Toward biomarkers of the addicted human brain: Using neuroimaging to predict relapse and sustained abstinence in substance use disorder

Neurofeedback for alcohol addiction: Changes in resting state network activity✰

Alcohol dependence continues to be a major global burden despite significant research progress and treatment development. The aim of this study was to investigate whether neurofeedback training can alter resting state fMRI activity in brain regions that play a crucial role in addiction disorders in patients with alcohol dependence. For this purpose, a total of 52 patients were recruited for the present study, randomized, and divided into an active and a sham group. Patients in the active group received three sessions of neurofeedback training. We compared the resting state data in the active group as part of the NF training on six measurement days. When comparing the results of the active group from neurofeedback day 3 with baseline 1, a significant reduction in activated voxels in the ventral attention network area was seen. This suggests that reduced activity over the course of therapy in subjects may lead to greater independence from external stimuli. Overall, a global decrease in activated voxels within all three analysed networks compared to baseline was observed in the study. The use of resting-state data as potential biomarkers, as activity changes within these networks, may be to help restore cognitive processes and alcohol abuse-related craving and emotions.

Cigarette craving in virtual reality cue exposure in abstainers and relapsed smokers

Cue exposure therapy (CET) in substance-use disorders aims to reduce craving and ultimately relapse rates. Applying CET in virtual reality (VR) was proposed to increase its efficacy, as VR enables the presentation of social and environmental cues along with substance-related stimuli. However, limited success has been reported so far when applying VR-CET for smoking cessation. Understanding if effects of VR-CET differ between future abstainers and relapsing smokers may help to improve VR-CET. Data from 102 participants allocated to the intervention arm (VR-CET) of a recent RCT comparing VR-CET to relaxation in the context of smoking cessation was analyzed with respect to tolerability, presence, and craving during VR-CET. Cue exposure was conducted in four VR contexts (Loneliness/Rumination, Party, Stress, Café), each presented twice. Relapsed smokers compared to abstainers experienced higher craving during VR-CET and stronger craving responses especially during the Stress scenario. Furthermore, lower mean craving during VR-CET positively predicted abstinence at 6-month follow-up. Attempts to improve smoking cessation outcomes of VR-CET should aim to identify smokers who are more at risk of relapse based on high craving levels during VR-CET. Specifically measuring craving responses during social stress seems to be well suited to mark relapse. We propose to investigate individualized treatment approaches accordingly.

Anterior cingulate and medial prefrontal cortex alcohol cue reactivity varies as a function of drink preference in alcohol use disorder

BACKGROUND

Functional MRI visual cue reactivity studies have not considered that brain responses to various alcohol-containing beverage types may vary as a function of an individual's drinking patterns and preferences. This study tested whether the brain's reward system responds differently to visual cues associated with an individuals' most commonly consumed ("preferred") alcohol beverage compared with less commonly consumed ("non-preferred") alcohol beverages in individuals with alcohol use disorder (AUD).

METHODS

Participants (N=70) with current AUD completed a standard visual alcohol cue reactivity procedure during fMRI and reported recent alcohol use through the Timeline Followback interview. Alcohol use patterns were used to infer drink preference. Repeated measure ANCOVAs were used to evaluate differences in subjective craving (alcohol urge) and neural reactivity to cues of individual's "preferred" versus "non-preferred" alcohol beverages.

RESULTS

Fifty-four (77%) participants were determined to have a "preferred" alcohol beverage, as defined by their pattern of alcohol use. These participants reported greater subjective alcohol urge (p=0.02) and activation in the anterior cingulate cortex (ACC) (p=0.005) and medial prefrontal cortex (mPFC) (p=0.001)) in response to visual cues associated with their "preferred" versus "non-preferred" alcohol beverage. Individuals with an alcohol preference did not differ from those with no alcohol preference on subjective or neural responses to their "preferred" and "non-preferred" alcohol cues.

DISCUSSION

Results suggest alcohol cue-elicited subjective and neural responses vary as a function of alcohol beverage preference in individuals with AUD and a behaviorally defined alcohol preference. Stronger ACC and mPFC activation may reflect greater subjective value of an individual's "preferred" alcohol beverage cue.

Blunted Neurobehavioral Loss Anticipation Predicts Relapse to Stimulant Drug Use

BACKGROUND

Patients with stimulant use disorder experience high rates of relapse. While neurobehavioral mechanisms involved in initiating drug use have been studied extensively, less research has focused on relapse.

METHODS

To assess motivational processes involved in relapse and diagnosis, we acquired functional magnetic resonance imaging responses to nondrug (monetary) gains and losses in detoxified patients with stimulant use disorder (n = 68) and community control participants (n = 42). In a prospective multimodal design, we combined imaging of brain function, brain structure, and behavior to longitudinally track subsequent risk for relapse.

RESULTS

At the 6-month follow-up assessment, 27 patients remained abstinent, but 33 had relapsed. Patients with blunted anterior insula (AIns) activity during loss anticipation were more likely to relapse, an association that remained robust after controlling for potential confounds (i.e., craving, negative mood, years of use, age, and gender). Lower AIns activity during loss anticipation was associated with lower self-reported negative arousal to loss cues and slower behavioral responses to avoid losses, which also independently predicted relapse. Furthermore, AIns activity during loss anticipation was associated with the structural coherence of a tract connecting the AIns and the nucleus accumbens, as was functional connectivity between the AIns and nucleus accumbens during loss processing. However, these neurobehavioral responses did not differ between patients and control participants.

CONCLUSIONS

Taken together, the results of the current study show that neurobehavioral markers predicted relapse above and beyond conventional self-report measures, with a cross-validated accuracy of 72.7%. These findings offer convergent multimodal evidence that implicates blunted avoidance motivation in relapse to stimulant use and may therefore guide interventions targeting individuals who are most vulnerable to relapse.

Cortical and subcortical microstructure integrity changes after repetitive transcranial magnetic stimulation therapy in cocaine use disorder and relates to clinical outcomes

Cocaine use disorder (CUD) is a worldwide public health condition that is suggested to induce pathological changes in macrostructure and microstructure. Repetitive transcranial magnetic stimulation (rTMS) has gained attention as a potential treatment for CUD symptoms. Here, we sought to elucidate whether rTMS induces changes in white matter (WM) microstructure in frontostriatal circuits after 2 weeks of therapy in patients with CUD and to test whether baseline WM microstructure of the same circuits affects clinical improvement. This study consisted of a 2-week, parallel-group, double-blind, randomized controlled clinical trial (acute phase) (sham [n = 23] and active [n = 27]), in which patients received two daily sessions of rTMS on the left dorsolateral prefrontal cortex (lDLPFC) as an add-on treatment. T1-weighted and high angular resolution diffusion-weighted imaging (DWI-HARDI) at baseline and 2 weeks after served to evaluate WM microstructure. After active rTMS, results showed a significant increase in neurite density compared with sham rTMS in WM tracts connecting lDLPFC with left and right ventromedial prefrontal cortex (vmPFC). Similarly, rTMS showed a reduction in orientation dispersion in WM tracts connecting lDLPFC with the left caudate nucleus, left thalamus, and left vmPFC. Results also showed a greater reduction in craving Visual Analogue Scale (VAS) after rTMS when baseline intra-cellular volume fraction (ICVF) was low in WM tracts connecting left caudate nucleus with substantia nigra and left pallidum, as well as left thalamus with substantia nigra and left pallidum. Our results evidence rTMS-induced WM microstructural changes in fronto-striato-thalamic circuits and support its efficacy as a therapeutic tool in treating CUD. Further, individual clinical improvement may rely on the patient's individual structural connectivity integrity.

