Nonlinear Dynamic Complexity and Sources of Resting-state EEG in Abstinent Heroin Addicts

Qinglin Zhao, Hua Jiang, Bin Hu, Yonghui Li, Ning Zhong, Mi Li, Wenhua Lin, Quanying Liu

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

It has been reported that chronic heroin intake induces both structural and functional changes in human brain; however, few studies have investigated the carry-over adverse effects on brain after heroin withdrawal. In this paper, we examined the neurophysiological differences between the abstinent heroin addicts (AHAs) and healthy controls (HCs) using nonlinear dynamic analysis and source localization analysis in resting-state electroencephalogram (EEG) data; 5 min resting EEG data from 20 AHAs and twenty age-, education-, and gender-matched HCs were recorded using 64 electrodes. The results of nonlinear characteristics (e.g., the correlation dimension, Kolmogorov entropy, and Lempel-Ziv complexity) showed that the EEG signals in alpha band from AHAs were significantly more irregular. Moreover, the source localization results confirmed the neuronal activities in alpha band in AHAs were significantly weaker in parietal lobe (BA3 and BA7), frontal lobe (BA4 and BA6), and limbic lobe (BA24). Together, our analysis at both the sensor level and source level suggested the functional abnormalities in the brain during heroin abstinence, in particular for the neuronal oscillations in alpha band.

Original languageEnglish
Article number7935504
Pages (from-to)349-355
Number of pages7
JournalIEEE Transactions on Nanobioscience
Volume16
Issue number5
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes

Keywords

  • Heroin addicts
  • correlation dimension
  • kolmogorov entropy
  • lempel-Ziv complexity
  • resting EEG
  • sLORETA

Fingerprint

Dive into the research topics of 'Nonlinear Dynamic Complexity and Sources of Resting-state EEG in Abstinent Heroin Addicts'. Together they form a unique fingerprint.

Cite this