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Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation.
Spiliotis, Konstantinos; Appali, Revathi; Fontes Gomes, Anna Karina; Payonk, Jan Philipp; Adrian, Simon; van Rienen, Ursula; Starke, Jens; Köhling, Rüdiger.
Affiliation
  • Spiliotis K; Institute of Mathematics, University of Rostock, Rostock, Germany. konstantinos.spiliotis@uni-rostock.de.
  • Appali R; Laboratory of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece. konstantinos.spiliotis@uni-rostock.de.
  • Fontes Gomes AK; Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.
  • Payonk JP; Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany.
  • Adrian S; Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.
  • van Rienen U; Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.
  • Starke J; Faculty of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany.
  • Köhling R; Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.
Sci Rep ; 14(1): 18919, 2024 08 14.
Article in En | MEDLINE | ID: mdl-39143173
ABSTRACT
A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin-Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Brain Stimulation / Models, Neurological Limits: Humans Language: En Journal: Sci Rep Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Brain Stimulation / Models, Neurological Limits: Humans Language: En Journal: Sci Rep Year: 2024 Document type: Article