Effects of Dementia and MCI on Diffusion Tensor Metrics Using the Updated ADNI3 DTI Preprocessing Pipeline. (20th December 2022)

• Record Type: Journal Article
• Authors: Thomopoulos, Sophia I; Nir, Talia M; Reina, Julio E Villalon; Zavaliangos‐Petropulu, Artemis; Maiti, Piyush; Nourollahimoghadam, Elnaz; Zheng, Hong; Jahanshad, Neda; Thompson, Paul M
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.066333
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Diffusion magnetic resonance imaging (dMRI) provides insight into white matter (WM) microstructural changes in Alzheimer's disease and mild cognitive impairment (MCI). The Alzheimer's Disease Neuroimaging Initiative's (ADNI) has currently extended its ADNI3 imaging protocol to include dMRI from Siemens, Philips and General Electric (GE) scanners, resulting in seven vendor‐specific dMRI acquisition protocols (Table1 ). Here we present the updated ADNI3 preprocessing pipeline 1 (Figure1 ) for diffusion tensor imaging (DTI) metrics made available via ADNI's database, and evaluate their utility for detecting WM differences associated with clinical impairment. Method: Raw dMRI downloaded from ADNI are first denoised using DiPy's principal component analysis (PCA) denoising algorithms: local PCA for zero‐padded k‐space data (GE), or Marchenko‐Pastur PCA for data with the original acquisition matrix (Siemens/Philips). dMRI are then corrected for Gibbs ringing, eddy currents using FSL's eddy_cuda with repol outlier estimation and slice‐to‐volume correction, intensity inhomogeneity with ANTS N4, and echo‐planar imaging distortions with a three‐channel non‐linear registration of the subject‐level preprocessed average B0 image to the subject's skull‐stripped T1‐weighted MRI. Bias field correction is applied again to dMRI with FSL‐FAST. DTI fractional anisotropy (FA), axial, mean and radial diffusivity (AxD, MD, RD) are estimated with FSL's dtifit, using weighted least squares. The JHU ICBM‐DTI‐81 atlas FA is warped to subject‐level FA maps using ANTS, and the transformations are applied to atlas labels. Subject‐level mean and robust mean (using M‐estimator from R's 'WRS2' package) of DTI metrics within 73 WM regions of interest (ROIs; Table2 ) are extracted. Here, we fit linear mixed effects models of diagnosis (N=733) and clinical dementia rating (CDR‐sob; N=682; Table3 ) in 28 of the 73 available ROIs, covarying for age, sex, age * sex, and including nested protocol|site variables as random effects; multiple comparisons were corrected for using false discovery rate (q=0.05). Result: Lower anisotropy and greater diffusivity were associated with greater impairment, particularly in the hippocampal cingulum (Figure2 ). Results from mean and robust mean metrics were comparable. Overall, our results follow previous work 2, 3 using publicly available ADNI dMRI. Conclusion: Subject‐level ADNI3 dMRI measures, sensitive to clinical indices of impairment as presented here, are available at https://ida.loni.usc.edu/ . References : [1 ]Thomopoulos (2021), https://doi.org/10.1117/12.2606337 ; [2] Nir (2013), https://doi.org/10.1016/j.nicl.2013.07.006 ; [3] Zavaliangos‐Petropulu (2019), https://doi.org/10.3389/fninf.2019.00002 .