title: Graph theory for feature extraction and classification: a migraine pathology case study.
abstract: Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.
authors.affiliation: Deustotech Life Department, University of Deusto, Bilbao 48007, Spain.
authors.forename: Fernando
authors.initials: F
authors.lastname: Jorge-Hernandez
authors.affiliation: Deustotech Life Department, University of Deusto, Bilbao 48007, Spain.
authors.forename: Yolanda
authors.initials: Y
authors.lastname: Garcia Chimeno
authors.affiliation: Deustotech Life Department, University of Deusto, Bilbao 48007, Spain.
authors.forename: Begonya
authors.initials: B
authors.lastname: Garcia-Zapirain
authors.affiliation: Department of Neuroradiology, Osatek, Galdakao 48960, Spain.
authors.forename: Alberto
authors.initials: A
authors.lastname: Cabrera Zubizarreta
authors.affiliation: Department of Neurology, Galdakao-Usansolo Hospital, Galdaka 48960, Spain.
authors.forename: Maria Angeles
authors.initials: MA
authors.lastname: Gomez Beldarrain
authors.affiliation: Research Department, Osatek, Bilbao 48011, Spain.
authors.forename: Begonya
authors.initials: B
authors.lastname: Fernandez-Ruanova
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D008881:Migraine Disorders / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology*; D009415:Nerve Net / Q000503:physiopathology*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019966:Substance-Related Disorders / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology*
keywords: Functional MRI (fMRI); graph theory; machine learning; migraine; synchronization likelihood