Dynamic Parameter Estimation of Brain Mechanisms

TL;DR

This paper surveys dynamic models and methods for estimating brain connectivity, aiding understanding of neurological diseases through electrophysiological data analysis.

Contribution

It provides a comprehensive review of six dynamic models, three network estimation methods, and various parameter estimation techniques, highlighting their strengths and limitations.

Findings

Comparison of dynamic models and their applicability.

Evaluation of network estimation approaches.

Discussion of parameter estimation techniques.

Abstract

Demystifying effective connectivity among neuronal populations has become the trend to understand the brain mechanisms of Parkinson's disease, schizophrenia, mild traumatic brain injury, and many other unlisted neurological diseases. Dynamic modeling is a state-of-the-art approach to explore various connectivities among neuronal populations corresponding to different electrophysiological responses. Through estimating the parameters in the dynamic models, including the strengths and propagation delays of the electrophysiological signals, the discovery of the underlying connectivities can lead to the elucidation of functional brain mechanisms. In this report, we survey six dynamic models that describe the intrinsic function of a single neuronal/subneuronal population and three effective network estimation methods that can trace the connections among the neuronal/subneuronal populations. The six dynamic models are event related potential, local field potential, conductance-based neural mass model, mean field model, neural field model, and canonical micro-circuits; the three effective network estimation approaches are dynamic causal modeling, structural causal model, and vector autoregression. Subsequently, we discuss dynamic parameter estimation methods including variational Bayesian, particle filtering, Metropolis-Hastings algorithm, Gauss-Newton algorithm, collocation method, and constrained optimization. We summarize the merits and drawbacks of each model, network estimation approach, and parameter estimation method. In addition, we demonstrate an exemplary effective network estimation problem statement. Last, we identify possible future work and challenges to develop an elevated package.