Mieke H. J. Schulte

Recovery of neurocognitive functions following sustained abstinence after substance dependence and implications for treatment

BACKGROUND Substance Use Disorders (SUDs) have been associated with impaired neurocognitive functioning, which may (partly) improve with sustained abstinence. New treatments are emerging, aimed at improving cognitive functions, and being tested. However, no integrated review is available regarding neurocognitive recovery following sustained abstinence. OBJECTIVES In this review, results from prospective studies on neurocognitive recovery using neuropsychological assessments before and after sustained abstinence from SUDs are summarized and discussed. RESULTS Thirty-five prospective studies were selected for this review, including twenty-two alcohol, three cannabis, four cocaine, three (meth)amphetamine, and three opioid studies. Results suggest that some cognitive functions (partially) recover after sustained abstinence, and that there are predictors of an unfavorable course such as poly-substance use and number of previous detoxifications. CONCLUSIONS Prospective studies indicate that sustained abstinence after SUDs generally results in (partial) neurocognitive recovery. However, a final answer regarding full recovery awaits prospective studies with neurocognitive assessments before, during, and after sustained abstinence from SUDs. New interventions that might enhance neurocognitive recovery after abstinence are discussed, including neurocognitive training, medication and neuromodulation.

An evolutionary approach for fMRI big data classification

Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patients brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. The image slices of brain are used as the variable and as results here we deal with a big data problem with about 240,000 inputs. To handle this problem, the data had to be reduced and the first process in doing that was to create a mask to apply to all images. The mask was created by averaging the before images for all patients and selecting the top 40% of voxels from that average. This mask was then applied to all fMRI images for all patients. The average of the difference in the before treatment and after fMRI images for each patient were found and these were flattened to one dimension. Then a matrix was made by stacking these 1D arrays on top of each other and a data reduction algorithm was applied on it. Lastly, this matrix was fed into some machine learning and Genetic Programming algorithms and leave-one-out cross-validation was used to test the accuracy. Out of all the data reduction machine learning algorithms used, the best accuracy was obtained using Principal Component Analysis along with Genetic Programming classifier. This gave an accuracy of 74%, which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.

The effect of N-acetylcysteine on brain glutamate and gamma-aminobutyric acid concentrations and on smoking cessation : A randomized, double-blind, placebo-controlled trial

Using data form a 14-day double-blind trial with 48 smokers randomized to either N-acetylcysteine (2400 mg) or placebo, we tested the effect of N-acetylcysteine on glutamate and gamma-aminobutyric acid concentrations in the dorsal anterior cingulate cortex and on smoking cessation. Smoking related behaviors and neurotransmitter concentrations in the dorsal anterior cingulate cortex were assessed before and after treatment. Forty-seven non-smoking males served as baseline controls. Smokers showed higher baseline glutamate but similar gamma-aminobutyric acid concentrations than non-smokers. There were no treatment effects on dorsal anterior cingulate cortex neurotransmitter concentrations, smoking cessation, craving, or withdrawal symptoms. These results confirm glutamate disbalance in smokers, but not efficacy of N-acetylcysteine.

The effect of N-acetylcysteine and working memory training on cocaine use, craving and inhibition in regular cocaine users: correspondence of lab assessments and Ecological Momentary Assessment

INTRODUCTION Effective treatment for cocaine use disorder should dampen hypersensitive cue-induced motivational processes and/or strengthen executive control. Using a randomized, double-blind, placebo-controlled intervention, the primary aim of this study was to investigate the effect of N-Acetylcysteine (NAC) and working memory (WM)-training to reduce cocaine use and craving and to improve inhibition assessed in the laboratory and during Ecological Momentary Assessment (EMA). The second aim was to examine correspondence between laboratory and EMA data. METHODS Twenty-four of 38 cocaine-using men completed a 25-day intervention with 2400mg/day NAC or placebo and WM-training as well as two lab-visits assessing cocaine use, craving and inhibition (Stop Signal task). Additionally, cocaine use, craving and cognition (Stroop task) were assessed using EMA during treatment, with 26 participants completing 819 assessments. RESULTS Cocaine problems according to the Drug Use Disorder Identification Test (DUDIT) decreased more after NAC than after placebo, and the proportion of cocaine-positive urines at lab-visit 2 was lower in the NAC group. No NAC effects were found on craving. For cocaine use and craving, results from the lab data were generally similar to EMA results. NAC also showed some effects on cognitive control: improved inhibition assessed with the Stop Signal task in the lab, and decreased classic Stroop performance during EMA. There were no significant effects of number of completed WM-training sessions. CONCLUSIONS Overall this study revealed mixed findings regarding the treatment of cocaine use disorders with NAC and WM-training. The effect of NAC on inhibition should be further investigated.

