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


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 ( and the paper-specific code is available at:

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Alzheimer’s disease


Normal control


Mild Cognitive Impairment


Stable MCI


Progressive MCI




Region of interest


Mini-mental state examination


Clinical dementia rating


Quality check


Optimal margin hyperplane

T1w MRI:

T1-weighted MRI


Diffusion MRI


Functional MRI


Fractional anisotropy


Mean diffusivity


Radial diffusivity


Axial diffusivity


Mode of anisotropy


Feature selection


Feature rescaling


Diffusion tensor imaging


Magnetic resonance imaging


Positron emission tomography


Gray matter


White matter


Brain Imaging Data Structure


Random forests


Logistic regression


Nearest neighbors


Naive Bayes




Balanced accuracy


Area under the curve


Support vector machine


Relevance vector machine


Linear discriminant analysis


Alzheimer’s Disease Neuroimaging Initiative


European DTI Study on Dementia


Sydney Memory and Aging


Research and Resource Memory


Hospital de Santiago Apostol


Prospective Registry on Dementia study


Ilsan Dementia Cohort


Memory Clinical at Xuan Wu Hospital


Tong Ji Hospital


Memory Impairment Clinic of Pusan National University Hospital


University Hospital of Geneva


German Center for Neurodegenerative Diseases Rostock database


Private database


Duke-UNC Brain Imaging and Analysis Center


National Alzheimer’s Coordinating Center


Norwegian registry for persons being evaluated for cognitive symptoms in specialized health care


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The research leading to these results has received funding from the program “Investissements d’avenir” ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) ANR-11-IDEX-004 (Agence Nationale de la Recherche-11-Initiative d’Excellence-004, project LearnPETMR number SU-16-R-EMR-16), from the European Union H2020 program (project EuroPOND, grant number 666992, project HBP SGA1 grant number 720270), from the joint NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience” (project HIPLAY7, grant number ANR-16-NEUC-0001-01),

from Agence Nationale de la Recherche (project PREVDEMALS, grant number ANR-14-CE15-0016-07), from the European Research Council (to Dr. Durrleman project LEASP, grant number 678304), from the Abeona Foundation (project Brain@Scale), and from the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute). J.W. receives financial support from China Scholarship Council (CSC). O.C. is supported by a “Contrat d’Interface Local” from Assistance Publique-Hôpitaux de Paris (AP-HP). N.B. receives funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. PCOFUND-GA-2013-609102, through the PRESTIGE programme coordinated by Campus France.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Correspondence to Junhao Wen or Olivier Colliot.

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Anne Bertrand Deceased, March 2nd, 2018

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

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Wen, J., Samper-González, J., Bottani, S. et al. Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer’s Disease. Neuroinform 19, 57–78 (2021).

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  • Classification
  • Machine learning
  • Reproducibility
  • Alzheimer’s disease
  • Diffusion magnetic resonance imaging
  • DTI
  • Open-source