Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities

Pereira, Inês; Frässle, Stefan; Heinzle, Jakob; Schöbi, Dario; Do, Cao Tri; Gruber, Moritz; Stephan, Klaas Enno

doi:10.1016/j.neuroimage.2021.118662

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Pereira, I., Frässle, S., Heinzle, J., Schöbi, D., Do, C. T., Gruber, M., & Stephan, K. E. (2021). Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities. NeuroImage, 245, 118662. https://doi.org/10.1016/j.neuroimage.2021.118662

Abstract

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance