Neuroelectrophysiological approaches in heroin addiction research: A review of literatures

Electrophysiological biomarkers in dual pathology

INTRODUCTION

The co-occurrence of substance use disorder with at least one other mental disorder is called dual pathology, which in turn is characterised by heterogeneous symptoms that are difficult to diagnose and have a poor response to treatment. For this reason, the identification and validation of biomarkers is necessary. Within this group, possible electroencephalographic biomarkers have been reported to be useful in diagnosis, treatment and follow-up, both in neuropsychiatric conditions and in substance use disorders. This article aims to review the existing literature on electroencephalographic biomarkers in dual pathology.

METHODS

A narrative review of the literature. A bibliographic search was performed on the PubMed, Science Direct, OVID, BIREME and Scielo databases, with the keywords: electrophysiological biomarker and substance use disorder, electrophysiological biomarker and mental disorders, biomarker and dual pathology, biomarker and substance use disorder, electroencephalography, and substance use disorder or comorbid mental disorder.

RESULTS

Given the greater amount of literature found in relation to electroencephalography as a biomarker of mental illness and substance use disorders, and the few articles found on dual pathology, the evidence is organised as a biomarker in psychiatry for the diagnosis and prediction of risk and as a biomarker for dual pathology.

CONCLUSIONS

Although the evidence is not conclusive, it suggests the existence of a subset of sites and mechanisms where the effects of psychoactive substances and the neurobiology of some mental disorders could overlap or interact.

Gender-Specific Interactions in a Visual Object Recognition Task in Persons with Opioid Use Disorder

Opioid use disorder (OUD)-associated overdose deaths have reached epidemic proportions worldwide over the past two decades, with death rates for men reported at twice the rate for women. Using a controlled, cross-sectional, age-matched (18-56 y) design to better understand the cognitive neuroscience of OUD, we evaluated the electroencephalographic (EEG) responses of male and female participants with OUD vs. age- and gender-matched non-OUD controls during a simple visual object recognition Go/No-Go task. Overall, women had significantly slower reaction times (RTs) than men. In addition, EEG N200 and P300 event-related potential (ERP) amplitudes for non-OUD controls were significantly larger for men, while their latencies were significantly shorter than for women. However, while N200 and P300 amplitudes were not significantly affected by OUD for either men or women in this task, latencies were also affected differentially in men vs. women with OUD. Accordingly, for both N200 and P300, male OUD participants exhibited longer latencies while female OUD participants exhibited shorter ones than in non-OUD controls. Additionally, robust oscillations were found in all participants during a feedback message associated with performance in the task. Although alpha and beta power during the feedback message were significantly greater for men than women overall, both alpha and beta oscillations exhibited significantly lower power in all participants with OUD. Taken together, these findings suggest important gender by OUD differences in cognitive processing and reflection of performance in this simple visual task.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

Resting State EEG Activity Related to Impulsivity in People with Prescription Opioid Use Disorder

Previous studies on EEG activity in prescription opioid use disorder (OUD) have reported neuronal dysfunction related to heroin use, most consistently reflected by increases in β-brain oscillations. As similar research has yet to examine EEG associated with non-medical use of prescription opioid and as inhibitory deficits are associated with OUD, this pilot study compared quantitative EEGs of 18 patients with prescription OUD and 18 healthy volunteers and assessed relationships between oscillatory activity and impulsivity with the Barratt Impulsiveness Scale (BIS-11). Spectral EEGs showed greater amplitude density in β₁, β₂, and β₃ frequencies across frontal, temporal-central and posterior recording areas in patients. Similar abnormal amplitude density increases were seen in δ but not in θ or α frequency bands. Patients exhibited greater scores (impaired impulse control) on BIS-11 subscales (attention, motor, self-control) and impairment of these impulsive subtypes was associated with increases in β and δ oscillations. In patients, β1, β2, and δ activity was positively associated with disorder severity. Taken together, the results suggest that altered brain oscillations in persons with prescription OUD show some similarities with reported oscillatory changes in heroin use and may indicate a chronic state of imbalance in neuronal networks regulating impulsive and inhibitory control systems.

