Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests.Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features.Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms

Développement de méthodes d’IRM avancées pour l’étude longitudinale de la Sclérose en Plaques

While conventional MRI is the reference tool for the diagnosis and monitoring of MS, it remains only moderately correlated with the patient’s clinical status. In order to better characterize pathological alterations occurring in MS, we use in this work non-conventional MRI techniques, namely magnetic resonance spectroscopy (MRS) and diffusion MRI.A first weekly follow-up revealed the sensitivity of the diffusion metrics and the specificity of the SRM to detect the initial processes of lesion formation.A second follow-up revealed changes in diffusivity in several white matter fiber bundles, including a decrease in fraction of anisotropy and an increase in radial diffusivity, worsening with advancing disease and more marked in the progressive forms.Finally, the application of graph theory allowed to characterize the brain connectivity in the four clinical forms and to study their evolution. This study allowed us to highlight alterations in all the four clinical phenotypes, including a decrease in the cerebral network density, more marked in the progressive forms of the disease and tending to increase with its progression.This work shows the sensitivity of advanced MRI techniques for the characterization of pathological alterations and their evolution in MSBien qu'outil de référence pour le diagnostic et le suivi de la SEP, l'IRM conventionnelle ne reste que modérément corrélée à l'état clinique du patient. Afin de mieux caractériser les altérations pathologiques, nous employons dans ce travail les techniques d'IRM dites non conventionnelles que sont la spectroscopie par résonance magnétique (SRM) et l'IRM de diffusion. Un premier suivi hebdomadaire, a permis de mettre en évidence la sensibilité des métriques de diffusion et la spécificité de la SRM pour détecter les processus initiaux de la formation d'une lésion.Un second suivi a permis de mettre en évidence des modifications de la diffusivité dans plusieurs faisceaux de substance blanche, avec notamment une diminution de la fraction d'anisotropie et une augmentation de diffusivité radiale, s'aggravant avec l'avancée de la maladie et plus marquée dans les formes progressives.Enfin, l'application de la théorie des graphes a permis de caractériser la connectivité cérébrale dans les quatre formes cliniques et d'étudier leur évolution. Cette étude a permis de mettre en évidence des altérations dans tous les phénotypes cliniques, avec notamment une diminution de la densité du réseau cérébral, plus importante dans les formes progressives de la maladie et tendant à s'accentuer avec la progression de la maladie.Ce travail montre la sensibilité des techniques avancées d'IRM pour la caractérisation des altérations pathologiques et de leur évolution dans la SE

Development of Advanced MRI Techniques for the Longitudinal Study of Multiple Sclerosis

Bien qu'outil de référence pour le diagnostic et le suivi de la SEP, l'IRM conventionnelle ne reste que modérément corrélée à l'état clinique du patient. Afin de mieux caractériser les altérations pathologiques, nous employons dans ce travail les techniques d'IRM dites non conventionnelles que sont la spectroscopie par résonance magnétique (SRM) et l'IRM de diffusion. Un premier suivi hebdomadaire, a permis de mettre en évidence la sensibilité des métriques de diffusion et la spécificité de la SRM pour détecter les processus initiaux de la formation d'une lésion.Un second suivi a permis de mettre en évidence des modifications de la diffusivité dans plusieurs faisceaux de substance blanche, avec notamment une diminution de la fraction d'anisotropie et une augmentation de diffusivité radiale, s'aggravant avec l'avancée de la maladie et plus marquée dans les formes progressives.Enfin, l'application de la théorie des graphes a permis de caractériser la connectivité cérébrale dans les quatre formes cliniques et d'étudier leur évolution. Cette étude a permis de mettre en évidence des altérations dans tous les phénotypes cliniques, avec notamment une diminution de la densité du réseau cérébral, plus importante dans les formes progressives de la maladie et tendant à s'accentuer avec la progression de la maladie.Ce travail montre la sensibilité des techniques avancées d'IRM pour la caractérisation des altérations pathologiques et de leur évolution dans la SEPWhile conventional MRI is the reference tool for the diagnosis and monitoring of MS, it remains only moderately correlated with the patient’s clinical status. In order to better characterize pathological alterations occurring in MS, we use in this work non-conventional MRI techniques, namely magnetic resonance spectroscopy (MRS) and diffusion MRI.A first weekly follow-up revealed the sensitivity of the diffusion metrics and the specificity of the SRM to detect the initial processes of lesion formation.A second follow-up revealed changes in diffusivity in several white matter fiber bundles, including a decrease in fraction of anisotropy and an increase in radial diffusivity, worsening with advancing disease and more marked in the progressive forms.Finally, the application of graph theory allowed to characterize the brain connectivity in the four clinical forms and to study their evolution. This study allowed us to highlight alterations in all the four clinical phenotypes, including a decrease in the cerebral network density, more marked in the progressive forms of the disease and tending to increase with its progression.This work shows the sensitivity of advanced MRI techniques for the characterization of pathological alterations and their evolution in M

A genetic algorithm-based model for longitudinal changes detection in white matter fiber-bundles of patient with multiple sclerosis

International audienceAnalysis of white matter (WM) tissue is essential to understand the mechanisms of neurodegenerative pathologies like multiple sclerosis (MS). Recently longitudinal studies started to show how the temporal component is important to investigate temporal diffuse effects of neurodegenerative pathologies.Diffusion tensor imaging (DTI) constitutes one of the most sensitive techniques for the detection and characterization of brain related pathological processes and allows also the reconstruction of WM fibers. The analysis of spatial and temporal pathological changes along the fibers are thus possible by merging quantitative maps with structural information provided by DTI.In this work, we present a new genetic algorithm (GA) based method to analyze longitudinal changes occurring along WM fiber-bundles. In the first part of this paper, we describe the data processing pipeline, including data registration and fiber tract post-processing. In the second part, we focus our attention to the description of our GA model. In the last part, we show the tests we performed on simulated and real MS longitudinal data. Our method reached a high level of precision, recall and F-Measure in the detection of longitudinal pathological alterations occurring along different WM fiber-bundles

Multi-Parametric Non-Negative Matrix Factorization for Longitudinal Variations Detection in White Matter Fiber-Bundles

International audienceProcessing of longitudinal diffusion tensor imaging (DTI) data is a crucial challenge to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white matter (WM) fiber-bundles are variably altered by inflammatory events

Hemispheric Differences in White Matter Microstructure between Two Profiles of Children with High Intelligence Quotient vs. Controls: A Tract-Based Spatial Statistics Study

International audienceObjectives: The main goal of this study was to investigate and compare the neural substrate of two children's profiles of high intelligence quotient (HIQ). Methods: Two groups of HIQ children were included with either a homogeneous (Hom-HIQ: n = 20) or a heterogeneous IQ profile (Het-HIQ: n = 24) as defined by a significant difference between verbal comprehension index and perceptual reasoning index. Diffusion tensor imaging was used to assess white matter (WM) microstructure while tract-based spatial statistics (TBSS) analysis was performed to detect and localize WM regional differences in fractional anisotropy (FA), mean diffusivity, axial (AD), and radial diffusivities. Quantitative measurements were performed on 48 regions and 21 fiber-bundles of WM. Results: Hom-HIQ children presented higher FA than Het-HIQ children in widespread WM regions including central structures, and associative intra-hemispheric WM fasciculi. AD was also greater in numerous WM regions of Total-HIQ, Hom-HIQ, and Het-HIQ groups when compared to the Control group. Hom-HIQ and Het-HIQ groups also differed by their hemispheric lateralization in AD differences compared to Controls. Het-HIQ and Hom-HIQ groups showed a lateralization ratio (left/right) of 1.38 and 0.78, respectively. Conclusions: These findings suggest that both inter-and intra-hemispheric WM integrity are enhanced in HIQ children and that neural substrate differs between Hom-HIQ and Het-HIQ. The left hemispheric lateralization of Het-HIQ children is concordant with their higher verbal index while the relative right hemispheric lateralization of Hom-HIQ children is concordant with their global brain processing and adaptation capacities as evidenced by their homogeneous IQ

Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

International audiencePurpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel. Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks. Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles

A longitudinal model for variations detection in white matter fiber-bundles

International audienc

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

International audiencePurpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Ion-M ˘ argineanu et al. Classifiers for Follow-up MS Patients Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms

A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis.

Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations