Dynamic Causal Models and Physiological Inference: A Validation Study Using Isoflurane Anaesthesia in Rodents

dc.contributor.authorMoran, Rosalyn J.en
dc.contributor.authorJung, Fabienneen
dc.contributor.authorKumagai, Tetsuyaen
dc.contributor.authorEndepols, Heikeen
dc.contributor.authorGraf, Rudolfen
dc.contributor.authorDolan, Raymond J.en
dc.contributor.authorFriston, Karl J.en
dc.contributor.authorStephan, Klaas Ennoen
dc.contributor.authorTittgemeyer, Marcen
dc.description.abstractGenerative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABAA receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.en
dc.description.sponsorshipThis work was supported by the Max Planck Society (MT, FJ, TK, HE, RG, RJM), the NEUROCHOICE project of SystemsX.ch (KES), and the Wellcome Trust (RJD, KJF, RJM).en
dc.format.extent10 pagesen
dc.identifier.citationMoran RJ, Jung F, Kumagai T, Endepols H, Graf R, et al. (2011) Dynamic Causal Models and Physiological Inference: A Validation Study Using Isoflurane Anaesthesia in Rodents. PLoS ONE 6(8): e22790. doi:10.1371/journal.pone.0022790en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.titleDynamic Causal Models and Physiological Inference: A Validation Study Using Isoflurane Anaesthesia in Rodentsen
dc.title.serialPLOS Oneen
dc.typeArticle - Refereeden


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