title: Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy.
abstract: In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space - identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). 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. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. 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.
authors.affiliation: Department of Data-analysis, University of Ghent, B9000 Ghent, Belgium.
authors.forename: Margarita
authors.initials: M
authors.lastname: Papadopoulou
authors.affiliation: UCL Institute of Neurology, Department of Clinical and Experimental Epilepsy, WC1N 3BG London, UK; Institute of systems and robotics, Instituto Superior Técnico, Universidade de Lisboa, 1049-001, Portugal.
authors.forename: Marco
authors.initials: M
authors.lastname: Leite
authors.affiliation: Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University-IBBT, B9000 Ghent, Belgium.
authors.forename: Pieter
authors.initials: P
authors.lastname: van Mierlo
authors.affiliation: Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent B9000, Belgium.
authors.forename: Kristl
authors.initials: K
authors.lastname: Vonck
authors.affiliation: UCL Institute of Neurology, Department of Clinical and Experimental Epilepsy, WC1N 3BG London, UK.
authors.forename: Louis
authors.initials: L
authors.lastname: Lemieux
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, WC1N 3BG London, UK.
authors.forename: Karl
authors.initials: K
authors.lastname: Friston
authors.affiliation: Department of Data-analysis, University of Ghent, B9000 Ghent, Belgium. Electronic address: daniele.marinazzo@gmail.com.
authors.forename: Daniele
authors.initials: D
authors.lastname: Marinazzo
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D003198:Computer Simulation; D000069279:Drug Resistant Epilepsy / Q000503:physiopathology; D004569:Electroencephalography; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology; D009474:Neurons; D017966:Pyramidal Cells; D012640:Seizures / Q000503:physiopathology; D013569:Synapses
keywords: Dynamical causal modelling; Dynamical connectivity; Electroencephalography; Epilepsy; Neural mass models; Seizure onset