Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network

Abstract

Brain functional hypernetworks have been successfully utilized in the diagnosis of brain diseases.In the previous study,the different hyper-edge generation method was mainly used to improve the construction of the hyper-network,which ignored the influence of different nodes definitions on the brain functional hyper-network topology.Therefore,in light of this problem,it is proposed to construct a brain functional hyper-network based on parcellation of different scales,so as to analyze its impact on brain functional hyper-network topology and classification performance.Specifically,firstly,based on the anatomical automatic labeling atlas,the brain was segmented by the method of clustering algorithm and the random dynamic seed point;secondly,based on the average time series obtained under each node scale,the brain functional hyper-network was constructed by the LASSO method respectively;then multiple sets of local features (node degree,shortest path,clustering coefficient) were extracted,and non-parametric tests and correlation-based methods were used to select features with difference;finally,support vector machine was adopted to build classification model.The classification results show that as the size of nodes increases,the classification accuracy of the constructed brain functional hyper-network is higher.When the node scale is 1501,the classification accuracy can reach 95.45%.Meanwhile,the classification accuracy of multi-scale fusion is better than that of any scale,which indicate different node definitions will affect the topology of the brain functional hyper-network.In future research,besides focusing to the construction method of the hyper-edge,the choice of brain parcellation scheme needs more attention in hyper-network.Moreover,combining multi-scale features can supplement more classification information to enhance the classification performance of depression and normal control.In addition,regardless of the size of the node,the classification performance of multiple sets of local properties is better than that of a single type of properties,which illustrates multiple sets of local property can make up for the missing information of a type of single feature,thereby discovering more brain disease biological markers.While effectively representing the brain functional hyper-network,it is also necessary to quantify the brain functional hyper-network topology information from multiple angles,so that the ability to characterize differences between groups can be enhanced,and the ability to diagnose and predict brain diseases can be improved.

Keywords

• resting state functional magnetic resonance imaging|multi-scale|brain functional hyper-network|local property feature|machine learning|depression