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：
10414018
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 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10414018
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 Dependence Cocaine use disorder Cognition Cognitive Communities Corpus striatum structure Data Analyses Data Set Diagnosis Diagnostic 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 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 psychiatric comorbidity 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.

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Functional Connectivity Mapping for rTMS Target Selection in Depression.

抑郁症 rTMS 目标选择的功能连接映射。

DOI：
10.1176/appi.ajp.20220306
发表时间：
2023
期刊：
The American journal of psychiatry
影响因子：
0
作者：
Elbau,ImmanuelG;Lynch,CharlesJ;Downar,Jonathan;Vila-Rodriguez,Fidel;Power,JonathanD;Solomonov,Nili;Daskalakis,ZafirisJ;Blumberger,DanielM;Liston,Conor
通讯作者：
Liston,Conor