Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson's disease and obsessive-compulsive disorder in a data-driven computational model.
Identifieur interne : 001299 ( Main/Merge ); précédent : 001298; suivant : 001300Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson's disease and obsessive-compulsive disorder in a data-driven computational model.
Auteurs : Sofia D. Karamintziou [Grèce] ; Nick G. Deligiannis ; Brigitte Piallat ; Mircea Polosan ; Stephan Chabardès ; Olivier David ; Pantelis G. Stathis ; George A. Tagaris ; Efstathios J. Boviatsis ; Damianos E. Sakas ; Georgia E. Polychronaki ; George L. Tsirogiannis ; Konstantina S. NikitaSource :
- Journal of neural engineering [ 1741-2552 ] ; 2016.
Descripteurs français
- KwdFr :
- Humains, Maladie de Parkinson (), Maladie de Parkinson (physiopathologie), Modèles neurologiques, Noyau subthalamique (physiopathologie), Résultat thérapeutique, Simulation numérique, Stimulation cérébrale profonde (), Thérapie assistée par ordinateur (), Trouble obsessionnel compulsif (), Trouble obsessionnel compulsif (physiopathologie).
- MESH :
English descriptors
- KwdEn :
- Computer Simulation, Deep Brain Stimulation (methods), Humans, Models, Neurological, Obsessive-Compulsive Disorder (physiopathology), Obsessive-Compulsive Disorder (therapy), Parkinson Disease (physiopathology), Parkinson Disease (therapy), Subthalamic Nucleus (physiopathology), Therapy, Computer-Assisted (methods), Treatment Outcome.
- MESH :
- methods : Deep Brain Stimulation, Therapy, Computer-Assisted.
- physiopathology : Obsessive-Compulsive Disorder, Parkinson Disease, Subthalamic Nucleus.
- therapy : Obsessive-Compulsive Disorder, Parkinson Disease.
- Computer Simulation, Humans, Models, Neurological, Treatment Outcome.
Abstract
Almost 30 years after the start of the modern era of deep brain stimulation (DBS), the subthalamic nucleus (STN) still constitutes a standard stimulation target for advanced Parkinson's disease (PD), but the use of STN-DBS is also now supported by level I clinical evidence for treatment-refractory obsessive-compulsive disorder (OCD). Disruption of neural synchronization in the STN has been suggested as one of the possible mechanisms of action of standard and alternative patterns of STN-DBS at a local level. Meanwhile, recent experimental and computational modeling evidence has signified the efficiency of alternative patterns of stimulation; however, no indications exist for treatment-refractory OCD. Here, we comparatively simulate the desynchronizing effect of standard (regular at 130 Hz) versus temporally alternative (in terms of frequency, temporal variability and the existence of bursts or pauses) patterns of STN-DBS for PD and OCD, by means of a stochastic dynamical model and two microelectrode recording (MER) datasets.
DOI: 10.1088/1741-2560/13/1/016013
PubMed: 26695534
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pubmed:26695534Le document en format XML
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<front><div type="abstract" xml:lang="en">Almost 30 years after the start of the modern era of deep brain stimulation (DBS), the subthalamic nucleus (STN) still constitutes a standard stimulation target for advanced Parkinson's disease (PD), but the use of STN-DBS is also now supported by level I clinical evidence for treatment-refractory obsessive-compulsive disorder (OCD). Disruption of neural synchronization in the STN has been suggested as one of the possible mechanisms of action of standard and alternative patterns of STN-DBS at a local level. Meanwhile, recent experimental and computational modeling evidence has signified the efficiency of alternative patterns of stimulation; however, no indications exist for treatment-refractory OCD. Here, we comparatively simulate the desynchronizing effect of standard (regular at 130 Hz) versus temporally alternative (in terms of frequency, temporal variability and the existence of bursts or pauses) patterns of STN-DBS for PD and OCD, by means of a stochastic dynamical model and two microelectrode recording (MER) datasets.</div>
</front>
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