Brain potentials measured during a Go/NoGo task predict completion of substance abuse treatment

Electrophysiological correlates of cognitive control and performance monitoring in risk propensity: An event-related potential study

Investigating the cognitive control processes and error detection mechanisms involved in risk-taking behaviors is essential for understanding risk propensity. This study investigated the relationship between risk propensity and cognitive control processes using an event-related potentials (ERP) approach. The study employed a Cued Go/Nogo paradigm to elicit ERP components related to cognitive control processes, including contingent negative variation (CNV), P300, error-related negativity (ERN), and error positivity (Pe). Healthy participants were categorized into high-risk and low-risk groups based on their performance in the Balloon Analogue Risk Task (BART). The results revealed risk-taking behavior influenced CNV amplitudes, indicating heightened response preparation and inhibition for the high-risk group. In contrast, the P300 component showed no group differences but revealed enhanced amplitudes in Nogo trials, particularly in high-risk group. Furthermore, despite the lack of difference in the Pe component, the high-risk group exhibited smaller ERN amplitudes compared to the low-risk group, suggesting reduced sensitivity to error detection. These findings imply that risk-taking behaviors may be associated with a hypoactive avoidance system rather than impaired response inhibition. Understanding the neural mechanisms underlying risk propensity and cognitive control processes can contribute to the development of interventions aimed at reducing risky behaviors and promoting better decision-making.

An exploration of neural predictors of treatment compliance in cognitive-behavioral group therapy for hoarding disorder

A persistent and influential barrier to effective cognitive-behavioral therapy (CBT) for patients with hoarding disorder (HD) is treatment retention and compliance. Recent research has suggested that HD patients have abnormal brain activity identified by functional magnetic resonance (fMRI) in regions often engaged for executive functioning (e.g., right superior frontal gyrus, anterior insula, and anterior cingulate), which raises questions about whether these abnormalities could relate to patients' ability to attend, understand, and engage in HD treatment. We examined data from 74 HD-diagnosed adults who completed fMRI-measured brain activity during a discarding task designed to elicit symptom-related brain dysfunction, exploring which regions' activity might predict treatment compliance variables, including treatment engagement (within-session compliance), homework completion (between-session compliance), and treatment attendance. Brain activity that was significantly related to within- and between-session compliance was found largely in insula, parietal, and premotor areas. No brain regions were associated with treatment attendance. The results add to findings from prior research that have found prefrontal, cingulate, and insula activity abnormalities in HD by suggesting that some aspects of HD brain dysfunction might play a role in preventing the engagement needed for therapeutic benefit.

Neurophysiological error processing and addiction self-awareness correlates of reduced insight in cannabis use disorder

BACKGROUND AND AIMS

Cannabis use disorder (CUD) prevalence has increased, while perceived risks of cannabis use and CUD treatment need have decreased. Chronic cannabis use may also impair the neural and behavioral mechanisms of insight, further hampering treatment-seeking. This study aimed to measure whether CUD is characterized by reduced self-monitoring in drug-related contexts (objectively-assessed insight), subserved by functional neural abnormalities in error-processing and manifested clinically as decreased awareness of the need to change.

DESIGN

Case-control laboratory study was used.

SETTING

University setting was in Alabama, USA.

PARTICIPANTS

There were 42 CUD participants and 47 age-, sex-, and nicotine use-matched controls.

MEASUREMENTS

Participants completed a probabilistic choice task, adapted for the first time for CUD, in which they selected pleasant, unpleasant, neutral, and cannabis-related images according to their preference. Reduced versus accurate insight was operationalized as the correspondence between self-reported and actual most chosen image type. Neurophysiological error-processing during an inhibitory control task was recorded using electroencephalography. Participants with CUD completed measures of cannabis problem recognition and motivation to change.

FINDINGS

Compared with controls, the CUD group made significantly more cannabis selections on the choice task (mean difference [MD] = 8.11, 95% confidence interval [CI] [4.88 11.35], p < 0.001) and had significantly reduced insight into cannabis choice (odds ratio [OR] = 9.69, 95% CI [1.06 88.65], p = 0.04). CUD participants with reduced insight on the choice task had significantly decreased neurophysiological reactivity to errors on the inhibitory control task (error-related negativity) compared with CUD participants with accurate insight (MD = 2.64 μV, 95% CI [0.74 μV 4.54 μV], p = 0.008) and controls (MD = 4.05 μV, 95% CI [1.29 μV 6.80 μV], p = 0.005). Compared with CUD participants with accurate insight on the choice task, CUD participants with reduced insight reported significantly less agreement that they had a cannabis problem (MD = -5.06, 95% CI [-8.49-1.62], p = 0.003).

CONCLUSIONS

People with CUD who show reduced insight on a drug-related choice task may also have decreased early neural error-processing and less cannabis problem recognition.

Transcranial Magnetic Stimulation for Reducing the Relative Reinforcing Value of Food in Adult Patients With Obesity Pursuing Metabolic and Bariatric Surgery: Protocol for a Pilot, Within-Participants, Sham-Controlled Trial

BACKGROUND

Metabolic and bariatric surgery (MBS) is the most effective and durable obesity treatment. However, there is heterogeneity in weight outcomes, which is partially attributed to variability in appetite and eating regulation. Patients with a strong desire to eat in response to the reward of palatable foods are more likely to overeat and experience suboptimal outcomes. This subgroup, classified as at risk, may benefit from repetitive transcranial magnetic stimulation (rTMS), a noninvasive brain stimulation technique that shows promise for reducing cravings and consumption of addictive drugs and food; no study has evaluated how rTMS affects the reinforcing value of food and brain reward processing in the context of MBS.

OBJECTIVE

The goal of the Transcranial Magnetic Stimulation to Reduce the Relative Reinforcing Value of Food (RESTRAIN) study is to perform an initial rTMS test on the relative reinforcing value (RRV) of food (the reinforcing value of palatable food compared with money) among adult patients who are pursuing MBS and report high food reinforcement. Using a within-participants sham-controlled crossover design, we will compare the active and sham rTMS conditions on pre- to posttest changes in the RRV of food (primary objective) and the neural modulation of reward, measured via electroencephalography (EEG; secondary objective). We hypothesize that participants will show larger decreases in food reinforcement and increases in brain reward processing after active versus sham rTMS.

METHODS

Participants (n=10) will attend 2 study sessions separated by a washout period. They will be randomized to active rTMS on 1 day and sham rTMS on the other day using a counterbalanced schedule. For both sessions, participants will arrive fasted in the morning and consume a standardized breakfast before being assessed on the RRV of food and reward tasks via EEG before and after rTMS of the left dorsolateral prefrontal cortex.

