# Past, Present and Future of Brain Stimulation

J. Modolo; R. Edwards; J. Campagnaud; B. Bhattacharya; A. Beuter

Mathematical Modelling of Natural Phenomena (2010)

- Volume: 5, Issue: 2, page 185-207
- ISSN: 0973-5348

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topModolo, J., et al. "Past, Present and Future of Brain Stimulation." Mathematical Modelling of Natural Phenomena 5.2 (2010): 185-207. <http://eudml.org/doc/197691>.

@article{Modolo2010,

abstract = {Recent technological advances including brain imaging (higher resolution in space and
time), miniaturization of integrated circuits (nanotechnologies), and acceleration of
computation speed (Moore’s Law), combined with interpenetration between neuroscience,
mathematics, and physics have led to the development of more biologically plausible
computational models and novel therapeutic strategies. Today, mathematical models of
irreversible medical conditions such as Parkinson’s disease (PD) are developed and
parameterised based on clinical data. How do these evolutions have a bearing on deep brain
stimulation (DBS) of patients with PD? We review how the idea of DBS, a standard
therapeutic strategy used to attenuate neurological symptoms (motor, psychiatric), has
emerged from past experimental and clinical observations, and present how, over the last
decade, computational models based on different approaches (phase oscillator models,
spiking neuron network models, population-based models) have started to shed light onto
DBS mechanisms. Finally, we explore a new mathematical modelling approach based on neural
field equations to optimize mechanisms of brain stimulation and achieve finer control of
targeted neuronal populations. We conclude that neuroscience and mathematics are crucial
partners in exploring brain stimulation and this partnership should also include other
domains such as signal processing, control theory and ethics.},

author = {Modolo, J., Edwards, R., Campagnaud, J., Bhattacharya, B., Beuter, A.},

journal = {Mathematical Modelling of Natural Phenomena},

keywords = {brain stimulation; spiking neurons; neural field models},

language = {eng},

month = {3},

number = {2},

pages = {185-207},

publisher = {EDP Sciences},

title = {Past, Present and Future of Brain Stimulation},

url = {http://eudml.org/doc/197691},

volume = {5},

year = {2010},

}

TY - JOUR

AU - Modolo, J.

AU - Edwards, R.

AU - Campagnaud, J.

AU - Bhattacharya, B.

AU - Beuter, A.

TI - Past, Present and Future of Brain Stimulation

JO - Mathematical Modelling of Natural Phenomena

DA - 2010/3//

PB - EDP Sciences

VL - 5

IS - 2

SP - 185

EP - 207

AB - Recent technological advances including brain imaging (higher resolution in space and
time), miniaturization of integrated circuits (nanotechnologies), and acceleration of
computation speed (Moore’s Law), combined with interpenetration between neuroscience,
mathematics, and physics have led to the development of more biologically plausible
computational models and novel therapeutic strategies. Today, mathematical models of
irreversible medical conditions such as Parkinson’s disease (PD) are developed and
parameterised based on clinical data. How do these evolutions have a bearing on deep brain
stimulation (DBS) of patients with PD? We review how the idea of DBS, a standard
therapeutic strategy used to attenuate neurological symptoms (motor, psychiatric), has
emerged from past experimental and clinical observations, and present how, over the last
decade, computational models based on different approaches (phase oscillator models,
spiking neuron network models, population-based models) have started to shed light onto
DBS mechanisms. Finally, we explore a new mathematical modelling approach based on neural
field equations to optimize mechanisms of brain stimulation and achieve finer control of
targeted neuronal populations. We conclude that neuroscience and mathematics are crucial
partners in exploring brain stimulation and this partnership should also include other
domains such as signal processing, control theory and ethics.

LA - eng

KW - brain stimulation; spiking neurons; neural field models

UR - http://eudml.org/doc/197691

ER -

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