Increased neural activity in the right dorsolateral prefrontal cortex during a risky decision-making task is associated with cocaine use in methadone-maintained patients

Prevalence of high impulsivity and its relation to sleep indices in opioid use disorder patients receiving methadone maintenance treatment

BACKGROUND

The relation between impulsivity and sleep indices is not well determined in patients receiving methadone maintenance treatment (MMT).

AIMS

to evaluate high impulsivity prevalence, its risk factors and relation with sleep indices.

METHODS

a random MMT sample (n = 61) plus MMT current cocaine users (n = 20) were assessed for impulsivity (Barratt impulsivity scale [BIS-11] and Balloon Analogue Risk task [BART]), sleep quality (Pittsburg Sleep Quality Index [PSQI]), sleepiness (The Epworth sleepiness scale [ESS]), and substance in urine.

RESULTS

81 patients, aged 56.6 ± 10, 54.3% tested positive to any substance, 53.1% with poor sleep (PSQI>5) and 43.2% with daytime sleepiness (ESS >7) were studied. Impulsivity (BIS-11 ≥ 72) prevalence was 27.9% (of the representative sample), and 30.9% of all participants. These patients characterized with any substance and shorter duration in MMT with no sleep indices or other differences including BART balloon task performance (that was higher only in any substance than non-substance user group). However, impulsive score linearly correlated with daytime sleepiness (R = 0.2, p = 0.05). Impulsivity proportion was lowest among those with no cocaine followed by cocaine use and the highest in those who used cocaine and opiates (20.8%, 33.3% and 60% respectively, p = 0.02), as daily sleep (38.3%, 42.1% and 60%, p = 0.3) although not statistically significant.

CONCLUSION

Daytime sleepiness correlated with impulsivity, but cocaine usage is the robust factor. Further follow-up is warranted to determine whether substance discontinuing will lead to a reduction in impulsivity, and improved vigilance. Sleep quality did not relate to daytime sleepiness and impulsivity and need further research.

Friend or foe: classifying collaborative interactions using fNIRS

To succeed, effective teams depend on both cooperative and competitive interactions between individual teammates. Depending on the context, cooperation and competition can amplify or neutralize a team's problem solving ability. Therefore, to assess successful collaborative problem solving, it is first crucial to distinguish competitive from cooperative interactions. We investigate the feasibility of using lightweight brain sensors to distinguish cooperative from competitive interactions in pairs of participants (N=84) playing a decision-making game involving uncertain outcomes. We measured brain activity using functional near-infrared spectroscopy (fNIRS) from social, motor, and executive areas during game play alone and in competition or cooperation with another participant. To distinguish competitive, cooperative, and alone conditions, we then trained support vector classifiers using combinations of features extracted from fNIRS data. We find that features from social areas of the brain outperform other features for discriminating competitive, cooperative, and alone conditions in cross-validation. Comparing the competitive and alone conditions, social features yield a 5% improvement over motor and executive features. Social features show promise as means of distinguishing competitive and cooperative environments in problem solving settings. Using fNIRS data provides a real-time measure of subjective experience in an ecologically valid environment. These results have the potential to inform intelligent team monitoring to provide better real-time feedback and improve team outcomes in naturalistic settings.

Adaptations of the balloon analog risk task for neuroimaging settings: a systematic review

Introduction

The Balloon Analog Risk Task (BART), a computerized behavioral paradigm, is one of the most common tools used to assess the risk-taking propensity of an individual. Since its initial behavioral version, the BART has been adapted to neuroimaging technique to explore brain networks of risk-taking behavior. However, while there are a variety of paradigms adapted to neuroimaging to date, no consensus has been reached on the best paradigm with the appropriate parameters to study the brain during risk-taking assessed by the BART. In this review of the literature, we aimed to identify the most appropriate BART parameters to adapt the initial paradigm to neuroimaging and increase the reliability of this tool.

Methods

A systematic review focused on the BART versions adapted to neuroimaging was performed in accordance with PRISMA guidelines.

Results

A total of 105 articles with 6,879 subjects identified from the PubMed database met the inclusion criteria. The BART was adapted in four neuroimaging techniques, mostly in functional magnetic resonance imaging or electroencephalography settings.

