Functional networks related to cocaine dependence and its treatment and relapse

与可卡因依赖及其治疗和复发相关的功能网络

• 批准号: 9262879
• 负责人: Marc N Potenza
• 金额: $ 25.13万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9262879
• 关键词: Abstinence; Aftercare; Attention; Biological Markers; Brain; Brain region; Cocaine; Cocaine Dependence; Conflict (Psychology); Confusion; Data; Data Set; Dorsal; Drug abuse; Drug usage; Functional Magnetic Resonance Imaging; Heterogeneity; Impairment; Incentives; Inpatients; Insula of Reil; Linear Models; Midbrain structure; Motivation; Nature; Neurons; Outcome; Outpatients; Participant; Pathological Gambling; Patients; Recovery; Relapse; Reporting; Research; Research Personnel; Rewards; Sampling; Signal Transduction; Source; Substance Use Disorder; Task Performances; Testing; Thalamic structure; Treatment outcome; Urine; Ventral Striatum; addiction; base; blood oxygen level dependent; cocaine use; cognitive control; diagnostic biomarker; effective therapy; follow-up; high dimensionality; imaging study; incentive salience; independent component analysis; insight; neural correlate; neuropathology; neuropsychiatric disorder; non-drug; novel strategies; predictive marker; prospective; public health relevance; secondary analysis; theories; therapy development

 DESCRIPTION (provided by applicant): This application responds to PAR-13-080 - Accelerating the Pace of Drug Abuse Research Using Existing Data. Functional magnetic resonance imaging (fMRI) and the traditional general-linear-model-based analysis (GLM-BA) have been used to assess neural correlates of cocaine dependence (CD) and other neuropsychiatric disorders. However, these studies have reported inconsistent findings including reduced and increased brain activity in patients relative to healthy controls (HCs), and have not been able to provide a clear picture on CD neuropathology and define reliable biomarkers for CD treatment. Investigators have suggested multiple factors such as addiction stages of patients contributing to conflicting findings. We recently applied spatial independent component analysis (sICA) to several fMRI datasets and found extensive overlaps of functional networks (FNs) with opposite timecourses of task-related activities. Based on these findings, we predict: 1) that blood-oxygenation-level-dependent (BOLD) signal mixtures reflecting concurrent co-localized activation and deactivation (CCAD) of different neurons in the same voxels contribute to the opposite findings, while CCAD is determined by balanced excitation and inhibition and functional heterogeneity in the brain; 2) that due to the nature of signal mixtures,a reduced brain deactivation might be expressed as an increased BOLD signal and misinterpreted as a greater brain activation by studies using GLM-BA, which cannot differentiate mixed signals from same voxels; and 3) that sICA will overcome the limitation of GLM-BA and reveal consistent fMRI findings in patients, because sICA can separate signal mixtures from the same voxels into different FNs, and thus mitigate the confusion of reduced deactivation vs. increased activation. For testing our predictions and better understanding CD neuropathology, this project will use sICA to perform secondary analyses on fMRI data acquired from 419 participants, including 179 HCs and 196 CD patients. They performed fMRI tasks for assessing cognitive control and/or monetary reward/loss- motivation. Most patients were treated for CD and followed for 3 ~ 12 months after treatment. We hypothesize that sICA will consistently reveal reduced task-related modulations of FNs related to cognitive control and/or reward/loss-motivation in CD patients relative to HCs, increased (i.e., "recovery") modulations of these FNs in patients after effective treatment for CD, and positive correlations between greater modulations of these FNs and better long-term outcomes after treatment. Findings supporting our hypotheses will not only provide a consistent insight into CD neuropathology and reconcile extant conflicting data, but also demonstrate that task- related modulations in FNs related to cognitive control and/or reward/loss-motivation in CD patients as revealed by sICA are excellent candidates for reliable diagnostic and predictive biomarkers for optimizing and developing CD treatments. Furthermore, these findings will have a sustained, powerful impact on fMRI theories and practices in a general and broad sense and will help move the entire field forward.

 描述（由应用程序提供）：此申请对PAR-13-080的响应 - 使用现有数据加速了药物滥用研究的速度。功能磁共振成像（fMRI）和传统的一般线性模型分析（GLM-BA）已用于评估可卡因依赖性（CD）和其他神经精神疾病的神经元相关性。但是，这些研究报告了相对于健康对照组（HCS）的患者的脑活动减少和增加的发现，并且无法提供有关CD神经病理学的清晰图片，并为CD治疗定义了可靠的生物标志物。调查人员提出了多种因素，例如导致发现冲突的患者的成瘾阶段。我们最近将空间独立组件分析（SICA）应用于几个fMRI数据集，并发现了与任务相关活动的相反时间代理的功能网络（FNS）的广泛重叠。基于这些发现，我们预测：1）同一体素中不同神经元的同时共定位的激活和失活（CCAD）的血氧依赖性（BOLD）信号混合物（CCAD）在同一体素中的同时进行了相反的发现，而CCAD则可以通过平衡的兴奋和抑制和抑制和抑制作用和功能型在脑周中确定。 2）由于信号混合物的性质，通过使用GLM-BA的研究，脑部失活的减少可能表示为增加的大脑信号，并被误解为更大的大脑激活，该研究无法将混合信号与同一体素区分开； 3）SICA将克服GLM-BA的局限性并揭示患者的FMRI发现一致，因为SICA可以将信号混合物与相同的体素分开为不同的FNS，从而减轻了减少失活的混淆与增加激活的混淆。为了测试我们的预测并更好地理解CD神经病理学，该项目将使用SICA对从419名参与者获得的fMRI数据进行辅助分析，包括179名HCS和196例CD患者。他们执行了FMRI任务，以评估认知控制和/或金钱奖励/损失动机。大多数患者接受了CD的治疗，并在治疗后进行了3 〜12个月。我们假设SICA将始终显示与HC相对于HCS的CD患者的认知控制和/或奖励/或奖励/损失动机的任务相关的FN的调制减少，在有效治疗后，这些FN的这些FN的调制（即“恢复”）在CD治疗后对这些FN进行了调制，以及对这些FNS和更好的长期治疗之间的正相关性和更好的长期相关性。支持我们假设的发现不仅将提供对CD神经病理学的一致洞察力并调和现有的相互矛盾的数据，而且还证明了与认知控制和/或SICA中CD患者中与任务相关的FNS中与任务相关的调制是可靠的诊断和预测性生物标志的良好候选者，以实现可靠的诊断和预测性生物标志。此外，这些发现将对fMRI理论和实践产生持续，强大的影响，并有助于向前推进整个领域。