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.

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers

“…We should note that the sample sizes of our subject groups were still small, although they exceeded the sample sizes reported in many of the previously reported classification studies in neuropsychiatry literature [ 58 , 61 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. As a continuation of this study, we will test the performance of our methodology on a larger subject cohort.…”

“…They achieved 80% accuracy, 81% sensitivity, and 77% specificity with a relatively small sample size (n = 16 for OCD and n = 13 for HC). Similarly, three studies utilized MVPA methods and MRI-based neuroimaging markers for accurate prediction of the presence of migraine by use of two-class classification schemes, and the reported accuracies ranged between 80% and 96% [ 75 , 76 , 77 ].…”

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

“…The results showed that in the classifier trained with fine features, the linear -SVM and RBF-SVM both obtained the best classification accuracy rate of 93.97%. Jorge-Hernandez et al (2014) developed a set of machine learning algorithms through graph theory analysis, demonstrating that the classification accuracies of migraine patients using NN and SVM classifiers were 92.86% and 87%, respectively. Wang Q. et al (2022) combined degree centrality (DC) and SVM analysis, revealing a significant decrease in the DC values of the bilateral inferior temporal gyrus (ITG) among migraine sufferers.…”