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

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

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

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-851D-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-0889-0

Genre: Journal Article

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Creators:
Pereira, Ines, Author
Frässle, Stefan, Author
Heinzle, Jakob, Author
Schöbi, Dario, Author
Do, Cao Tri, Author
Gruber, Moritz, Author
Stephan, Klaas Enno¹, Author

Affiliations:
1Stephan – Translational Neuromodelling, External Scientific Members, Max Planck Institute for Metabolism Research, Max Planck Society, ou_3485601

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Language(s): eng - English

Dates: Date issued: 2021

Publication Status: Issued

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Rev. Type: Peer

Identifiers: DOI: 10.1016/j.neuroimage.2021.118662

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Title: NeuroImage

Source Genre: Journal

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Publ. Info: Orlando, FL : Academic Press

Pages: - Volume / Issue: 245 Sequence Number: 118662 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166