Study of mental disorders has entered into an exciting new era where biological measures from multiple platforms such as neuroimaging and genetics are being collected to help deepen the understanding of the disorders and improve diagnosis and treatment. Multi-dimensional data are becoming more common and hold great promise for advancing mental health research. However, effective statistical methods for extracting useful and complementary information from multi-dimensional data are still in their infancy. One of the major challenges is that multi-dimensional data often have different scales (continuous/discrete), data representations (scalar/array/matrix) and dimensions. Current analytical approaches typically conduct separate analysis within each dimension or apply simple correlative analyses. These methods are of very limited nature for uncovering latent patterns and associations in these data. This project seeks to develop novel statistical independent component analysis (ICA) methods to provide effective tools for reducing dimension, denoising and extracting features from large- scale multi-dimensional data. Specifically, the proposed methods would 1) provide a unified framework for decomposing and integrating multimodal neuroimaging data such as fMRI and DTI, 2) provide a discrete ICA model for extracting latent signals from large-scale discrete outcomes such as single-nucleotide polymorphism (SNP) genotype data, and 3) provide a joint ICA model for simultaneously decomposing neuroimaging and SNP genotype data to extract integrated imaging genetics features. The proposed statistical methods will be applied to a major depressive disorder (MDD) study, and user-friendly software will be developed and made available to general research communities. Our proposed method developments will directly benefit mental health research by providing innovative statistical tools to combine information from multi-dimensional datasets that can facilitate diagnosis, deepen mechanistic understanding and improve treatment of mental disorders. Our methods are also ubiquitous enough to be generally useful to statistical practice.
Many mental health studies now collect data from multiple platforms including neuroimaging, genetics, behavioral sciences and clinical research, which provide an unprecedented opportunity for crosscutting investigations that may offer new insights to mechanisms underlying mental disorders. There is great need of effective statistical methods for extracting complementary information from these multi-dimensional massive datasets. In this project, we seek to develop novel statistical independent component analysis (ICA) methods that can jointly decompose data from multiple platforms to extract integrated multi-dimensional profiles to facilitate diagnosis, deepen mechanistic understanding and improve treatment of mental disorders.
|Zhang, Guosheng; Huang, Kuan-Chieh; Xu, Zheng et al. (2016) Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression. Genet Epidemiol 40:333-40|
|Cordova, J Scott; Gurbani, Saumya S; Holder, Chad A et al. (2016) Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery. Mol Imaging Biol 18:454-62|
|Wang, Yikai; Kang, Jian; Kemmer, Phebe B et al. (2016) An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Front Neurosci 10:123|
|Peng, Limin; Manatunga, Amita; Wang, Ming et al. (2016) A general approach to categorizing a continuous scale according to an ordinal outcome. J Stat Plan Inference 172:23-25|
|Kang, Jian; Bowman, F DuBois; Mayberg, Helen et al. (2016) A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. Neuroimage 141:431-41|
|An, Qian; Kang, Jian; Song, Ruiguang et al. (2016) A Bayesian hierarchical model with novel prior specifications for estimating HIV testing rates. Stat Med 35:1471-87|
|Mayberg, Helen S (2016) Implementing Recommendations for Depression Screening of Adults: How Can Neurology Contribute to the Dialogue? JAMA Neurol 73:270-1|
|Chen, Shuo; Kang, Jian; Xing, Yishi et al. (2015) A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks. Hum Brain Mapp 36:5196-206|
|Kemmer, Phebe B; Guo, Ying; Wang, Yikai et al. (2015) Network-based characterization of brain functional connectivity in Zen practitioners. Front Psychol 6:603|
|Ray, Meredith; Kang, Jian; Zhang, Hongmei (2015) Identifying Activation Centers with Spatial Cox Point Processes Using FMRI Data. IEEE/ACM Trans Comput Biol Bioinform :|
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