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

TL;DR

This study demonstrates that EEG gamma band power can objectively distinguish methamphetamine-dependent individuals from healthy controls and assess rTMS treatment efficacy, paving the way for real-time neuromodulation in addiction therapy.

Contribution

It introduces the use of EEG gamma band power as a biomarker for methamphetamine dependence and rTMS treatment evaluation, employing machine learning for classification.

Findings

Gamma RBP effectively distinguishes MBT from HC with 90% accuracy.

rTMS treatment normalizes EEG spectra towards healthy control patterns.

Gamma RBP during cue exposure can serve as a real-time biomarker for addiction and treatment response.

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.