Quantifying Analytic Flexibility of Free Water Corrected Diffusion Tensors in Parkinson’s Disease
Poster No:
224
Submission Type:
Abstract Submission
Authors:
Brent McPherson1, Michelle Wang1, Ariel Rokem2, Kelly Chang3, Maxime Descoteaux4, Alain Dagher5, Nikhil Bhagwat1, Jean-Baptiste Poline1
Institutions:
1McGill University, Montreal, Quebec, 2University of Washington, Seattle, CA, 3University of Washington, Seattle, WA, 4University of Sherbrooke, Sherbrooke, Quebec, 5McGill University, Montreal, QC
First Author:
Co-Author(s):
Introduction:
Diffusion imaging (dMRI) is offering a set of promising biomarkers for Parkinson's Disease (PD) as the modality is capable of reflecting the microstructure of the brain in regions known to be affected by the disease1. Recently, the free water compartment estimated from diffusion tensor imaging (fwDTI) has been identified as a potential biomarker of PD progression. fwDTI is a modification of diffusion tensor imaging (DTI) that estimates an additional "free water" component in addition to the diffusion tensor. This model describes both the general orientation of axons as well as the amount of unconstrained movement within the same space. This unconstrained free water is hypothesized to correspond to changes in the brain associated with neurodegeneration. For instance, Yang et al. 2019 report increased fwDTI signals in PD patients compared to healthy subjects in the posterior substantia nigra that inversely correlate with motor and cognitive deficits.
In this context, assessing and understanding analytic flexibility (AF) that has been seen in structural or functional MRI studies is important to establish the best and most robust dMRI biomarkers of PD. A dMRI biomarker has the potential to be used for diagnosis, progression tracking, or disease course prediction, and may improve the predictive validity of models3,4. fwDTI models an additional free water parameter to characterize the loss of cell bodies within the tissue of the brain (white and subcortical gray matter). The abundant availability of dMRI data in biobanks and existing initiatives makes this derivative readily available to many research groups and biobank coordinators. The variation in data quality between different dMRI scans, like the difference between single shell and multi-shell datasets, can greatly impact the quality of fwDTI results3. Ultimately, a deeper understanding of how free water estimation varies with data and pipeline is essential for advancing PD imaging research based on this biomarker. We explore free water estimation in the PPMI dataset using two pipelines, providing preliminary information on the robustness of free water based PD biomarkers.
In this context, assessing and understanding analytic flexibility (AF) that has been seen in structural or functional MRI studies is important to establish the best and most robust dMRI biomarkers of PD. A dMRI biomarker has the potential to be used for diagnosis, progression tracking, or disease course prediction, and may improve the predictive validity of models3,4. fwDTI models an additional free water parameter to characterize the loss of cell bodies within the tissue of the brain (white and subcortical gray matter). The abundant availability of dMRI data in biobanks and existing initiatives makes this derivative readily available to many research groups and biobank coordinators. The variation in data quality between different dMRI scans, like the difference between single shell and multi-shell datasets, can greatly impact the quality of fwDTI results3. Ultimately, a deeper understanding of how free water estimation varies with data and pipeline is essential for advancing PD imaging research based on this biomarker. We explore free water estimation in the PPMI dataset using two pipelines, providing preliminary information on the robustness of free water based PD biomarkers.
Methods:
The Parkinson's Progression Markers Initiative5 (PPMI, N=414 at baseline processed) was processed through Tractoflow6 and QSIPrep7 for both multi-shell and single shell data. fwDTI corrected FA values were averaged within FreeSurfer aseg ROIs8 for comparison.
We also investigated two models for fwDTI parameterizations8,9 implemented in scilpy9 and dipy10 on the QSIprep processed multi-shell data.
We also investigated two models for fwDTI parameterizations8,9 implemented in scilpy9 and dipy10 on the QSIprep processed multi-shell data.
Results:
Figure 1 shows the difference of FA results between Tractoflow and QSIprep processings for single shell (top left, fwDTI-FA) versus multi-shell data (top right: fwDTI-FA values corrected by free water estimation, see also bottom panel). The two pipelines show clear differences of estimation across most regions, QSIprep yielding larger fwDTI-FA values than Tractoflow.
While both pipelines (scilpy9 and dipy10) perform an equivalent necessary denoising operation, there is important variation in the resulting model noise. We also have preliminary findings to show that the two model parameterizations of the free water component implemented in scilpy9 and dipy10 lead to important differences between estimated remaining noise in data.
While both pipelines (scilpy9 and dipy10) perform an equivalent necessary denoising operation, there is important variation in the resulting model noise. We also have preliminary findings to show that the two model parameterizations of the free water component implemented in scilpy9 and dipy10 lead to important differences between estimated remaining noise in data.
Conclusions:
We assessed the analytic flexibility of diffusion biomarkers for Parkinson's Disease research and found that Tractoflow and QSIprep lead to significantly different estimation of FA after correcting for free water estimation. In the future we hope to advance our understanding of processing decisions made when modeling fwDTI data by evaluating the model fit parameters from the estimation, such as the NRMSE. We hope to use the variability of these estimations as well as other advanced IDPs to improve the prediction of disease progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Methods Development
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
Aging
Data analysis
Degenerative Disease
Design and Analysis
Open Data
Statistical Methods
Sub-Cortical
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Yang, J., et al., 2019. Multimodal dopaminergic and free-water imaging in Parkinson’s disease. Parkinsonism & Related Disorders 62, 10–15. https://doi.org/10.1016/j.parkreldis.2019.01.007
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