RESULTS

Recruitment and data collection began in December 2022. As of October 2023, overall, 52 patients have been screened; 36 (69%) screened eligible, and 17 (47%) were enrolled. Of these 17 patients, 3 (18%) were excluded before rTMS, 5 (29%) withdrew, 4 (24%) are in the process of completing the protocol, and 5 (29%) completed the protocol.

CONCLUSIONS

The RESTRAIN study is the first to test whether rTMS can target neural reward circuits to reduce behavioral (RRV) and neural (EEG) measures of food reward in patients who are pursuing MBS. If successful, the results would provide a rationale for a fully powered trial to examine whether rTMS-related changes in food reinforcement translate into healthier eating patterns and improved MBS outcomes. If the results do not support our hypotheses, we will continue this line of research to evaluate whether additional rTMS sessions and pulses as well as different stimulation locations produce clinically meaningful changes in food reinforcement.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05522803; https://clinicaltrials.gov/study/NCT05522803.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50714.

Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior

Importance

Valid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts.

Objective

To determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD).

Design

A longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up.

Participants

Eighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session.

Main Outcomes and Measures

Outcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison.

Results

Models using the non-speech variables showed the best predictive performance at three(r>0.45, P<2×10-3) and six months follow-up (r>0.37, P<3×10-2). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P=3×10-2), craving (r=0.72, P=5×10-5), days of abstinence (r=0.76, P=1×10-5), and cocaine use in the past 90 days (r=0.61, P=2×10-3), significantly outperforming the other models for abstinence prediction.

Conclusions and Relevance

At short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity.

Addiction as a brain disease? A meta-regression comparison of error-related brain potentials between addiction and neurological diseases

The notion that addiction is a "brain disorder" is widespread. However, there is a lack of evidence on the degree of disorder in terms of error processing in addiction. The present meta-analysis aimed at shedding light on this by comparing error-processes with populations with well-recognized brain disorders. We included 17 addiction and 32 neurological disorder studies that compared error-related negativity (ERN) or error positivity (Pe) amplitudes/latencies between experimental and healthy-control groups. Meta-regression analyses were performed for the intergroup comparison and other moderators. Both diagnoses were accompanied by a diminished ERN amplitude, although the degree of impairment was marginally larger in neurological disorders. Neurological disorders presented shorter ERN latencies than addiction when compared with controls. The two groups did not differ in Pe amplitude/latency. Except for a reduced ERN amplitude found along with aging, no other moderator contributed significantly to divergent findings about these four ERP indexes. The results support the brain disease model of addiction, while stressing the importance of quantifying the degrees of brain dysfunctions as a next step.

The influence of Facebook intrusion and task context on cognitive control

Social networking sites, especially Facebook, have become increasingly popular over the past decades. However, besides the benefits of using Facebook, negative effects in the form of Facebook intrusion are also increasingly pointed out. Much of the research focuses on personality, emotional and social factors related to Facebook intrusion. However, there has been limited research on the relationship between this type of behavioural addiction and cognitive functioning. Consequently, the current study aimed to verify the relationship between Facebook intrusion and cognitive control in light of the dual mechanism of cognitive control model. Additionally, the study aim was to verify the impact of the Facebook-related context on cognitive control (proactive and reactive modes) compared to neutral and positive contexts. The participants (N = 82 young adults, 57 female, M = 22.24 years, SD = 2.67 years, age range 18-35 years) were divided into two groups based on their level of Facebook intrusion. The Facebook intrusion scale was used to assess the level of Facebook intrusion. The AX-CPT task was used to assess proactive and reactive control modes in three task contexts: Facebook-related, neutral and positive. The current study results show that the participants with high Facebook intrusion had greater reactive control than participants with low Facebook intrusion. The differences between Facebook-related, neutral context and positive context were not found. However, the present findings demonstrate the interaction between Facebook intrusion and task context in cognitive control. More specifically, participants with low Facebook intrusion had greater proactive control than participants with high Facebook intrusion in the Facebook-related and positive context.

Hemodynamic activity in the limbic system predicts reoffending in women

Previous research (Aharoni et al., 2013, 2014) found that hemodynamic activity in the dorsal anterior cingulate cortex (dACC) during error monitoring predicted non-violent felony rearrest in men released from prison. This article reports an extension of the Aharoni et al. (2013, 2014) model in a sample of women released from state prison (n = 248). Replicating aspects of prior work, error monitoring activity in the dACC, as well as psychopathy scores and age at release, predicted non-violent felony rearrest in women. Sex differences in the directionality of dACC activity were observed-high error monitoring activity predicted rearrest in women, whereas prior work found low error monitoring activity predicted rearrest in men. As in prior analyses, the ability of the dACC to predict rearrest outcomes declines with more generalized outcomes (i.e., general felony). Implications for future research and clinical and forensic risk assessment are discussed.

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.

Multitarget high-definition transcranial direct current stimulation improves response inhibition more than single-target high-definition transcranial direct current stimulation in healthy participants

Prior studies have focused on single-target anodal transcranial direct current stimulation (tDCS) over the right inferior frontal gyrus (rIFG) or pre-supplementary motor area (pre-SMA) to improve response inhibition in healthy individuals. However, the results are contradictory and the effect of multitarget anodal stimulation over both brain regions has never been investigated. The present study aimed to investigate the behavioral and neurophysiological effects of different forms of anodal high-definition tDCS (HD-tDCS) on improving response inhibition, including HD-tDCS over the rIFG or pre-SMA and multitarget HD-tDCS over both areas. Ninety-two healthy participants were randomly assigned to receive single-session (20 min) anodal HD-tDCS over rIFG + pre-SMA, rIFG, pre-SMA, or sham stimulation. Before and immediately after tDCS intervention, participants completed a stop-signal task (SST) and a go/nogo task (GNG). Their cortical activity was recorded using functional near-infrared spectroscopy (fNIRS) during the go/nogo task. The results showed multitarget stimulation produced a significant reduction in stop-signal reaction time (SSRT) relative to baseline. The pre-to-post SSRT change was not significant for rIFG, pre-SMA, or sham stimulation. Further analyses revealed multitarget HD-tDCS significantly decreased SSRT in both the high-performance and low-performance subgroups compared with the rIFG condition which decreased SSRT only in the low-performance subgroup. Only the multitarget condition significantly improved neural efficiency as indexed by lower △oxy-Hb after stimulation. In conclusion, the present study provides important preliminary evidence that multitarget HD-tDCS is a promising avenue to improve stimulation efficacy, establishing a more effective montage to enhance response inhibition relative to the commonly used single-target stimulation.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

