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.
