Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation

Hell, Franz; Palleis, Carla; Mehrkens, Jan H.; Koeglsperger, Thomas und Boetzel, Kai (3. April 2019): Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation. In: Frontiers in Neurology, Bd. 10, 314

Abstract

Deep brain stimulation has developed into an established treatment for movement disorders and is being actively investigated for numerous other neurological as well as psychiatric disorders. An accurate electrode placement in the target area and the effective programming of DBS devices are considered the most important factors for the individual outcome. Recent research in humans highlights the relevance of widespread networks connected to specific DBS targets. Improving the targeting of anatomical and functional networks involved in the generation of pathological neural activity will improve the clinical DBS effect and limit side-effects. Here, we offer a comprehensive overview over the latest research on target structures and targeting strategies in DBS. In addition, we provide a detailed synopsis of novel technologies that will support DBS programming and parameter selection in the future, with a particular focus on closed-loop stimulation and associated biofeedback signals.

• Dokumententyp: Zeitschriftenartikel
• Keywords: Deep brain stimulation; machine learning; adaptive; feedback; DBS target; reinforcement learning
• Fakultät: Medizin
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit ; 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• ISSN: 1664-2295
• Sprache: Englisch