Brain modeling for control: A review - Acharya, Gagan; Ruf, Sebastian F.; Nozari, Erfan (Frontiers in Control Engineering, 2022)
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 epilepsy, Parkinson's Disease, and depression. The provided stimulation can be of different types, such as electric, magnetic, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global neural dynamics to a desired state of (in)activity. For most neurostimulation techniques, however, an underlying theoretical understanding of their efficacy is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the inherent complex network dynamics of the brain in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile systematically and in closed loop. 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 and commonly used 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. In all cases, we highlight the strength and weaknesses of the reviewed works and conclude the review with discussions on outstanding challenges and critical avenues for future work.