title: Estimation of effective connectivity via data-driven neural modeling.
abstract: This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used to track the mechanisms involved in seizure initiation and termination.
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne Fitzroy, VIC, Australia ; NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia.
authors.forename: Dean R
authors.initials: DR
authors.lastname: Freestone
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne Fitzroy, VIC, Australia ; NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia.
authors.forename: Philippa J
authors.initials: PJ
authors.lastname: Karoly
authors.affiliation: NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia.
authors.forename: Dragan
authors.initials: D
authors.lastname: Nešić
authors.affiliation: Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, UK.
authors.forename: Parham
authors.initials: P
authors.lastname: Aram
authors.affiliation: Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne Fitzroy, VIC, Australia.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Cook
authors.affiliation: NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia ; Centre for Neural Engineering, The University of Melbourne Parkville, VIC, Australia.
authors.forename: David B
authors.initials: DB
authors.lastname: Grayden
keywords: Kalman filter; effective connectivity; epilepsy; functional connectivity; model inversion; neural mass model; parameter estimation; seizures