Comparison of Emotional Processing Between Patients with Substance Use Disorder and Those with Dual Diagnosis: Relationship with Severity of Dependence and Use During Treatment

This study analyzed, in a Spanish sample, the differences in emotional processing in patients diagnosed with substance use disorder (SUD) and patients with a dual diagnosis (DD), and tested whether alterations in emotional regulation were related to the severity of dependence and consumption during treatment. A descriptive follow-up study was conducted with 88 adult outpatients (83% men) who were receiving treatment for alcohol and cocaine SUD. Of the sample, 43.2% presented dual diagnosis according to DSM-IV-TR criteria. Emotional processing was assessed with the IAPS, and dependence severity with the SDSS. Consumption was determined with self-reports and toxicological tests. Regression analyses revealed that the DD group had more difficulties in identifying the valence and arousal of the images than patients with SUD. Patients with DD presented more difficulty in identifying images in which valence was manipulated, but not in those in which arousal was manipulated. Cocaine use during treatment was associated with difficulties in identifying unpleasant (U = 734.0; p < .05) and arousing (U = 723.5; p < .05) images. Although these results are preliminary, findings suggest that impaired emotional processing is aggravated in dual patients, although it may be a common transdiagnostic factor in SUD and other comorbid mental disorders. Findings highlight the importance of evaluating emotional regulation to better understand its possible role in the maintenance of substance use.

Neural mechanisms linked to treatment outcomes and recovery in substance-related and addictive disorders

The present review focuses on potential neural mechanisms underlying recovery from psychiatric conditions characterised by impaired impulse control, specifically substance use disorders, gambling disorder, and internet gaming disorder. Existing treatments (both pharmacological and psychological) for these addictions may impact brain processes, and these have been evaluated in neuroimaging studies. Medication challenge and short-term intervention administration will be considered with respect to treatment utility. Main models of addiction (e.g., dual process, reward deficiency syndrome) will be considered in the context of extant data. Additionally, advanced analytic approaches (e.g., machine-learning approaches) will be considered with respect to guiding treatment development efforts. Thus, this narrative review aims to provide directions for treatment development for addictive disorders.

Mood Variability, Craving, and Substance Use Disorders: From Intrinsic Brain Network Connectivity to Daily Life Experience

BACKGROUND

Substance use disorders (SUDs) are major contributors to morbidity and mortality rates worldwide, and this global burden is attributable in large part to the chronic nature of these conditions. Increased mood variability might represent a form of emotional dysregulation that may have particular significance for the risk of relapse in SUD, independent of mood severity or diagnostic status. However, the neural biomarkers that underlie mood variability remain poorly understood.

METHODS

Ecological momentary assessment was used to assess mood variability, craving, and substance use in real time in 54 patients treated for addiction to alcohol, cannabis, or nicotine and 30 healthy control subjects. Such data were jointly examined relative to spectral dynamic causal modeling of effective brain connectivity within 4 networks involved in emotion generation and regulation.

RESULTS

Differences in effective connectivity were related to daily life variability of emotional states experienced by persons with SUD, and mood variability was associated with craving intensity. Relative to the control participants, effective connectivity was decreased for patients in the prefrontal control networks and increased in the emotion generation networks. Findings revealed that effective connectivity within the patient group was modulated by mood variability.

CONCLUSIONS

The intrinsic causal dynamics in large-scale neural networks underlying emotion regulation play a predictive role in a patient's susceptibility to experiencing mood variability (and, subsequently, craving) in daily life. The findings represent an important step toward informing interventional research through biomarkers of factors that increase the risk of relapse in persons with SUD.

Resting-state electroencephalography theta predicts neurofeedback treatment 4-month follow-up response in nicotine addiction

Background

The high rate of long-term relapse is a major cause of smoking cessation failure. Recently, neurofeedback training has been widely used in the treatment of nicotine addiction; however, approximately 30% of subjects fail to benefit from this intervention. Our previous randomised clinical trial (RCT) examined cognition-guided neurofeedback and demonstrated a significant decrease in daily cigarette consumption at the 4-month follow-up. However, significant individual differences were observed in the 4-month follow-up effects of decreased cigarette consumption. Therefore, it is critical to identify who will benefit from pre-neurofeedback.

Aims

We examined whether the resting-state electroencephalography (EEG) characteristics from pre-neurofeedback predicted the 4-month follow-up effects and explored the possible mechanisms.

Methods

This was a double-blind RCT. A total of 60 participants with nicotine dependence were randomly assigned to either the real-feedback or yoked-feedback group. They underwent 6 min closed-eye resting EEG recordings both before and after two neurofeedback sessions. A follow-up assessment was conducted after 4 months.

Results

The frontal resting-state theta power spectral density (PSD) was significantly altered in the real-feedback group after two neurofeedback visits. Higher theta PSD in the real-feedback group before neurofeedback was the only predictor of decreased cigarette consumption at the 4-month follow-up. Further reliability analysis revealed a significant positive correlation between theta PSD pre-neurofeedback and post-neurofeedback. A leave-one-out cross-validated linear regression of the theta PSD pre-neurofeedback demonstrated a significant correlation between the predicted and observed reductions in cigarette consumption at the 4-month follow-up. Finally, source analysis revealed that the brain mechanisms of the theta PSD predictor were located in the orbital frontal cortex.

Conclusions

Our study demonstrated changes in the resting-state theta PSD following neurofeedback training. Moreover, the resting-state theta PSD may serve as a prognostic marker of neurofeedback effects. A higher resting-state theta PSD predicts a better long-term response to neurofeedback treatment, which may facilitate the selection of individualised interventions.

Trial registration number

ChiCTR-IPR-17011710.

