Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

Specific endophenotypes in EEG microstates for methamphetamine use disorder

Background

Electroencephalogram (EEG) microstates, which reflect large-scale resting-state networks of the brain, have been proposed as potential endophenotypes for methamphetamine use disorder (MUD). However, current endophenotypes lack refinement at the frequency band level, limiting their precision in identifying key frequency bands associated with MUD.

Methods

In this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.

Results

During the resting state, the highest classification accuracy for detecting MUD was 85.5%, achieved using microstate parameters in the alpha band. Among these, the coverage of microstate class A contributed the most, suggesting it as the most promising endophenotype for specifying MUD.

Discussion

We accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.

Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals

Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (p < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.

Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder

Methamphetamine use disorder (MUD) is a substance use disorder. Because MUD has become more prevalent due to the COVID-19 pandemic, alternative ways to help the efficiency of mass screening of MUD are important. Previous studies used electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) aberrations during the virtual reality (VR) induction of drug craving to accurately separate patients with MUD from the healthy controls. However, whether these abnormalities present without induction of drug-cue reactivity to enable separation between patients and healthy subjects remains unclear. Here, we propose a clinically comparable intelligent system using the fusion of 5-channel EEG, HRV, and GSR data during resting state to aid in detecting MUD. Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. The analytic results revealed that the fusion of HRV and GSR features leads to the most accurate separation rate of 79%. The use of EEG, HRV, and GSR features provides more robust information, leading to relatively similar and enhanced accuracy across different classifiers. In conclusion, we demonstrated that a clinically applicable intelligent system using resting-state EEG, ECG, and GSR features without the induction of drug cue reactivity enhances the detection of MUD. This system is easy to implement in the clinical setting and can save a lot of time on setting up and experimenting while maintaining excellent accuracy to assist in mass screening of MUD.

Enhanced brain network flexibility by physical exercise in female methamphetamine users

Methamphetamine (MA) abuse is increasing worldwide, and evidence indicates that MA causes degraded cognitive functions such as executive function, attention, and flexibility. Recent studies have shown that regular physical exercise can ameliorate the disturbed functions. However, the potential functional network alterations resulting from physical exercise have not been extensively studied in female MA users. We collaborated with a drug rehabilitation center for this study to investigate changes in brain activity and network dynamics after two types of acute and long-term exercise interventions based on 64-channel electroencephalogram recordings of seventy-nine female MA users, who were randomly divided into three groups: control group (CG), dancing group (DG) and bicycling group (BG). Over a 12-week period, we observed a clear drop in the rate of brain activity in the exercise groups, especially in the frontal and temporal regions in the DG and the frontal and occipital regions in the BG, indicating that exercise might suppress hyperactivity and that different exercise types have distinct impacts on brain networks. Importantly, both exercise groups demonstrated enhancements in brain flexibility and network connectivity entropy, particularly after the acute intervention. Besides, a significantly negative correlation was found between Δattentional bias and Δbrain flexibility after acute intervention in both DG and BG. Analysis strongly suggested that exercise programs can reshape patient brains into a highly energy-efficient state with a lower activity rate but higher information communication capacity and more plasticity for potential cognitive functions. These results may shed light on the potential therapeutic effects of exercise interventions for MA users.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09848-5.

Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks

Methamphetamine (MA) is a neurological drug, which is harmful to the overall brain cognitive function when abused. Based on this property of MA, people can be divided into those with MA abuse and healthy people. However, few studies to date have investigated automatic detection of MA abusers based on the neural activity. For this reason, the purpose of this research was to investigate the difference in the neural activity between MA abusers and healthy persons and accordingly discriminate MA abusers. First, we performed event-related potential (ERP) analysis to determine the time range of P300. Then, the wavelet coefficients of the P300 component were extracted as the main features, along with the time and frequency domain features within the selected P300 range to classify. To optimize the feature set, F_score was used to remove features below the average score. Finally, a Bidirectional Long Short-term Memory (BiLSTM) network was performed for classification. The experimental result showed that the detection accuracy of BiLSTM could reach 83.85%. In conclusion, the P300 component of EEG signals of MA abusers is different from that in normal persons. Based on this difference, this study proposes a novel way for the prevention and diagnosis of MA abuse.

Analysing the behaviour change of brain regions of methamphetamine abusers using electroencephalogram signals: Hope to design a decision support system

Long-term use of methamphetamine (meth) causes cognitive and neuropsychological impairments. Analysing the impact of this substance on the human brain can aid prevention and treatment efforts. In this study, the electroencephalogram (EEG) signals of meth abusers in the abstinence period and healthy subjects were recorded during eyes-closed and eyes-opened states to distinguish the brain regions that meth can significantly influence. In addition, a decision support system (DSS) was introduced as a complementary method to recognize substance users accompanied by biochemical tests. According to these goals, the recorded EEG signals were pre-processed and decomposed into frequency bands using the discrete wavelet transform (DWT) method. For each frequency band, energy, KS entropy, Higuchi and Katz fractal dimensions of signals were calculated. Then, statistical analysis was applied to select features whose channels contain a p-value less than 0.05. These features between two groups were compared, and the location of channels containing more features was specified as discriminative brain areas. Due to evaluating the performance of features and distinguishing the two groups in each frequency band, features were fed into a k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron neural networks (MLP) and linear discriminant analysis (LDA) classifiers. The results indicated that prolonged consumption of meth has a considerable impact on the brain areas responsible for working memory, motor function, attention, visual interpretation, and speech processing. Furthermore, the best classification accuracy, almost 95.8%, was attained in the gamma band during the eyes-closed state.