Electrophysiological indexes for impaired response inhibition and salience attribution in substance (stimulants and depressants) use disorders: A meta-analysis

The impairment of inhibitory control and reward system is the core feature underlying substance use disorder (SUD). Previous studies suggested that it can be regarded as impaired response inhibition and salience attribution syndrome (iRISA). The neural substrates of the two deficit functions were widely investigated in neuroimaging studies, and the impaired prefrontal cortex, limbic-orbitofrontal network, and fronto-insular-parietal network were observed. Previous Event-related potential (ERP) studies were also conducted to explore EEG indexes related to abnormal brain function. In the current meta-analysis, we aimed to explore the consistency of ERP indexes that can reflect the two aberrant processes: P300/slow potential (SP) for salience attribution and Error-related negativity (ERN)/Nogo-N200/Nogo-P300 for inhibitory control and conflict monitoring. Subgroup analyses for drug type and drug use conditions were also conducted. According to the 60 research studies, we found significantly enhanced drug-cue-induced P300 amplitude and attenuated Nogo-N200 amplitude in SUD individuals relative to Healthy control (HC), which supports the dual model. Moreover, the drug-cue-induced P300 displayed time-dependence recovery, suggesting a potential index for treatment evaluation. In conclusion, drug-cue-induced P300 and Nogo-N200 demonstrated high consistency, and the drug-cue-induced P300 can be used to track the changes of functional recovery for SUD. The integration of the two ERP components could be regarded as a potential biomarker for SUD, which may provide a new insight for clinical treatment and investigation.

Treating cocaine and opioid use disorder with transcranial magnetic stimulation: A path forward

Developing new, effective treatments for substance use disorders (SUDs), especially cocaine and opioid use disorders (CUD and OUD), are of immense importance. These are chronic, relapsing brain diseases characterized by dysregulated circuits manifesting from neuroplastic change brought on by repeated exposure to substances of abuse. A potential treatment is therapeutically inducing neuroplastic change in targeted dysregulated circuits. One such intervention, repetitive transcranial magnetic stimulation (rTMS) has gained traction over the past two decades as a method of noninvasively stimulating cortical structures in order to induce subcortical neuroplastic change. By doing so, rTMS ameliorates symptoms that are consequent of dysregulations in disease-related circuits, such as craving, and reduces drug use. Although rTMS has been successfully applied as a treatment for other clinical disorders, progress toward treatment applications for SUDs has been stymied by what we dub "known unknowns". These are fundamental lines of research within the rTMS-SUD field that have yet to be systematically understood which could help to optimize TMS as an intervention for SUDs. Because progress in treatment for CUD and OUD is imperative given the widespread severity of OUD and the lack of treatment for CUD, it is necessary to critically reflect on the ways in which rTMS research for these disorders can most effectively move forward to help patients. We articulate six "known unknowns" and outline a direction of research to address each. Briefly, the "known unknowns" in the field are: 1) Cortical target selection, 2) subcortical circuit engagement, 3) optimizing rTMS sequences, 4) rTMS as an adjuvant to existing interventions, 5) manipulating brain state, and 6) selecting outcome measures. We also outline research design approaches to address these "known unknowns" in the rTMS-SUDs field. Unification of efforts across research laboratories is necessary to develop empirically validated treatments that will benefit patients in a timely fashion.

Event-related potential (ERP) measures of error processing as biomarkers of externalizing disorders: A narrative review

Previous studies have shown that electrophysiological measures of error processing are affected in patients at risk or diagnosed with internalizing disorders, hence, suggesting that error processing could be a suitable biomarker for internalizing disorders. In this narrative review, we will evaluate studies that address the role of event-related potential (ERP) measures of error-processing in externalizing disorders and discuss to what extend these can be considered a biomarker for externalizing disorders. Currently, there is evidence for the notion that electrophysiological indices of error processing such as the error-related negativity (ERN) and error positivity (Pe) are reduced in individuals with substance use disorders, attention-deficit/hyperactivity disorder, and in forensic populations. However, it remains unclear whether this is also the case for other understudied disorders such as behavioral addiction. Furthermore, to fully understand how these deficits affect day to day behavior, we encourage research to focus on testing current theories and hypotheses of ERN and Pe. In addition, we argue that within an externalizing disorder, individual differences in error processing deficits may be related to prognosis and gender of the patient, methodological issues and presence of comorbidity. Next, we review studies that have related treatment trajectories with ERP measures of error processing, and we discuss the prospect of improving error processing as a treatment option. We conclude that ERP measures of error processing are candidate biomarkers for externalizing disorders, albeit we strongly urge researchers to continue looking into the predictive value of these measures in the etiology and treatment outcome through multi-method and longitudinal designs.

Neural signature of error processing in major depression

The clinical presentation of major depression (MD) is heterogenous and comprises various affective and cognitive symptoms including an increased sensitivity to errors. Various electrophysiological but only few functional magnetic resonance imaging (fMRI) studies investigated neural error processing in MD with inconsistent findings. Thus, reliable evidence regarding neural signatures of error processing in patients with current MD is limited despite its potential relevance as viable neurobiological marker of psychopathology. We therefore investigated a sample of 16 young adult female patients with current MD and 17 healthy controls (HC). During fMRI, we used an established Erikson-flanker Go/NoGo-paradigm and focused on neural alterations during errors of commission. In the absence of significant differences in rates of errors of commission in MD compared to HC, we observed significantly (p < 0.05, FWE-corrected on cluster level) enhanced neural activations of the dorsal anterior cingulate cortex (dACC) and the pre-supplementary motor area (pre-SMA) in MD relative to HC and thus, in brain regions consistently associated to neural error processing and corresponding behavioral adjustments. Considering comparable task performance, in particular similar commission error rates in MD and HC, our results support the evidence regarding an enhanced responsivity of neural error detection mechanisms in MD as a potential neural signature of increased negative feedback sensitivity as one of the core psychopathological features of this disorder.

Acute Aerobic Exercise Ameliorates Cravings and Inhibitory Control in Heroin Addicts: Evidence From Event-Related Potentials and Frequency Bands

Objective

Aerobic exercise is considered a potential adjunctive treatment for heroin addicts, but little is known about its mechanisms. Less severe cravings and greater inhibitory control have been associated with reduced substance use. The aim of the current study was to determine the effects, as measured by behavioral and neuroelectric measurements, of acute aerobic exercise on heroin cravings and inhibitory control induced by heroin-related conditions among heroin addicts.

