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.

Keywords:

Brain network, depression, feature selection, graph theory, machine learning, support vector machine,

References

Competing interests

The authors have no competing interests.

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The authors have no competing interests.