Academic Journal
Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic noise.
| Τίτλος: | Computational modelling of Parkinson's disease: A multiscale approach with deep brain stimulation and stochastic noise. |
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| Συγγραφείς: | Herrera A; Department of Statistics, University of Manitoba, Winnipeg, Canada. Electronic address: herrera8@myumanitoba.ca., Chowdhury M; Department of Statistics, University of Manitoba, Winnipeg, Canada. Electronic address: chowdh62@myumanitoba.ca., Shaheen H; Department of Statistics, Faculty of Science, University of Manitoba, Winnipeg, Canada. Electronic address: hina.shaheen@umanitoba.ca. |
| Πηγή: | Journal of neuroscience methods [J Neurosci Methods] 2026 Aug; Vol. 432, pp. 110752. Date of Electronic Publication: 2026 Apr 06. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press. |
| Ιατρικοί όροι (MeSH): | Deep Brain Stimulation*/methods , Parkinson Disease*/physiopathology , Parkinson Disease*/therapy , Brain*/physiopathology , Computer Simulation* , Models, Neurological*, Action Potentials/physiology ; Neurons/physiology ; Stochastic Processes ; Humans |
| Περίληψη: | Multiscale modelling presents a multifaceted perspective into understanding the mechanisms of the brain and how neurodegenerative disorders like Parkinson's disease (PD) manifest and evolve over time. In this study, we propose a novel co-simulation multiscale approach that unifies both micro- and macroscales to more rigorously capture brain dynamics. The presented design considers the electrodiffusive activity across the brain and in the network defined by the cortex, basal ganglia, and thalamus that is implicated in the mechanics of PD, as well as the contribution of presynaptic inputs in the highlighted regions. The application of deep brain stimulation (DBS) and its effects, along with the inclusion of stochastic noise are also examined. We found that the thalamus exhibits large, fluctuating spiking in both the deterministic and stochastic conditions, suggesting that noise contributes primarily to neural variability, rather than driving the overall spiking activity. Ultimately, this work intends to provide greater insights into the dynamics of PD and the brain which can eventually be converted into clinical use. (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Σχόλια: | Update of: ArXiv. 2025 Sep 16:arXiv:2509.08179v2.. (PMID: 41001568) |
| Contributed Indexing: | Keywords: Brain models; Computational modelling; Computational neuroscience; Neuron dynamics; Neuronal modelling; Non-linear dynamics; Statistical modelling; Stochastic noise |
| Entry Date(s): | Date Created: 20260408 Date Completed: 20260714 Latest Revision: 20260714 |
| Update Code: | 20260715 |
| DOI: | 10.1016/j.jneumeth.2026.110752 |
| PMID: | 41951129 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1872-678X |
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| DOI: | 10.1016/j.jneumeth.2026.110752 |