Part 1. For our initial analysis, we parcellated the whole brain into 116 anatomic regions and 1000 functional networks, then analyzed connectivity within and among brain regions and networks. To test our results, we analyzed the same data using a multivariate classification method, which confirmed most of the connectivity features our new method selected. As expected, both comparisons showed widespread differences in brain connectivity between schizophrenia cases and healthy unrelated controls. More unusually, they also revealed unexpectedly large differences between cases and their healthy siblings. This project will continue in FY2014, when (in accordance with formal IRB approval and detailed collaborative agreement) extramural collaborators are scheduled to provide us with new datasets for analysis. Part 2. Following our initial analytic method development, proof-of-concept simulations, and real data validation tests , we now aim to refine our multi-sequenced mixture model for the analysis of series of data points in order to predict the tipping point when, in our test case, disease symptoms or therapeutic drug effects become evident. In extensive simulation tests, our mixture model has produced encouraging results. We will therefore continue to improve our model by making it more flexible in terms of handling missing data and will test its feasibility using real data, including data sets from the massive STAR*D study of persons diagnosed with depressive disorders.

Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
U.S. National Institute of Mental Health
Zip Code
Luo, Xin; Jin, Chunhui; Zhou, Zhenhe et al. (2015) New findings support the association of DISC1 genetic variants with susceptibility to schizophrenia in the Han Chinese population. Psychiatry Res 228:966-8
Wang, Yi; Li, Yi; Cao, Hongbao et al. (2015) Efficient test for nonlinear dependence of two continuous variables. BMC Bioinformatics 16:260
Wang, Jicai; Cao, Hongbao; Liao, Yanhui et al. (2015) Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings. Neuroimage Clin 8:95-103
Zhang, Fuquan; Xu, Yong; Shugart, Yin Yao et al. (2015) Converging evidence implicates the abnormal microRNA system in schizophrenia. Schizophr Bull 41:728-35
Merikangas, K R; Cui, L; Heaton, L et al. (2014) Independence of familial transmission of mania and depression: results of the NIMH family study of affective spectrum disorders. Mol Psychiatry 19:214-9
Cao, Hongbao; Duan, Junbo; Lin, Dongdong et al. (2014) Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs. Neuroimage 102 Pt 1:220-8
Yuan, Jianmin; Jin, Chunhui; Qin, Hai-De et al. (2013) Replication study confirms link between TSPAN18 mutation and schizophrenia in Han Chinese. PLoS One 8:e58785