An electroencephalographic signature predicts craving for methamphetamine

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence

Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.

Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification

Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.

An automated drug dependence detection system based on EEG

OBJECTIVE

Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser.

METHODS

EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification.

RESULTS

The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score.

CONCLUSIONS AND SIGNIFICANCE

The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction

Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

Resting-state EEG, Substance use and Abstinence After Chronic use: A Systematic Review

Resting-state EEG reflects intrinsic brain activity and its alteration represents changes in cognition that are related to neuropathology. Thereby, it provides a way of revealing the neurocognitive mechanisms underpinning chronic substance use. In addition, it is documented that some neurocognitive functions can recover following sustained abstinence. We present a systematic review to synthesize how chronic substance use is associated with resting-state EEG alterations and whether these spontaneously recover from abstinence. A literature search in Medline, PsycINFO, Embase, CINAHL, Web of Science, and Scopus resulted in 4088 articles, of which 57 were included for evaluation. It covered the substance of alcohol (18), tobacco (14), cannabis (8), cocaine (6), opioids (4), methamphetamine (4), and ecstasy (4). EEG analysis methods included spectral power, functional connectivity, and network analyses. It was found that long-term substance use with or without substance use disorder diagnosis was associated with broad intrinsic neural activity alterations, which were usually expressed as neural hyperactivation and decreased neural communication between brain regions. Some studies found the use of alcohol, tobacco, cocaine, cannabis, and methamphetamine was positively correlated with these changes. These alterations can partly recover from abstinence, which differed between drugs and may reflect their neurotoxic degree. Moderating factors that may explain results inconsistency are discussed. In sum, resting-state EEG may act as a potential biomarker of neurotoxic effects of chronic substance use. Recovery effects awaits replication in larger samples with prolonged abstinence. Balanced sex ratio, enlarged sample size, advanced EEG analysis methods, and transparent reporting are recommended for future studies.

Effects of Transcranial Direct Current Stimulation on Attentional Bias to Methamphetamine Cues and Its Association With EEG-Derived Functional Brain Network Topology

BACKGROUND

Although transcranial direct current stimulation (tDCS) has shown to potentially mitigate drug craving and attentional bias to drug-related stimuli, individual differences in such modulatory effects of tDCS are less understood. In this study, we aimed to investigate a source of the inter-subject variability in the tDCS effects that can be useful for tDCS-based treatments of individuals with methamphetamine (MA) use disorder (IMUD).

METHODS

Forty-two IMUD (all male) were randomly assigned to receive a single-session of either sham or real bilateral tDCS (anodal right/cathodal left) over the dorsolateral prefrontal cortex. The tDCS effect on MA craving and biased attention to drug stimuli were investigated by quantifying EEG-derived P3 (a measure of initial attentional bias) and late positive potential (LPP; a measure of sustained motivated attention) elicited by these stimuli. To assess the association of changes in P3 and LPP with brain connectivity network (BCN) topology, the correlation between topology metrics, specifically those related to the efficiency of information processing, and the tDCS effect was investigated.

RESULTS

The P3 amplitude significantly decreased following the tDCS session, whereas the amplitudes increased in the sham group. The changes in P3 amplitudes were significantly correlated with communication efficiency measured by BCN topology metrics (r = -0.47, P = .03; r = -0.49, P = .02). There was no significant change in LPP amplitude due to the tDCS application.

CONCLUSIONS

These findings validate that tDCS mitigates initial attentional bias, but not the sustained motivated attention, to MA stimuli. Importantly, however, results also show that the individual differences in the effects of tDCS may be underpinned by communication efficiency of the BCN topology, and therefore, these BCN topology metrics may have the potential to robustly predict the effectiveness of tDCS-based interventions on MA craving and attentional bias to MA stimuli among IMUD.

