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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-19543

Moran, R J; Stephan, K E; Seidenbecher, T; Pape, H C; Dolan, R J; Friston, K J (2009). Dynamic causal models of steady-state responses. NeuroImage, 44(3):796-811.

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Abstract

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
DDC:330 Economics
Language:English
Date:2009
Deposited On:01 Jul 2009 16:02
Last Modified:27 Nov 2013 21:17
Publisher:Elsevier
ISSN:1053-8119
Additional Information:Elsevier free full text article
Publisher DOI:10.1016/j.neuroimage.2008.09.048
PubMed ID:19000769
Citations:Web of Science®. Times Cited: 46
Google Scholar™
Scopus®. Citation Count: 48

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