# Statistical Methods for Biomarkers Identification Using High-resolution Diffusion MRI

> **NIH NIH R03** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2024 · $79,950

## Abstract

PROJECT SUMMARY/ABSTRACT
Parkinson's disease (PD) is a neurodegenerative disease that leads to the abnormalities of patients' movement
and other functions. The current clinical diagnosis of PD cannot accomplish the desired accuracy. Imaging
biomarkers of PD has shown great promising in improving the diagnosis accuracy. Our co-investigators
developed a high spatial resolution diffusion MRI, which can improve the spatial resolution substantially and
diminish geometric distortion. Pioneer studies conducted by our co-investigators identified changes in left side
of substantia nigra of right-handed patients with PD. However, the potential of the data has not been fully
realized due to the lack of appropriate statistical methods for this new type of data. This proposal aims to
develop new statistical and computational tools to identify imaging biomarkers via parameters in the continuous
time random walk (CTRW) model using the high resolution MRI data. Existing statistical methods for the CTRW
model did not take the advantage of the high resolution data. The proposed statistical methods will perform high
dimensional inference to integrate the information from a large number of pixels in the MRI data to achieve the
power that cannot be attained by conventional low dimensional methods. This proposal has two specific
objectives (1 ): develop high dimensional statistical inference methods for the CTRW model using high spatial
resolution diffusion MRI; (2): integrate patients' clinical characteristics, such as disease duration, and
neurological test scores and relevant biological variables such as age and sex, with imaging biomarkers in
improving the diagnosis accuracy for PD. The developed statistics methods will be applied to diffusion MRI data
sets of PD patients collected by co-investigators and their collaborators. User friendly software and
computational tools will be made available for public use.

## Key facts

- **NIH application ID:** 10828863
- **Project number:** 5R03NS128450-02
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Ping-Shou Zhong
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $79,950
- **Award type:** 5
- **Project period:** 2023-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10828863

## Citation

> US National Institutes of Health, RePORTER application 10828863, Statistical Methods for Biomarkers Identification Using High-resolution Diffusion MRI (5R03NS128450-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10828863. Licensed CC0.

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