Harmonization of Diffusion Weighted MRI
Primary Investigator/s:
Bennett Landman
Brief Description of Project:
Alzheimer’s Disease and related dementia are a growing public health crisis affecting 5.8 million Americans, yet there are only four FDA-approved medications for Alzheimer’s Disease, none of which are disease-modifying. Hence, early detection and diagnosis are key to successful patient management and biomarkers are needed for evaluating new therapies in clinical trials. White matter changes are increasingly implicated in early Alzheimer’s Disease progression, and diffusion weighted magnetic resonance imaging (DW-MRI) has been included in many national-scale studies. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variation in acquisition protocols, sites, and scanners. DW-MRI enables quantification of brain microstructure and facilitates structural connectivity mapping. Substantial recent progress has been made with calibration and harmonization to reduce inter-subject variance and improve interpretability of computed measures. Yet, the fundamental challenge remains that clinical application of DW-MRI (as currently implemented) is confounded by inter-scanner and inter-site effects.
To improve understanding of structural changes in Alzheimer’s Disease, we will construct and evaluate three separate analysis strategies to characterize, calibrate, and optimize DW-MRI for single-subject biomarker development for Alzheimer’s Disease. We will integrate and optimize our strategies using large retrospective multi-site studies and validate the approaches on two distinct prospective cohorts.
Specifically, we aim to:
Aim 1: Optimize data-driven techniques for stability across sessions, scanners/sites, and field strengths Impact: Harmonized DW-MRI methods will increase sensitivity to Alzheimer’s Disease and its prodromal stages.
Aim 2: Translate innovations in microstructural harmonization to structural connectivity (tractography)
Impact: Harmonizing structural connectivity will improve understanding of white matter in Alzheimer’s Disease.
Aim 3: Advance statistical tools for single-subject inference through normative database construction
Impact: Data-driven resources for uncertainty estimation will enable robust single-single subject inference.
Relevance and Impact on Healthcare: The proposed research will advance understanding of Alzheimer’s Disease through (1) quantitative harmonization of DW-MRI biomarkers, (2) protocols for harmonization of retrospective and prospective DW-MRI studies, and (3) new tools for single subject inference targeting older cohorts. We will organize workshops/challenges to maximize the translational impact on clinical science. The long-term goal of our research is to (1) provide a well-validated strategy to quantitatively evaluate DW-MRI data across sites, (2) enhance DW-MRI biomarkers for Alzheimer’s Disease, and (3) advance patient care. Our research strategy will transform the manner in which DW-MRI data are interpreted and enable single-subject machine learning to interpret brain properties. The resources, software, and visualization tools will be made freely available in open source through DIPY to facilitate continued innovation.
Desired Qualifications:
MRI, neuroscience OR 3-D imaging experience
Interest in quantitative imaging
Python programming
Nature of Supervision:
Weekly meetings. Collaboration with graduate students.
A Brief Research Plan (period is for 10 weeks):
2 weeks - project overview and plan development
6 weeks - project implementation
1 week - statistical evaluation
1 week - project write-up
Number of Slots: 2
Contact Information
Bennett Landman
Electrical & Computer Engineering
Email: bennett.landman@vanderbilt.edu