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dc.contributor.author
Daunizeau, Jean
dc.contributor.author
Lemieux, Louis
dc.contributor.author
Vaudano, Anna E.
dc.contributor.author
Friston, Karl J.
dc.contributor.author
Stephan, Klaas E.
dc.date.accessioned
2019-07-01T16:23:27Z
dc.date.available
2017-06-10T14:12:10Z
dc.date.available
2019-07-01T16:23:27Z
dc.date.issued
2013-01
dc.identifier.issn
1662-5188
dc.identifier.other
10.3389/fncom.2012.00103
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/63933
dc.identifier.doi
10.3929/ethz-b-000063933
dc.description.abstract
In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data—acquired in epilepsy patients—to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.subject
dynamic causal modeling
en_US
dc.subject
neural noise
en_US
dc.subject
EEG
en_US
dc.subject
fMRI
en_US
dc.subject
effective connectivity
en_US
dc.subject
neural field
en_US
dc.subject
separation of time scales
en_US
dc.title
An electrophysiological validation of stochastic DCM for fMRI
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2013-01-18
ethz.journal.title
Frontiers in Computational Neuroscience
ethz.journal.volume
6
en_US
ethz.journal.abbreviated
Front. comput. neurosci.
ethz.pages.start
103
en_US
ethz.size
21 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
ethz.date.deposited
2017-06-10T14:14:24Z
ethz.source
ECIT
ethz.identifier.importid
imp5936505e48ff518235
ethz.ecitpid
pub:101364
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T07:53:10Z
ethz.rosetta.lastUpdated
2019-07-01T16:23:39Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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