CAREER: Discovering Common Human Brain Architecture

职业：发现常见的人脑结构

• 批准号: 1149260
• 负责人: Tianming Liu
• 金额: $ 44.74万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149260&HistoricalAwards=false
• 关键词: CAREER; Discovering; Common; Human; Brain

Is there a common human brain architecture that can be quantitatively encoded and precisely reproduced across individuals? This CAREER project aims to discover and represent common human brain architecture through a map of Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). Each of the landmarks will be defined by group-wise consistent white matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. In parallel, large-scale multimodal fMRI and DTI datasets will be employed to determine predictive relationships between DICCCOLs and functional localizations. The resulting DICCCOL representation of common brain architecture will be applied to create a universal and individualized brain reference system, construct human brain connectomes, and elucidate the brain's functional interactions. The education objective of this CAREER project is to create and assess a fundamentally novel interdisciplinary higher education approach, namely, transformative interdisciplinary group learning (TIGL). Students and instructors from three courses that are related but emerge from different disciplinary perspectives (Biomedical Image Analysis, Introduction to MRI Physics, and Functional Brain Imaging) will work together in one classroom. During these common sessions, the students will have synergistic learning activities, engage in interdisciplinary group discussions, and design and conduct interdisciplinary group projects.The discovery and representation of common brain architecture will fundamentally advance scientific understanding of the human brain. Broad dissemination of the DICCCOL map and its prediction framework will transform numerous applications that rely on structural/functional correspondences across individuals. The DICCCOL map offers a generic bridge to compare and integrate neuroimaging data across laboratories, which will stimulate and enable plentiful collaborative efforts. While this project has a focus on brain imaging, the general methodology of predictive modeling of structure and function is expected to influence many other imaging domains. The TIGL approach will advance fundamental understanding of interdisciplinary learning. The TIGL approach will be scaled up to other institutions and disciplines, and will be widely disseminated. This continuous effort will establish the TIGL approach as a general interdisciplinary education methodology to increase the capacity of the next generation of scientists who have an interdisciplinary mindset.

是否存在一种通用的人类大脑结构，可以在个体之间进行定量编码和精确复制？该职业项目旨在通过密集个体化和基于共同连接的皮质地标 (DICCCOL) 地图来发现和代表常见的人类大脑结构。每个地标将由源自扩散张量成像 (DTI) 数据的分组一致的白质纤维连接模式定义。同时，大规模多模态 fMRI 和 DTI 数据集将用于确定 DICCCOL 和功能定位之间的预测关系。由此产生的常见大脑结构的 DICCCOL 表示将用于创建通用和个性化的大脑参考系统，构建人脑连接组，并阐明大脑的功能相互作用。该职业项目的教育目标是创建和评估一种全新的跨学科高等教育方法，即变革性跨学科小组学习（TIGL）。来自三门相关但来自不同学科视角的课程（生物医学图像分析、MRI 物理概论和功能性脑成像）的学生和教师将在一间教室里一起工作。在这些共同会议期间，学生将进行协同学习活动，参与跨学科小组讨论，设计和开展跨学科小组项目。共同大脑结构的发现和表达将从根本上推进对人类大脑的科学理解。 DICCCOL 图谱及其预测框架的广泛传播将改变依赖于个体间结构/功能对应关系的众多应用。 DICCCOL 地图提供了一个通用桥梁来比较和整合各个实验室的神经影像数据，这将刺激并实现大量的协作努力。虽然该项目的重点是脑成像，但结构和功能预测建模的一般方法预计会影响许多其他成像领域。 TIGL 方法将增进对跨学科学习的基本理解。 TIGL 方法将扩展到其他机构和学科，并将得到广泛传播。这种持续的努力将把 TIGL 方法确立为一种通用的跨学科教育方法，以提高具有跨学科思维的下一代科学家的能力。