Motivated Learning and Memory Neuroimaging Data Repository

脑成像是一种新兴的神经科学工具，已成为表征药物成瘾病理生理学的核心。结构和功能性脑成像研究有助于理解神经系统和认知过程的组成部分，介导适应不良的药物寻求，风险选择和回避行为，包括那些与学习和记忆。由于成像研究的快速发展，研究的成本，增加统计能力的需求以及收集的数据的复杂性，与其他研究人员共享现有的神经成像数据集至关重要，以促进对药物相关行为的原因和后果的科学发现。该提案是对NIDA资助的R01赠款的一年行政补充，用于建立与动机性学习和记忆（LAM）相关的脑成像研究的公共数据库。作为母基金的一部分，收集的结构和功能成像数据将被共享并链接到另一个成瘾性疾病（RAD）静息状态数据的公共存储库。共享数据的一些独特功能包括关注LAM过程，涉及药物成瘾的内侧颞叶，纹状体和额叶区域的高分辨率功能成像，以及适用于荟萃分析的奖励和惩罚相关任务和操作的核心集。将制定知识产权和其他数据存取和公布条例，并将建立数据库链接结构。虽然早期尝试建立大型，单片脑成像数据存储库失败，由于规模和维护的问题，我们设想了一种替代方法的链接利基数据库，其组织的灵感来自大脑架构和社交网络。

Brain imaging is an emerging neuroscientific tool that has become central to characterizing the pathophysiology of drug addiction. Structural and functional brain imaging studies have contributed to an understanding of the component neural systems and cognitive processes that mediate maladaptive drugseeking, risky choice, and avoidant behaviors, including those related to learning and memory. Due to the rapid pace of imaging research, the cost of the studies, the need to increase statistical power, and the complexity of the data collected, it is vital to share existing neuroimaging data sets with other researchers to facilitate scientific discovery into the causes and consequences of drug-related behaviors. This proposal is a one-year administrative supplement to a NIDA-funded R01 grant to establish a public data repository on brain imaging studies related to motivated learning and memory (LAM). Structural and functional imaging data collected as part of the parent grant will be shared and linked to another public repository of resting state data in addictive disorders (RAD). Some unique features of the data to be shared include the focus on LAM processes, high-resolution functional imaging of medial temporal, striatal, and frontal regions involved in drug addiction, and a core set of reward and punishment-related tasks and manipulations suitable for meta-analysis. Intellectual property rights and other regulations for data access and publication will be developed, and database linking structures will be created. While early attempts to establish large, monolithic brain imaging data repositories failed due to problems of scale and maintenance, we envision an alternate approach of linked ¿niche¿ databases whose organization is inspired by brain architecture and social networks.