ArticleCognitive neurodynamics2019
Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG.
Article in Cognitive neurodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Cortical Complexity Alterations in Methamphetamine, Cannabis, and Opioid Users: An EEG-Based Analysis.Journal of biomedical physics & engineering · 2026Article
- A Convolutional Neural Network Combining Multi-scale Convolution and Functional Connectivity for Predicting rTMS Efficacy in MUD.Annals of biomedical engineering · 2026Article
- Nonlinear EEG Complexity as a Marker of Maladaptive Brain Plasticity in Substance Use Disorders: A Multi-Group Machine Learning Classification Study.Brain sciences · 2026Article
- Abnormal DLPFC activation in severe methamphetamine use disorder during abstinence: fNIRS evidence from Stroop task.Frontiers in psychiatry · 2026Article
- Resting-state EEG power and machine-learning classification in adult males with gambling disorder.Frontiers in human neuroscience · 2025Article
- Resting state EEG delta-beta amplitude-amplitude coupling: a neural predictor of cortisol response under stress.Cognitive neurodynamics · 2024Article
- Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks.Brain topography · 2024Article
- Assessment of rTMS treatment effects for methamphetamine addiction based on EEG functional connectivity.Cognitive neurodynamics · 2024Article
- Analysing the behaviour change of brain regions of methamphetamine abusers using electroencephalogram signals: Hope to design a decision support system.Addiction biology · 2024Article
- An electroencephalographic signature predicts craving for methamphetamine.Cell reports. Medicine · 2024Article
- Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups.Journal of biomedical physics & engineering · 2023Article
- Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction.Frontiers in psychiatry · 2023Review
- NDCN-Brain: An Extensible Dynamic Functional Brain Network Model.Diagnostics (Basel, Switzerland) · 2022Article
- The Counterproductive Effect of Right Anodal/Left Cathodal Transcranial Direct Current Stimulation Over the Dorsolateral Prefrontal Cortex on Impulsivity in Methamphetamine Addicts.Frontiers in psychiatry · 2022Article
- The Effectiveness of Mindfulness-Based Relapse Prevention on Chinese Methamphetamine Dependent Patients: A Pilot Study.Frontiers in psychiatry · 2022Article
- Construction of embedded fMRI resting-state functional connectivity networks using manifold learning.Cognitive neurodynamics · 2021Article
- Brain functional network modeling and analysis based on fMRI: a systematic review.Cognitive neurodynamics · 2021Review
- Energy features in spontaneous up and down oscillations.Cognitive neurodynamics · 2021Article
- Effects of exercise programs on neuroelectric dynamics in drug addiction.Cognitive neurodynamics · 2021Article
- The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.Cognitive neurodynamics · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.
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