Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses.
Purpose: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.Materials and Methods:Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] 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 bestF-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 bestF-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.
CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1 Lyon, France.
Gabriel
G
Kocevar
CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1 Lyon, France.
Claudio
C
Stamile
CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France; Faculty of Medicine, Abu-Haidar Neuroscience Institute, American University of BeirutBeirut, Lebanon.
Salem
S
Hannoun
CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France; Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de LyonLyon, France.
François
F
Cotton
Service de Neurologie A, Hôpital Neurologique, Hospices Civils de Lyon Lyon, France.
Sandra
S
Vukusic
CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France; Service de Neurologie A, Hôpital Neurologique, Hospices Civils de LyonLyon, France.
Françoise
F
Durand-Dubief
CREATIS Centre National de la Recherche Scientifique UMR5220 and Institut National de la Santé et de la Recherche Médicale U1206, INSA-Lyon, Université de Lyon, Université Claude Bernard-Lyon 1Lyon, France; CERMEP-Imagerie du Vivant, Université de LyonLyon, France.
Dominique
D
Sappey-Marinier
MRI; SVM; classification; diffusion tensor imaging; graph theory; multiple sclerosis; structural connectivity