Theta and Alpha Oscillatory Activity During Working Memory Maintenance in Long-Term Cannabis Users: The Importance of the Polydrug Use Context

Background: Impairments in various subdomains of memory have been associated with chronic cannabis use, but less is known about their neural underpinnings, especially in the domain of the brain's oscillatory activity. Aims: To investigate neural oscillatory activity supporting working memory (WM) in regular cannabis users and non-using controls. We focused our analyses on frontal midline theta and posterior alpha asymmetry as oscillatory fingerprints for the WM's maintenance process. Methods: 30 non-using controls (CG) and 57 regular cannabis users-27 exclusive cannabis users (CU) and 30 polydrug cannabis users (PU) completed a Sternberg modified WM task with a concurrent electroencephalography recording. Theta, alpha and beta frequency bands were examined during WM maintenance. Results: When compared to non-using controls, the PU group displayed increased frontal midline theta (FMT) power during WM maintenance, which was positively correlated with RT. The posterior alpha asymmetry during the maintenance phase, on the other hand, was negatively correlated with RT in the CU group. WM performance did not differ between groups. Conclusions: Both groups of cannabis users (CU and PU), when compared to the control group, displayed differences in oscillatory activity during WM maintenance, unique for each group (in CU posterior alpha and in PU FMT correlated with performance). We interpret those differences as a reflection of compensatory strategies, as there were no differences between groups in task performance. Understanding the psychophysiological processes in regular cannabis users may provide insight on how chronic use may affect neural networks underlying cognitive processes, however, a polydrug use context (i.e., combining cannabis with other illegal substances) seems to be an important factor.

Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine- and alcohol-use disorders

Electroencephalography (EEG) likely reflects activity of cortical neurocircuits, making it an insightful estimation for mental health in patients with substance use disorder (SUD). EEG signals are recorded as sinusoidal waves, containing spectral amplitudes across several frequency bands with high spatio-temporal resolution. Prior work on EEG signal analysis has been made mainly at individual electrodes. These signals can be evaluated from advanced aspects, including sub-regional and hemispheric analyses. Due to limitation of computational techniques, few studies in earlier work could conduct data analyses from these aspects. Therefore, EEG in patients with SUD is not fully understood. In the present retrospective study, spectral powers from a data house containing opioid (OUD), methamphetamine/stimulants (MUD), and alcohol use disorder (AUD) were extracted, and then converted into five distinct topographic data (i.e., electrode-based, cortical subregion-based, left-right hemispheric, anterior-posterior based, and total cortex-based analyses). We found that data conversion and reorganization in the topographic way had an impact on EEG spectral powers in patients with OUD significantly different from those with MUD or AUD. Differential changes were observed from multiple perspectives, including individual electrodes, subregions, hemispheres, anterior-posterior cortices, and across the cortex as a whole. Understanding the differential changes in EEG signals may be useful for future work with machine learning and artificial intelligence (AI), not only for diagnostic but also for prognostic purposes in patients with SUD.

Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform

Few studies have investigated the functional patterns of methamphetamine abstainers. A better understanding of the underlying neurobiological mechanism in the brains of methamphetamine abstainers will help to explain their abnormal behaviors. Forty-two male methamphetamine abstainers, currently in a long-term abstinence status (for at least 14 months), and 32 male healthy controls were recruited. All subjects underwent functional MRI while responding to drug-associated cues. This study proposes to combine a convolutional neural network with a short-time Fourier transform to identify different brain patterns between methamphetamine abstainers and controls. The short-time Fourier transformation provides time-localized frequency information, while the convolutional neural network extracts the structural features of the time-frequency spectrograms. The results showed that the classifier achieved a satisfactory performance (98.9% accuracy) and could extract robust brain voxel information. The highly discriminative power voxels were mainly concentrated in the left inferior orbital frontal gyrus, the bilateral postcentral gyri, and the bilateral paracentral lobules. This study provides a novel insight into the different functional patterns between methamphetamine abstainers and healthy controls. It also elucidates the pathological mechanism of methamphetamine abstainers from the view of time-frequency spectrograms.

Not Just a Pot: Visual Episodic Memory in Cannabis Users and Polydrug Cannabis Users: ROC and ERP Preliminary Investigation

Background While research has consistently identified an association between long-term cannabis use and memory impairments, few studies have examined this relationship in a polydrug context (i.e., when combining cannabis with other substances). Aims: In this preliminary study, we used event-related potentials to examine the recognition process in a visual episodic memory task in cannabis users (CU) and cannabis polydrug users (PU). We hypothesized that CU and PU will have both-behavioral and psychophysiological-indicators of memory processes affected, compared to matched non-using controls with the PU expressing more severe changes. Methods 29 non-using controls (CG), 24 CU and 27 PU were enrolled into the study. All participants completed a visual learning recognition task while brain electrical activity was recorded. Event-related potentials were calculated for familiar (old) and new images from a signal recorded during a subsequent recognition test. We used receiver operating characteristic curves for behavioral data analysis. Results The groups did not differ in memory performance based on receiver operating characteristic method in accuracy and discriminability indicators nor mean reaction times for old/new images. The frontal old/new effect expected from prior research was observed for all participants, while a parietal old/new effect was not observed. While, the significant differences in the late parietal component (LPC) amplitude was observed between CG and PU but not between CG and CU nor CU and PU. Linear regression analysis was used to examine the mean amplitude of the LPC component as a predictor of memory performance accuracy indicator. LPC amplitude predicts recognition accuracy only in the CG. Conclusion The results showed alterations in recognition memory processing in CU and PU groups compared to CG, which were not manifested on the behavioral level, and were the most prominent in cannabis polydrug users. We interpret it as a manifestation of the cumulative effect of multiple drug usage in the PU group.