Accumbal-thalamic connectivity and associated glutamate alterations in human cocaine craving: A state-dependent rs-fMRI and 1H-MRS study

Craving is a core symptom of cocaine use disorder and a major factor for relapse risk. To date, there is no pharmacological therapy to treat this disease or at least to alleviate cocaine craving as a core symptom. In animal models, impaired prefrontal-striatal signalling leading to altered glutamate release in the nucleus accumbens appear to be the prerequisite for cocaine-seeking. Thus, those network and metabolic changes may constitute the underlying mechanisms for cocaine craving and provide a potential treatment target. In humans, there is recent evidence for corresponding glutamatergic alterations in the nucleus accumbens, however, the underlying network disturbances that lead to this glutamate imbalance remain unknown. In this state-dependent randomized, placebo-controlled, double-blinded, cross-over multimodal study, resting state functional magnetic resonance imaging in combination with small-voxel proton magnetic resonance spectroscopy (voxel size: 9.4 × 18.8 × 8.4 mm3) was applied to assess network-level and associated neurometabolic changes during a non-craving and a craving state, induced by a custom-made cocaine-cue film, in 18 individuals with cocaine use disorder and 23 healthy individuals. Additionally, we assessed the potential impact of a short-term challenge of N-acetylcysteine, known to normalize disturbed glutamate homeostasis and to thereby reduce cocaine-seeking in animal models of addiction, compared to a placebo. We found increased functional connectivity between the nucleus accumbens and the dorsolateral prefrontal cortex during the cue-induced craving state. However, those changes were not linked to alterations in accumbal glutamate levels. Whereas we additionally found increased functional connectivity between the nucleus accumbens and a midline part of the thalamus during the cue-induced craving state. Furthermore, obsessive thinking about cocaine and the actual intensity of cocaine use were predictive of cue-induced functional connectivity changes between the nucleus accumbens and the thalamus. Finally, the increase in accumbal-thalamic connectivity was also coupled with craving-related glutamate rise in the nucleus accumbens. Yet, N-acetylcysteine had no impact on craving-related changes in functional connectivity. Together, these results suggest that connectivity changes within the fronto-accumbal-thalamic loop, in conjunction with impaired glutamatergic transmission, underlie cocaine craving and related clinical symptoms, pinpointing the thalamus as a crucial hub for cocaine craving in humans.

Distinct neural networks predict cocaine versus cannabis treatment outcomes

Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: "Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)", registration number: NCT01442597 . "Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management", registration number: NCT00350649 . "Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)", registration number: NCT01406899 .

Psychedelic therapy in the treatment of addiction: the past, present and future

Psychedelic therapy has witnessed a resurgence in interest in the last decade from the scientific and medical communities with evidence now building for its safety and efficacy in treating a range of psychiatric disorders including addiction. In this review we will chart the research investigating the role of these interventions in individuals with addiction beginning with an overview of the current socioeconomic impact of addiction, treatment options, and outcomes. We will start by examining historical studies from the first psychedelic research era of the mid-late 1900s, followed by an overview of the available real-world evidence gathered from naturalistic, observational, and survey-based studies. We will then cover modern-day clinical trials of psychedelic therapies in addiction from first-in-human to phase II clinical trials. Finally, we will provide an overview of the different translational human neuropsychopharmacology techniques, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), that can be applied to foster a mechanistic understanding of therapeutic mechanisms. A more granular understanding of the treatment effects of psychedelics will facilitate the optimisation of the psychedelic therapy drug development landscape, and ultimately improve patient outcomes.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Relapse Prevention and Prediction Strategies in Substance Use Disorder: A Scoping Review

BACKGROUND

Relapse prevention for those with substance use disorder (SUD) is an evolving practice. Initiatives focused on relapse prevention from other populations may provide the foundation for future considerations and recommendations for recovering anesthesia providers in the workplace. The purpose of this scoping review was to examine what is known about return-to-use prediction and prevention strategies in various populations struggling with SUDs to inform future considerations and implications for recovering anesthesia providers with a history of SUD.

METHODS

The Arksey and O'Malley framework was used to conduct a scoping review of the literature. A systematic search was conducted across three databases (PubMed, CINAHL, and PsycInfo) for relevant literature. Search terms used were "measures predicting relapse in substance use disorder" and "relapse prevention in substance use disorder AND anesthesia." Data from articles that met the eligibility criteria were extracted and summarized by the primary author.

RESULTS

The search identified 46 articles highlighting various relapse prediction and prevention strategies related to craving and stress, underlying biological factors, neuroimaging, and mindfulness. Relapse prediction and prevention strategies ranged from cell phone applications, monitoring biological markers, and functional neuroimaging of the brain.

CONCLUSIONS

Relapse is a concern for individuals with a history of SUD. For anesthesia providers, immediate access to powerful anesthesia medications requires return-to-use prediction and prevention strategies when anesthesia providers return to work after SUD treatment. Although some identified strategies are practical, more research is needed to predict and prevent return to use for recovering anesthesia providers.

Toward neuromarkers for tailored smoking cessation treatments

Vulnerability to compulsive drug use stems from dysregulated activity within the neural networks that underlie reward and executive functions. Empirical evidence suggests that a) attributing high motivational salience to drug-related stimuli leads to compulsive drug seeking and b) cognitive control deficits lead to compulsive drug taking. Noninvasive neuroimaging techniques enable brain activity monitoring during affective and cognitive processing and are paving the way to precision medicine for substance use disorders. Identifying robust neuromarkers of affective and cognitive dysregulation would allow clinicians to personalize treatments by targeting individual psychophysiological vulnerabilities. However, methodological choices have biased the field toward experimental paradigms that cannot optimally assess individual differences in the motivational salience of drug-related cues and in the ability to control drug-related decisions, choices which have hindered the identification of clinically relevant neuromarkers. Here, we show that once these shortcomings are amended, replicable neuromarkers of the tendency to attribute motivational salience to drug-related cues and the ability to control drug-related decisions emerge. While we use tobacco use disorder as a model, we also show that the methodological issues highlighted here are relevant to other disorders characterized by maladaptive appetitive behaviors.

Threats to external validity in the neuroprediction of substance use treatment outcomes

Background: Tools predicting individual relapse risk would invaluably inform clinical decision-making (e.g. level-of-care) in substance use treatment. Studies of neuroprediction - use of neuromarkers to predict individual outcomes - have the dual potential to create such tools and inform etiological models leading to new treatments. However, financial limitations, statistical power demands, and related factors encourage restrictive selection criteria, yielding samples that do not fully represent the target population. This problem may be further compounded by a lack of statistical optimism correction in neuroprediction research, resulting in predictive models that are overfit to already-restricted samples.Objectives: This systematic review aims to identify potential threats to external validity related to restrictive selection criteria and underutilization of optimism correction in the existing neuroprediction literature targeting substance use treatment outcomes.Methods: Sixty-seven studies of neuroprediction in substance use treatment were identified and details of sample selection criteria and statistical optimism correction were extracted.Results: Most publications were found to report restrictive selection criteria (e.g. excluding psychiatric (94% of publications) and substance use comorbidities (69% of publications)) that would rule-out a considerable portion of the treatment population. Furthermore, only 21% of publications reported optimism correction.Conclusion: Restrictive selection criteria and underutilization of optimism correction are common in the existing literature and may limit the generalizability of identified neural predictors to the target population whose treatment they would ultimately inform. Greater attention to the inclusivity and generalizability of addiction neuroprediction research, as well as new opportunities provided through open science initiatives, have the potential to address this issue.

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.

