Multimodal evidence suggests the linearity of brain dynamics at the macroscale
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,Biotechnology
Link
https://www.nature.com/articles/s41551-023-01127-w.pdf
Reference5 articles.
1. Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017). A review article that presents an overview of different classes of linear and nonlinear models most commonly used for modelling large-scale brain dynamics, as well as the theory and assumptions underlying them.
2. Stam, C. J. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin. Neurophysiol. 116, 2266–2301 (2005). A review article on various methods for analysis and estimation of nonlinearity in brain signals using indirect measures from nonlinear dynamical systems theory (also known as chaos theory).
3. Ahmed, S. & Nozari, E. On the linearizing effect of spatial averaging in large-scale populations of homogeneous nonlinear systems. In Proc. 2023 IEEE CDC 641–648 (IEEE, 2023). Follow-up study mathematically proving macroscopic linearity under spatial averaging.
4. Ahmed, S. & Nozari, E. On the linearizing effect of temporal averaging in nonlinear dynamical systems. In Proc. 2023 ACC 4185–4190 (IEEE, 2023). Follow-up study mathematically proving macroscopic linearity under temporal averaging.
5. Acharya, G., Davis, K. A. & Nozari, E. Predictive modeling of evoked intracranial EEG response to medial temporal lobe stimulation in patients with epilepsy. Preprint at bioRxiv https://doi.org/10.1101/2023.08.07.552297 (2023). Follow-up study showing that iEEG dynamics become switched-linear under electrical stimulation.
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