Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome
Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods.Materials and Methods: A dataset composed of 90 MS patients acquired at...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2022.926255/full |
| _version_ | 1798019524766728192 |
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| author | Berardino Barile Berardino Barile Pooya Ashtari Claudio Stamile Aldo Marzullo Frederik Maes Françoise Durand-Dubief Françoise Durand-Dubief Sabine Van Huffel Dominique Sappey-Marinier Dominique Sappey-Marinier |
| author_facet | Berardino Barile Berardino Barile Pooya Ashtari Claudio Stamile Aldo Marzullo Frederik Maes Françoise Durand-Dubief Françoise Durand-Dubief Sabine Van Huffel Dominique Sappey-Marinier Dominique Sappey-Marinier |
| author_sort | Berardino Barile |
| collection | DOAJ |
| description | Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods.Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (Eg), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed.Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8.Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures. |
| first_indexed | 2024-04-11T16:42:10Z |
| format | Article |
| id | doaj.art-db330e942bb64ffd9b9714fe80ec93c5 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-04-11T16:42:10Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-db330e942bb64ffd9b9714fe80ec93c52022-12-22T04:13:39ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442022-10-01910.3389/frobt.2022.926255926255Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectomeBerardino Barile0Berardino Barile1Pooya Ashtari2Claudio Stamile3Aldo Marzullo4Frederik Maes5Françoise Durand-Dubief6Françoise Durand-Dubief7Sabine Van Huffel8Dominique Sappey-Marinier9Dominique Sappey-Marinier10CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, FranceDepartment of Electrical Engineering, KU Leuven, Leuven, BelgiumDepartment of Electrical Engineering, KU Leuven, Leuven, BelgiumBIP SpA, Milan, ItalyDepartment of Mathematics and Computer Science, University of Calabria, Rende, ItalyDepartment of Electrical Engineering, KU Leuven, Leuven, BelgiumCREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, FranceHôpital Neurologique, Service de Neurologie, Hospices Civils de Lyon, Bron, FranceDepartment of Electrical Engineering, KU Leuven, Leuven, BelgiumCREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, FranceCERMEP–Imagerie du Vivant, Université de Lyon, Lyon, FrancePurpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods.Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (Eg), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed.Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8.Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.https://www.frontiersin.org/articles/10.3389/frobt.2022.926255/fullmultiple sclerosisbrain connectivitygrey mattermachine learningartificial intelligence–AI |
| spellingShingle | Berardino Barile Berardino Barile Pooya Ashtari Claudio Stamile Aldo Marzullo Frederik Maes Françoise Durand-Dubief Françoise Durand-Dubief Sabine Van Huffel Dominique Sappey-Marinier Dominique Sappey-Marinier Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome Frontiers in Robotics and AI multiple sclerosis brain connectivity grey matter machine learning artificial intelligence–AI |
| title | Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome |
| title_full | Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome |
| title_fullStr | Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome |
| title_full_unstemmed | Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome |
| title_short | Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome |
| title_sort | classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome |
| topic | multiple sclerosis brain connectivity grey matter machine learning artificial intelligence–AI |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2022.926255/full |
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