ArticleCognitive neurodynamics2024
Assessment of rTMS treatment effects for methamphetamine addiction based on EEG functional connectivity.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Convolutional Neural Network Combining Multi-scale Convolution and Functional Connectivity for Predicting rTMS Efficacy in MUD.Annals of biomedical engineering · 2026Article
- Modulatory Effects of Methadone on Brain Network Instability in Methamphetamine Use; A Cross-sectional Study.Archives of academic emergency medicine · 2026Article
- Effect of rTMS on cerebral functional hierarchical structure in schizophrenia.Psychoradiology · 2026Article
- Dynamic Neural Network States During Social and Non-Social Cueing in Virtual Reality Working Memory Tasks: A Leading Eigenvector Dynamics Analysis Approach.Brain sciences · 2024Article
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Authors and funding
6 authors.
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Abstract
Methamphetamine (MA) addiction leads to impairment of neural communication functions in the brain, and functional connectivity (FC) may be a valid indicator. However, it is unclear how FC in the brain changes in methamphetamine use disorder (MUD) after treatment with repetitive transcranial magnetic stimulation (rTMS). Thirty-four patients with MUD participated in this study. The subjects were randomized to receive the active or sham rTMS for four weeks. Subjects performed electroencephalography (EEG) examinations and visual analogue scale (VAS) assessments before and after the treatment. The FC networks were constructed and visualized, and then the graph theory analysis was carried out. Finally, machine learning was used to classify FC networks before and after rTMS. The results showed that (1) the active group showed a significant enhancement in connectivity in the beta band; (2) the global efficiency, local efficiency, and aggregation coefficient of the active group in the beta band decreased significantly; (3) the LDA algorithm combined with the beta band FC matrix achieved an average accuracy of 82.5% in distinguishing before and after treatment. This study demonstrated that brain FC could effectively assess the therapeutic effect of rTMS, among which the beta band was the most sensitive and effective frequency band. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-024-10097-x.
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