MRI: Acquisition of a high performance 3T magnetic resonance system for high resolution human brain imaging

美国国家科学基金会的主要研究仪器（MRI）奖将为加州理工学院的神经科学研究人员及其国内和国际合作者购买用于研究人类大脑功能和结构的磁共振成像扫描仪的重大升级提供三年的资助。该合同将支持加州理工学院脑成像中心现有的西门子Tim Trio 3T扫描仪升级到最新的西门子Prisma平台。升级后的扫描仪将提供更清晰、更详细的人脑图像。这种成像能力的提高将使加州理工学院的研究人员能够解决一些基本问题，比如大脑如何从经验中学习，大脑如何做出决定，以及大脑如何支持在社会环境中向他人学习和互动的能力。这种新设备最终将帮助加州理工学院的研究人员更好地了解大脑是如何工作的，它是如何连接的，以及它是如何在疾病中出现功能障碍的。反过来，这些知识将有助于构建人工智能系统。该资助还将使学生和博士后获得使用最先进的脑成像设备的经验，通过在加州理工学院教授的课程提供实践经验，以及通过学员参与使用该设备的研究项目。总之，新设备带来的尖端科学，以及对下一代年轻科学家的培训，将对加州理工学院、美国乃至全世界的认知、决策和社会神经科学做出重大贡献。为了进一步了解大脑如何支持人类学习、决策和调解社会互动的能力，有必要在三个不同的领域取得进展。首先，有必要通过解决每个相关大脑区域（如杏仁核、腹内侧前额叶皮层、纹状体和中脑）内离散神经解剖学分支的功能特性，对这些不同计算过程背后的神经机制进行更详细的回路级理解。其次，有必要解决在这些不同的子系统中实现的各种子过程如何最终在系统级别集成在一起以驱动复杂的行为。第三，描述各种计算和神经实现在时间、任务和个人之间的差异是很重要的。西门子Prisma扫描仪提供了独特的技术能力，适合于推动加州理工学院这三个领域的进步。新平台将显著提高从感兴趣的大脑结构获得的高分辨率fMRI扫描的质量，最大限度地减少丢失和几何失真，并增加信噪比。这些功能还将增强所获得图像的稳定性，从而提高测试-重测试的可靠性，而改进的梯度集将大大提高扩散加权成像和功能连接数据的质量。

This NSF Major Research Instrumentation (MRI) Award will enable a three-year grant to purchase a major upgrade to the magnetic resonance imaging scanner used for studying the function and structure of the human brain by neuroscience researchers at the California Institute of Technology and their national and international collaborators. The award will support the upgrade of the existing Siemens Tim Trio 3T scanner at the Caltech Brain Imaging Center to the latest Siemens Prisma platform. The upgraded scanner will provide clearer and more detailed images of the human brain. Such an improvement in imaging capabilities will enable Caltech researchers to address fundamental problems such as how the brain learns from experience, how the brain makes decisions and how brains support the ability to learn from and interact with other people in social contexts. This new equipment will ultimately help Caltech researchers obtain a better understanding of how the brain works, how it is wired up, and how it may dysfunction in disease. That knowledge, in turn, will contribute to efforts to build artificially intelligent systems. The grant will also enable students and post-docs to obtain experience in using state-of-the-art brain imaging equipment, through classes taught at Caltech that offer hands-on-experience as well as through the participation of trainees in research projects that utilize the equipment. Taken together, the cutting-edge science enabled by the new equipment, and the training of the next generation of young scientists on it, will contribute substantially to cognitive, decision and social neuroscience at Caltech, in the US and worldwide. To advance understanding about how the brain supports the capacity of humans to learn, make decisions and mediate social interactions it will be necessary to make progress in three distinct domains. First, there is a need to develop a much more detailed circuit-level understanding of the neural mechanisms underlying these various computational processes by resolving the functional properties of discrete neuroanatomical sub-divisions within each of the relevant brain areas of interest such as the amygdala, ventromedial prefrontal cortex, striatum and midbrain. Second, it is necessary to address how the various sub-processes that are implemented in these distinct sub-systems are ultimately integrated together at the systems level to drive complex behavior. Third, it will be important to characterize how the various computations and neural implementations differ across time, tasks and individuals. The Siemens Prisma scanner provides technical capabilities that are uniquely suited to advance progress in each of these three domains at the California Institute of Technology. The new platform will offer significant improvements in the quality of high resolution fMRI scans obtained from brain structures of interest, by minimizing dropout and geometric distortion, and by increasing signal-to-noise. These capabilities will also enhance the stability of the images obtained and hence improve test-retest reliability, while the improved gradient set will offer major gains in the quality of diffusion weighted imaging, and of functional connectivity data.