EEG Spectral Power Analysis: A Comparison Between Heroin Dependent and Control Groups

Previous studies indicated that heroin abuse would result in abnormal functional organization of the brain. However, studies of heroin abuse- related brain dysfunction are scarce. The purpose of the present study was to investigate heroin effects on brain function by studying relationships between Electroencephalograph (EEG) spectral power and heroin abuse. The resting EEG signals were acquired from 15 male heroin dependent group and 15 male control group. The differences in the EEG components of each group were evaluated using the statistical Mann-Whitney examination and Davis Bouldin Index. The results show that heroin dependent group has an attenuated relative beta-2 power compared with other EEG frequency sub bands. Nevertheless, the results indicate heroin dependent group have an increase of power spectrum density for theta at all locations, as well as delta in the temporal, frontal and central areas compared with control group. Compared to control group, the heroin dependent group decreased its spectral power more than the control group in all three alpha bands. The present findings using the Davis Bouldin Index provide evidence that alpha-3 band in the FZ channel is more affected by heroin abuse than other frequency sub bands.

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.

Attentional bias and response inhibition in severe obesity with food disinhibition: a study of P300 and N200 event-related potential

BACKGROUND/OBJECTIVE

In obesity there is growing evidence for common mechanism between food intake regulation and substance use disorders, especially more attentional bias and less cognitive control. In the present study we investigated whether severely obese subjects with or without disordered eating exhibit electroencephalographic (EEG) event-related potential (ERP) modifications as observed in substance abusers.

SUBJECTS/METHODS

A total of 90 women were included; 30 in the normal-weight (NW) group (18.5 < BMI < 24.5 kg/m²; no food disinhibition or restriction on the Three-Factor Eating Questionnaire) and 60 participants with BMI ≥ 35 kg/m² were separated into two groups (n = 30): without food disinhibition (disinhibition score ≤8; ObFD- group) and with food disinhibition (score >8; ObFD+). Clinical and metabolic parameters as well as compartmental aspects (Eating Disorders Inventory-2, EDI-2) were assessed. Participants underwent an ERP recording with an auditory oddball paradigm.

RESULTS

The mean ± SD P300 amplitudes in Pz were significantly (p < 0.05) lower in ObFD- (12.4 ± 4.6) and ObFD+ (12.5 ± 4.4) groups than in the NW group (15.8 ± 5.9). The mean ± SD N200 amplitude in Cz was significantly lower in the ObFD- group (-2.0 ± 5.4) than in the NW group (-5.2 ± 4.2 vs; p = 0.035). N200 Cz amplitude was correlated with EDI-2 Binge eating risk score (ρ = 0.331; p = 0.01), EDI-2 Body Dissatisfaction score (ρ = 0.351; p = 0.007), and Drive for Thinness score (ρ = 0.26; p = 0.05).

CONCLUSIONS

The present study provides evidence for reduction of P300 and N200 amplitude in obese women and that N200 amplitude may be related to more disordered eating and eating disorder risk. This leads to consider attentional bias and response inhibition as core mechanisms in obesity and as possible targets for new therapeutic strategy.

