Functional networks related to cocaine dependence and its treatment and relapse

 描述（由申请人提供）：本申请响应PAR-13-080 -使用现有数据加快药物滥用研究的步伐。功能磁共振成像（fMRI）和传统的基于一般线性模型的分析（GLM-BA）已被用来评估可卡因依赖（CD）和其他神经精神疾病的神经相关性。然而，这些研究报告了不一致的结果，包括患者相对于健康对照（HC）的脑活动减少和增加，并且无法提供CD神经病理学的清晰图像并定义CD治疗的可靠生物标志物。研究人员提出了多种因素，如患者的成瘾阶段，导致了相互矛盾的发现。最近，我们应用空间独立成分分析（sICA）的几个功能磁共振成像数据集，并发现广泛的重叠功能网络（FN）与相反的时间过程的任务相关的活动。基于这些发现，我们预测：1）反映同一体素中不同神经元同时共定位激活和失活（CCAD）的血氧水平依赖（BOLD）信号混合物导致相反的结果，而CCAD是由大脑中平衡的兴奋和抑制以及功能异质性决定的; 2）由于信号混合的性质，减少的大脑失活可能被表示为增加的BOLD信号，并且被使用GLM-BA的研究误解为更大的大脑激活，其不能区分来自相同体素的混合信号; 3）sICA可以克服GLM-BA的局限性，并在患者中显示一致的fMRI结果，因为sICA可以将来自相同体素的信号混合物分离到不同的FN中，从而减轻了减少的失活与增加的激活的混淆。为了验证我们的预测和更好地理解CD神经病理学，本项目将使用sICA对419名参与者（包括179名HC和196名CD患者）的fMRI数据进行二次分析。他们执行功能磁共振成像任务，以评估认知控制和/或金钱奖励/损失动机。大多数患者接受CD治疗，治疗后随访3 ~ 12个月。我们假设sICA将一致地揭示与CD患者的认知控制和/或奖励/损失动机相关的FN的任务相关调制相对于HC减少，增加（即，这些FN的更大调节与治疗后更好的长期结果之间的正相关性。支持我们假设的发现不仅提供了对CD神经病理学的一致见解并调和了现存的冲突数据，而且还证明了如sICA所揭示的与CD患者中的认知控制和/或奖励/损失动机相关的FN中的任务相关调制是用于优化和开发CD治疗的可靠诊断和预测生物标志物的极好候选者。此外，这些发现将在一般和广泛的意义上对功能磁共振成像理论和实践产生持续的，强大的影响，并将有助于推动整个领域的发展。

 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.