• Energy Research

Le trouble de la consommation d'alcool (AUD) est une maladie chronique et mortelle. Le principal obstacle au traitement par AUD est une forte probabilité de rechute à l'abus d'alcool, même après une abstinence prolongée. Les mécanismes moléculaires de la rechute induite par les signaux ne sont pas bien établis, malgré le fait qu'ils peuvent offrir de nouvelles cibles pour le traitement de l'AUD. En utilisant un modèle animal complet d'AUD, des manipulations génétiques à médiation virale et ciblées sur l'amygdale par la technologie CRISPR/Cas9 et l'électrophysiologie ex vivo, nous identifions un mécanisme qui contrôle sélectivement la rechute d'alcool induite par les signaux et la gravité des symptômes d'AUD. Ce mécanisme est basé sur l'activité régulée de la protéine associée au cytosquelette (ARC)/plasticité dépendante de l'Arg3.1 des synapses de l'amygdale. Chez l'homme, nous avons identifié des polymorphismes mononucléotidiques dans le gène ARC et leur méthylation prédisant non seulement la taille de l'amygdale, mais aussi la fréquence de la consommation d'alcool, même au début de la consommation régulière. Cibler l'arc pendant l'exposition à l'alcool peut donc être un nouveau mécanisme sélectif pour la prévention des rechutes. El trastorno por consumo de alcohol (TCA) es una enfermedad crónica y mortal. El principal impedimento de la terapia AUD es una alta probabilidad de recaída en el abuso de alcohol incluso después de una abstinencia prolongada. Los mecanismos moleculares de la recaída inducida por señales no están bien establecidos, a pesar de que pueden ofrecer nuevas dianas para el tratamiento de la AUD. Utilizando un modelo animal integral de AUD, manipulaciones genéticas mediadas por virus y dirigidas a la amígdala mediante tecnología CRISPR/Cas9 y electrofisiología ex vivo, identificamos un mecanismo que controla selectivamente la recaída de alcohol inducida por señales y la gravedad de los síntomas de AUD. Este mecanismo se basa en la actividad regulada de la proteína asociada al citoesqueleto (ARC)/plasticidad dependiente de Arg3.1 de las sinapsis de la amígdala. En humanos, identificamos polimorfismos de un solo nucleótido en el gen ARC y su metilación prediciendo no solo el tamaño de la amígdala, sino también la frecuencia del consumo de alcohol, incluso al inicio del consumo regular. Dirigirse a Arc durante la exposición al alcohol puede ser, por lo tanto, un nuevo mecanismo selectivo para la prevención de recaídas. Alcohol use disorder (AUD) is a chronic and fatal disease. The main impediment of the AUD therapy is a high probability of relapse to alcohol abuse even after prolonged abstinence. The molecular mechanisms of cue-induced relapse are not well established, despite the fact that they may offer new targets for the treatment of AUD. Using a comprehensive animal model of AUD, virally-mediated and amygdala-targeted genetic manipulations by CRISPR/Cas9 technology and ex vivo electrophysiology, we identify a mechanism that selectively controls cue-induced alcohol relapse and AUD symptom severity. This mechanism is based on activity-regulated cytoskeleton-associated protein (Arc)/ARG3.1-dependent plasticity of the amygdala synapses. In humans, we identified single nucleotide polymorphisms in the ARC gene and their methylation predicting not only amygdala size, but also frequency of alcohol use, even at the onset of regular consumption. Targeting Arc during alcohol cue exposure may thus be a selective new mechanism for relapse prevention. Alcohol use disorder (AUD) is a chronic and fatal disease. The main impediment of AUD therapy is a high probability of relapse to alcohol abuse even after prolonged abstinence. The molecular mechanisms of cue-induced relapse are not well established, despite the fact that they may offer new targets for the treatment of AUD. Using a comprehensive animal model of AUD, virally-mediated and amygdala-targeted genetic manipulations by CRISPR/Cas9 technology and ex vivo electrophysiology, we identify a mechanism that selectively controls cue-induced alcohol relapse and AUD symptom severity. This mechanism is based on activity-regulated cytoskeleton-associated protein (arc)/Arg3.1-dependent plasticity of the amygdala synapses. In humans, we identified single nucleotide polymorphisms in the ARC gene and their methylation predicting not only amygdala size, but also frequency of alcohol use, even at the onset of regular consumption. Targeting Arc during alcohol exposure may thus be a selective new mechanism for relapse prevention. اضطراب تعاطي الكحول (AUD) هو مرض مزمن ومميت. العائق الرئيسي لعلاج AUD هو احتمال كبير للانتكاس إلى تعاطي الكحول حتى بعد الامتناع عن ممارسة الجنس لفترات طويلة. الآليات الجزيئية للانتكاس الناجم عن الإشارة ليست راسخة، على الرغم من حقيقة أنها قد تقدم أهدافًا جديدة لعلاج AUD. باستخدام نموذج حيواني شامل من AUD، والتلاعب الجيني بوساطة فيروسية واستهداف اللوزة بواسطة تقنية CRISPR/Cas9 والفسيولوجيا الكهربية خارج الجسم الحي، نحدد آلية تتحكم بشكل انتقائي في انتكاس الكحول الناجم عن الإشارة وشدة أعراض AUD. تعتمد هذه الآلية على البروتين المرتبط بالهياكل الخلوية المنظم للنشاط (ARC )/ اللدونة المعتمدة على Arg3.1 في مشابك اللوزة الدماغية. في البشر، حددنا تعدد أشكال النيوكليوتيدات المفردة في جين القوس وتنبأت مثيلتها ليس فقط بحجم اللوزة، ولكن أيضًا بتكرار تعاطي الكحول، حتى في بداية الاستهلاك المنتظم. وبالتالي، قد يكون استهداف القوس أثناء التعرض للكحول آلية جديدة انتقائية للوقاية من الانتكاس.

AbstractAlcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidating neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long‐lasting abstinence. In the present study, we analysed resting‐state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified patients with AD (n = 93) who were followed up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age‐ and gender‐matched controls (CON, n = 83). Our results suggest increased whole‐brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared with CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared with CON, encompassing regions associated with higher‐order executive functions, sensory and reward processing. Among patients with AD, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow‐up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.

Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted 10 phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM.

AbstractIn alcohol-dependent (AD) patients, alcohol cues induce strong activations in brain areas associated with alcohol craving and relapse, such as the nucleus accumbens (NAc) and amygdala. However, little is known about the influence of depressive symptoms, which are common in AD patients, on the brain’s reactivity to alcohol cues. The methylation state of the dopamine transporter gene (DAT) has been associated with alcohol dependence, craving and depression, but its influence on neural alcohol cue reactivity has not been tested. Here, we compared brain reactivity to alcohol cues in 38 AD patients and 17 healthy controls (HCs) using functional magnetic resonance imaging and assessed the influence of depressive symptoms and peripheral DAT methylation in these responses. We show that alcoholics with low Beck’s Depression Inventory scores (n=29) had higher cue-induced reactivity in NAc and amygdala than those with mild/moderate depression scores (n=9), though subjective perception of craving was higher in those with mild/moderate depression scores. We corroborated a higher DAT methylation in AD patients than HCs, and showed higher DAT methylation in AD patients with mild/moderate than low depression scores. Within the AD cohort, higher methylation predicted craving and, at trend level (P=0.095), relapse 1 year after abstinence. Finally, we show that amygdala cue reactivity correlated with craving and DAT methylation only in AD patients with low depression scores. These findings suggest that depressive symptoms and DAT methylation are associated with alcohol craving and associated brain processes in alcohol dependence, which may have important consequences for treatment. Moreover, peripheral DAT methylation may be a clinically relevant biomarker in AD patients.

AbstractWith the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psychometric variables such as personality traits or mental health comorbidities in a data-driven approach. We analyzed a subset of adolescents who did not have any prior binge drinking experience at age 14 (IMAGEN dataset, n = 555, 52.61% female). Participants underwent sMRI at age 14, binge drinking assessments at ages 14 and 22, and psychometric questionnaire assessments at ages 14 and 22. We derived structural brain features from T1-weighted magnetic resonance and diffusion tensor imaging. Using Machine Learning (ML), we predicted binge drinking (age 22) from brain structure (age 14) and used counterbalancing with oversampling to systematically control for 110+ variables from a wide range of social, personality, and other psychometric characteristics potentially associated with binge drinking. We evaluated if controlling for any variable resulted in a significant reduction in ML prediction accuracy.Sensation-seeking (-13.98±1.68%) assessed via the Substance Use Risk Profile Scale at age 14 and uncontrolled eating (-13.98±3.28%) assessed via the Three-Factor-Eating-Questionnaire at age 22 led to significant reductions in ML prediction accuracy upon controlling for them. Thus, sensation-seeking and binge eating could partially explain the prediction of future binge drinking from adolescent brain structure.Our findings suggest that binge drinking and binge eating at age 22 share common neurobiological precursors discovered by the ML model. These neurobiological precursors seem to be associated with sensation-seeking at age 14. Our results facilitate early detection of increased risk for binge drinking and inform future clinical research in trans-diagnostic prevention approaches for adolescent alcohol misuse.