A Neural Signature of Drug Craving in Methamphetamine Users
Post by Christopher Chen
The takeaway
By measuring brain activity and applying machine learning to detect patterns, researchers have identified a neurobiological signature of drug craving in methamphetamine use disorder (MUD). These findings represent a significant advancement toward developing personalized and effective therapeutic interventions for MUD and other substance use disorders.
What's the science?
Whether a person becomes addicted to a drug or suffers from drug relapse is strongly grounded in the neurobiological mechanisms of craving and drug cue reactivity. Therefore, researchers have focused on developing treatments that target craving to reduce drug use.
Machine learning tools can be used to develop predictive models correlating brain activity and drug craving. In combination with EEG and behavioral data, machine learning has previously helped classify individuals with MUD and healthy controls with over 90% accuracy. However, while these studies demonstrated promising results, they used conventional low-density EEG. Conventional EEGs at the 32- and 64-channel scale lack the specificity to localize the source of certain brain signals. High-density (128 channels or higher) EEG can better assess the regional and global brain activity related to craving in MUD.
In a recent article in Cell Reports Medicine, Tian and colleagues leveraged high-density resting-state EEG and machine learning to investigate the neurophysiological signatures of MUD. Ultimately, this study aimed to identify individual-level functional connectivity in individuals with and without MUD in the hopes of generating reliable biomarkers that could be used to predict MUD.
How did they do it?
Researchers generated brain functional connectivity networks (FCNs), essentially visualizations of synchrony between brain regions, using data from resting-state high-density EEG from individuals with MUD and healthy controls (HC) who watched a 5-minute video depicting various scenarios of methamphetamine use. The data included 465 region of interest (ROI) pairs, spanning different frequency bands (delta, theta, alpha, beta, and gamma) and two resting conditions (eyes closed and eyes open).
The researchers quantified FCNs using a measurement called imaginary coherence (iCoh), a measurement of the synchrony between EEG signals from two channels. After characterizing FCNs in each subject, researchers used a machine learning technique called a relevance vector machine (RVM) to build models to predict craving scores for individuals with MUD. To further validate these predictive models, researchers applied them to another resting-state EEG dataset of 44 different individuals with MUD.
Additionally, the researchers were interested in seeing whether their models could distinguish between individuals with MUD and HC. They applied the RVM model to classify individuals with MUD and HC using FCNs from various brain signaling frequency bands and resting conditions. Finally, they compared the predictive capacity of their models with models derived from another well-known marker of brain activity called EEG spectral power.
What did they find?
EEG-tailored machine learning models pinpointed crucial brain regions like the medial prefrontal cortex (mPFC), angular gyrus, orbital gyrus, and insula, along with their connections, as critical in mediating craving — findings that align with previous MUD studies. Interestingly, there were also unique connections at certain frequencies in the brain — delta and beta bands — that correlated with craving, suggesting these may be potential therapeutic targets.
The researchers found that the most robust biomarker for MUD came from activity networks in the beta frequency, while participants had their eyes open (REO beta). Using data from REO beta conditions, the models exhibited the strongest predictive capabilities for cue-induced craving, correlating well with craving in individuals with MUD. Importantly, the prediction was prospectively replicated in an independent EEG dataset. The model also effectively identified abnormalities in MUD individuals, bettering previous methods by associating brain activities and interactions with MUD using source localization and iCoh. REO beta models were also best at classification performance across all frequency bands and resting conditions, expressing over 80% accuracy in determining whether an individual had MUD.
What's the impact?
The results illustrate the effectiveness of integrating advanced brain imaging techniques with machine learning tools in identifying robust neurobiological biomarkers for drug addiction. Furthermore, the study successfully developed replicable predictive models for craving and showed that FCNs are potent measurement tools for characterizing brain activity related to drug craving. Looking ahead, these insights underscore the potential of leveraging similar combinations of imaging and AI-driven techniques to create more personalized and effective therapeutics for alleviating MUD and other drug-use disorders.
