Model-based estimation of intra-cortical connectivity using electrophysiological data

P Aram,D.R Freestone,M.J Cook,V Kadirkamanathan,D.B Grayden

doi:10.1016/j.neuroimage.2015.06.048

P Aram, D.R Freestone + Show 3 more

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https://doi.org/10.1016/j.neuroimage.2015.06.048

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Abstract

This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.

Highlights

The human brain is arguably nature's most complex system
This paper provides a method for data-driven neural field modeling that does not rely on complicated, computationally intense estimation algorithms
The spatial dynamics are governed by the connectivity function, w(r), that collects all the presynaptic firing rates that drive the field of postsynaptic potentials, v(t, r), and r ∈ Ω ⊂ Rn are spatial locations in n-dimensional physical space, n ∈ {1, 2, 3}

Summary

Introduction

The development and validation of a theory that underpins its function is one of the greatest challenges faced by scientists today. This great challenge is being addressed by both the experimental and theoretical neuroscience communities. The theoretical neuroscience community is developing mathematical models that can explain generators of data and make non-trivial predictions about system behavior (to be validated by experiments). Accumulating more facts has not brought us closer to an understanding of what appears to be emergent phenomena in the brain. Understanding such phenomena requires the development and acceptance of theory. Theoretical developments have been limited by the inability to accurately measure model parameters

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