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A minimally invasive neurostimulation method for controlling abnormal synchronisation in the neuronal activity
File | Description | Size | Format | |
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journal.pcbi.1006296.pdf | Published version | 6.69 MB | Adobe PDF | View/Open |
Title: | A minimally invasive neurostimulation method for controlling abnormal synchronisation in the neuronal activity |
Authors: | Asllani, M Expert, P Carletti, T |
Item Type: | Journal Article |
Abstract: | Many collective phenomena in Nature emerge from the -partial- synchronisation of the units comprising a system. In the case of the brain, this self-organised process allows groups of neurons to fire in highly intricate partially synchronised patterns and eventually lead to high level cognitive outputs and control over the human body. However, when the synchronisation patterns are altered and hypersynchronisation occurs, undesirable effects can occur. This is particularly striking and well documented in the case of epileptic seizures and tremors in neurodegenerative diseases such as Parkinson’s disease. In this paper, we propose an innovative, minimally invasive, control method that can effectively desynchronise misfiring brain regions and thus mitigate and even eliminate the symptoms of the diseases. The control strategy, grounded in the Hamiltonian control theory, is applied to ensembles of neurons modelled via the Kuramoto or the Stuart-Landau models and allows for heterogeneous coupling among the interacting unities. The theory has been complemented with dedicated numerical simulations performed using the small-world Newman-Watts network and the random Erdős-Rényi network. Finally the method has been compared with the gold-standard Proportional-Differential Feedback control technique. Our method is shown to achieve equivalent levels of desynchronisation using lesser control strength and/or fewer controllers, being thus minimally invasive. |
Issue Date: | 19-Jul-2018 |
Date of Acceptance: | 12-Jun-2018 |
URI: | http://hdl.handle.net/10044/1/62694 |
DOI: | https://dx.doi.org/10.1371/journal.pcbi.1006296 |
ISSN: | 1553-734X |
Publisher: | Public Library of Science (PLoS) |
Journal / Book Title: | PLoS Computational Biology |
Volume: | 14 |
Issue: | 7 |
Copyright Statement: | © 2018 Asllani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Science & Technology Life Sciences & Biomedicine Biochemical Research Methods Mathematical & Computational Biology Biochemistry & Molecular Biology DEEP BRAIN-STIMULATION COUPLED OSCILLATORS PERTURBATION-THEORY PARKINSONS-DISEASE EPILEPSY MODEL MECHANISMS NETWORKS CHAOS POPULATIONS 06 Biological Sciences 08 Information And Computing Sciences 01 Mathematical Sciences Bioinformatics |
Publication Status: | Published |
Open Access location: | http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006296 |
Article Number: | ARTN e1006296 |
Appears in Collections: | Applied Mathematics and Mathematical Physics Mathematics |