Design

The present study used a randomized controlled design.

Methods

Sixty male heroin addicts who met the DSM-V criteria were recruited from the Isolated Detoxification Center in China and randomly assigned to one of two groups; one group completed a 20-min bout of acute stationary cycle exercise with vigorous intensity (70-80% of maximum heart rate, exercise group), and the other group rested (control group). The self-reported heroin craving levels and inhibitory control outcomes (measured by a heroin-related Go/No-Go task) were assessed pre- and post-exercise.

Results

The heroin craving levels in the exercise group were significantly attenuated during, immediately following, and 40 min after vigorous exercise compared with before exercise; moreover, during exercise, a smaller craving was observed in the exercise group than in the control group. Acute exercise also facilitated inhibition performance in the No-Go task. After exercise, the participants' accuracy, the N2d amplitudes, and the theta two band spectral power during the No-Go conditions were higher in the exercise group than in the control group. Interestingly, significant correlations between the changes in these sensitive measurements and the changes in cravings were observed.

Conclusions

This is the first empirical study to demonstrate that aerobic exercise may be efficacious for reducing heroin cravings and promoting inhibitory control among heroin addicts.

Neurocognitive predictors of treatment completion and daytime activities at follow-up in multiproblem young adults

Previous research has shown an association between cognitive control deficits and problematic behavior such as antisocial behavior and substance use, but little is known about the predictive value of cognitive control for treatment outcome. The current study tests whether selected markers of baseline cognitive control predict (1) treatment completion of a day treatment program involving a combination of approaches for multiproblem young adults and (2) daytime activities a year after the start of treatment, over and above psychological, social, and criminal characteristics. We assessed individual, neurobiological, and neurobehavioral measures, including functional brain activity during an inhibition task and two electroencephalographic measures of error processing in 127 male multiproblem young adults (age 18-27 years). We performed two hierarchical regression models to test the predictive power of cognitive control for treatment completion and daytime activities at follow-up. The overall models did not significantly predict treatment completion or daytime activities at follow-up. However, activity in the anterior cingulate cortex (ACC) during response inhibition, years of regular alcohol use, internalizing problems, and ethnicity were all significant individual predictors of daytime activity at follow-up. In conclusion, cognitive control could not predict treatment completion or daytime activities a year after the start of treatment over and above individual characteristics. However, results indicate a direct association between brain activity during response inhibition and participation in daytime activities, such as work or school, after treatment. As adequate baseline inhibitory control is associated with a positive outcome at follow-up, this suggests interventions targeting cognitive control might result in better outcomes at follow-up.

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.

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.

Bouncing back: Brain rehabilitation amid opioid and stimulant epidemics

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Frontoparietal event related potentials predict/track recovery.

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Frontostriatal functional magnetic resonance imaging signals predict/track recovery.

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Transcranial magnetic left prefrontal stimulation reduces craving and drug use.

Recent methamphetamine and opioid use epidemics are a major public health concern. Chronic stimulant and opioid use are characterized by significant psychosocial, physical and mental health costs, repeated relapse, and heightened risk of early death. Neuroimaging research highlights deficits in brain processes and circuitry that are linked to responsivity to drug cues over natural rewards as well as suboptimal goal-directed decision-making. Despite the need for interventions, little is known about (1) how the brain changes with prolonged abstinence or as a function of various treatments; and (2) how symptoms change as a result of neuromodulation. This review focuses on the question: What do we know about changes in brain function during recovery from opioids and stimulants such as methamphetamine and cocaine? We provide a detailed overview and critique of published research employing a wide array of neuroimaging methods – functional and structural magnetic resonance imaging, electroencephalography, event-related potentials, diffusion tensor imaging, and multiple brain stimulation technologies along with neurofeedback – to track or induce changes in drug craving, abstinence, and treatment success in stimulant and opioid users. Despite the surge of methamphetamine and opioid use in recent years, most of the research on neuroimaging techniques for recovery focuses on cocaine use. This review highlights two main findings: (1) interventions can lead to improvements in brain function, particularly in frontal regions implicated in goal-directed behavior and cognitive control, paired with reduced drug urges/craving; and (2) the targeting of striatal mechanisms implicated in drug reward may not be as cost-effective as prefrontal mechanisms, given that deep brain stimulation methods require surgery and months of intervention to produce effects. Overall, more studies are needed to replicate and confirm findings, particularly for individuals with opioid and methamphetamine use disorders.

Accelerated Intermittent Theta-Burst Stimulation as a Treatment for Cocaine Use Disorder: A Proof-of-Concept Study

There are no effective treatments for cocaine use disorder (CUD), a chronic, relapsing brain disease characterized by dysregulated circuits related to cue reactivity, reward processing, response inhibition, and executive control. Transcranial magnetic stimulation (TMS) has the potential to modulate circuits and networks implicated in neuropsychiatric disorders, including addiction. Although acute applications of TMS have reduced craving in urine-negative cocaine users, the tolerability and safety of administering accelerated TMS to cocaine-positive individuals is unknown. As such, we performed a proof-of-concept study employing an intermittent theta-burst stimulation (iTBS) protocol in an actively cocaine-using sample. Although our main goal was to assess the tolerability and safety of administering three iTBS sessions daily, we also hypothesized that iTBS would reduce cocaine use in this non-treatment seeking cohort. We recruited 19 individuals with CUD to receive three open-label iTBS sessions per day, with approximately a 60-min interval between sessions, for 10 days over a 2-week period (30 total iTBS sessions). iTBS was delivered to left dorsolateral prefrontal cortex (dlPFC) with neuronavigation guidance. Compliance and safety were assessed throughout the trial. Cocaine use behavior was assessed before, during, and after the intervention and at 1- and 4-week follow-up visits. Of the 335 iTBS sessions applied, 73% were performed on participants with cocaine-positive urine tests. Nine of the 14 participants who initiated treatment received at least 26 of 30 iTBS sessions and returned for the 4-week follow-up visit. These individuals reduced their weekly cocaine consumption by 78% in amount of dollars spent and 70% in days of use relative to pre-iTBS cocaine use patterns. Similarly, individuals reduced their weekly consumption of nicotine, alcohol, and THC, suggesting iTBS modulated a common circuit across drugs of abuse. iTBS was well-tolerated, despite the expected occasional headaches. A single participant developed a transient neurological event of uncertain etiology on iTBS day 9 and cocaine-induced psychosis 2 weeks after discontinuation. It thus appears that accelerated iTBS to left dlPFC administered in active, chronic cocaine users is both feasible and tolerable in actively using cocaine participants with preliminary indications of efficacy in reducing both the amount and frequency of cocaine (and other off target drug) use. The neural underpinnings of these behavioral changes could help in the future development of effective treatment of CUD.

