Brain Modeling for Control: A Review

TL;DR

This review explores computational models of neurostimulation technologies, emphasizing dynamical system models for control design, to enhance understanding and efficacy of brain stimulation therapies.

Contribution

It provides a comprehensive overview of biophysical, stimulus-response, and dynamical system models across major neurostimulation methods, highlighting their roles in control applications.

Findings

Dynamical system models are crucial for closed-loop control design.

Biophysical models help understand low-level stimulation effects.

Data-driven models capture brain responses to stimulation.

Abstract

Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as Parkinson's Disease, and depression. The provided stimulation can be of different types, such as electric, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global dynamics to a desired state of (in)activity. However, an underlying theoretical understanding of the efficacy of neurostimulation is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the complex brain network in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile in a closed-loop manner. In this paper, we review the computational modeling studies of (i) deep brain stimulation, (ii) transcranial magnetic stimulation, (iii) direct current stimulation, (iv) transcranial electrical stimulation, and (v) optogenetics as five of the most popular neurostimulation technologies in research and clinical settings. For each technology, we split the reviewed studies into (a)theory-driven biophysical models capturing the low-level physics of the interactions between the stimulation source and neuronal tissue, (b) data-driven stimulus-response models which capture the end-to-end effects of stimulation on various biomarkers of interest and (c) data-driven dynamical system models that extract the precise dynamics of the brain's response to neurostimulation from neural data. While our focus is particularly on the latter category due to their greater utility in control design, we review key works in the former two categories as the basis and context in which dynamical system models have been and will be developed.