# Advancing methods for structural connectome acquisition and estimation in older adults

> **NIH NIH R21** · UNIVERSITY OF ROCHESTER · 2020 · $438,448

## Abstract

Abstract Diffusion MRI (dMRI) plays critical roles in understanding the neural underpinnings in the development
of Alzheimer's disease (AD). Obtaining high quality image with high spatial and q-space resolutions requires
prolonged data acquisition, which leads increased mental stress and higher risk of motion artifacts. Acquisition
time is a particularly important issue in clinical dMRI in AD and at-risk subjects. But shortening data acquisition
time through reducing image resolution will reduce the ability to estimate white matter tracts and degenerate the
statistical power. In-plane acceleration (IPA), and simultaneous multislice (SMS), are potential solutions to
balance imaging quality and acquisition time. However, IPA and SMS have important limitations, as they require
reconstruction methods that introduce noise. The literature studying the impacts of acceleration on dMRI and
dMRI-based structural connectome analysis is very limited. Some important questions remain open, including
how IPA and SMS affect structural connectome estimation and whether we can further shorten the acquisition
time without sacrificing image quality. In this study, we will collect and analyze repeated scans from mild cognitive
impairment (MCI) subjects, a group at high risk for AD, and age-matched controls. A set of novel statistical
methods and toolboxes will be developed to improve both dMRI image reconstruction and connectome
estimation and analysis under acceleration. The collected data will be made publicly available. The project has
three specific aims: (1) Determine the impact of accelerated imaging on structural connectome (SC)
analysis in MCI subjects and age-matched controls. Subjects will have repeated scans under identical
acquisition protocols. We will conduct quantitative assessments of reproducibility and discriminative ability of
white matter structure and SC. (2) Machine learning reconstruction methods for reducing noise in
accelerated dMRI. We will develop machine learning methods to improve vendor-implemented approaches to
image reconstruction of k-space data to reduce noise and allow faster acquisition. (3) Develop novel methods
for SC estimation and analysis in older adults. Utilizing the output from Aim 2, we will develop novel
approaches that can better estimate and analyze SC for older adults. The methodologies developed in this
project will facilitate the development of fast and high-quality dMRI acquisition and SC analysis and thus facilitate
the development of early imaging biomarkers for AD.

## Key facts

- **NIH application ID:** 9952875
- **Project number:** 1R21AG066970-01
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** KATHI L HEFFNER
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $438,448
- **Award type:** 1
- **Project period:** 2020-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9952875

## Citation

> US National Institutes of Health, RePORTER application 9952875, Advancing methods for structural connectome acquisition and estimation in older adults (1R21AG066970-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9952875. Licensed CC0.

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