Repetitive Transcranial Magnetic Stimulation Delivered With an H-Coil to the Right Insula Reduces Functional Connectivity Between Insula and Medial Prefrontal Cortex

OBJECTIVE

Insula neurocircuitry alterations are reported in a range of neuropsychiatric disorders holding promise for clinical interventions. We measured, in a pilot study, acute neuroplastic modulations resulting from high- and low-frequency stimulation with repetitive transcranial magnetic stimulation (rTMS) delivered via an H-coil that targeted the right insula and overlying prefrontal cortex.

METHODS

Healthy, nonsmoking, adult participants (N = 28), in a within-participant, sham-controlled experiment, received a single rTMS session on four separate days. Participants received one session each of low- (1 Hz) and high (10 Hz)-frequency stimulation and two sessions of sham stimulation matched to each rTMS frequency. After each rTMS session, participants completed a functional magnetic resonance imaging (fMRI) scan while performing two cognitive tasks and a resting-state scan. The effect of rTMS was examined on task behavior as well as blood oxygenated level-dependent (BOLD) response during task performance and resting state. We expected low- and high-frequency stimulation to decrease and increase, respectively, insula and overlying cortical BOLD signal and network connectivity.

RESULTS/CONCLUSIONS

There was no effect of rTMS, regardless of frequency, on task behavior or task-based BOLD response. There was an effect of rTMS compared to sham on rsFC between insula and medial prefrontal cortex, with connectivity reduced after rTMS compared to sham, regardless of frequency. Implications for using rTMS to the insula as a treatment for neuropsychiatric disorders are discussed in light of insula-medial prefrontal cortex connectivity.

Transcranial electrical and magnetic stimulation (tES and TMS) for addiction medicine: A consensus paper on the present state of the science and the road ahead

There is growing interest in non-invasive brain stimulation (NIBS) as a novel treatment option for substance-use disorders (SUDs). Recent momentum stems from a foundation of preclinical neuroscience demonstrating links between neural circuits and drug consuming behavior, as well as recent FDA-approval of NIBS treatments for mental health disorders that share overlapping pathology with SUDs. As with any emerging field, enthusiasm must be tempered by reason; lessons learned from the past should be prudently applied to future therapies. Here, an international ensemble of experts provides an overview of the state of transcranial-electrical (tES) and transcranial-magnetic (TMS) stimulation applied in SUDs. This consensus paper provides a systematic literature review on published data - emphasizing the heterogeneity of methods and outcome measures while suggesting strategies to help bridge knowledge gaps. The goal of this effort is to provide the community with guidelines for best practices in tES/TMS SUD research. We hope this will accelerate the speed at which the community translates basic neuroscience into advanced neuromodulation tools for clinical practice in addiction medicine.

Alterations in resting-state functional connectivity in substance use disorders and treatment implications

Substance use disorders (SUD) are diseases of the brain, characterized by aberrant functioning in the neural circuitry of the brain. Resting state functional connectivity (rsFC) can illuminate these functional changes by measuring the temporal coherence of low-frequency fluctuations of the blood oxygenation level-dependent magnetic resonance imaging signal in contiguous or non-contiguous regions of the brain. Because this data is easy to obtain and analyze, and therefore fairly inexpensive, it holds promise for defining biological treatment targets in SUD, which could help maximize the efficacy of existing clinical interventions and develop new ones. In an effort to identify the most likely "treatment targets" obtainable with rsFC we summarize existing research in SUD focused on 1) the relationships between rsFC and functionality within important psychological domains which are believed to underlie relapse vulnerability 2) changes in rsFC from satiety to deprived or abstinent states 3) baseline rsFC correlates of treatment outcome and 4) changes in rsFC induced by treatment interventions which improve clinical outcomes and reduce relapse risk. Converging evidence indicates that likely "treatment target" candidates, emerging consistently in all four sections, are reduced connectivity within executive control network (ECN) and between ECN and salience network (SN). Other potential treatment targets also show promise, but the literature is sparse and more research is needed. Future research directions include data-driven prediction analyses and rsFC analyses with longitudinal datasets that incorporate time since last use into analysis to account for drug withdrawal. Once the most reliable biological markers are identified, they can be used for treatment matching, during preliminary testing of new pharmacological compounds to establish clinical potential ("target engagement") prior to carrying out costly clinical trials, and for generating hypotheses for medication repurposing.

Distress intolerance modulation of neurophysiological markers of cognitive control during a complex go/no-go task

Distress intolerance (DI), a trait-like individual difference reflective of the inability to endure aversive affective states, is relevant to multiple forms of psychopathology, but its relations to theoretically relevant neurobiological systems have received little attention. Altered cognitive control-related neurobiology has been theorized to underlie individual differences in DI, but little empirical work has been conducted. To test this hypothesis, baseline data from a large community sample with elevated high levels of emotional psychopathology and comorbidity was utilized (N = 256). Participants completed a complex go/no-go task while EEG was recorded, and P2, N2, and P3 amplitudes were measured. Based upon prior findings on the relations between these components and response inhibition, a core cognitive control function, we hypothesized that DI would predict reduced no-go N2 and P3 amplitude while controlling for current anxious/depressive symptom severity (i.e., negative affect). Peak amplitudes from the raw data and principal components analysis were used to quantify amplitude of ERP components. Partially consistent with predictions, high DI was independently associated with reduced no-go N2 peak amplitude in the raw ERP data, and was significantly related to a frontal positivity factor in the N2 time window across no-go and go trials. Contrary to predictions, no relations between DI and the P3 were found. Overall, results support the theorized relevance of cognitive control-linked neurobiology to individual differences in tolerance of distress over and above distress severity itself, and suggest specific relations between DI and alterations in early controlled attention/conflict-monitoring but not response inhibition or response inhibition-related sequelae. (PsycINFO Database Record

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.

