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Past, Present and Future of Brain Stimulation

Published online by Cambridge University Press:  10 March 2010

J. Modolo ,
R. Edwards ,
J. Campagnaud ,
B. Bhattacharya  and
A. Beuter
J. Modolo
Affiliation:
Lawson Health Research Institute, Department of Medical Biophysics University of Western Ontario, N6A 4V2 London, ON Canada Laboratoire IMS, Site ENSCPB, Institut Polytechnique de Bordeaux 16 avenue Pey-Berland, 33607 Pessac, France
R. Edwards
Affiliation:
Department of Mathematics and Statistics, University of Victoria V8W 3R4 Victoria, B.C. Canada
J. Campagnaud
Affiliation:
Laboratoire IMS, Site ENSCPB, Institut Polytechnique de Bordeaux 16 avenue Pey-Berland, 33607 Pessac, France
B. Bhattacharya
Affiliation:
Laboratoire IMS, Site ENSCPB, Institut Polytechnique de Bordeaux 16 avenue Pey-Berland, 33607 Pessac, France
A. Beuter*
Affiliation:
Laboratoire IMS, Site ENSCPB, Institut Polytechnique de Bordeaux 16 avenue Pey-Berland, 33607 Pessac, France
*
* Corresponding author. E-mail:[email protected]
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Abstract

Recent technological advances including brain imaging (higher resolution in space andtime), miniaturization of integrated circuits (nanotechnologies), and acceleration ofcomputation speed (Moore’s Law), combined with interpenetration between neuroscience,mathematics, and physics have led to the development of more biologically plausiblecomputational models and novel therapeutic strategies. Today, mathematical models ofirreversible medical conditions such as Parkinson’s disease (PD) are developed andparameterised based on clinical data. How do these evolutions have a bearing on deep brainstimulation (DBS) of patients with PD? We review how the idea of DBS, a standardtherapeutic strategy used to attenuate neurological symptoms (motor, psychiatric), hasemerged from past experimental and clinical observations, and present how, over the lastdecade, computational models based on different approaches (phase oscillator models,spiking neuron network models, population-based models) have started to shed light ontoDBS mechanisms. Finally, we explore a new mathematical modelling approach based on neuralfield equations to optimize mechanisms of brain stimulation and achieve finer control oftargeted neuronal populations. We conclude that neuroscience and mathematics are crucialpartners in exploring brain stimulation and this partnership should also include otherdomains such as signal processing, control theory and ethics.

Keywords

brain stimulationspiking neuronsneural field models
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 5 , Issue 2: Mathematics and neuroscience , 2010 , pp. 185 - 207
Copyright
© EDP Sciences, 2010

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