Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment

Article

Capecci, Elisa, Kasabov, Nikola and Wang, Grace Y.. 2015. "Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment." Neural Networks. 68, pp. 62-77. https://doi.org/10.1016/j.neunet.2015.03.009
Article Title

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment

ERA Journal ID18090
Article CategoryArticle
AuthorsCapecci, Elisa (Author), Kasabov, Nikola (Author) and Wang, Grace Y. (Author)
Journal TitleNeural Networks
Journal Citation68, pp. 62-77
Number of Pages16
Year2015
Place of PublicationUnited Kingdom
ISSN0893-6080
1879-2782
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neunet.2015.03.009
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0893608015000659
Abstract

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging.

KeywordsEEG; Methadone maintenance; NeuCube; Opiates; Response to treatment; Spiking neural networks
ANZSRC Field of Research 2020520205. Psychopharmacology
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Byline AffiliationsAuckland University of Technology, New Zealand
Institution of OriginUniversity of Southern Queensland
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