Dynamic network analysis of electrophysiological task data

• DOI: 10.1162/imag_a_00226
• Publication Date: 2024-07-01T20:00:57Z
• Authors: Chetan Gohil; Oliver Kohl; Rukuang Huang; Mats W.J. van Es; Oiwi Parker Jones; Laurence T. Hunt; Andrew J. Quinn; Mark W. Woolrich
• Keywords: Network Dynamics; Dynamic functional connectivity

Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves time-frequency analysis, in which recorded activity transformed and epoched around task events of interest, followed by trial-averaging power. While simple can reveal fast oscillatory dynamics, regions are analysed one at time. This causes difficulties interpretation debilitating number multiple comparisons. addition, it now recognised that responds tasks through coordinated networks areas. As such, techniques take whole-brain network perspective needed. Here, we show how responses from conventional approaches be represented more parsimoniously level using two state-of-the-art methods: HMM (Hidden Markov Model) DyNeMo (Dynamic Network Modes). Both methods frequency-resolved millisecond resolution. Comparing DyNeMo, HMM, traditional response identify activations/deactivations other fail detect. offers powerful new method analysing data dynamic networks.