Computed Axial Tomography

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Table of contents

1 Neuroimaging: a primer 
1.1 Neuroimaging acquisition techniques
1.1.1 Computed Axial Tomography
1.1.2 Positron emission tomography
1.1.3 Magnetic resonance imaging
1.1.4 Electroencephalography and magnetoencephalography
1.2 Inference in neuroimaging
1.3 Univariate techniques
1.3.1 The General Linear Model
1.3.2 Group analysis in fMRI
1.3.3 Voxel-based morphometry
1.4 Multivariate machine learning techniques
1.4.1 General setting: supervised learning
1.4.2 Multi-Voxel Pattern Analysis of functional MRI data
1.4.3 Computer-aided diagnosis tools for aMRI
2 Inter-subject learning as a multi-source problem 
2.1 Multi-source learning
2.1.1 Multi-source setting
2.1.2 Link with multi-view and multi-task learning
2.2 A multi-source setting for inter-subject prediction
2.2.1 Dataset and probabilistic model
2.2.2 Addressed problems
3 State of the art
3.1 Constructing invariant representations
3.1.1 Feature engineering
3.1.2 Structured representations
3.1.3 Representation learning
3.2 Domain adaptation
3.2.1 Looking for shared representations
3.2.2 Instance weighting
3.2.3 Iterative approaches
3.3 Multi-source-specific methods
3.3.1 Multi-source domain adaptation
3.3.2 Boosting-based methods
3.3.3 Multi-task models
3.3.4 Other approaches
3.4 Other approaches for inter-subject learning
3.4.1 Hyperalignment
3.4.2 Spatial regularization
4 Graph-based Support Vector Classification for inter-subject decoding of fMRI data
4.1 Introduction
4.2 Materials and methods
4.2.1 Graph-based Support Vector Classification (G-SVC)
4.2.2 Graphical representation of fMRI patterns
4.2.3 Graph similarity
4.2.4 Datasets
4.2.5 Evaluation framework
4.3 Results
4.3.1 Results on artificial data: G-SVC vs. vector-based methods
4.3.2 Results on real data: G-SVC vs. vector-based methods
4.3.3 Results on real data: G-SVC vs parcel-based methods
4.3.4 Results on real data: G-SVC with variable number of nodes
4.3.5 Results on real data: influence of each graph attribute
4.3.6 Kernel parameters
4.4 Discussion
4.4.1 Hyper-parameters estimation
4.4.2 Linear vs nonlinear classifiers
4.4.3 Examining assumptions and potential applications
4.4.4 Which graph kernel for fMRI graphs?
4.5 Conclusion
4.6 Appendix – Within-subject G-SVC decoding results
4.7 Appendix – Testing pattern symmetry using G-SVC
4.8 Appendix – Inter-region decoding using G-SVC
5 Mapping cortical shape differences using a searchlight approach based on classification of sulcal pit graphs 
5.1 Introduction
5.2 Methods
5.2.1 Extracting sulcal pits
5.2.2 Representing patterns of sulcal pits as graphs
5.2.3 Graph-based support vector classification
5.2.4 Searchlight mapping
5.2.5 Multi-scale spatial inference
5.2.6 Interpretation-aiding visualization tools
5.3 Experiments
5.3.1 Mapping gender and hemispheric differences
5.3.2 Results: methodological considerations
5.3.3 Results: neuroscience considerations
5.4 Discussion
5.4.1 Exploring the relevance of our results
5.4.2 Searchlight statistical analysis
5.4.3 On the necessity of the multi-scale approach
5.4.4 A kernel-based multivariate classification model
5.5 Conclusion
6 Multi-source kernel mean matching for inter-subject decoding of MEG data
6.1 Introduction
6.2 A reminder on kernel mean matching
6.2.1 Instance weighting for domain adaptation
6.2.2 Kernel Mean Matching
6.2.3 A transductive domain adaptation classifier
6.3 Multi-source kernel mean matching
6.3.1 Multi-source setting
6.3.2 Multi-source kernel mean matching
6.3.3 Limiting cases of the model
6.4 Simulations
6.4.1 Dataset and pre-processing
6.4.2 Experiments
6.4.3 Results
6.5 Discussion and conclusion
6.6 Appendix – Solving the KMM optimization problem using cvxopt
6.7 Appendix – Solving the MSKMM optimization problem using cvxopt
7 Conclusion 
Bibliographie