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。结构和功能脑成像研究有助于理解神经系统和认知过程的组成部分，这些神经系统和认知过程介导了适应性不良的药物寻求、风险选择和回避行为，包括与学习和记忆相关的行为。由于影像学研究的快速发展、研究成本、需要提高统计能力以及收集数据的复杂性，与其他研究人员共享现有的神经影像学数据集对于促进对药物相关行为的原因和后果的科学发现至关重要。该提案是nida资助的R01拨款的一年行政补充，旨在建立与动机学习和记忆（LAM）相关的脑成像研究的公共数据库。作为父母资助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障碍（RAD）静息状态数据的公共存储库。将分享的数据的一些独特特征包括关注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，以及一组适合meta分析的奖惩相关任务和操作的核心。将制定知识产权和其他数据访问和出版条例，并建立数据库链接结构。由于规模和维护问题，早期建立大型单片脑成像数据库的尝试失败了，我们设想了一种替代方法，即链接的“利基”数据库，其组织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.