Journal
NEUROIMAGE
Volume 183, Issue -, Pages 504-521Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2018.08.042
Keywords
Classification; Reproducibility; Alzheimer's disease; Magnetic resonance imaging; Positron emission tomography; Open-source
Funding
- program Investissements d'avenir [ANR-10-IAIHU-06, ANR-11-IDEX-004, SU-16-R-EMR-16]
- European Union H2020 program (project EuroPOND) [666992, 720270]
- ICM Big Brain Theory Program (project DYNAMO)
- European Research Council [678304]
- joint NSF/NIH/ANR program Collaborative Research in Computational Neuroscience (project HIPLAY7) [ANR-16-NEUC0001-01]
- Contrat d'Interface Local progam from Assistance publique-Hopitaux de Paris (AP-HP)
- People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant through the PRESTIGE programme [PCOFUND-GA-2013-609102]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- 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
- 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
- Transition Therapeutics
- Canadian Institutes of Health Research
- [P50 AG05681]
- [P01 AG03991]
- [R01 AG021910]
- [P20 MH071616]
- [U24 RR021382]
Ask authors/readers for more resources
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format ( BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML.
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