Estimation of effective connectivity via data-driven neural modeling

• Published in: Frontiers in neuroscience Vol. 8; p. 383
• Main Authors: Freestone, Dean R., Karoly, Philippa J., NeÅ¡ić, Dragan, Aram, Parham, Cook, Mark J., Grayden, David B.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 28.11.2014; Frontiers Media S.A
• Subjects: Biophysics; Brain research; Computational neuroscience; Control theory; Convulsions & seizures; Cortex; Engineering; Epilepsy; Kalman filters; Medical imaging; Membrane potential; Neural networks; Neuroimaging; Neuroscience; Neurosciences; Population; Seizures; Australia; New York; United States > US; epilepsy; model inversion; functional connectivity; Kalman filter; neural mass model; effective connectivity; parameter estimation; seizures
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2014.00383

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.