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Table of contents
Abstract
Résumé
Scientific production
Contents
List of Figures
List of Tables
List of Abbreviations
Introduction
1 Machine learning from neuroimaging data to assist the diagnosis of Alzheimer’s disease
1.1 Alzheimer’s disease
1.2 Interest of ML for identification of AD
1.3 Modalities involved in AD diagnosis
1.3.1 Mono-modal approaches
1.3.1.1 Anatomical MRI
1.3.1.2 PET
1.3.1.3 Diffusion MRI
1.3.1.4 Functional MRI
1.3.1.5 Non-imaging modalities
1.3.2 Multimodal approaches
1.3.2.1 Anatomical MRI and FDG PET
1.3.2.2 Other combinations
1.3.2.3 Combination with non-imaging modalities
1.4 Features
1.4.1 Voxel-based features
1.4.2 Regional features
1.4.3 Graph features
1.5 Dimensionality reduction
1.5.1 Feature selection
1.5.1.1 Univariate feature selection
1.5.1.2 Multivariate feature selection
1.5.2 Feature transformation
1.6 Learning approaches
1.6.1 Logistic regression
1.6.2 Support vector machine
1.6.3 Ensemble learning
1.6.4 Deep neural networks
1.6.5 Patch-based grading
1.6.6 Multimodality approaches
1.7 Validation
1.7.1 Cross-validation
1.7.2 Performance metrics
1.8 Datasets
1.9 Conclusion
2 Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort
2.1 Abstract
2.2 Introduction
2.3 Material and Methods
2.3.1 Participants
2.3.2 MRI acquisition
2.3.3 Fully automated volumetry software
2.3.4 Automatic classification using SVM
2.3.4.1 Preprocessing: extraction of whole gray matter maps
2.3.4.2 SVM classification
2.3.5 Radiological classification
2.4 Results
2.4.1 Automated segmentation software
2.4.2 Automatic SVM classification from whole-brain gray matter maps
2.4.3 Automatic SVM classification from AVS volumes
2.4.4 Radiological classification
2.5 Discussion
2.6 Conclusion
2.7 Supplementary material
3 Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data
3.1 Abstract
3.2 Introduction
3.3 Materials
3.3.1 Datasets
3.3.2 Participants
3.3.2.1 ADNI
3.3.2.2 AIBL
3.3.2.3 OASIS
3.3.3 Imaging data
3.3.3.1 ADNI
3.3.3.2 AIBL
3.3.3.3 OASIS
3.4 Methods
3.4.1 Converting datasets to a standardized data structure
3.4.1.1 Conversion of the ADNI dataset to BIDS
3.4.1.2 Conversion of the AIBL dataset to BIDS
3.4.1.3 Conversion of the OASIS dataset to BIDS
3.4.2 Preprocessing pipelines
3.4.2.1 Preprocessing of T1-weighted MR images
3.4.2.2 Preprocessing of PET images
3.4.3 Feature extraction
3.4.4 Classification models
3.4.4.1 Linear SVM
3.4.4.2 Logistic regression with L2 regularization
3.4.4.3 Random forest
3.4.5 Evaluation strategy
3.4.5.1 Cross-validation
3.4.5.2 Metrics
3.4.6 Classification experiments
3.5 Results
3.5.1 Influence of the atlas
3.5.2 Influence of the smoothing
3.5.3 Influence of the type of features
3.5.4 Influence of the classification method
3.5.5 Influence of the partial volume correction of PET images
3.5.6 Influence of the magnetic field strength
3.5.7 Influence of the class imbalance
3.5.8 Influence of the dataset
3.5.9 Influence of the training dataset size
3.5.10 Influence of the diagnostic criteria
3.5.11 Computation time
3.6 Discussion
3.7 Conclusions
4 Reproducible evaluation of methods for predicting progression to Alzheimer’s disease from clinical and neuroimaging data
4.1 Introduction
4.2 Materials and methods
4.2.1 Data
4.2.2 Data conversion
4.2.3 Preprocessing and feature extraction
4.2.4 Age correction
4.2.5 Classification approaches
4.2.5.1 Classification using clinical data
4.2.5.2 Image-based classification
4.2.5.3 Integrating clinical and imaging data
4.2.5.4 Integrating amyloid status
4.2.5.5 Prediction at different time-points
4.2.6 Validation
4.3 Results
4.3.1 Classification using clinical data
4.3.2 Integration of imaging and clinical data
4.3.3 Integration of amyloid status
4.3.4 Prediction at different time-points
4.4 Conclusions
Conclusion & Perspectives
A Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease
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