Functional connectivity analysis of resting-state fMRI networks in nicotine dependent patients

Brain imaging studies identified brain networks that play a key role in nicotine dependence-related behavior. Functional connectivity of the brain is dynamic; it changes over time due to different causes such as learning, or quitting a habit. Functional connectivity analysis is useful in discovering and comparing patterns between functional magnetic resonance imaging (fMRI) scans of patients’ brains. In the resting state, the patient is asked to remain calm and not do any task to minimize the contribution of external stimuli. The study of resting-state fMRI networks have shown functionally connected brain regions that have a high level of activity during this state. In this project, we are interested in the relationship between these functionally connected brain regions to identify nicotine dependent patients, who underwent a smoking cessation treatment. Our approach is on the comparison of the set of connections between the fMRI scans before and after treatment. We applied support vector machines, a machine learning technique, to classify patients based on receiving the treatment or the placebo. Using the functional connectivity (CONN) toolbox, we were able to form a correlation matrix based on the functional connectivity between different regions of the brain. The experimental results show that there is inadequate predictive information to classify nicotine dependent patients using the SVM classifier. We propose other classification methods be explored to better classify the nicotine dependent patients.

Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

Prefrontal Glx and GABA concentrations and impulsivity in cigarette smokers and smoking polysubstance users

Glutamate and GABA play an important role in substance dependence. However, it remains unclear whether this holds true for different substance use disorders and how this is related to risk-related traits such as impulsivity. We, therefore, compared Glx (as a proxy measure for glutamate) and GABA concentrations in the dorsal anterior cingulate cortex (dACC) of 48 male cigarette smokers, 61 male smoking polysubstance users, and 90 male healthy controls, and investigated the relationship with self-reported impulsivity and substance use. Glx and GABA concentrations were measured using proton Magnetic Resonance Spectroscopy. Impulsivity, smoking, alcohol and cocaine use severity and cannabis use were measured using self-report instruments. Results indicate a trend towards group differences in Glx. Post-hoc analyses showed a difference between smokers and healthy controls (p=0.04) and a trend towards higher concentrations in smoking polysubstance users and healthy controls (p=0.09), but no differences between smokers and smoking polysubstance users. dACC GABA concentrations were not significantly different between groups. Smoking polysubstance users were more impulsive than smokers, and both groups were more impulsive than controls. No significant associations were observed between dACC neurotransmitter concentrations and impulsivity and level and severity of smoking, alcohol or cocaine use or the presence of cannabis use. The results indicate that differences in dACC Glx are unrelated to type and level of substance use. No final conclusion can be drawn on the lack of GABA differences due to assessment difficulties. The relationship between dACC neurotransmitter concentrations and cognitive impairments other than self-reported impulsivity should be further investigated.

Causal Factors of Increased Smoking in ADHD: A Systematic Review

ABSTRACT Background: ADHD is a highly prevalent disorder and poses a risk for a variety of mental disorders and functional impairments into adulthood. One of the most striking comorbidities of ADHD is nicotine dependence. Youth diagnosed with ADHD are 2–3 times more likely to smoke than their peers without ADHD, initiate smoking earlier in life and progress more quickly and more frequently to regular use and dependence. Possible explanations for these increased risks are: (a) self-medication of ADHD symptoms with the stimulant nicotine; (b) ADHD symptoms like inattention and hyperactivity/impulsivity predispose for smoking initiation and impede smoking cessation; (c) peer pressure; and/or (d) common genetic or environmental determinants for ADHD and smoking. Objective: Identify the most probable causes of the high prevalence of smoking and nicotine dependence in subjects with ADHD. Methods: A systematic literature review was performed and the causality of the observed relations was ranked using the Bradford Hill criteria. Findings: ADHD medication reduces early smoking initiation and alleviates smoking withdrawal. Nicotine patches, bupropion and (probably) varenicline ameliorate ADHD symptoms. Imitation of and interaction with peers and genetic and environmental determinants may contribute to the comorbidity, but seem to contribute less than self-medication. Conclusion: Smoking is probably best explained by a combination of imitation, peer pressure and typical traits of ADHD. In contrast, the positive relation between ADHD and nicotine dependence is currently best explained by the self-medication hypothesis. This hypothesis has a clear pharmacological rationale and is supported by ample evidence, but awaits confirmation from longitudinal naturalistic studies.

The relation between gray matter volume and the use of alcohol, tobacco, cocaine and cannabis in male polysubstance users

BACKGROUND Neuroimaging studies have demonstrated gray matter (GM) volume abnormalities in substance users. While the majority of substance users are polysubstance users, very little is known about the relation between GM volume abnormalities and polysubstance use. METHODS In this study we assessed the relation between GM volume, and the use of alcohol, tobacco, cocaine and cannabis as well as the total number of substances used, in a sample of 169 males: 15 non-substance users, 89 moderate drinkers, 27 moderate drinkers who also smoke tobacco, 13 moderate drinkers who also smoke tobacco and use cocaine, 10 heavy drinkers who smoke tobacco and use cocaine and 15 heavy drinkers who smoke tobacco, cannabis and use cocaine. RESULTS Regression analyses showed that there was a negative relation between the number of substances used and volume of the dorsal medial prefrontal cortex (mPFC) and the ventral mPFC. Without controlling for the use of other substances, the volume of the dorsal mPFC was negatively associated with the use of alcohol, tobacco, and cocaine. After controlling for the use of other substances, a negative relation was found between tobacco and cocaine and volume of the thalami and ventrolateral PFC, respectively. CONCLUSION These findings indicate that mPFC alterations may not be substance-specific, but rather related to the number of substances used, whereas, thalamic and ventrolateral PFC pathology is specifically associated with tobacco and cocaine use, respectively. These findings are important, as the differential alterations in GM volume may underlie different cognitive deficits associated with substance use disorders.