Using Mismatch Negativity to Investigate the Pathophysiology of Substance Use Disorders and Comorbid Psychosis

Substance use disorders (SUDs) have a devastating impact on society and place a heavy burden on health care systems. Given that alcohol, tobacco, and cannabis use have the highest prevalence, further understanding of the underlying pathophysiology of these SUDs is crucial. Electroencephalography is an inexpensive, temporally superior, and translatable technique which enables investigation of the pathobiology of SUDs through the evaluation of various event-related potential components, including mismatch negativity (MMN). The goals of this review were to investigate the effects of acute and chronic alcohol, tobacco, and cannabis use on MMN among nonpsychiatric populations and patients with comorbid psychosis. A literature search was performed using the database PubMed, and 36 articles met our inclusion and exclusion criteria. We found a pattern of attenuation of MMN amplitude among patients with alcoholism across acute and chronic alcohol use, and this dysregulation was not heritable. Reports were limited, and results were mixed on the effects of acute and chronic tobacco and cannabis use on MMN. Reports on comorbid SUDs and psychosis were even fewer, and also presented mixed findings. These preliminary results suggest that MMN deficits may be associated with SUDs, specifically alcohol use disorder, and serve as a possible biomarker for treating these common disorders.

dACC response to presentation of negative feedback predicts stimulant dependence diagnosis and stimulant use severity

Error-monitoring abnormalities in stimulant-dependent individuals (SDIs) may be due to reduced awareness of committed errors, or to reduced sensitivity upon such awareness. The distinction between these alternatives remains largely undifferentiated, but may have substantial clinical relevance. We sought to better characterize the nature, and clinical relevance, of SDIs' error-monitoring processes by comparing carefully isolated neural responses during the presentation of negative feedback to a) stimulant dependence status and b) lifetime stimulant use. Forty-eight SDIs and twenty-three non-SDIs performed an fMRI-based time-estimation task specifically designed to isolate neural responses associated with the presentation (versus expectation) of contingent negative feedback. SDIs showed reduced dACC response compared to non-SDIs following the presentation of negative feedback, but only when error expectancies were controlled. Moreover, lifetime stimulant use correlated negatively with magnitude of expectancy-controlled dACC attenuation. While this finding was minimized after controlling for age, these results suggest that SDIs may be characterized by a core reduction in neural activity following error feedback, in the context of intact feedback expectancies. Correlations with lifetime stimulant use suggest that this neural attenuation may hold clinical significance.

Investigating error-related processing in incarcerated adolescents with self-report psychopathy measures

Disparate results have been found in previous reports when incorporating both interview-based and self-report measures of psychopathic traits within the same sample, suggesting such assessments should not be used interchangeably. We previously found Total and Facet 4 scores from Hare's Psychopathy Checklist: Youth Version (PCL:YV) were negatively related to amplitude of the error-related positivity (Pe) event-related potential (ERP) component. Here, we investigated using the same previously published sample whether scores on four different self-report measures of adolescent psychopathic traits (the Antisocial Process Screening Device [APSD], Child Psychopathy Scale [CPS], Inventory of Callous-Unemotional Traits [ICU], and Youth Psychopathic Traits Inventory [YPI]) were similarly associated with reduced Pe amplitude. Unlike our previous results, adolescent self-report psychopathy scores were not associated with reduced Pe amplitude in multiple regression analyses. Results obtained in the current report support previous research observing incongruent findings when incorporating different assessment types within the same sample.

The Patient Repository for EEG Data + Computational Tools (PRED+CT)

Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canonical neural computations, making them a uniquely insightful measure of brain function. As evidence of these virtues, numerous candidate biomarkers of different psychiatric and neurological diseases have been advanced. Presumably, we would only need to apply powerful machine-learning methods to validate these ideas and provide novel clinical tools. Yet, the reality of this advancement is more complex: the scale of data required for robust and reliable identification of a clinical biomarker transcends the ability of any single laboratory. To surmount this logistical hurdle, collective action and transparent methods are required. Here we introduce the Patient Repository of EEG Data + Computational Tools (PRED+CT: predictsite.com). The ultimate goal of this project is to host a multitude of available tasks, patient datasets, and analytic tools, facilitating large-scale data mining. We hope that successful completion of this aim will lead to the development of novel EEG biomarkers for differentiating populations of neurological and psychiatric disorders.

Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion

BACKGROUND

Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop specialized, efficacious treatments. We investigated whether functional network connectivity (FNC) measures were predictive of substance abuse treatment completion using machine learning pattern classification of functional magnetic resonance imaging data.

METHODS

Treatment-seeking stimulant- or heroin-dependent incarcerated participants (n = 139; 89 women) volunteered for a 12-week substance abuse treatment program. Participants performed a response inhibition Go/NoGo functional magnetic resonance imaging task prior to onset of the substance abuse treatment. We tested whether FNC related to the anterior cingulate cortex would be predictive of those who would or would not complete a 12-week substance abuse treatment program.

RESULTS

Machine learning pattern classification models using FNC between networks incorporating the anterior cingulate cortex, striatum, and insula predicted which individuals would (sensitivity: 81.31%) or would not (specificity: 78.13%) complete substance abuse treatment. FNC analyses predicted treatment completion above and beyond other clinical assessment measures, including age, sex, IQ, years of substance use, psychopathy, anxiety and depressive symptomatology, and motivation for change.

CONCLUSIONS

Aberrant neural network connections predicted substance abuse treatment outcomes, which could illuminate new targets for developing interventions designed to reduce or eliminate substance use while facilitating long-term outcomes. This work represents the first application of machine-learning models of FNC analyses of functional magnetic resonance imaging data to predict which substance abusers would or would not complete treatment.

A trans-diagnostic perspective on obsessive-compulsive disorder

Progress in understanding the underlying neurobiology of obsessive-compulsive disorder (OCD) has stalled in part because of the considerable problem of heterogeneity within this diagnostic category, and homogeneity across other putatively discrete, diagnostic categories. As psychiatry begins to recognize the shortcomings of a purely symptom-based psychiatric nosology, new data-driven approaches have begun to be utilized with the goal of solving these problems: specifically, identifying trans-diagnostic aspects of clinical phenomenology based on their association with neurobiological processes. In this review, we describe key methodological approaches to understanding OCD from this perspective and highlight the candidate traits that have already been identified as a result of these early endeavours. We discuss how important inferences can be made from pre-existing case-control studies as well as showcasing newer methods that rely on large general population datasets to refine and validate psychiatric phenotypes. As exemplars, we take 'compulsivity' and 'anxiety', putatively trans-diagnostic symptom dimensions that are linked to well-defined neurobiological mechanisms, goal-directed learning and error-related negativity, respectively. We argue that the identification of biologically valid, more homogeneous, dimensions such as these provides renewed optimism for identifying reliable genetic contributions to OCD and other disorders, improving animal models and critically, provides a path towards a future of more targeted psychiatric treatments.

