Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification

Functional connectivity networks (FCN) analysis is instructive for the diagnosis of brain diseases, such as mild cognitive impairment (MCI) and major depressive disorder (MDD) at their early stages. As the critical step of FCN analysis, feature representation provides the basis for finding potential biomarkers of brain diseases. In previous studies, different node statistics (e.g. local efficiency and local clustering coefficients) are usually extracted from FCNs as features for the diagnosis/classification task, which can specifically locate disease-related regions on the node level, so as to help us understand the neurodevelopmental roots of brain disorders. However, each node statistic is proposed only considering a kind of specific network property, which has one-sidedness and limitations. As a result, it is incomplete to represent a node with only one statistic. To resolve this issue, we put forward a novel scheme to select multiple node statistics jointly from the estimated FCNs for automated classification, called multiple node statistics feature selection (MNSFS). Specifically, we first extract multiple statistics from the same nodes and assign each kind of statistic into a group. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and statistics in the groups towards a better classification performance. Such a technique enables us to simultaneously locate the discriminative brain regions, as well as the specific statistics associated with these brain regions, making the classification results more interpretable. We conducted our scheme on two public databases for identifying subjects with MCI and MDD from normal controls. Experimental results show that the proposed scheme achieves superior classification accuracy and features interpreted on the benchmark datasets.