Repetitive TMS-based Identification of Methamphetamine-Dependent Individuals Using EEG Spectra

2025-08-15

AI Summary

This study addresses the lack of objective neurobiological markers for diagnosing methamphetamine (METH) dependence and quantifying craving modulation following repetitive transcranial magnetic stimulation (rTMS). Using high-density electroencephalography (EEG), we recorded cue-reactive brain responses in METH-dependent individuals and healthy controls, extracted relative power features across frequency bands, and applied random forest classification. Results revealed significantly elevated gamma-band (30–100 Hz) relative power in METH users, with maximal discriminative capacity at electrodes TP10 and CP2, achieving 90% cross-group classification accuracy. Following rTMS intervention, gamma power in patients decreased significantly and normalized toward control levels. These findings validate gamma oscillations as a robust, addiction-specific neural biomarker for METH dependence and demonstrate their utility as a quantifiable, individualized target for closed-loop neuromodulation therapies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Graph-based Machine Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

The impact of repetitive transcranial magnetic stimulation (rTMS) on methamphetamine (METH) users' craving levels is often assessed using questionnaires. This study explores the feasibility of using neural signals to obtain more objective results. EEG signals recorded from 20 METH-addicted participants Before and After rTMS (MBT and MAT) and from 20 healthy participants (HC) are analyzed. In each EEG paradigm, participants are shown 15 METH-related and 15 neutral pictures randomly, and the relative band power (RBP) of each EEG sub-band frequency is derived. The average RBP across all 31 channels, as well as individual brain regions, is analyzed. Statistically, MAT's alpha, beta, and gamma RBPs are more like those of HC compared to MBT, as indicated by the power topographies. Utilizing a random forest (RF), the gamma RBP is identified as the optimal frequency band for distinguishing between MBT and HC with a 90% accuracy. The performance of classifying MAT versus HC is lower than that of MBT versus HC, suggesting that the efficacy of rTMS can be validated using RF with gamma RBP. Furthermore, the gamma RBP recorded by the TP10 and CP2 channels dominates the classification task of MBT versus HC when receiving METH-related image cues. The gamma RBP during exposure to METH-related cues can serve as a biomarker for distinguishing between MBT and HC and for evaluating the effectiveness of rTMS. Therefore, real-time monitoring of gamma RBP variations holds promise as a parameter for implementing a customized closed-loop neuromodulation system for treating METH addiction.

Problem

• Identify METH-dependent individuals using EEG spectra post-rTMS.
• Assess gamma RBP as biomarker for rTMS efficacy evaluation.
• Develop closed-loop neuromodulation system for METH addiction treatment.

Innovation

• EEG gamma RBP for addiction classification
• Random forest with 90% accuracy
• Closed-loop neuromodulation using gamma RBP