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Bayesian estimation of synaptic physiology from the spectral responses of neural masses

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dc.contributor.author Moran, R.J
dc.contributor.author Stephan, K.E
dc.contributor.author Kiebel, S.J
dc.contributor.author Rombach, N
dc.contributor.author O'Connor, William T.
dc.contributor.author Murphy, K.J
dc.contributor.author Reilly, R.B
dc.contributor.author Friston, K.J
dc.date.accessioned 2015-09-24T11:07:08Z
dc.date.available 2015-09-24T11:07:08Z
dc.date.issued 2008
dc.identifier.citation Moran, RJ; Stephan, KE; Kiebel, SJ; Rombach, N; O'Connor, WT; Murphy, KJ; Reilly, RB; Friston, KJ (2008) 'Bayesian estimation of synaptic physiology from the spectral responses of neural masses'. NEUROIMAGE, 42 (1):272-284. en_US
dc.identifier.uri http://hdl.handle.net/10344/4659
dc.description non-peer-reviewed en_US
dc.description.abstract We describe a Bayesian inference scheme for quantifying the active physiology of neuronal ensembles using local field recordings of synaptic potentials. This entails the inversion of a generative neural mass model of steady-state spectral activity. The inversion uses Expectation Maximization (EM) to furnish the posterior probability of key synaptic parameter and the marginal likelihood of the model itself. The neural mass model embeds prior knowledge pertaining to both the anatomical [synaptic, circuitry and plausible trajectories of neuronal dynamics. This model comprises a population of excitatory pyramidal cells, under local interneuron inhibition and driving excitation from layer IV stellate cells. Under quasi-stationary assumptions, the model can predict the spectral profile of local field potentials (LFP). This means model parameters can be optimised given real electrophysiological observations. The validity of inferences about synaptic parameters is demonstrated using simulated data and experimental recordings from the medial prefrontal cortex of control and isolation-reared Wistar rats. Specifically, we examined the maximum a posteriori estimates of parameters describing synaptic function in the two groups and tested predictions derived from concomitant microdialysis measures. The modelling of the LFP recordings revealed (i) a sensitization of post-synaptic excitatory responses, particularly marked in pyramidal cells, in the medial prefrontal cortex of socially isolated rats and (ii) increased neuronal adaptation. These inferences were consistent with predictions derived from experimental microdialysis measures of extracellular glutamate levels. (c) 2008 Elsevier Inc. All rights reserved. en_US
dc.language.iso eng en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries NeuroImage;42, pp. 272-284
dc.relation.uri http://dx.doi.org/10.1016/j.neuroimage.2008.01.025
dc.subject dynamic causal modelling en_US
dc.subject schizophrenia en_US
dc.subject glutamate en_US
dc.subject GABA en_US
dc.title Bayesian estimation of synaptic physiology from the spectral responses of neural masses en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.date.updated 2015-09-24T10:51:59Z
dc.description.version SUBMITTED
dc.contributor.sponsor IRC en_US
dc.contributor.sponsor SFI en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1132936
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed


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