Alcohol use disorder is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use (...) to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity. (shrink)

Aims: Attentional bias, alcohol craving, and anxiety have important implications in the development and maintenance of alcohol use disorder. The current study aims to test the effectiveness of a Virtual Reality Cue-Exposure Therapy to reduce levels of alcohol craving and anxiety and prompt changes in AB toward alcohol content.Method: A 49-year-old male participated in this study, diagnosed with severe AUD, who also used tobacco and illicit substances on an occasional basis and who made several failed (...) attempts to cease substance misuse. The protocol consisted of six VR-CET booster sessions and two assessment sessions over the course of 5 weeks. The VR-CET program consisted of booster therapy sessions based on virtual reality exposure to preferred alcohol-related cues and contexts. The initial and final assessment sessions were focused on exploring AB, alcohol craving, and anxiety using paper-and-pencil instruments and the eye-tracking and VR technologies at different time points.Results: Pre and post assessment sessions indicated falls on the scores of all instruments assessing alcohol craving, anxiety, and AB.Conclusions: This case report, part of a larger project, demonstrates the effectiveness of the VR-CET booster sessions in AUD. In the post-treatment measurements, a variety of instruments showed a change in the AB pattern and an improvement in craving and anxiety responses. As a result of the systematic desensitization, virtual exposure gradually reduced the responses to significant alcohol-related cues and contexts. The implications for AB, anxiety and craving are discussed. (shrink)

Current cognitive models of addiction imply that speeded detection and increased distraction from substance cues might both independently contribute to the persistence of addictive behavior. Speeded detection might lower the threshold for experiencing craving, whereas increased distraction might further increase the probability of entering a bias-craving-bias cycle, thereby lowering the threshold for repeated substance use. This study was designed to examine whether indeed both attentional processes are involved in substance use disorders. Both attentional processes were indexed by an Odd-One-Out visual (...) search task in individuals diagnosed with alcohol use disorder and cannabis use disorder. To test whether the detection and/or the distraction component are characteristic for AUD and CUD, their indices were compared with matched individuals without these diagnoses. Individuals with CUD showed speeded detection of cannabis cues; the difference in detection between AUD and the comparison group remained inconclusive. Neither the AUD nor the CUD group showed more distraction than the comparison groups. The sample size of the CUD group was relatively small. In addition, participants made relatively many errors in the attentional bias task, which might have lowered its sensitivity to detect ABs. The current study provided no support for the proposed role of increased distraction in CUD and AUD. The findings did, however, provide support for the view that speeded detection might be involved in CUD. Although a similar trend was evident for AUD, the evidence was weak and remained therefore inconclusive. (shrink)

Excessive alcohol use has adverse effects on the central nervous system (CNS) and can lead to alcohol use disorders (AUDs). Recent studies have suggested that myelin reductions may directly contribute to CNS dysfunctions associated with AUDs. Myelin consists of compact lipid membranes wrapped around axons to provide electrical insulation and trophic support. Regulation of myelin is considered as a new form of neural plasticity due to its profound impacts on the computation of neural networks. In this review, the (...) authors first discuss experimental evidence showing how alcohol exposure causes demyelination in different brain regions, often accompanied by deficits in cognition and emotion. Next, they discuss postulated molecular and cellular mechanisms underlying alcohol's impact on myelin. It is clear that more extensive investigations are needed in this important but underexplored research field in order to gain a better understanding of the myelin‐behavior relationship and to develop new treatment strategies for AUDs. (shrink)

Alcohol use disorder is one of the most common substance use disorders contributing to both behavioral and cognitive impairments in patients with AUD. Recent neuroimaging studies point out that AUD is a typical disorder featured by altered functional connectivity. However, the details about how voxel-wise functional coordination remain unknown. Here, we adopted a newly proposed method named functional connectivity density to depict altered voxel-wise functional coordination in AUD. The novel functional imaging technique, FCD, provides a comprehensive analytical (...) method for brain's “scale-free” networks. We applied resting-state functional MRI toward subjects to obtain their FCD, including global FCD, local FCD, and long-range FCD. Sixty-one patients with AUD and 29 healthy controls were recruited, and patients with AUD were further divided into alcohol-related cognitive impairment group and non-cognitive impairment group. All subjects were asked to stay stationary during the scan in order to calculate the resting-state gFCD, lFCD, and lrFCD values, and further investigate the abnormal connectivity alterations among AUD-NCI, ARCI, and HC. Compared to HC, both AUD groups exhibited significantly altered gFCD in the left inferior occipital lobe, left calcarine, altered lFCD in right lingual, and altered lrFCD in ventromedial frontal gyrus. It is notable that gFCD of the ARCI group was found to be significantly deviated from AUD-NCI and HC in left medial frontal gyrus, which changes probably contributed by the impairment in cognition. In addition, no significant differences in gFCD were found between ARCI and HC in left parahippocampal, while ARCI and HC were profoundly deviated from AUD-NCI, possibly reflecting a compensation of cognition impairment. Further analysis showed that within patients with AUD, gFCD values in left medial frontal gyrus are negatively correlated with MMSE scores, while lFCD values in left inferior occipital lobe are positively related to ADS scores. In conclusion, patients with AUD exhibited significantly altered functional connectivity patterns mainly in several left hemisphere brain regions, while patients with AUD with or without cognitive impairment also demonstrated intergroup FCD differences which correlated with symptom severity, and patients with AUD cognitive impairment would suffer less severe alcohol dependence. This difference in symptom severity probably served as a compensation for cognitive impairment, suggesting a difference in pathological pathways. These findings assisted future AUD studies by providing insight into possible pathological mechanisms. (shrink)

Background Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals’ judgments of stigma, empathy, and compassion. Methods We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments (...) of stigma, empathy, and compassion. Results Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists. Conclusions The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others. (shrink)