MRI Technology for Measurement of Functional and Structural Connectivity in Brain

项目概要： 磁共振成像已经证明了非侵入性映射的结构和 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 包括扩散成像和静止状态功能连接映射。虽然这些方法具有 虽然这些技术在连通性绘图方面的能力得到验证，但它们也面临着限制其实用性的技术限制。 扩散成像受到低灵敏度和对扩散数据进行编码的低效率的阻碍。同样地， 静息状态下的功能连接在时间分辨率上受到全脑空间编码的限制 连通性映射在这个研究项目中，我们假设，我们可以大大提高效率的 这些方法中的数据采集方案通过多切片编码和同时重聚焦采集。 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6个切片， 我们既增加了数据采集的灵敏度，又大大减少了成像时间。这一发展 将有助于推进一个新兴的扩散方法，探索水的扩散， 白色物质和灰色物质的连通性在传统的扩散张量图像上增加细节。 同样，它将提高静息态泛函的时空分辨率和灵敏度 连通性映射提高灵敏度和减少采集时间将为常规临床和 这些技术的临床科学应用。 在项目的指导阶段，候选人将利用他的信号处理和优化 理论专业知识，设计射频脉冲和重建算法，同时获得神经科学知识 和磁共振成像来开发采集序列，以及处理和解释大脑连接数据。 后期将结合本项目的各个组成部分进行实验， 用于静息状态功能连接性映射临床相关时间范围内的高信号体内数据， 通过DTI、Q-ball和DSI进行弥散成像。该项目符合候选人的长期职业目标， 关于MRI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编程和底层 神经科学指导阶段将在MGH Martinos生物医学成像中心进行 在那里，候选人将利用先进的高场MRI设备和专业知识。而且 候选人将利用世界知名的教育机会，在中心的附属机构 (MIT和哈佛）。他的职业发展计划包括MR物理和序列设计方面的培训， 扩散成像和脑连接组学，与专家的咨询和神经科学课程; 参加研讨会和科学会议。作为启动自己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.