Neural Correlates of Cue Reactivity and the Regulation of Craving in Substance Use Disorders

Abstract. Theoretical background: Considerable progress has been made in illuminating the neural basis of the compulsive use patterns characterizing substance use disorders. It has been suggested to utilize these findings to alleviate the health burden associated with substance use. Objective: We address how neuroimaging research can provide these benefits. Methods: Based on neurobiological models of addiction, we highlight neuroimaging research elucidating neural predictors of relapse and how treatments modify these markers. Results: With the focus on cue reactivity, brain activity related to the motivational salience of drugs and automatized use behaviors can predict relapse. Cue reactivity changes with abstinence, and it remains to be determined whether such changes confer periods of critical relapse susceptibility. Conclusions: Several established and emerging interventions modulate brain activity associated with drug value. However, executive deficits in addiction may compromise interventions targeting control-related prefrontal brain areas. Lastly, it remains more difficult to change the brain responses mediating habitual behaviors.

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.

The brain in social context: A systematic review of substance use and social processing from adolescence to young adulthood

Substance use escalates between adolescence and young adulthood, and most experimentation occurs among peers. To understand underlying mechanisms, research has focused on neural response during relevant psychological processes. Functional magnetic resonance imaging (fMRI) research provides a wealth of information about brain activity when processing monetary rewards; however, most studies have used tasks devoid of social stimuli. Given that adolescent neurodevelopment is sculpted by the push-and-pull of peers and emotions, identifying neural substrates is important for intervention. We systematically reviewed 28 fMRI studies examining substance use and neural responses to stimuli including social reward, emotional faces, social influence, and social stressors. We found substance use was positively associated with social-reward activity (e.g., in the ventral striatum), and negatively with social-stress activity (e.g., in the amygdala). For emotion, findings were mixed with more use linked to heightened response (e.g., in amygdala), but also with decreased response (e.g., in insula). For social influence, evidence supported both positive (e.g., cannabis and nucleus accumbens during conformity) and negative (e.g., polydrug and ventromedial PFC during peers' choices) relations between activity and use. Based on the literature, we offer recommendations for future research on the neural processing of social information to better identify risks for substance use.

Electrophysiological responses to emotional and cocaine cues reveal individual neuroaffective profiles in cocaine users

Smokers with stronger neuroaffective responses to drug-related cues compared to nondrug-related pleasant images (C > P) are more vulnerable to compulsive smoking than individuals with the opposite brain reactivity profile (P > C). However, it is unknown if these neurobehavioral profiles exist in individuals abusing other drugs. We tested whether individuals with cocaine use disorder (CUD) show similar neuroaffective profiles to smokers. We also monitored eye movements to assess attentional bias toward cues and we further performed exploratory analyses on demographics, personality, and drug use between profiles. Participants with CUD (n = 43) viewed pleasant, unpleasant, cocaine, and neutral images while we recorded electroencephalogram. For each picture category, we computed the amplitude of the late positive potential (LPP), an event-related potential component that reflects motivational relevance. k-means clustering classified participants based on their LPP responses. In line with what has been observed in smokers, clustering participants using LPP responses revealed the presence of two groups: one with larger LPPs to pleasant images compared to cocaine images (P > C) and one group with larger LPPs to cocaine images compared to pleasant images (C > P). Individuals with the C > P reactivity profile also had higher attentional bias toward drug cues. The two groups did not differ on demographic and drug use characteristics, however individuals with the C > P profile reported lower distress tolerance, higher anhedonia, and higher posttraumatic stress symptoms compared to the P > C group. This is the first study to report the presence of these neuroaffective profiles in individuals with CUD, indicating that this pattern may cut across addiction populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Trauma exposure among cannabis use disorder individuals was associated with a craving-correlated non-habituating amygdala response to aversive cues

The relationship of cannabis-use disorder and trauma exposure at the level of the brain is not well-understood. Cue-reactivity paradigms have largely focused on characterizing aberrant subcortical function by averaging across the entire task. However, changes across the task, including a non-habituating amygdala response (NHAR), may be a useful biomarker for relapse vulnerability and other pathology. This secondary analysis utilized existing fMRI data from a CUD population with (TR-Y, n = 18) or without trauma (TR-N, n = 15). Amygdala reactivity to novel and repeated aversive cues was examined between TR-Y vs. TR-N groups, using a repeated measures ANOVA. Analysis revealed a significant interaction between TR-Y vs. TR-N and amygdala response to novel vs. repeated cues in the amygdala (right: F (1,31) = 5.31, p = 0.028; left: F (1,31) = 7.42, p = 0.011). In the TR-Y group, a NHAR was evident, while the TR-N group exhibited amygdala habituation, resulting in a significant difference between groups of amygdala reactivity to repeated cues (right: p = 0.002; left: p < 0.001). The NHAR in the TR-Y (but not TR-N) group was significantly correlated with higher cannabis craving scores, yielding a significant group difference (z = 2.1, p = 0.018). Results suggest trauma interacts with the brain's sensitivity to aversive cues, offering a neural explanation for the relationship between trauma and CUD vulnerability. These findings suggest the importance of considering the temporal dynamics of cue reactivity and trauma history in future studies and treatment planning, as this distinction may help decrease relapse vulnerability.

A review of functional brain differences predicting relapse in substance use disorder: Actionable targets for new methods of noninvasive brain stimulation

Neuroimaging studies have identified a variety of brain regions whose activity predicts substance use (i.e., relapse) in patients with substance use disorder (SUD), suggesting that malfunctioning brain networks may exacerbate relapse. However, this knowledge has not yet led to a marked improvement in treatment outcomes. Noninvasive brain stimulation (NIBS) has shown some potential for treating SUDs, and a new generation of NIBS technologies offers the possibility of selectively altering activity in both superficial and deep brain structures implicated in SUDs. The goal of the current review was to identify deeper brain structures involved in relapse to SUD and give an account of innovative methods of NIBS that might be used to target them. Included studies measured fMRI in currently abstinent SUD patients and tracked treatment outcomes, and fMRI results were organized with the framework of the Addictions Neuroclinical Assessment (ANA). Four brain structures were consistently implicated: the anterior and posterior cingulate cortices, ventral striatum and insula. These four deeper brain structures may be appropriate future targets for the treatment of SUD using these innovative NIBS technologies.

Compounding Vulnerability in the Neurocircuitry of Addiction: Longitudinal Functional Connectivity Changes in Alcohol Use Disorder

AIMS

The addiction neurocircuitry model describes the role of several brain circuits (drug reward, negative emotionality and craving/executive control) in alcohol use and subsequent development of alcohol use disorder (AUD). Human studies examining longitudinal change using resting-state functional magnetic resonance imaging (rs-fMRI) are needed to understand how functional changes to these circuits are caused by or contribute to continued AUD.

METHODS

In order to characterize how intrinsic functional connectivity changes with sustained AUD, we analyzed rs-fMRI data from individuals with (n = 18; treatment seeking and non-treatment seeking) and without (n = 21) AUD collected on multiple visits as part of various research studies at the NIAAA intramural program from 2012 to 2020.

RESULTS

Results of the seed correlation analysis showed that individuals with AUD had an increase in functional connectivity over time between emotionality and craving neurocircuits, and a decrease between executive control and reward networks. Post hoc investigations of AUD severity and alcohol consumption between scans revealed an additive effect of these AUD features in many of the circuits, such that more alcohol consumption or more severe AUD was associated with more pronounced changes to synchronicity.

CONCLUSIONS

These findings suggest an increased concordance of networks underlying emotionality and compulsions toward drinking while also a reduction in control network connectivity, consistent with the addiction neurocircuitry model. Further, they suggest a compounding effect of continued heavy drinking on these vulnerabilities in neurocircuitry. More longitudinal research is necessary to understand the trajectories of individuals with AUD not adequately represented in this study, as well as whether this can inform effective harm reduction strategies.

Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis

Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p < 0.001) of ROP and 59 % of CHR patients. The classifications of CCu based on sMRI and cognition were non-significant (ps > 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Structural and functional brain recovery in individuals with substance use disorders during abstinence: A review of longitudinal neuroimaging studies

BACKGROUND

Neuroimaging studies reveal structural and functional including neurochemical brain abnormalities in individuals with substance use disorders compared to healthy controls. However, whether and to what extent such dysfunction is reversible with abstinence remains unclear, and a review of studies with longitudinal within-subject designs is lacking. We performed a systematic review of longitudinal neuroimaging studies to explore putative brain changes associated with abstinence in treatment-seeking individuals with substance use disorders.

METHODS

Following PRISMA guidelines, we examined articles published up to May 2021 that employed a neuroimaging technique and assessed neurobiological recovery in treatment-seeking participants at a minimum of two time-points separated by a period of abstinence (longer than 24 h apart) or significant reduction in drug use.

RESULTS

Forty-five studies met inclusion criteria. Encouragingly, in this limited but growing literature, the majority of studies demonstrated at least partial neurobiological recovery with abstinence. Structural recovery appeared to occur predominantly in frontal cortical regions, the insula, hippocampus, and cerebellum. Functional and neurochemical recovery was similarly observed in prefrontal cortical regions but also in subcortical structures. The onset of structural recovery appears to precede neurochemical recovery, which begins soon after cessation (particularly for alcohol); functional recovery may require longer periods of abstinence.

CONCLUSIONS

The literature is still growing and more studies are warranted to better understand abstinence-mediated neural recovery in individuals with substance use disorders. Elucidating the temporal dynamics between neuronal recovery and abstinence will enable evidence-based planning for more effective and targeted treatment of substance use disorders, potentially pre-empting relapse.

Positive alcohol expectancy and resting-state functional connectivity of the insula in problem drinking

Positive alcohol expectancy (AE), a significant predictor of excessive alcohol consumption, is associated with heightened drinking motivation and reduced control. As the insula interacts with the limbic and prefrontal structures to integrate stimulus saliency, interoception, and cognitive control, the region may play a unique role in modulating AE. Here, we examined resting-state functional connectivity of the right and left insula in relation to AE in 180 adult drinkers. Whole-brain multiple regressions and path analysis were performed to delineate the inter-relationship between AE, insular connectivity, and drinking severity. We found that heightened AE was associated with diminished right insular connectivity with regions involved in negative emotion processing and self-control, including the amygdala, putamen, and ventromedial prefrontal cortex. In contrast, there was a positive relationship between AE and right insular connectivity with regions implicated in motivated responses to alcohol stimuli, including the superior parietal lobule, postcentral and superior frontal gyri. Path analysis showed that the two sets of right insular connectivity exhibited opposing associations with AE and that their net strength (i.e., "control minus motivation") was negatively correlated with AE and drinking severity. Analyses of the left insula seed, in contrast, did not yield regional connectivity in significant correlation with AE. These findings highlight the roles of right insula connectivity in motivational and regulatory processes that may differentially modulate drinking behavior. Recruitment of the motivational circuit and/or disengagement of the affective control circuit would be associated with heightened AE and heavier alcohol consumption.

Decreased reward-related brain function prospectively predicts increased substance use

Substance use and addiction are prominent global health concerns and are associated with abnormalities in reward sensitivity. Reward sensitivity and approach motivation are supported by a fronto-striatal neural circuit including the orbitofrontal cortex (OFC), ventral striatum (VS), and dorsal striatum (DS). Although research highlights abnormalities in reward neural circuitry among individuals with problematic substance use, questions remain about whether such use arises from excessively high, or excessively low, reward sensitivity. This study examined whether reward-related brain function predicted subsequent substance use course. Participants were 79 right-handed individuals (Mage = 21.52, SD = 2.19 years), who completed a monetary incentive delay (MID) fMRI task, and follow-up measures assessing substance use frequency and impairment. The average duration of the follow-up period was 9.1 months. Regions-of-interest analyses focused on the reward anticipation phase of the MID. Decreased activation in the VS during reward anticipation predicted increased substance use frequency at follow-up. Decreased DS activation during reward anticipation predicted increased substance use frequency at follow-up, but this finding did not pass correction for multiple comparisons. Analyses adjusted for relevant covariates, including baseline substance use and the presence or absence of a lifetime substance use disorder prior to MRI scanning. Results support the reward hyposensitivity theory, suggesting that decreased reward-related brain function is a risk factor for increased substance use. Results have implications for understanding the pathophysiology of problematic substance use and highlight the importance of the fronto-striatal reward circuit in the development and maintenance of addiction. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Sustained brain response to repeated drug cues is associated with poor drug-use outcomes

A threefold increase in fatal cocaine overdoses during the past decade highlights the critical lack of medications for cocaine use disorders. The brain response to drug cues can predict future drug use; however, results have been mixed. We present preliminary evidence that a sustained response to repeated cocaine cues within a single task is a significant predictor of drug-use outcomes. Seventy-three cocaine inpatients were administered a passive-viewing fMRI task, featuring 500 ms novel evocative (cocaine, sexual, aversive) and neutral comparator cues in the first half (Half1), which were then repeated in the second half (Half2). After the baseline scan, patients received eight outpatient treatment weeks with twice-weekly drug screens. Drug-use outcome groups were empirically defined based on cocaine-positive or missing urines averaged across the outpatient phase: GOOD (<40%), POOR (>85%), and Intermediate (INT, between 40% and 85%) outcomes. Differences of response to initial (Half1) and repeated (Half2) cues in a priori (cue-reactive) regions were tested between outcome groups (3 [Group] × 2 [Halves] ANOVA). An interaction was found in the brain response to drug (but not sex or aversive) cues, with a significant difference between the GOOD and POOR outcome groups in Half2, driven by a significant decrease in brain response by the GOOD outcome group and a sustained brain response by the POOR outcome group, to repeated cocaine cues. The brain response to repeated drug cues may be a useful predictor of future drug use, encouraging future intervention studies to restore a "healthy" (decreasing) response to the repeated presentation of drug cues.

Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling

Opioid use disorder is a major public health crisis. While effective treatments are available, outcomes vary widely across individuals and relapse rates remain high. Understanding neural mechanisms of treatment response may facilitate the development of personalized and/or novel treatment approaches. Methadone-maintained, polysubstance-using individuals (n = 53) participated in fMRI scanning before and after substance-use treatment. Connectome-based predictive modeling (CPM)-a recently developed, whole-brain approach-was used to identify pretreatment connections associated with abstinence during the 3-month treatment. Follow-up analyses were conducted to determine the specificity of the identified opioid abstinence network across different brain states (cognitive vs. reward task vs. resting-state) and different substance use outcomes (opioid vs. cocaine abstinence). Posttreatment fMRI data were used to assess network changes over time and within-subject replication. To determine further clinical relevance, opioid abstinence network strength was compared with healthy subjects (n = 38). CPM identified an opioid abstinence network (p = 0.018), characterized by stronger within-network motor/sensory connectivity, and reduced connectivity between the motor/sensory network and medial frontal, default mode, and frontoparietal networks. This opioid abstinence network was anatomically distinct from a previously identified cocaine abstinence network. Relationships between abstinence and opioid and cocaine abstinence networks replicated across multiple brain states but did not generalize across substances. Network connectivity measured at posttreatment related to abstinence at 6-month follow-up (p < 0.009). Healthy comparison subjects displayed intermediate network strengths relative to treatment responders and nonresponders. These data indicate dissociable anatomical substrates of opioid vs. cocaine abstinence. Results may inform the development of novel opioid-specific treatment approaches to combat the opioid epidemic.

Effective connectivity predicts cognitive empathy in cocaine addiction: a spectral dynamic causal modeling study

Social cognition plays a crucial role in the development and treatment of cocaine dependence. However, studies investigating social cognition, such as empathy and its underlying neural basis, are lacking. To explore the neural interactions among reward and memory circuits, we applied effective connectivity analysis on resting-state fMRI data collected from cocaine-dependent subjects. The relationship between effective connectivity within these two important circuits and empathy ability - evaluated with the Interpersonal Reactivity Index (IRI) - was assessed by machine learning algorithm using multivariate regression analysis. In accordance with the neurocircuitry disruptions of cocaine addiction, the results showed that cocaine-dependent subjects relative to healthy controls had altered resting state effective connectivity between parts of the memory and reward systems. Furthermore, effective connectivity between the memory and reward system could predict the fantasy empathy (FE) subscale scores in cocaine dependence. Overall, our findings provide further evidence for the neural substrates of social cognition in cocaine-dependent patients. These new insights could be useful for the development of new treatment programs for this substance dependency disorder.

Dopamine-related striatal neurophysiology is associated with specialization of frontostriatal reward circuitry through adolescence

Characterizing developmental changes in frontostriatal circuitry is critical to understanding adolescent development and can clarify neurobiological mechanisms underlying increased reward sensitivity and risk-taking and the emergence of psychopathology during this period. However, the role of striatal neurobiology in the development of frontostriatal circuitry through human adolescence remains largely unknown. We examined background connectivity during a reward-guided decision-making task ("reward-state"), in addition to resting-state, and assessed the association between age-related changes in frontostriatal connectivity and age-related changes in reward learning and risk-taking through adolescence. Further, we examined the contribution of dopaminergic processes to changes in frontostriatal circuitry and decision-making using MR-based assessments of striatal tissue-iron as a correlate of dopamine-related neurobiology. Connectivity between the nucleus accumbens (NAcc) and ventral anterior cingulate, subgenual cingulate, and orbitofrontal cortices decreased through adolescence into adulthood, and decreases in reward-state connectivity were associated with improvements reward-guided decision-making as well as with decreases in risk-taking. Finally, NAcc tissue-iron mediated age-related changes and was associated with variability in connectivity, and developmental increases in NAcc R2' corresponded with developmental decreases in connectivity. Our results provide evidence that dopamine-related striatal properties contribute to the specialization of frontostriatal circuitry, potentially underlying changes in risk-taking and reward sensitivity into adulthood.

Neural reward circuit dysfunction as a risk factor for bipolar spectrum disorders and substance use disorders: A review and integration

Bipolar spectrum disorders (BSDs) and substance use disorders (SUDs) are associated with neural reward dysfunction. However, it is unclear what pattern of neural reward function underlies pre-existing vulnerability to BSDs and SUDs, or whether neural reward function explains their high co-occurrence. The current paper provides an overview of the separate literatures on neural reward sensitivity in BSDs and SUDs. We provide a systematic review of 35 studies relevant to identifying neural reward function vulnerability to BSDs and SUDs. These studies include those examining neural reward processing on a monetary reward task with prospective designs predicting initial onset of SUDs, familial risk studies that examine unaffected offspring or first-degree relatives of family members with BSDs or SUDs, and studies that examine individuals with BSDs or SUDs who are not currently in an episode of the disorder. Findings from the review highlight that aberrant responding and connectivity across neural regions associated with reward and cognitive control confers risk for the development of BSDs and SUDs. Discussion focuses on limitations of the extant literature. We conclude with an integration and theoretical model for understanding how aberrant neural reward responding may constitute a vulnerability to the development of both BSDs and SUDs.

The longitudinal stability of fMRI activation during reward processing in adolescents and young adults

BACKGROUND

The use of functional neuroimaging has been an extremely fruitful avenue for investigating the neural basis of human reward function. This approach has included identification of potential neurobiological mechanisms of psychiatric disease and examination of environmental, experiential, and biological factors that may contribute to disease risk via effects on the reward system. However, a central and largely unexamined assumption of much of this research is that neural reward function is an individual difference characteristic that is relatively stable and trait-like over time.

METHODS

In two independent samples of adolescents and young adults studied longitudinally (Ns = 145 & 139, 100% female and 100% male, ages 15-21 and 20-22, 2-4 scans and 2 scans respectively), we tested within-person stability of reward-task BOLD activation, with a median of 1 and 2 years between scans. We examined multiple commonly used contrasts of active states and baseline in both the anticipation and feedback phases of a card-guessing reward task. We examined the effects of cortical parcellation resolution, contrast, network (reward regions and resting-state networks), region-size, and activation strength and variability on the stability of reward-related activation.

RESULTS

In both samples, contrasts of an active state relative to a baseline were more stable (ICC: intra-class correlation; e.g., Win>Baseline; mean ICC = 0.13 - 0.33) than contrasts of two active states (e.g., Win>Loss; mean ICC = 0.048 - 0.05). Additionally, activation in reward regions was less stable than in many non-task networks (e.g., dorsal attention), and activation in regions with greater between-subject variability showed higher stability in both samples.

CONCLUSIONS

These results show that some contrasts from functional neuroimaging activation during a card guessing reward task have partially trait-like properties in adolescent and young adult samples over 1-2 years. Notably, results suggest that contrasts intended to map cognitive function and show robust group-level effects (i.e. Win > Loss) may be less effective in studies of individual differences and disease risk. The robustness of group-level activation should be weighed against other factors when selecting regions of interest in individual difference fMRI studies.

Functional evaluation of NK1 antagonism on cue reactivity in opiate dependence; An fMRI study

BACKGROUND

Opiate addiction is a major health challenge with substantial societal cost. Though harm minimisation strategies have been effective, there is a growing need for new treatments for detoxification and relapse prevention. Preclinical research has found neurokinin 1 (NK1) receptors have prominent effects on opiate reward and reinforcement, and human studies have found NK1 antagonism led to reductions in craving and withdrawal. However, its effect on brain mechanisms in opiate addiction has not yet been examined.

METHODS

This study aims to assess the impact of NK1 antagonist aprepitant on heroin cue-elicited changes in blood-oxygenation level dependent (BOLD) signal in opiate dependent individuals undergoing detoxification. Participants will attend two scanning sessions and receive a single dose of aprepitant (320 mg) and a placebo in a randomised, cross-over design. During functional magnetic resonance imaging participants will undergo two runs of a cue reactivity task, which consists of passive viewing of drug cues or neutral cues in a block design fashion. We hypothesise that NK1 antagonism will attenuate the BOLD response to drug cues in the caudate nucleus and amygdala. Regions of interest were selected based on NK1 receptor density and their role in cue reactivity and craving.

Altered intrinsic connectivity distribution in internet gaming disorder and its associations with psychotherapy treatment outcomes

Alterations in brain connectivity have been implicated in internet gaming disorder (IGD). However, little is known about alterations in whole-brain connectivity and their associations with long-term treatment outcomes. Here, we used a relatively new analytic approach, intrinsic connectivity distribution (ICD) analysis, to examine brain connectivity in 74 IGD participants and 41 matched healthy controls (HCs) and conducted post hoc seed-based resting-state functional connectivity (rsFC) analyses based on the ICD findings. We also examined how these findings related to outcomes involving a craving behavioral intervention (CBI) for IGD. IGD participants showed less whole-brain connectivity in the left angular gyrus and ventromedial prefrontal cortex (vmPFC) compared with HC participants. Seed-based rsFC analyses revealed that the left angular gyrus in the IGD group showed less connectivity with areas involved in the default-mode network and greater connectivity with areas in the salience and executive control networks. CBI was associated with improved connectivity within regions in the default-mode network and regions across the default-mode and salience networks. ICD-identified connectivity differences in the left angular gyrus and vmPFC were related to changes in craving and severity of addiction 6 months after the intervention. The findings suggest that IGD is associated with alterations in brain connectivity that may be sensitive to interventions. Thus, the findings have implications for understanding mechanisms underlying CBI effects and for further treatment development.

Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse

INTRODUCTION

By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C").

AIM AND METHOD

The goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers.

RESULTS

The predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials.

CONCLUSION

These results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm.

IMPLICATIONS

We assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

Evaluating the resource allocation index as a potential fMRI-based biomarker for substance use disorder

BACKGROUND

There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples.

METHODS

The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4-365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature.

RESULTS

First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures.

CONCLUSION

Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.

The neurobiology of substance use and addiction: evidence from neuroimaging and relevance to treatment

Addiction is a global health problem with a chronic relapsing nature for which there are few treatment options. In the past few decades, neuroimaging has allowed us to better understand the neurobiology of addiction. Functional neuroimaging paradigms have been developed to probe the neural circuits underlying addiction, including reward, inhibitory control, stress, emotional processing and learning/memory networks. Functional neuroimaging has also been used to provide biological support for the benefits of psychosocial and pharmacological interventions, although evidence remains limited and often inconclusive in this area, which may contribute to the variability in treatment efficacy. In this article, we discuss the changing definitions and clinical criteria that describe and classify addictive disorders. Using examples from functional neuroimaging studies we summarise the neurobiological mechanisms that underpin drug use, dependence, tolerance, withdrawal and relapse. We discuss the links between functional neuroimaging and treatment, outline clinical management in the UK and give an overview of future directions in research and addiction services.

Neurobiology, Clinical Presentation, and Treatment of Methamphetamine Use Disorder: A Review

IMPORTANCE

The prevalence of and mortality associated with methamphetamine use has doubled during the past 10 years. There is evidence suggesting that methamphetamine use disorder could be the next substance use crisis in the United States and possibly worldwide.

OBSERVATION

The neurobiology of methamphetamine use disorder extends beyond the acute effect of the drug as a monoaminergic modulator and includes intracellular pathways focused on oxidative stress, neurotoxic and excitotoxic effects, and neuroinflammation. Similarly, the clinical picture extends beyond the acute psychostimulatory symptoms to include complex cardiovascular and cerebrovascular signs and symptoms that need to be identified by the clinician. Although there are no pharmacologic treatments for methamphetamine use disorder, cognitive behavioral therapy, behavioral activation, and contingency management show modest effectiveness.

CONCLUSIONS AND RELEVANCE

There is a need to better understand the complex neurobiology of methamphetamine use disorder and to develop interventions aimed at novel biological targets. Parsing the disorder into different processes (eg, craving or mood-associated alterations) and targeting the neural systems and biological pathways underlying these processes may lead to greater success in identifying disease-modifying interventions. Finally, mental health professionals need to be trained in recognizing early cardiovascular and cerebrovascular warning signs to mitigate the mortality associated with methamphetamine use disorder.

Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research

Substance use is a leading cause of disability and death worldwide. Despite the existence of evidence-based treatments, clinical outcomes are highly variable across individuals, and relapse rates following treatment remain high. Within this context, methods to identify individuals at particular risk for unsuccessful treatment (i.e., limited within-treatment abstinence), or for relapse following treatment, are needed to improve outcomes. Cumulatively, the literature generally supports the hypothesis that individual differences in brain function and structure are linked to differences in treatment outcomes, although anatomical loci and directions of associations have differed across studies. However, this work has almost entirely used methods that may overfit the data, leading to inflated effect size estimates and reduced likelihood of reproducibility in novel clinical samples. In contrast, cross-validated predictive modeling (i.e., machine learning) approaches are designed to overcome limitations of traditional approaches by focusing on individual differences and generalization to novel subjects (i.e., cross-validation), thereby increasing the likelihood of replication and potential translation to novel clinical settings. Here, we review recent studies using these approaches to generate brain-behavior models of treatment outcomes in addictions and provide recommendations for further work using these methods.

Predicting relapse after alcohol use disorder treatment in a high-risk cohort: The roles of anhedonia and smoking

On average, two-thirds of individuals treated for alcohol use disorder (AUD) relapse within six months. There is a critical need to identify modifiable risk factors associated with relapse that can be addressed during AUD treatment. Candidate factors include mood disorders and cigarette smoking, which frequently co-occur with AUD. We predicted that co-occurrence of mood disorders, cigarette smoking, and other modifiable conditions will predict relapse within six months of AUD treatment. Ninety-five Veterans, 23-91 years old, completed assessments of multiple characteristics including demographic information, co-occurring psychiatric disorders, and medical conditions during residential treatment for AUD. Participants' alcohol consumption was monitored over six months after participation. Logistic regression was used to determine if, mood disorders, cigarette smoking status, alcohol consumption, educational level, and comorbid general medical conditions are associated with relapse after AUD treatment. Sixty-nine percent of Veterans (n = 66) relapsed within six months of study while 31% remained abstinent (n = 29). While education, comorbid general medical conditions, and mood disorder diagnoses were not predictors of relapse, Veterans with greater symptoms of anhedonia, active smokers, and fewer days of abstinence prior to treatment showed significantly greater odds for relapse within six months. Anhedonia and cigarette smoking are modifiable risk factors, and effective treatment of underlying anhedonic symptoms and implementation of smoking cessation concurrent with AUD-focused interventions may decrease risk of relapse.

