Abstract: The study of brain function hypernetwork plays an important role in the accurate diagnosis of brain diseases.At pre-sent,there are a variety of hypernetwork construction methods used in the classification of brain diseases,but these methods do not take into account the overlap between groups.Studies have shown that the overlap between groups may affect the construction of related hypernetwork models and the classification application after construction.Therefore,if only non-overlapping group structures are used,it will limit its applicability in hypernetwork.Aiming at the hypernetwork construction method that has been applied to the study of brain disease classification,when constructing the hypernetwork model,the problem of partial overlap between groups and the problem of attribute singleness in the feature extraction stage are not considered,a method of overlapping group lasso with multi-feature fusion analysis is proposed.This research method is used in the construction of hypernetwork and applied to the diagnosis of depression.The results show that the classification performance of overlapping group lasso method is better than that of other existing methods in both pure clustering coefficient attribute and multi-feature fusion analysis.Under the overlapping group lasso method,the multi-feature fusion analysis achieves a higher classification accuracy than use the clustering coefficient attribute analysis alone,reaches 87.87%.

Key words: Classification, Depression, Functional hypernetwork, Multi-feature fusion, Overlapping group lasso