Comparison of brain activation patterns during executive function tasks in hoarding disorder and non-hoarding OCD

We examined differences in regional brain activation during tests of executive function in individuals with Hoarding Disorder (HD), Obsessive Compulsive Disorder (OCD), and healthy controls (HC) using functional magnetic resonance imaging (fMRI). Participants completed computerized versions of the Stroop and Go/No-Go task. We found that during the conflict monitoring and response inhibition condition in the Go/No-Go task, individuals with HD had significantly greater activity than controls in the anterior cingulate cortex (ACC) and right dorsolateral prefrontal cortex (DLPFC). HD also exhibited significantly greater right DLPFC activity than OCD. We also observed significant differences in activity between HD and HC and between HD and OCD in regions (ACC, anterior insula, orbitofrontal cortex, and striatum) involved in evaluating stimulus-response-reward associations, or the personal and task-relevant value of stimuli and behavioral responses to stimuli. These results support the hypothesis that individuals with HD have difficulty deciding on the value or task relevance of stimuli, and may perceive an abnormally high risk of negative feedback for difficult or erroneous cognitive behavior.

Brain potentials predict substance abuse treatment completion in a prison sample

INTRODUCTION

National estimates suggest that up to 80% of prison inmates meet diagnostic criteria for a substance use disorder. Because more substance abuse treatment while incarcerated is associated with better post-release outcomes, including a reduced risk of accidental overdose death, the stakes are high in developing novel predictors of substance abuse treatment completion in inmate populations.

METHODS

Using electroencephalography (EEG), this study investigated stimulus-locked ERP components elicited by distractor stimuli in three tasks (VO-Distinct, VO-Repeated, Go/NoGo) as a predictor of treatment discontinuation in a sample of male and female prison inmates. We predicted that those who discontinued treatment early would exhibit a less positive P3a amplitude elicited by distractor stimuli.

RESULTS

Our predictions regarding ERP components were partially supported. Those who discontinued treatment early exhibited a less positive P3a amplitude and a less positive PC4 in the VO-D task. In the VO-R task, however, those who discontinued treatment early exhibited a more negative N200 amplitude rather than the hypothesized less positive P3a amplitude. The discontinuation group also displayed less positive PC4 amplitude. Surprisingly, there were no time-domain or principle component differences among the groups in the Go/NoGo task. Support Vector Machine (SVM) models of the three tasks accurately classified individuals who discontinued treatment with the best model accurately classifying 75% of inmates. PCA techniques were more sensitive in differentiating groups than the classic time-domain windowed approach.

CONCLUSIONS

Our pattern of findings are consistent with the context-updating theory of P300 and may help identify subtypes of ultrahigh-risk substance abusers who need specialized treatment programs.

Dysfunctional error-related processing in female psychopathy

Neurocognitive studies of psychopathy have predominantly focused on male samples. Studies have shown that female psychopaths exhibit similar affective deficits as their male counterparts, but results are less consistent across cognitive domains including response modulation. As such, there may be potential gender differences in error-related processing in psychopathic personality. Here we investigate response-locked event-related potential (ERP) components [the error-related negativity (ERN/Ne) related to early error-detection processes and the error-related positivity (Pe) involved in later post-error processing] in a sample of incarcerated adult female offenders (n = 121) who performed a response inhibition Go/NoGo task. Psychopathy was assessed using the Hare Psychopathy Checklist-Revised (PCL-R). The ERN/Ne and Pe were analyzed with classic windowed ERP components and principal component analysis (PCA). Consistent with previous research performed in psychopathic males, female psychopaths exhibited specific deficiencies in the neural correlates of post-error processing (as indexed by reduced Pe amplitude) but not in error monitoring (as indexed by intact ERN/Ne amplitude). Specifically, psychopathic traits reflecting interpersonal and affective dysfunction remained significant predictors of both time-domain and PCA measures reflecting reduced Pe mean amplitude. This is the first evidence to suggest that incarcerated female psychopaths exhibit similar dysfunctional post-error processing as male psychopaths.

Dysfunctional error-related processing in incarcerated youth with elevated psychopathic traits

Adult psychopathic offenders show an increased propensity towards violence, impulsivity, and recidivism. A subsample of youth with elevated psychopathic traits represent a particularly severe subgroup characterized by extreme behavioral problems and comparable neurocognitive deficits as their adult counterparts, including perseveration deficits. Here, we investigate response-locked event-related potential (ERP) components (the error-related negativity [ERN/Ne] related to early error-monitoring processing and the error-related positivity [Pe] involved in later error-related processing) in a sample of incarcerated juvenile male offenders (n=100) who performed a response inhibition Go/NoGo task. Psychopathic traits were assessed using the Hare Psychopathy Checklist: Youth Version (PCL:YV). The ERN/Ne and Pe were analyzed with classic windowed ERP components and principal component analysis (PCA). Using linear regression analyses, PCL:YV scores were unrelated to the ERN/Ne, but were negatively related to Pe mean amplitude. Specifically, the PCL:YV Facet 4 subscale reflecting antisocial traits emerged as a significant predictor of reduced amplitude of a subcomponent underlying the Pe identified with PCA. This is the first evidence to suggest a negative relationship between adolescent psychopathy scores and Pe mean amplitude.

Neuroimaging measures of error-processing: Extracting reliable signals from event-related potentials and functional magnetic resonance imaging

Error-related brain activity has become an increasingly important focus of cognitive neuroscience research utilizing both event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI). Given the significant time and resources required to collect these data, it is important for researchers to plan their experiments such that stable estimates of error-related processes can be achieved efficiently. Reliability of error-related brain measures will vary as a function of the number of error trials and the number of participants included in the averages. Unfortunately, systematic investigations of the number of events and participants required to achieve stability in error-related processing are sparse, and none have addressed variability in sample size. Our goal here is to provide data compiled from a large sample of healthy participants (n=180) performing a Go/NoGo task, resampled iteratively to demonstrate the relative stability of measures of error-related brain activity given a range of sample sizes and event numbers included in the averages. We examine ERP measures of error-related negativity (ERN/Ne) and error positivity (Pe), as well as event-related fMRI measures locked to False Alarms. We find that achieving stable estimates of ERP measures required four to six error trials and approximately 30 participants; fMRI measures required six to eight trials and approximately 40 participants. Fewer trials and participants were required for measures where additional data reduction techniques (i.e., principal component analysis and independent component analysis) were implemented. Ranges of reliability statistics for various sample sizes and numbers of trials are provided. We intend this to be a useful resource for those planning or evaluating ERP or fMRI investigations with tasks designed to measure error-processing.

