Open AccessSparse deep neural networks on imaging genetics for schizophrenia case–control classification
Author(s) -
Chen Jiayu,
Li Xiang,
Calhoun Vince D.,
Turner Jessica A.,
Erp Theo G. M.,
Wang Lei,
Andreassen Ole A.,
Agartz Ingrid,
Westlye Lars T.,
Jönsson Erik,
Ford Judith M.,
Mathalon Daniel H.,
Macciardi Fabio,
O'Leary Daniel S.,
Liu Jingyu,
Ji Shihao
Publication year - 2021
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25387
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , generalizability theory , artificial neural network , feature selection , discriminative model , support vector machine , false discovery rate , machine learning , mathematics , statistics , biology , biochemistry , gene
Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case–control classification. An L 0 ‐norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi‐study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols ( N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools.