Reproducible evaluation of diffusion MRI features for automatic
classification of patients with Alzheimers disease
release_ge7355bpf5anppx45yqzviklgm
by
Junhao Wen, Jorge Samper-Gonzalez, Simona Bottani, Alexandre Routier,
Ninon Burgos, Thomas Jacquemont, Sabrina Fontanella, Stanley Durrleman,
Stephane Epelbaum, Anne Bertrand, Olivier Colliot (for the Alzheimers Disease
Neuroimaging Initiative)
2020
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 AD. However, classification performance obtained with
different approaches is difficult to compare and these studies are also
difficult to reproduce. In the present paper, we first extend a previously
proposed framework to diffusion MRI data for AD classification. 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 gives better
performance than regional features. Fractional anisotropy (FA) and mean
diffusivity (MD) provided comparable results for voxel-wise features. Moreover,
we observe that the poor performance obtained in tasks involving MCI were
potentially caused by the small data samples, rather than by the data
imbalance. Furthermore, no extensive classification difference exists for
different degree of smoothing and registration methods. Besides, we demonstrate
that using non-nested validation of FS leads to unreliable and over-optimistic
results: 0.05 up to 0.40 relative increase in BA. Lastly, with proper FR and
FS, the performance of diffusion MRI features is comparable to that of T1w MRI.
All the code of the framework and the experiments are publicly available:
general-purpose tools have been integrated into the Clinica software package
(www.clinica.run) and the paper-specific code is available at:
https://github.com/aramis-lab/AD-ML.
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