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. 2011 Feb 6;1(1):61-74.
doi: 10.1098/rsfs.2010.0509. Epub 2010 Nov 17.

Model-driven Therapeutic Treatment of Neurological Disorders: Reshaping Brain Rhythms With Neuromodulation

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Free PMC article

Model-driven Therapeutic Treatment of Neurological Disorders: Reshaping Brain Rhythms With Neuromodulation

Julien Modolo et al. Interface Focus. .
Free PMC article

Abstract

Electric stimulation has been investigated for several decades to treat, with various degrees of success, a broad spectrum of neurological disorders. Historically, the development of these methods has been largely empirical but has led to a remarkably efficient, yet invasive treatment: deep brain stimulation (DBS). However, the efficiency of DBS is limited by our lack of understanding of the underlying physiological mechanisms and by the complex relationship existing between brain processing and behaviour. Biophysical modelling of brain activity, describing multi-scale spatio-temporal patterns of neuronal activity using a mathematical model and taking into account the physical properties of brain tissue, represents one way to fill this gap. In this review, we illustrate how biophysical modelling is beginning to emerge as a driving force orienting the development of innovative brain stimulation methods that may move DBS forward. We present examples of modelling works that have provided fruitful insights in regards to DBS underlying mechanisms, and others that also suggest potential improvements for this neurosurgical procedure. The reviewed literature emphasizes that biophysical modelling is a valuable tool to assist a rational development of electrical and/or magnetic brain stimulation methods tailored to both the disease and the patient's characteristics.

Keywords: biophysical modelling; brain rhythms; brain stimulation; electric and magnetic stimulation; neuromodulation.

Figures

Figure 1.
Figure 1.
Localization, Fourier transform and coherence values between primary motor cortex activity (as measured by magnetoencephalography) and peripheral rest tremor (as measured by electromyography) in a PD patient. (a) Indication of where the magnetoencephalographic (MEG) signal is recorded (red area). (b) The power spectrum of MEG signal in the primary motor cortex, which features a noticeable peak centred at 10 Hz. (c) The cerebro-muscular coherence between the MEG signal in the primary motor cortex and the electromyographic signal measured at the extensor digitorum communis (EDC) level, with a maximum value around 10 Hz. (Figure modified from Timmermann et al. [19], with permission.)
Figure 2.
Figure 2.
Illustration of the relationship between the oscillation phase and the polarization state of the neuron membrane (see [47]). A neuron regularly spiking ((a) for a representation of neuron trajectory in the phase space) can be represented as a point oscillating around a circle with an angular frequency ω = 2πf, the instantaneous position on the circle being the phase φ (b). From (a), one can see that different parts of the limit cycle correspond to different neuronal excitability states.
Figure 3.
Figure 3.
Three states of a phase oscillator network, the phase of each neuron being its position on the unit circle. Each black dot indicates the phase of a given neuron (N = 12 here). (a) Neurons have their phases regularly distributed on the circle, so that the amplitude R(t) of the synchronization (coherent neuronal activity) index Z(t) vanishes, resulting in a null vector Z(t). (b) Some neurons have a similar phase, thus there is partial synchronization in the network resulting in non-zero real amplitude R(t) less than 1. (c) All neurons have the same phase yielding complete network synchronization characterized by R(t) = 1 [47].
Figure 4.
Figure 4.
Activity of thalamic relay neurons (a(i,ii), TC cells), internal globus pallidus neurons (b(i,ii), GPi cells) and subthalamic neurons (c(i,ii), STN cells) without DBS—left column—and during DBS applied to subthalamic neurons—right column. (Figure modified from Rubin & Terman [40], with permission.) (a) The incoming sensorimotor inputs are shown on the bottom trace on the activity plot of the thalamic relay cells. Whereas all of these cells exhibit low-frequency bursting in the absence of stimulation—left column—thereby simulating neuronal dynamics in PD, DBS suppresses them and drives neurons to fire in a 1∶1 manner (one spike per stimulation pulse)—right column. Therefore, the pathological rhythm is replaced by another high frequency one that appears compatible with a reliable transmission of sensorimotor signals.
Figure 5.
Figure 5.
Principle of the linear delayed feedback method proposed by Rosenblum & Pikovsky [63,64]. The target neural population is monitored using a recording electrode, and after a given delay, a rescaled value of the recorded signal (i.e. a given multiplicative factor is applied) is computed and used as a stimulation signal applied to the target neural population. (Figure adapted from Rosenblum et al. [65] with permission.)

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