# Quantification of microstructures in the entorhinal-hippocampus pathway as a sensitive biomarker for neurodegeneration during the preclinical stage of Alzheimer's disease

> **NIH NIH K99** · JOHNS HOPKINS UNIVERSITY · 2024 · $97,795

## Abstract

Project Summary/Abstract
Alzheimer's disease (AD) is a global issue that must be solved urgently because of its significant impact on public
health and economics, as well as the quality of life of individuals in the United States and other aging societies.
Once cognitive impairment occurs in the AD continuum, there are great difficulties in modifying the devastating
disease process. Pathological changes inside the brain begin silently many years before the onset of cognitive
impairment. This long “preclinical” stage provides us with an opportunity for timely therapeutic and preventive
interventions. Therefore, the development of tools that can predict future cognitive decline during the preclinical
stage of AD is crucial. There is a consensus that neurodegeneration has a stronger correlation with cognition in
the disease progression along the AD continuum, compared to the diagnostic AD biomarkers such as amyloid
and tau proteins. In contrast, neuroimaging modalities currently used to detect biomarkers for neurodegeneration
are not sensitive enough to detect minute changes during the preclinical stage of AD. Here, Dr. Yuto Uchida
hypothesized that myeloarchitectonic features observed in the entorhinal-hippocampus pathway could serve as
sensitive neurodegenerative biomarkers given that AD pathogenesis occurs in the entorhinal cortices. In this
project, he will conduct a proof-of-concept study to examine microstructural neurodegeneration of the entorhinal-
hippocampus pathway in a combined framework: ex vivo ultra-high-field quantitative MRI followed by histological
verification in Aim 1, and in vivo ultra-high-field quantitative MRI in clinical settings for healthy controls in Aim 2
and for preclinical and prodromal AD individuals in Aim 3. In Aim 1, postmortem hemibrains will be scanned on
a human 7T MRI scanner and compared with the corresponding histology in the entorhinal-hippocampus
pathway to fill the gap between the MRI findings and microscopic observations. In Aim 2 and Aim 3, cutting-edge,
deep learning-based susceptibility tensor imaging (DeepSTI) and DeepSTI-based tractography will be applied
to an ongoing cohort study (RF1AG071515), which comprises healthy, preclinical, and prodromal AD individuals.
In Aim 2, reference ranges for quantitative MRI measures in the entorhinal layer II and the perforant path fibers
will be established. In Aim 3, comparative analyses of these quantitative MRI measures among the groups will
be done cross-sectionally, which will be followed by a longitudinal study to examine these associations with
cognitive decline along the AD continuum. In summary, the long-term objective of this K99/R00 application is to
support Dr. Yuto Uchida’s ability to conduct studies aimed at developing biomarkers for neurodegeneration that
can visualize and quantify microstructural brain alterations during the preclinical stage of AD using ultra-high-
field quantitative MRI. Dr. Uchida will be co-mentored by Drs. Kenichi Oishi, Xu L...

## Key facts

- **NIH application ID:** 10949187
- **Project number:** 1K99AG088363-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Yuto Uchida
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $97,795
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10949187

## Citation

> US National Institutes of Health, RePORTER application 10949187, Quantification of microstructures in the entorhinal-hippocampus pathway as a sensitive biomarker for neurodegeneration during the preclinical stage of Alzheimer's disease (1K99AG088363-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10949187. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*