Browse by author
Lookup NU author(s): Professor Yujiang WangORCiD, Dr Frances TurnerORCiD, Professor Marcus Kaiser
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand.This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects.We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.
Author(s): Wang YJ, Hutchings F, Kaiser M
Publication type: Review
Publication status: Published
Journal: Progress in Brain Research
Year: 2015
Volume: 222
Pages: 191-228
Online publication date: 29/07/2015
Acceptance date: 01/01/1900
ISSN (print): 0079-6123
Publisher: ELSEVIER SCIENCE BV
URL: http://dx.doi.org/10.1016/bs.pbr.2015.06.012
DOI: 10.1016/bs.pbr.2015.06.012
