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

• 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
• 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

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.

项目摘要 物质使用障碍（SUD）的患病率正在增加，已经是残疾的主要原因， 部分原因是我们对潜在的病理生理学的理解不完整。像大多数 神经精神综合征，SUD是高度异质性的，不同的机制可能是有效的， 有些人，但不是在其他人，甚至在一个单一的诊断类别。此外，SUD经常与 与抑郁症、焦虑症和其他精神病综合征一起发生，使识别分子和 电路水平的机制，并解开他们从那些参与情绪和焦虑症。诊断 因此，异质性是开发更好的治疗方法、鉴定生物标志物以 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 功能连接性指标，进而预测亚型特异性临床症状特征， 治疗结果。在这里，作为对PAR-18-062的回应，我们提出了一个二级数据分析， 将这种方法扩展到SUD，利用多种深入表征的大规模神经成像 数据集。我们的中心假设是，个体差异的机制，潜在的损害， 反应抑制和显著性归因（iRISA）是由不同形式的功能障碍性连接介导的 在成瘾相关回路中，这些回路相互作用并引起不同的神经生理成瘾 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亚型 并优化分类器（功能磁共振成像生物标志物），用于诊断个体患者，最初专注于 可卡因成瘾在目标2中，我们将通过首先将它们复制到一个 新的数据集，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 亚型特异性回路机制一般化为在其他形式的成瘾中介导iRISA功能， 定义他们的相互作用与不同的机制介导快感缺失和焦虑唤醒患者 抑郁和焦虑并存