Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.

Neuroinformatics
Junhao WenAlzheimer’s Disease Neuroimaging Initiative

Abstract

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally...Continue Reading

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Citations

Oct 4, 2020·BMC Neurology·Peter LeeUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Jun 25, 2021·Physics in Medicine and Biology·Jorge Perez-GonzalezVerónica Medina-Bañuelos
Aug 10, 2021·Frontiers in Aging Neuroscience·David B StoneUNKNOWN Alzheimer’s Disease Neuroimaging Initiative

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Methods Mentioned

BETA
feature extraction

Key Resources (RRID) Mentioned

SCR_003007

Software Mentioned

NorCog
FMRIB Software Library ( FSL )
dcm2niix
ANTs
DUBIAC
AIBL
FSL flirt
scikit
SVM
DARTEL

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