Clinical and biobehavioral perspectives: Is medication overuse headache a behavior of dependence?

Medication overuse headache (MOH), previously known as analgesic abuse headache or medication misuse headaches, is a common form of chronic headache disorder that has a detrimental impact on health and society. Although it has been widely accepted that overusing abortive medications is paradoxically the cause of MOH and drug discontinuation is the treatment of choice, ongoing debates exist as to whether drug consumption per se is the cause or consequence of headache chronification. Certain features in MOH such as their compulsive drug-seeking behavior, withdrawal headaches and high relapse rates share similarities with drug dependence, suggesting that there might be common underlying biological and psychobehavioral mechanisms. In this regard, this article will discuss the updated evidence and current debates on the possible biobehavioral overlap between MOH and drug dependence. To begin with, we will discuss whether MOH has characteristics of substance dependence based on standard psychiatry diagnostic criteria and other widely used dependence scales. Recent epidemiological studies underscoring common psychiatric comorbidities between the two disorders will also be presented. Although both demonstrate seemingly distinct personality traits, recent studies revealed similar decision-making impairment from a cognitive perspective, indicating the presence of a maladaptive reward system in both disorders. In addition, emerging imaging studies also support this notion by showing reversible morphological and functional brain changes related to the mesocorticolimbic reward circuitry in MOH, with a strong resemblance to those in addiction. Finally, an increased familial risk for drug dependence and genetic association with dopaminergic and drug dependence molecular pathways in MOH also support a possible link between MOH and addiction. Understanding the role of dependence in MOH will have a great impact on disease management as this will provide the missing piece of the puzzle in current therapeutic strategies.

Modeling neuroaffective biomarkers of drug addiction: A Bayesian nonparametric approach using dirichlet process mixtures

BACKGROUND

The properties of neurophysiological processes related to addiction have received much attention in the literature. However, empirical evidence of meaningful and useful characterization of these processes is limited. Recent studies have found that electrophysiological responses to emotional and drug-related cues can be used to create profiles that reliably predict smoking relapse.

NEW METHOD

This paper evaluates the validity of classifying electrophysiological responses into distinct profiles using a Bayesian dirichlet process mixture (DPM) model. The DPM is a Bayesian nonparametric (BNP) method to modeling unknown number of profiles characterized by uncertainty in cluster membership and in cluster number.

RESULTS

The DPM model confirmed previously identified neuroaffective reactivity profiles, but also revealed a finer level of granularity in the clustering. Specifically, in addition to the two clusters previously identified in the literature, the BNP methods identified a cluster of individuals showing similar responses to smoking, pleasant, neutral and unpleasant cues.

COMPARISON WITH EXISTING METHODS

BNP models provide an alternative to the k-mean clustering approach to modeling EEG-based neuroaffective profiles. Unlike k-means clustering, BNP models compute the probability that a subject belongs to a cluster while taking into consideration uncertainty in the number of clusters.

CONCLUSIONS

Our results confirm the reliability of the two clusters previously identified in these data, but also provide new insights by revealing a cluster that presented similar responses to stimuli with different contents. This finding may be related to the uncertainty in classification or overlapping brain-reactivity profiles.

Prospective testing of a neurophysiologic biomarker for treatment decisions in major depressive disorder: The PRISE-MD trial

Management of Major Depressive Disorder (MDD) might be improved by a biomarker to predict whether a selected medication is likely to lead to remission. We previously reported on a quantitative electroencephalogram-based biomarker, the Antidepressant Treatment Response (ATR) index, that integrated recordings at baseline and after one week of treatment. The present study prospectively tested whether treatment directed by the biomarker increased the likelihood of remission; we hypothesized that continued treatment with a drug predicted to lead to remission (i.e., high ATR values) would be associated with better outcomes than if the drug was predicted not to lead to remission (i.e., low ATR values). We enrolled 180 adult outpatients with unipolar MDD from the community. After one week of escitalopram treatment to determine the biomarker, stratified randomization (high vs. low ATR) was used to assign subjects to either continued escitalopram or a switch to bupropion as a blinded control condition, for seven additional weeks. For the 73 evaluable subjects assigned to continued escitalopram treatment, the remission rate was significantly higher for those in whom ATR had predicted remission versus non-remission (60.4% vs. 30.0%, respectively, p = 0.01). Accuracy was enhanced by combining 1-week depressive symptom change with ATR (68.6% vs 28.9%). This prospective validation study supports further development of the ATR biomarker, alone or together with early symptom change, to improve care by identifying individuals unlikely to remit with their current treatment, and support the decision to change treatment after one week rather than after failing a full, prolonged course of medication.

Inhibitory control and problematic Internet-pornography use - The important balancing role of the insula

BACKGROUND AND AIMS

Diminished control over a specific behavior is a core characteristic in addictive behaviors such as problematic Internet-pornography (IP) use. First studies suggest that a hyperactivity of the impulsive system is one reason for impulsive behaviors in the context of problematic IP use. The tripartite-process theory of addiction explains neurocognitive mechanisms beyond common dual-process theories in addictive behaviors. However, the role of the reflective and interoceptive system is still unresolved.

METHODS

The study comprised a stop-signal task (SST) including neutral and pornographic images during fMRI and questionnaires to investigate associations between symptoms of problematic IP use, craving, and neural activity of the impulsive, reflective, and interoceptive system. We examined 28 heterosexual males with varying symptom severity of problematic IP use.

RESULTS

Data indicates that individuals with more symptoms of problematic IP use showed better performance in the SST which was linked to decreased insula and inferior frontal gyrus activity during pornographic image processing. An increase in craving was associated with lower activity of the ventral striatum during pornographic image processing. The interoceptive system showed varying effects. Increased insula activity during inhibitory control and decreased activity during pornographic image processing were associated with higher inhibitory control performance.

DISCUSSION AND CONCLUSION

Effects of tolerance and motivational aspects may explain the better inhibitory control performance in individuals with higher symptom severity which was associated with differential activity of the interoceptive and reflective system. Diminished control over IP use presumably results from the interaction between the impulsive, reflective, and interoceptive systems.

Mapping sign-tracking and goal-tracking onto human behaviors

As evidenced through classic Pavlovian learning mechanisms, environmental cues can become incentivized and influence behavior. These stimulus-outcome associations are relevant in everyday life but may be particularly important for the development of impulse control disorders including addiction. Rodent studies have elucidated specific learning profiles termed 'sign-tracking' and 'goal-tracking' which map onto individual differences in impulsivity and other behaviors associated with impulse control disorders' etiology, course, and relapse. Whereas goal-trackers are biased toward the outcome, sign-trackers fixate on features that are associated with but not necessary for achieving an outcome; a pattern of behavior that often leads to escalation of reward-seeking that can be maladaptive. The vast majority of the sign- and goal-tracking research has been conducted using rodent models and very few have bridged this concept into the domain of human behavior. In this review, we discuss the attributes of sign- and goal-tracking profiles, how these are manifested neurobiologically, and how these distinct learning styles could be an important tool for clinical interventions in human addiction.