Error-related processing in adult males with elevated psychopathic traits

Psychopathy is a serious personality disorder characterized by dysfunctional affective and behavioral symptoms. In incarcerated populations, elevated psychopathic traits have been linked to increased rates of violent recidivism. Cognitive processes related to error processing have been shown to differentiate individuals with high and low psychopathic traits and may contribute to poor decision making that increases the risk of recidivism. Error processing abnormalities related to psychopathy may be attributable to error-monitoring (error detection) or posterror processing (error evaluation). A recent 'bottleneck' theory predicts deficiencies in posterror processing in individuals with high psychopathic traits. In the current study, incarcerated males (n = 93) performed a Go/NoGo response inhibition task while event-related potentials (ERPs) were recorded. Classic time-domain windowed component and principal component analyses were used to measure error-monitoring (as measured with the error-related negativity [ERN/Ne]) and posterror processing (as measured with the error positivity [Pe]). Psychopathic traits were assessed using Hare's Psychopathy Checklist-Revised (PCL-R). PCL-R Total score, Factor 1 (interpersonal-affective traits), and Facet 3 (lifestyle traits) scores were positively related to posterror processes (i.e., increased Pe amplitude) but unrelated to error-monitoring processes (i.e., ERN/Ne). These results support the attentional bottleneck theory and further describe deficiencies related to elevated psychopathic traits that could be beneficial for new treatment strategies for psychopathy.

Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders

Classification models are becoming useful tools for finding patterns in neuroimaging data sets that are not observable to the naked eye. Many of these models are applied to discriminating clinical groups such as schizophrenic patients from healthy controls or from patients with bipolar disorder. A more nuanced model might be to discriminate between levels of personality traits. Here, as a proof of concept, we take an initial step toward developing prediction models to differentiate individuals based on a personality disorder: psychopathy. We included three groups of adolescent participants: incarcerated youth with elevated psychopathic traits (i.e., callous and unemotional traits and conduct disordered traits; n=71), incarcerated youth with low psychopathic traits (n=72), and non-incarcerated youth as healthy controls (n=21). Support vector machine (SVM) learning models were developed to separate these groups using an out-of-sample cross-validation method on voxel-based morphometry (VBM) data. Regions of interest from the paralimbic system, identified in an independent forensic sample, were successful in differentiating youth groups. Models seeking to classify incarcerated individuals to have high or low psychopathic traits achieved 69.23% overall accuracy. As expected, accuracy increased in models differentiating healthy controls from individuals with high psychopathic traits (82.61%) and low psychopathic traits (80.65%). Here we have laid the foundation for using neural correlates of personality traits to identify group membership within and beyond psychopathy. This is only the first step, of many, toward prediction models using neural measures as a proxy for personality traits. As these methods are improved, prediction models with neural measures of personality traits could have far-reaching impact on diagnosis, treatment, and prediction of future behavior.

Multimodal imaging measures predict rearrest

Rearrest has been predicted by hemodynamic activity in the anterior cingulate cortex (ACC) during error-processing (Aharoni et al., 2013). Here, we evaluate the predictive power after adding an additional imaging modality in a subsample of 45 incarcerated males from Aharoni et al. (2013). Event-related potentials (ERPs) and hemodynamic activity were collected during a Go/NoGo response inhibition task. Neural measures of error-processing were obtained from the ACC and two ERP components, the error-related negativity (ERN/Ne) and the error positivity (Pe). Measures from the Pe and ACC differentiated individuals who were and were not subsequently rearrested. Cox regression, logistic regression, and support vector machine (SVM) neuroprediction models were calculated. Each of these models proved successful in predicting rearrest and SVM provided the strongest results. Multimodal neuroprediction SVM models with out of sample cross-validating accurately predicted rearrest (83.33%). Offenders with increased Pe amplitude and decreased ACC activation, suggesting abnormal error-processing, were at greatest risk of rearrest.

Reward salience and risk aversion underlie differential ACC activity in substance dependence

The medial prefrontal cortex, especially the dorsal anterior cingulate cortex (ACC), has long been implicated in cognitive control and error processing. Although the association between ACC and behavior has been established, it is less clear how ACC contributes to dysfunctional behavior such as substance dependence. Evidence from neuroimaging studies investigating ACC function in substance users is mixed, with some studies showing disengagement of ACC in substance dependent individuals (SDs), while others show increased ACC activity related to substance use. In this study, we investigate ACC function in SDs and healthy individuals performing a change signal task for monetary rewards. Using a priori predictions derived from a recent computational model of ACC, we find that ACC activity differs between SDs and controls in factors related to reward salience and risk aversion between SDs and healthy individuals. Quantitative fits of a computational model to fMRI data reveal significant differences in best fit parameters for reward salience and risk preferences. Specifically, the ACC in SDs shows greater risk aversion, defined as concavity in the utility function, and greater attention to rewards relative to reward omission. Furthermore, across participants risk aversion and reward salience are positively correlated. The results clarify the role that ACC plays in both the reduced sensitivity to omitted rewards and greater reward valuation in SDs. Clinical implications of applying computational modeling in psychiatry are also discussed.

Error-related brain activity in the age of RDoC: A review of the literature

The ability to detect and respond to errors is critical to successful adaptation to a changing environment. The error-related negativity (ERN), an event-related potential (ERP) component, is a well-validated neural response to errors and reflects the error monitoring activity of the anterior cingulate cortex (ACC). Additionally, the ERN is implicated in several processes key to adaptive functioning. Abnormalities in error-related brain activity have been linked to multiple forms of psychopathology and individual differences. As such, the component is likely to be useful in NIMH's Research Domain Criteria (RDoC) initiative to establish biologically-meaningful dimensions of psychological dysfunction, and currently appears as a unit of measurement in three RDoC domains: Positive Valence Systems, Negative Valence Systems, and Cognitive Systems. In this review paper, we introduce the ERN and discuss evidence related to its psychometric properties, as well as important task differences. Following this, we discuss evidence linking the ERN to clinically diverse forms of psychopathology, as well as the implications of one unit of measurement appearing in multiple RDoC dimensions. And finally, we discuss important future directions, as well as research pathways by which the ERN might be leveraged to track the ways in which dysfunctions in multiple neural systems interact to influence psychological well-being.