nottingham) Stam, Cornelis J (Amsterdam UMC; Vrije Universiteit Amsterdam) Brookes, Matthew J. (University of Nottingham) Van Mieghem, P.F.A. (TU Delft Network Architectures and Services) Sotiropoulos, Stamatios N. (University of Nottingham; University of Oxford; Nottingham University Hospitals NHS Trust) Hillebrand, Arjan (Amsterdam UMC; Vrije Universiteit Amsterdam) Date 2022 Abstract How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time-resolved amplitude connectivity. Time-resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co-occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time-resolved resting-state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions. Subject dynamic functional connectivityeigenmodesmagnetoencephalography To reference this document use: http://resolver.tudelft.nl/uuid:1b4f06f8-7ebf-4bf5-aa7c-ec826e45d2a3 DOI https://doi.org/10.1002/hbm.25967 ISSN 1065-9471 Source Human Brain Mapping, 43 (14), 4475-4491 Part of collection Institutional Repository Document type journal article Rights © 2022 Prejaas Tewarie, B. Prasse, Jil Meier, Kanad Mandke, Shaun Warrington, Cornelis J Stam, Matthew J. Brookes, P.F.A. Van Mieghem, Stamatios N. Sotiropoulos, Arjan Hillebrand Files PDF Human_Brain_Mapping_2022_ ... ctural.pdf 4.72 MB Close viewer /islandora/object/uuid:1b4f06f8-7ebf-4bf5-aa7c-ec826e45d2a3/datastream/OBJ/view