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 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: 5% up to 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.

扩散MRI是研究白质改变的一种选择方式。在过去的几年中，各种工作已使用扩散MRI对阿尔茨海默氏病进行自动分类。但是，由于输入数据，参与者选择，图像预处理，特征提取，特征重缩放（FR），特征选择（FS）和交叉验证（CV）程序等组件的差异，很难比较使用不同方法获得的分类性能。 。此外，由于这些不同的成分不易获得，因此这些研究也难以复制。在先前的工作中（Samper-González等人，2018），我们提出了一个开放源代码框架，用于从T1加权（T1w）MRI和PET数据可重复评估AD分类。在本文中，我们首先将此框架扩展到扩散MRI数据。具体来说，我们添加：将扩散MRI ADNI数据转换为BIDS标准和用于扩散MRI预处理和特征提取的管道。然后，我们将框架应用于比较不同的组件。首先，FS对分类结果有积极影响：任务CN与AD的最高平衡精度（BA）从0.76提高到0.82。其次，体素智能特征通常比区域特征具有更好的性能。分数各向异性（FA）和平均扩散率（MD）为三维像素特征提供了可比较的结果。此外，我们观察到，在涉及MCI的任务中获得的较差性能可能是由较小的数据样本引起的，而不是由数据不平衡引起的。此外，对于不同程度的平滑和配准方法，不存在广泛的分类差异。除了，我们证明，使用FS的非嵌套验证会导致结果不可靠且过于乐观：BA相对增加5％至40％。最后，通过适当的FR和FS，弥散MRI功能的性能可与T1w MRI媲美。框架和实验的所有代码均可公开获得：通用工具已集成到Clinica软件包中（www.clinica.run）和特定于纸张的代码可在以下网址获得：https：//github.com/aramis-lab/AD-ML。