# New Quantitative Neuroimaging Metrics of Structural and Functional Connectivity of the Locus Coeruleus as a Novel Biomarker of Alzheimer's Disease Pathogenesis and Progression > **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $358,977 ## Abstract The original theory offered by Braak & Braak (1991)—that neurofibrillary tangle pathology proceeds along well- defined predilection sites beginning in the medial temporal cortex—has been modified by the same author to suggest that the pathologic process instead commences in the lower brainstem (Braak et al. 2011). The first visible pathologic changes are now thought to occur in the locus coeruleus (LC) and then spread via its axonal projections to transentorhinal/entorhinal cortex (TEC). We propose to study LC change using a novel computational morphology method, combined with novel methods of measuring white matter microstructural tractography and functional connectivity to TEC. These new methods are designed to overcome major limitations in current neuro-MRI analysis methods that limit the ability to detect subtle structural and functional changes associated with early AD. Such alterations across the aging-MCI-AD continuum, as well as in those cognitively normal individuals with risk factors for AD (e.g., CSF AD biomarkers; apolipoprotein E ε4 carriers), would provide significant advances in our understanding of the pathogenesis of AD across clinical transition points and perhaps during this `silent' period (i.e., prior to the occurrence of traditional AD biomarker positivities). Using our newly developed diagnostic and MRI metrics, we propose to quantify variations in LC morphology and its projections to TEC (termed the LC-TEC system). Aim 1. Examine locus coeruleus morphology, contrast, and associated cortical thickness. In this supplement, we propose to augment the structural morphology estimates with our newly developed Joint Estimation Diffusion (JEDI) method (Frank et al., 2020) to provide improved sensitivity to the assessment of gray matter (GM) tissue characteristics. We hypothesize that this greater sensitivity will produce greater distinctions in LC morphology, contrast and associated cortical thickness—particularly in vulnerable TEC and hippocampal regions—along the aging-MCI-AD continuum. Aim 2. Examine structural connectivity of the LC-TEC system. In this supplement, we propose to augment the diffusion analysis with our newly developed JEDI method that provides sensitivity to sub-voxel (microscopic) diffusion anisotropy, facilitating assessment of GM tissue status, and improving anisotropy estimates in white matter (WM). We hypothesize that this greater sensitivity will produce more accurate estimates of structural connectivity derived from the local anisotropy measures in the LC-TEC system. Aim 3. Examine functional connectivity of the LC-TEC system.In this supplement, we propose to supplement the functional modes and connectivity estimates with functional tractography augmented by averaged structural GM/WM tissue constraints derived from our JEDI method to provide improved sensitivity to the assessment of functional modes and connectivity. We hypothesize that this greater sensitivity will produce better clustering of abnormal fu... ## Key facts - **NIH application ID:** 10326564 - **Project number:** 3R01AG054049-04S1 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO - **Principal Investigator:** Mark W Bondi - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $358,977 - **Award type:** 3 - **Project period:** 2017-09-15 → 2022-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10326564 ## Citation > US National Institutes of Health, RePORTER application 10326564, New Quantitative Neuroimaging Metrics of Structural and Functional Connectivity of the Locus Coeruleus as a Novel Biomarker of Alzheimer's Disease Pathogenesis and Progression (3R01AG054049-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10326564. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*