Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy.
Bottom Line: Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity.Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis.Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory-inhibitory balance.
Affiliation: Department of Data-analysis, University of Ghent, B9000 Ghent, Belgium.Show MeSH
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Mentions: In summary, DCM solves the inverse problem of recovering plausible parameters (of both neuronal dynamics and noise) that explain observed cross spectra. It uses standard variational Bayesian procedures (Friston et al., 2007) to fit time-series or cross spectra – under model complexity constraints – to provide maximum a posteriori estimates of the underlying model parameters and the evidence for any particular model; see Friston et al. (2012) for more details in this particular setting. Fig. 2 illustrates the basic idea behind the application of dynamic causal modelling to cross spectral responses. The key point made by this figure is that changes in connectivity can have profound effects on spectral behaviour responses to endogenous input. It is these effects that are used to estimate (changes in) the underlying connectivity (Friston, 2014). If we take the modifications in the amplitude and frequencies produced by changes in model parameters as a simple model of seizure onset, one can use the predicted spectral responses as a likelihood model of empirical responses and thereby estimate the time-dependent changes in parameters. The simulations reported in Fig. 2 can be reproduced using the seizure onset demonstration in the neuronal modelling toolbox of the academic SPM freeware (http://www.fil.ion.ucl.ac.uk/spm). These simulation results use standard parameter values (prior expectations: see Table 1).
Affiliation: Department of Data-analysis, University of Ghent, B9000 Ghent, Belgium.