Diagnostic and prognostic biomarkers for subtypes of addiction-related circuit dysfunction

成瘾相关回路功能障碍亚型的诊断和预后生物标志物

基本信息

批准号：
10177987
负责人：
Rita Z Goldstein
金额：
$ 60.4万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10177987
关键词：
Abstinence Amygdaloid structure Anhedonia Animal Model Anterior Anxiety Anxiety Disorders Arousal Behavior Biological Markers Brain Brain scan Cardiovascular Diseases Categories Clinical Cocaine Cocaine Dependence Cognition Cognitive Communities Corpus striatum structure Data Analyses Data Set Diagnosis Diagnostic Disease Dorsal Functional Magnetic Resonance Imaging Functional disorder Habits Heterogeneity Impairment Individual Individual Differences Lateral Lead Learning Malignant Neoplasms Maps Measures Medial Mediating Medical Mental Depression Molecular Mood Disorders Motivation Negative Reinforcements Neurobiology Nicotine Patient Care Patients Pattern Pharmaceutical Preparations Pharmacology Physicians Prediction of Response to Therapy Prevalence Process Prognostic Marker Psychiatric Diagnosis Regulation Relapse Research Research Personnel Rest Rewards Risk Scanning Subgroup Substance Use Disorder Symptoms Syndrome Testing Transcend Treatment outcome Ventral Striatum Withdrawal addiction animal data anxious base biobank biomarker validation cocaine use comorbid depression comorbidity craving diagnostic biomarker diagnostic platform disability disease classification disease diagnosis drug seeking behavior individual patient individualized medicine interest large scale data machine learning method negative affect negative emotional state network dysfunction neuroimaging neurophysiology neuropsychiatry novel novel therapeutic intervention novel therapeutics patient subsets reinforcer response specific biomarkers substance misuse tool

项目摘要

Project Summary Substance use disorders (SUDs) are increasing in prevalence and are already a leading cause of disability, due in part to the fact that our understanding of the underlying pathophysiology is incomplete. Like most neuropsychiatric syndromes, SUDs are highly heterogeneous, and distinct mechanisms may be operative in some individuals but not in others, even within a single diagnostic category. Furthermore, SUDs frequently co- occur with depression, anxiety, and other psychiatric syndromes, complicating efforts to identify molecular and circuit-level mechanisms, and disentangle them from those involved in mood and anxiety disorders. Diagnostic heterogeneity is thus a fundamental obstacle to developing better treatments, identifying biomarkers for quantifying risk for different forms of addiction, and predicting treatment response and relapse. Recently, we developed and validated an approach to discovering and diagnosing subtypes of depression using fMRI measures of functional connectivity, which in turn predicted subtype-specific clinical symptom profiles and treatment outcomes. Here, in response to PAR-18-062, we propose a secondary data analysis that would extend this approach to SUDs, leveraging multiple deeply characterized and large-scale neuroimaging datasets. Our central hypothesis is that individual differences in mechanisms underlying impairments in response inhibition and salience attribution (iRISA) are mediated by distinct forms of dysfunctional connectivity in addiction-related circuits, which in turn interact and give rise to distinct neurophysiological addiction subtypes. In Aim 1, we will use statistical clustering and machine learning methods to delineate these subtypes and optimize classifiers (fMRI biomarkers) for diagnosing them in individual patients, focusing initially on cocaine addiction. In Aim 2, we will validate these subtype-specific biomarkers by first replicating them in a new dataset and then evaluating their longitudinal stability and predictive utility. In Aim 3, we will test whether subtype-specific circuit mechanisms generalize to mediate iRISA functions in other forms of addiction, and define their interactions with distinct mechanisms mediating anhedonia and anxious arousal in patients with comorbid depression and anxiety.

项目概要物质使用障碍（SUD）的患病率正在增加，并且已经成为残疾的主要原因，部分原因是我们对潜在病理生理学的理解不完整。像大多数人一样神经精神综合征、SUD 具有高度异质性，不同的机制可能在其中起作用即使在单一诊断类别中，某些个体也如此，但其他个体则不然。此外，SUD 经常共同与抑郁、焦虑和其他精神综合症一起发生，使识别分子和精神疾病的努力变得复杂化。电路层面的机制，并将它们与情绪和焦虑症相关的机制分开。诊断因此，异质性是开发更好的治疗方法、识别生物标志物的根本障碍。量化不同形式成瘾的风险，并预测治疗反应和复发。最近，我们开发并验证了一种使用功能磁共振成像发现和诊断抑郁症亚型的方法功能连接的测量，进而预测亚型特异性的临床症状特征和治疗结果。在此，针对 PAR-18-062，我们提出了二次数据分析，该分析将利用多种深入表征的大规模神经影像，将此方法扩展到 SUD 数据集。我们的中心假设是，潜在损伤机制的个体差异反应抑制和显着性归因（iRISA）是由不同形式的功能失调连接介导的在与成瘾相关的回路中，这些回路反过来相互作用并产生独特的神经生理学成瘾亚型。在目标 1 中，我们将使用统计聚类和机器学习方法来描述这些子类型并优化分类器（fMRI 生物标志物）以对个体患者进行诊断，首先关注可卡因成瘾。在目标 2 中，我们将通过首先将这些亚型特异性生物标志物复制到新数据集，然后评估其纵向稳定性和预测效用。在目标 3 中，我们将测试是否亚型特异性电路机制普遍介导其他形式成瘾中的 iRISA 功能，并且定义它们与介导患者快感缺乏和焦虑唤醒的不同机制的相互作用共病抑郁和焦虑。