Abstract
Brain network is a widely used tool for identifying abnormal topological properties in whole-brain networks which has been applied to neurological disease diagnosis such as Major Depressive Disorder (MDD). But there is not any study showing that abnormal brain network topological metrics can be used in machine learning classification methods for the identification of MDD patients. In order to find an appropriate feature selection method, we hypothesize that MDD disrupts the topological organization of functional brain networks and the abnormal topological metrics could be used as effective features in constructing a classifier. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. The topological metrics, including global and local, were calculated using graph theory-based approaches. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. Result showed that both the MDD and control groups showed small-world architecture in brain functional networks. However, some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia and medial temporal and prefrontal regions. Support vector machine with radial basis kernel function algorithm exhibited the highest average accuracy (86.01%) with 28 features (p<0.05). Overall, the current study suggested that MDD is associated with abnormal functional brain network topological metrics and statistically significant network metrics can be successfully used in classification algorithms as features.
Highlights
Functional neuroimaging studies have suggested that Major Depressive Disorder (MDD) is related to some abnormal brain regions such as hippocampus, parahippocampal gyrus, precentral gyrus, caudate nucleus and so on Zhang et al (2011)
In order to find an appropriate feature selection method, we hypothesize that MDD disrupts the topological organization of functional brain networks and the abnormal topological metrics could be used as effective features in constructing a classifier
Global metrics: Result showed that comparing with random network, brain networks of both MDD and normal controls demonstrated an economic small-world
Summary
Functional neuroimaging studies have suggested that Major Depressive Disorder (MDD) is related to some abnormal brain regions such as hippocampus, parahippocampal gyrus, precentral gyrus, caudate nucleus and so on Zhang et al (2011). Complex graph theoretical analysis provided a powerful research method for characterizing topological properties of brain networks and proved that functional brain networks of normal controls have typical features of small-world (He et al, 2007). Costafreda (2009) and Fu et al (2008) constructed classifiers using structural and functional magnetic resonance imaging data and tested them with normal controls and MDD patients, respectively, reporting accuracy rates of 67 and 86%. Gong et al (2011) investigated differences between the use of gray matter and white matter as classification features and used Support Vector Machine (SVM) algorithms to distinguish refractory and non-refractory depressive disorder, reporting accuracy rates of 65.22 and 76.09%, respectively Costafreda (2009) and Fu et al (2008) constructed classifiers using structural and functional magnetic resonance imaging data and tested them with normal controls and MDD patients, respectively, reporting accuracy rates of 67 and 86%. Gong et al (2011) investigated differences between the use of gray matter and white matter as classification features and used Support Vector Machine (SVM) algorithms to distinguish refractory and non-refractory depressive disorder, reporting accuracy rates of 65.22 and 76.09%, respectively
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