Discussion

First, to adapt the BART to neuroimaging, a delay was included between each trial, the total number of inflations was reduced between 12 and 30 pumps, and the number of trials was increased between 80 and 100 balloons, enabling us to respect the recording constraints of neuroimaging. Second, explicit feedback about the balloon burst limited the decisions under ambiguity associated with the first trials. Third, employing an outcome index that provides more informative measures than the standard average pump score, along with a model incorporating an exponential monotonic increase in explosion probability and a maximum explosion probability between 50 and 75%, can yield a reliable estimation of risk profile. Additionally, enhancing participant motivation can be achieved by increasing the reward in line with the risk level and implementing payment based on their performance in the BART. Although there is no universal adaptation of the BART to neuroimaging, and depending on the objectives of a study, an adjustment of parameters optimizes its evaluation and clinical utility in assessing risk-taking.

The impact of opioid-stimulant co-use on tonic and cue-induced craving

The twin opioid-stimulant epidemics have led to increased overdose deaths and present unique challenges for individuals entering treatment with opioid-stimulant polysubstance use. This study examined tonic and cue-induced craving as a primary outcome among persons in substance use treatment who reported primary substances of opioids, methamphetamine, or cocaine. The sample consisted of 1974 individuals in 55 residential substance-use treatment centers in the United States in 2021. Weekly surveys were delivered via a third-party outcomes tracking system, including measures of tonic and cue-induced craving. Initial comparisons on tonic and cue-induced craving were made among those who primarily used opioids, cocaine, or methamphetamine. Further, the effect of opioid/stimulant polysubstance use on tonic and cue-induced craving was evaluated using marginal effect regression models. Primary methamphetamine use was associated with decreased tonic craving compared to primary opioid use (β = -5.63, p < 0.001) and primary cocaine use was also associate with decreased tonic craving compared to primary opioid use (β = -6.14, p < 0.001). Primary cocaine use was also associated with lower cue-induced cravings compared to primary opioid use (β = -0.53, p = 0.037). Opioid-methamphetamine polysubstance use was associated with higher tonic craving (β = 3.81, p = <0.001) and higher cue-induced craving (β = 1.55, p = 0.001); however, this was not the case for opioid-cocaine polysubstance use. The results of this study indicate that individuals who primarily use opioids and have secondary methamphetamine use experience higher cue-induced and tonic-induced craving, suggesting that these individuals may benefit from additional interventions that target craving and mitigate relapse risk and other negative sequelae.

Neural correlates of cognitive control in women with a history of sexual violence suggest altered prefrontal cortical activity during cognitive processing

OBJECTIVE

Women's experiences of sexual violence can be not only psychologically and physically traumatizing but may also have lasting effects on brain functions, including cognitive control relating to the inhibition and processing of emotion. Thus, the purpose of this pilot study is to explore underlying neural correlates of sexual violence's impact on cognitive control in women.

METHODS

Thirty women (aged 21-30 years) participants underwent a quantitative survey along with an affect-congruent Go-NoGo task. Prefrontal activity was monitored using functional near-infrared spectroscopy, a portable neuroimaging technology. An analysis of variance tested for main effects of the condition (Go versus NoGo), group (sexual violence versus no prior sexual violence), and potential interactions.

RESULTS

Fifteen of 30 women reported a history of childhood (n = 5) and/or adult (n = 12) sexual violence. Those with sexual violence histories reported significantly higher depression, anxiety, and posttraumatic stress symptoms, as well as increased impulsivity compared to their peers. Behavioral performance did not differ between the groups; however, functional near-infrared spectroscopy data revealed a significant (group × condition) interaction in Optodes 13 and 16. Women with histories of sexual violence had a significantly lower response during the "NoGo" condition and a heightened response during the "Go" condition, in the right dorsolateral prefrontal cortex.

CONCLUSION

These results suggest altered prefrontal cortical activity during cognitive processing in women with a history of sexual violence, showing hypoactivity during response inhibition and hyperactivity to the positive stimuli. These findings have strong translational promise for innovative assessment and prevention of untoward effects among women with sexual violence.

Prefrontal fNIRS-based clinical data analysis of brain functions in individuals abusing different types of drugs

Background

The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain.

Methods

Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics.

Results

Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres.

Conclusions

The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.

Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning

Taking different types of addictive drugs such as methamphetamine, heroin, and mixed drugs causes brain functional Changes. Based on the prefrontal functional near-infrared spectroscopy, this study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed type) were collected. For these three types of individuals, the convolutional neural networks (CNN) was designed to classify eight Brodmann areas and the entire prefrontal area, and the average accuracy of the three classifications on each functional area was obtained. As a result, the classification accuracy was lower on the left side than on the right in the dorsolateral prefrontal cortex (DLPFC) of the drug users, while it was higher on the left than on the right in the ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPC), and orbitofrontal cortex (OFC). Then the differences in eight functional areas between the three types of individuals were statistically analyzed, and results showed significant differences in the right VLPFC and right OFC.