Forging Neuroimaging Targets for Recovery in Opioid Use Disorder

The United States is in the midst of an opioid epidemic and lacks a range of successful interventions to reduce this public health burden. Many individuals with opioid use disorder (OUD) consume drugs to relieve physical and/or emotional pain, a pattern that may increasingly result in death. The field of addiction research lacks a comprehensive understanding of physiological and neural mechanisms instantiating this cycle of Negative Reinforcement in OUD, resulting in limited interventions that successfully promote abstinence and recovery. Given the urgency of the opioid crisis, the present review highlights faulty brain circuitry and processes associated with OUD within the context of the Three-Stage Model of Addiction (1). This model underscores Negative Reinforcement processes as crucial to the maintenance and exacerbation of chronic substance use together with Binge/Intoxication and Preoccupation/Anticipation processes. This review focuses on cross-sectional as well as longitudinal studies of relapse and treatment outcome that employ magnetic resonance imaging (MRI), functional near-infrared spectroscopy (fNIRs), brain stimulation methods, and/or electroencephalography (EEG) explored in frequency and time domains (the latter measured by event-related potentials, or ERPs). We discuss strengths and limitations of this neuroimaging work with respect to study design and individual differences that may influence interpretation of findings (e.g., opioid use chronicity/recency, comorbid symptoms, and biological sex). Lastly, we translate gaps in the OUD literature, particularly with respect to Negative Reinforcement processes, into future research directions involving operant and classical conditioning involving aversion/stress. Overall, opioid-related stimuli may lessen their hold on frontocingulate mechanisms implicated in Preoccupation/Anticipation as a function of prolonged abstinence and that degree of frontocingulate impairment may predict treatment outcome. In addition, longitudinal studies suggest that brain stimulation/drug treatments and prolonged abstinence can change brain responses during Negative Reinforcement and Preoccupation/Anticipation to reduce salience of drug cues, which may attenuate further craving and relapse. Incorporating this neuroscience-derived knowledge with the Three-Stage Model of Addiction may offer a useful plan for delineating specific neurobiological targets for OUD treatment.

Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents

Methadone as the most prevalent opioid substitution medication has been shown to influence the neurophysiological functions among heroin addicts. However, there is no firm conclusion on acute neuroelectrophysiological changes among methadone-treated subjects as well as the effectiveness of methadone in restoring brain electrical abnormalities among heroin addicts. This study aims to investigate the acute and short-term effects of methadone administration on the brain's electrophysiological properties before and after daily methadone intake over 10 weeks of treatment among heroin addicts. EEG spectral analysis and single-trial event-related potential (ERP) measurements were used to investigate possible alterations in the brain's electrical activities, as well as the cognitive attributes associated with MMN and P3. The results confirmed abnormal brain activities predominantly in the beta band and diminished information processing ability including lower amplitude and prolonged latency of cognitive responses among heroin addicts compared to healthy controls. In addition, the alteration of EEG activities in the frontal and central regions was found to be associated with the withdrawal symptoms of drug users. Certain brain regions were found to be influenced significantly by methadone intake; acute effects of methadone induction appeared to be associative to its dosage. The findings suggest that methadone administration affects cognitive performance and activates the cortical neuronal networks, resulting in cognitive responses enhancement which may be influential in reorganizing cognitive dysfunctions among heroin addicts. This study also supports the notion that the brain's oscillation powers and ERPs can be utilized as neurophysiological indices for assessing the addiction treatment traits.

Alcohol affects the P3 component of an adaptive stop signal task ERP

BACKGROUND

The P3 component of the event-related potential (ERP) has been particularly useful in alcohol research for identifying endophenotypes of alcohol-use disorder (AUD) risk in sober subjects. However, practice and/or fatigue reduce P3 amplitude, limiting the ability to ascertain acute and adaptive effects of alcohol exposure. Here, we report acute alcohol effects on P3 amplitude and latency using an adaptive stop signal task (aSST).

METHODS

One hundred forty-eight non-dependent moderate to heavy social drinkers, ages 21 to 27, participated in two single-blind, alcohol or placebo, counterbalanced sessions approximately 1 week apart. During each session, subjects performed an adaptive stop signal task (aSST) at 1) baseline, 2) upon reaching the target 60 mg/dL breath alcohol concentration or at the equivalent time during the placebo session, and 3) approximately 135 min later while the breath alcohol concentration was clamped. Here, we report on differences between baseline and first subsequent measurements across the experimental sessions. During each aSST run, the stop signal delay (SSD, the time between stop and go signals) adjusted trial-by-trial, based on the subject's performance.

