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

Elisa Capecci, Nikola Kasabov, Grace Wang

Research output: Contribution to journalArticle

19 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)62-77
Number of pages16
JournalNeural Networks
Volume68
Early online date20 Apr 2015
DOIs
Publication statusPublished - 31 Aug 2015

Keywords

  • spiking neural networks
  • NeuCube
  • EEG
  • Response to treatment
  • Methadone maintenance
  • Opiates

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