Neuroimaging of Alcohol Addiction

酒精成瘾的神经影像学

• 批准号: 9559244
• 负责人: Abdolreza Momenan
• 金额: $ 125.15万
• 依托单位: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9559244
• 关键词: Abstinence; Alcohol consumption; Alcohol dependence; Alcohol or Other Drugs use; Alcohol-Induced Disorders; Alcohols; Anterior; Appearance; Behavior; Behavioral; Big Data; Bilateral; Biological Markers; Brain; Cerebellum; Clinical; Collaborations; Comorbidity; Computer Assisted; Country; Data; Data Analyses; Data Set; Decision Making; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Drug Addiction; Emotional; Funding; Genes; Genetic; Genetic Polymorphism; Grant; Hippocampus (Brain); Human; Image; Imaging Device; Impulse Control Disorders; Impulsivity; Individual; Individual Differences; Inflammation; Information Systems; Insula of Reil; Intervention; Link; Machine Learning; Manuals; Masks; Measures; Medical Imaging; Meta-Analysis; Methodology; Modeling; Motion; Motivation; National Institute of Drug Abuse; National Institute on Alcohol Abuse and Alcoholism; Neuropsychology; Neurotransmitters; Nucleus Accumbens; Paper; Participant; Patient Self-Report; Patients; Personality Traits; Phenotype; Play; Process; Psychometrics; Publications; Questionnaires; Recovery; Relapse; Research; Rest; Sampling; Scanning; Shapes; Site; Societies; Standardization; Statistical Models; Structure; Substance Use Disorder; Thalamic structure; Training; Treatment Efficacy; United States National Institutes of Health; Visit; Work; addiction; alcohol use disorder; base; behavior measurement; brain volume; chronic alcohol ingestion; comparison group; disorder subtype; drinking; endophenotype; gray matter; imaging genetics; individualized medicine; innovation; instrument; morphometry; neural correlate; neurobehavioral; neuroimaging; phenotypic data; pre-clinical; prospective; relapse prediction; relating to nervous system; response; symposium; tool; white matter

Summary: Structural Data Analysis a. Subcortical Volume in alcohol dependence: The effect of polysubstance use Chronic alcohol use has widespread effects on brain morphometry. Alcohol dependent individuals are often diagnosed with comorbid substance use disorders. Alterations in brain morphometry may be different in individuals that are dependent on alcohol alone and in individuals dependent on alcohol and other substances (polysubstance users). We examined subcortical brain volumes in individuals with alcohol dependence only (ADO), individuals with polysubstance use disorder (PS) and 37 healthy control participants (HC). Participants underwent a structural MR scan and a model-based segmentation tool was used to measure the volume of 14 subcortical regions. Compared to HC, ADO had smaller volume in the bilateral hippocampus, right nucleus accumbens and right thalamus. PS only had volume reductions in the bilateral thalamus compared to HC. PS had a larger right caudate compared to ADO. Subcortical volume was negatively associated with drinking measures only in the ADO group. This study confirms the association between alcohol dependence and reductions in subcortical brain volume. It also suggests that polysubstance use interacts with alcohol use to produce limited subcortical volume reduction and at least one region of subcortical volume increase. These findings indicate that additional substance use may mask damage through inflammation or may function in a protective manner, shielding subcortical regions from alcohol-induced damage (Grodin and Momenan, 2016). b. Impulsivity, Compulsivity, and Salience Network Volume in Alcohol Dependence Convergent preclinical and clinical evidence has linked the anterior insula to impulsivity and alcohol-associated compulsivity. No studies had investigated the association between morphometric abnormalities in salience network regions and the phenotype of high levels of impulsivity and compulsivity seen in alcohol dependent individuals. In this study, we compared self-report impulsivity, decisional impulsivity, self-report compulsivity, and structural neuroimaging measures in a sample of alcohol dependent individuals (n = 60) and a comparison group of healthy controls (n = 49). We found that alcohol dependent individuals had smaller anterior insula and anterior cingulate volumes, as well as thinner anterior insula cortices. Anterior insula and anterior cingulate structural measures were negatively associated with self-report impulsivity, decisional impulsivity, and compulsivity measures. Our results suggest that addiction endophenotypes are associated with salience network morphometry in alcohol addiction. These relationships indicate that salience network hubs represent potential treatment targets for impulse control disorders, including alcohol addiction. This work has been accepted for publication in Drug and Alcohol Dependence. EXTRAMURAL COLLABORATIONS 1. Cerebellar recovery of alcohol dependent patients during short term abstinence We are conducting collaboration with Dr. George Fein of Neurobehavioral Research, Inc. (NRI) in a funded U01 grant to develop and apply a 30 parcel active appearance model of the cerebellum on prospective motion tracking and corrected structural MRIs. CNIRC plays a key role in identifying appropriate subjects from ongoing studies in order to maximize the clinical, behavioral, and neuropsychological data available for analysis. A total of 44 subjects have been acquired and made available for modeling since March. Each subject comprises 4 first-visit scans: 1 mm3 T1 motion-corrected, 1 mm3 T1 standard, and two 0.7 mm3 T1 motion-corrected images with re-entry. The second-visit scans comprising: 1 mm3 T1 motion-corrected, 1mm3 T1 standard, and one 0.7 mm3 T1 motion-corrected images. Statistical models of shape and intensity are being trained from expert manual delineations of a subset of the acquired images. Some preliminary results of this data and the methodology will be presented at the ENIGMA Cerbellum Challenge of The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, 2017. 2. Addiction ENIGMA Jointly, with our counterparts at the National Institute on Drug Abuses Neuroimaging Research Branch of Dr. Elliot Stein, we initiated the NIH Addiction Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA). This initiative is part of the Addiction-ENIGMA consortium, a large, multi-site, data-pooling initiative focused on genetics and the brain that has analyzed tens of thousands of study participants at more than 100 labs in over 30 countries. We have already contributed to a number of conference presentations, a review publication (Mackey et al, 2016), and two other submitted papers using structural data from structural images. We have recently completed our contribution to another study utilizing diffusion tensor imaging. OMNIBUS ALCOHOL NEUROIMAGING ASSESSMENTS The purpose of this study is to obtain a standard set of assessments, including brain behavioral, structural, functional, and connectivity (structural and functional) information, on all NIAAA research participants, referred to hereinafter as Characterization Imaging Instruments (CII); a) to determine how individual differences in brain structure and evoked responses relate to generalized trait personality and behavior differences (as assessed by psychometric questionnaire instruments and behavioral measures); and b) to determine whether these individual differences relate specifically to genetic polymorphisms in genes governing neurotransmitter activity. Currently we have structural and functional neuroimaging data for over 155 alcohol dependent patients and healthy controls. This data are collected from motivational, emotional, and decision making tasks. We are currently in the process of determining the analysis approach for this rich data set. This in turn might enable us to identify and establish an Addictions Neuroimaging Assessment (ANiA). Create a standardized neuroimaging assessment to provide AD subtyping phenotypes using information from: Resting state and task driven functional connectivity; Neurocircuitries associated with AD domains; Gray and white matter structural integrity of the human brain in various stages of this heterogeneous disease. Develop a big data system that enables the use of ANA and ANiA assessments and phenotypic data. Determine the neural correlates (i.e. networks) associated with the domains and neuroclinical measures of alcohol use disorders. Utilize innovative approaches such as machine learning to assist in individualized patient treatment, treatment efficacy, and relapse prediction.