MRI Technology for Measurement of Functional and Structural Connectivity in Brain

项目总结： 磁共振成像已经证明了非侵入性标测结构和 人类大脑在健康和疾病中的功能连接。已经出现的主要方法 包括扩散成像和静息状态功能连通性映射。尽管这些方法具有 除了经过验证的连接映射功能外，它们还面临技术限制，这些限制了它们的实用性。 扩散成像受到低灵敏度和对扩散数据进行编码的低效的阻碍。同样， 静息状态的功能连接在时间分辨率上受到全脑空间编码的限制 连接映射。在这个研究项目中，我们假设我们可以极大地提高 这些方法中的数据采集方案通过多切片编码和同时重新聚焦采集。 例如，通过将每个采集周期获得的图像切片的数量从1个切片增加到最多6个， 既提高了数据采集的灵敏度，又大大缩短了成像时间。这一发展 将有助于推动探索水扩散的整个新兴扩散方法的发展，从而 在传统的扩散张量图像上，白质和灰质的连通性越来越详细。 同样，它将提高空间-时间分辨率和静态泛函的灵敏度 连接映射。提高灵敏度和减少采集时间将为常规的临床和 这些技术在临床科学中的应用。 在项目的指导阶段，候选人将利用他的信号处理和优化 在获得神经科学知识的同时，具备设计射频脉冲和重建算法的理论专长 和MR PHYIC开发采集序列，以及处理和解释大脑连接数据。 在后期，通过结合本项目的各个组成部分，将进行实验以获得 用于静息状态功能连接标测的临床相关时间范围内的高信号活体数据和 通过DTI、Q-ball和DSI进行扩散成像。该项目符合候选人的长期职业目标，即建立 一个关于磁共振成像数据采集方法的高质量独立研究计划，将充分利用 知识以及软件算法开发、MR物理和基础知识之间的相互作用 神经科学。指导阶段将在MGH Martinos生物医学成像中心进行 应聘者将利用先进的高场磁共振设备和专业知识。此外， 应聘者将利用该中心附属机构的世界知名教育机会 (麻省理工学院和哈佛大学)。他的职业发展计划包括磁共振物理和序列设计方面的培训， 扩散成像和脑连接学，咨询专家和神经科学课程；以及 参加研讨会和科学会议。作为启动自己的独立研究计划的一部分， 候选人将帮助指导一名将参与该项目的研究生。

Project summary: Magnetic resonance imaging has demonstrated the potential for non-invasive mapping of the structural and functional connectivity of the human brain in health and disease. The primary methods that have emerged include diffusion imaging and resting-state functional connectivity mapping. Although these methods have validated capabilities for connectivity mapping, they also face technical limitations which constrain their utility. Diffusion imaging is hampered by low sensitivity and the inefficiency of encoding the diffusion data. Similarly, resting-state functional connectivity is limited in temporal resolution by spatial encoding during whole brain connectivity mapping. In this research project, we hypothesize that we can greatly improve the efficiency of the data acquisition schemes in these methods via multi-slice encoding and simultaneous refocusing acquisition. For example, by increasing the number of images slices obtained per acquisition period from 1 slice to up to 6, we both increase the sensitivity of the data acquisition and greatly reduce the imaging time. This development will help advance an entire class of emerging diffusion methodology which probe the water diffusion and thus white matter and grey matter connectivity in increasing detail over the traditional diffusion tensor image. Similarly, it will increase the spatial-temporal resolution and the sensitivity of resting-state functional connectivity mapping. Improving sensitivity and reduce acquisition time will pave way for routine clinical and clinical science applications of these technologies. During the mentored phase of the project, the candidate will draw on his signal processing and optimization theory expertise to design RF pulses and reconstruction algorithms, while gaining knowledge in neuroscience and MR physic to develop acquisition sequences, as well as process and interpret the brain connectivity data. In the later stage, by combining various components of this project, experiments will be carried out to obtain high signal in vivo data in clinically relevant time frame for resting-state functional connectivity mapping and diffusion imaging via DTI, Q-ball, and DSI. The project fits the candidate's long-term career goal of establishing a high-quality independent research program on data acquisition methodology in MRI that will fully utilizes the knowledge and the inter-play between software algorithm development, MR physic, and the underlying neuroscience. The mentored phase will be carried out at the MGH Martinos Center for Biomedical Imaging where the candidate will take advantage of the advanced high-field MRI facility and expertise. Furthermore, the candidate will make use of the world renowned educational opportunities at the Center's affiliated institutions (MIT and Harvard). His career development plan includes training in MR physics and sequence design, diffusion imaging and brain connectomics, consultations with experts and coursework in neuroscience; and participation in seminars and scientific meetings. As part of initiating his own independent research program, the candidate will help mentor a graduate student who will be involved in this project.