RESULTS

The aSST reliably generated a STOP P3 component that did not change significantly with repeated task performance. The pre-infusion SSD distribution was bimodal, with mean values several hundred msec apart (FAST: 153 msec and SLOW: 390 msec). This suggested different response strategies: FAST SSD favoring "going" over "stopping", and SLOW SSD favoring "stopping" over "going". Exposure to alcohol at 60 mg/dL differentially affected the amplitude and latency of the STOP P3 according to SSD group. Alcohol significantly reduced P3 amplitude in the SLOW SSD compared to the FAST SSD group, but significantly increased P3 latency in the FAST SSD compared to the SLOW SSD group.

CONCLUSIONS

The aSST is a robust and sensitive task for detecting alcohol-induced changes in inhibition behavior as measured by the P3 component in a within-subject design. Alcohol was associated with P3 component changes, which varied by SSD group, suggesting a differential effect as a function of task strategy. Overall, the data support the potential utility of the aSST in the detection of alcohol response-related AUD risk.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

OBJECTIVE

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools.

APPROACH

The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain.

MAIN RESULTS

This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways.

SIGNIFICANCE

These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Event-Related Potentials as Biomarkers of Behavior Change Mechanisms in Substance Use Disorder Treatment

Substance use disorders (SUDs) are one of the most prevalent psychiatric conditions and represent a significant public health concern. Substantial research has identified key processes related to reinforcement and cognition for the development and maintenance of SUDs, and these processes represent viable treatment targets for psychosocial and pharmacological interventions. Research on SUD treatments has suggested that most approaches are comparable in effectiveness. As a result, recent work has focused on delineating the underlying mechanisms of behavior change that drive SUD treatment outcome. Given the rapid fluctuations associated with the key neurocognitive processes associated with SUDs, high-temporal-resolution measures of human brain processing, namely event-related potentials (ERPs), are uniquely suited to expand our understanding of the underlying neural mechanisms of change during and after SUD treatment. The value of ERPs in the context of SUD treatment are discussed along with work demonstrating the predictive validity of ERPs as biomarkers of SUD treatment response. Example associations between multiple ERP components and psychosocial and/or pharmacological treatment outcome include the P3a and P3b (in response to neutral and substance-related cues), the attention-related negativities (e.g., N170, N200), the late positive potential, and the error-related negativity. Also addressed are limitations of the biomarker approach to underscore the need for research programs evaluating mechanisms of change. Finally, we emphasize the advantages of ERPs as indices of behavior change in SUD treatment and outline issues relevant for future directions in this context.

Delta coherence in resting-state EEG predicts the reduction in cigarette craving after hypnotic aversion suggestions

Cigarette craving is a key contributor of nicotine addiction. Hypnotic aversion suggestions have been used to help smoking cessation and reduce smoking relapse rates but its neural basis is poorly understood. This study investigated the underlying neural basis of hypnosis treatment for nicotine addiction with resting state Electroencephalograph (EEG) coherence as the measure. The sample consisted of 42 male smokers. Cigarette craving was measured by the Tobacco Craving Questionnaire. The 8-minute resting state EEG was recorded in baseline state and after hypnotic induction in the hypnotic state. Then a smoking disgust suggestion was performed. A significant increase in EEG coherence in delta and theta frequency, and significant decrease in alpha and beta frequency, between the baseline and the hypnotic state was found, which may reflect alterations in consciousness after hypnotic induction. More importantly, the delta coherence between the right frontal region and the left posterior region predicted cigarette craving reduction after hypnotic aversion suggestions. This suggests that the functional connectivity between these regions plays an important role in reducing cigarette cravings via hypnotic aversion suggestions. Thus, these brain regions may serve as an important target to treat nicotine addiction, such as stimulating these brain regions via repetitive transcranial magnetic stimulation.