High‐Order line graphs of fMRI data in major depressive disorder

Abstract

AbstractBackgroundResting‐state functional magnetic resonance imaging (rs‐fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high‐order topology of hypergraphs. In addition, the study of multi‐scale and multi‐layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression.PurposeThis work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high‐order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance.MethodsBased on rs‐fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high‐order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi‐kernel learning. The Kolmogorov–Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave‐one‐out cross‐validation method.ResultsThe high‐order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy = 92.42%, sensitivity = 92.86%, specificity = 92.11%, precision = 89.66%, F1 = 91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression.ConclusionsThis work validated the classification model based on the high‐order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.