How can DMD be used for analyzing brain connectivity and network dynamics from neuroimaging data?

The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections ... Feb 13, 2025 ... Based on simple least-square regression, the underlying dynamic mode decomposition algorithm can be used to construct a low-rank subspace ... In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets. Dynamic mode decomposition (DMD) is a recently developed method focused on discovering coherent spatial-temporal modes in high-dimensional data collected from ... Nov 12, 2021 ... The method is capable of characterizing nonlinear, time-varying, multidimensional time series data in a computationally efficient manner. The ... ... modes from dynamic mode decomposition', Eur. J. Mech. B/Fluids, 2017, 62, pp. 109–129. 10.1016/j.euromechflu. 2016.11.015. Crossref · Google Scholar. 26. Barata ... Aug 29, 2024 ... We apply here an unsupervised data-driven algorithm, Dynamic Mode Decomposition (DMD), to sEEG recordings from five focal epilepsy patients. Jul 28, 2023 ... When analyzing high-dimensional time series, the DMD algorithm requires extremely large amounts of computational power. To accelerate the DMD ... Mar 21, 2025 ... Using Dynamic Mode Decomposition with control (DMDc), we analyze surface air temperature patterns from climate simulations to elucidate the ... Jun 5, 2015 ... We will show that this approach is, in effect, an extension of dynamic mode decomposition (DMD), which has been used to approximate the Koopman ...