| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 31, 2012 |
| Latest Amendment Date: | September 9, 2013 |
| Award Number: | 1149260 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Kenneth Whang
kwhang@nsf.gov (703)292-5149 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2012 |
| End Date: | August 31, 2018 (Estimated) |
| Total Intended Award Amount: | $447,399.00 |
| Total Awarded Amount to Date: | $459,279.00 |
| Funds Obligated to Date: |
FY 2013 = $11,880.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
310 E CAMPUS RD RM 409 ATHENS GA US 30602-1589 (706)542-5939 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
200 D.W. Brooks Drive Athens GA US 30602-5016 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Robust Intelligence, Other Global Learning & Trng |
| Primary Program Source: |
01001314DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project aimed to discover and represent common human brain architecture through a map of Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). Conceptually, each of the DICCCOL landmarks is defined by group-wise consistent white matter fiber connection patterns derived from diffusion tensor imaging (DTI) data and large-scale multimodal fMRI and DTI datasets are employed to determine predictive relationships between DICCCOLs and functional localizations. In general, this project successfully defined and represented hundreds of such DICCCOL landmarks in multiple human neuroimaging datasets and the resulting DICCCOL representation of common brain architecture have been applied to create a universal and individualized brain reference system, construct human brain connectomes, and elucidate the brain's functional interactions in health and diseases. The robustness and commonality of those hundreds of DICCCOL landmarks have been replicated and reproduced in a variety of human subjects with different neuroimaging parameters, as described in over two dozen publications. In general, the discovery and representation of DICCCOL have fundamentally advanced scientific understanding of the human brain. Broad dissemination of the DICCCOL map and its prediction framework in open sources have advanced many applications that rely on structural/functional correspondences across individuals. The DICCCOL map offered a generic bridge to compare and integrate neuroimaging data across laboratories, which have stimulated and enabled multiple collaborative efforts.
Last Modified: 10/01/2018
Modified by: Tianming Liu
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