Deep brain stimulation for medically refractory epilepsy
Department of Neurosurgery, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA. Neurosurgical FOCUS
(Impact Factor: 2.11).
10/2008; 25(3):E11. DOI: 10.3171/FOC/2008/25/9/E11
Epilepsy is a chronic neurological disorder that affects 0.5-1% of the population. Up to one-third of patients will have incompletely controlled seizures or debilitating side effects of anticonvulsant medications. Although some of these patients may be candidates for resection, many are not. The desire to find alternative treatments for epilepsy has led to a resurgence of interest in the use of deep brain stimulation (DBS), which has been used quite successfully in movement disorders. Small pilot studies and open-label trials have yielded results that may support the use of DBS in selected patients with refractory seizures. Because of the diversity of regions involved with seizure initiation and propagation, a variety of targets for stimulation have been examined. Moreover, stimulation parameters such as amplitude, frequency, pulse duration, and continuous versus intermittent on vary from one study to the next. More studies are necessary to determine if there is an appropriate population of seizure patients for DBS, the optimal target, and the most efficacious stimulation parameters.
Available from: Marom Bikson
- "Clinical studies are often grouped by anatomical target  , but target-specific factors need to play a greater role in individualizing electrotherapy strategy and characterizing its mechanisms. In this section, we summarize what is known about the diversity in anatomical target response to electrical stimulation, and outline target-specific features that may distinguish response even to identical waveforms, and should be considered in rational electrotherapy . "
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ABSTRACT: Electrical stimulation is emerging as a viable alternative for patients with epilepsy whose seizures are not alleviated by drugs or surgery. Its attractions are temporal and spatial specificity of action, flexibility of waveform parameters and timing, and the perception that its effects are reversible unlike resective surgery. However, despite significant advances in our understanding of mechanisms of neural electrical stimulation, clinical electrotherapy for seizures relies heavily on empirical tuning of parameters and protocols. We highlight concurrent treatment goals with potentially conflicting design constraints that must be resolved when formulating rational strategies for epilepsy electrotherapy, namely, seizure reduction versus cognitive impairment, stimulation efficacy versus tissue safety, and mechanistic insight versus clinical pragmatism. First, treatment markers, objectives, and metrics relevant to electrical stimulation for epilepsy are discussed from a clinical perspective. Then the experimental perspective is presented, with the biophysical mechanisms and modalities of open-loop electrical stimulation, and the potential benefits of closed-loop control for epilepsy.
Epilepsy & Behavior 01/2010; 17(1-17):6-22. DOI:10.1016/j.yebeh.2009.10.017 · 2.26 Impact Factor
Available from: Joost H Heeroma
- "However, the evidence-base and rationale for thalamic DBS in epilepsy is limited. In particular, as noted by others   , the multiplicity of anatomical targets coupled with many variable stimulation parameters create obstacles in generalising from multiple small experimental and clinical studies. Hence, the motivation for this paper is to characterise the underlying mechanisms linking the stimulation input in the ventral-lateral nucleus of the thalamus and the system output, recorded via EEG from an electrode placed in the motor cortex. "
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ABSTRACT: Motivated by its success as a therapeutic treatment in other neurological disorders, most notably Parkinson's disease, Deep Brain Stimulation (DBS) is currently being trialled in a number of patients with drug unresponsive epilepsies. However, the mechanisms by which DBS interferes with neuronal activity linked to the disorder are not well understood. Furthermore, there is a need to identify optimized values of parameters (for example in amplitude/frequency space) of the stimulation protocol with which one aims to achieve the desired outcome. In this paper we characterise the system response to stimulation, to gain an understanding of the role different brain regions play in generating the output observed in EEG. We perform a number of experiments in healthy rats, where the ventral-lateral thalamic nucleus is stimulated using a train of square-waves with different frequency and amplitudes. The response to stimulation in the motor cortex is recorded and the drive-response relationship over frequency/amplitude space is considered. Subsequently, we compare the experimental data with simulations of a mean-field model, finding good agreement between the output of the model and the experimental data--both in the time and frequency domains--when considering a transition to oscillatory activity in the cortex as the frequency of stimulation is increased. Overall, our study suggests that mean-field models can appropriately characterise the stimulus-response relationship of DBS in healthy animals. In this way, it constitutes a first step towards the goal of developing a closed-loop feedback control protocol for suppressing epileptic activity, by adaptively adjusting the stimulation protocol in response to EEG activity.
Journal of Neuroscience Methods 08/2009; 183(1):77-85. DOI:10.1016/j.jneumeth.2009.06.044 · 2.05 Impact Factor
Available from: mcgill.ca
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ABSTRACT: This thesis presents a new methodology for automatically learning an optimal neurostimulation strategy for the treatment of epilepsy. The technical challenge is to automatically modulate neurostimulation parameters, as a function of the observed field potential recording, so as to minimize the frequency and duration of seizures. The methodology leverages recent techniques from the machine learning literature, in particular the reinforcement learning paradigm, to formalize this optimization problem. We present an algorithm which is able to learn an adaptive neurostimulation strategy directly from labeled training data acquired from animal brain tissues. Our results suggest that this methodology can be used to automatically find a stimulation strategy which effectively reduces the incidence of seizures, while also minimizing the amount of stimulation applied. This work highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders such as epilepsy. Cette thèse présente une nouvelle méthodologie pour apprendre, de façon automatique, une stratégie optimale de neurostimulation pour le traitement de l'épilepsie. Le défi technique est de moduler automatiquement les paramètres de stimulation, en fonction de l'enregistrement de potentiel de champ observé, afin de minimiser la fréquence et la durée des crises d'épilepsie. Cette méthodologie fait appel à des techniques récentes développées dans le domaine de l'apprentissage machine, en particulier le paradigme d'apprentissage par renforcement, pour formaliser ce problème d'optimisation. Nous présentons un algorithme qui est capable d'apprendre une stratégie adaptative de neurostimulation, et ce directement à partir de données d'apprentissage, étiquetées, acquises depuis des tissus de cerveaux d'animaux. Nos résultats suggèrent que cette méthodologie peut être utiliser pour trouver, automatiquement, une stratégie de stimulation qui réduit efficacement l'indicence des crises d'épilepsie tout en minimisant le nombre de stimulations appliquées. Ce travail met en évidence le rôle crucial que les techniques modernes d'apprentissage machine peuvent jouer dans l'optimisation de stratégies de traitements pour des patients souffrant de maladies chroniques telle l'épilepsie. fr
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