# Joint Estimate Diffusion Imaging (JEDI) for improved Tissue Characterization and Neural Connectivity in Aging and Alzheimer's Disease > **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $1,581,696 ## Abstract Alzheimer's Disease (AD) and related dementias (ADRD) are characterized by progressive structural changes of brain tissue that results in a debilitating loss of cognitive and functional abilities and has profound social and economic implications. While hallmark AD pathology (e.g. beta amyloid depositions and neurofibrillary tangles) are remarkably pronounced at the cellular level, there are currently no successful non- invasive brain imaging techniques to report these microstructural changes. Diffusion magnetic resonance imaging (dMRI) is a widely available non-invasive clinical imaging method with this potential, as it is sensitive to the subtle motion of water within the complex brain gray matter (GM) and white matter (WM) tissue architecture. In principle, dMRI can report both the local tissue structure and the long range connectivity of neural tracts in order to identify pathology and determine the effects of AD on functional brain networks. Unfortunately, the clinical utility of the standard dMRI methodology is severely compromised by its lack of specificity to microstructural tissue changes below the image resolution. Recently, however, we have developed a novel acquisition and analysis method called Joint Estimation Diffusion Imaging (JEDI) that is highly sensitive to microstructural features of GM and GM/WM border regions, and also provides improved connectivity maps from WM. JEDI is easily implemented on a clinical scanner and we have recently incorporated it into a first study on subjects ranging from cognitively normal (CN) to Mild Cognitive Impairment (MCI) to early AD in order to assess its ability to detect changes in these groups. Two critical steps in extending the clinical utility of JEDI in AD are: 1) To characterize the relationship between the JEDI data and specific tissue microstructural features in order to develop quantitative clinical metrics and 2) To develop efficient acquisition protocols for both microstructural sensitivity and limited patient scan time. That is the focus of this proposal, which will involve three lines of work: 1) Numerical computer simulations of the JEDI experiment in realistic tissue models that will allow us to efficiently optimize the acquisition protocol for maximum specificity and minimal time; 2) Validate these optimizations through in-vivo evaluation of normal aging processes in the ferret and in ex-vivo radiologic-pathologic analysis in post-mortem human tissue from patients with different stages of AD; 3) Incorporate these optimizations into the JEDI acquisition and analysis of human protocols on our clinical scanners with specific application to examining the prodromal microstructure tissue changes across the aging-MCI-AD continuum. By enabling a reliable, validated and clinically viable method for the quantitative characterization of subtle brain tissue changes across the aging-MCI-AD continuum, JEDI will significantly enhance our ability to understand the earliest neurodegenerative features o... ## Key facts - **NIH application ID:** 10843785 - **Project number:** 5R01AG079280-02 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO - **Principal Investigator:** Mark W Bondi - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $1,581,696 - **Award type:** 5 - **Project period:** 2023-06-01 → 2028-02-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10843785 ## Citation > US National Institutes of Health, RePORTER application 10843785, Joint Estimate Diffusion Imaging (JEDI) for improved Tissue Characterization and Neural Connectivity in Aging and Alzheimer's Disease (5R01AG079280-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10843785. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*