Blind Source Separation Methods 

Independent Component Analysis (ICA)

Independent component analysis (ICA) is the most commonly used computational tool for identifying and characterizing underlying brain functional networks. We have done pioneering work in developing formal statistical modeling, estimation and inference framework for ICA in neuroimaging studies, particularly in the area of investigating between-subject heterogeneity in ICA-derived brain networks and modeling longitudinal changes in brain functional networks.  


†:  denote students/postdocs advised/co-advised 

Blind Source Separation of Brain Connectome 

We have developed innovative blind source separation approaches for decomposing brain connectivity data to reveal latent connectivity source signals and characterize individuals' loadings on these sources.


Brain Network/Brain Connectivity Analysis

We have developed a variety of network analysis tools to generate more reliable and reproducible results for investigating brain network and connectivity. These tools derive from a variety of advanced statistical methodologies including sparse inverse convariance estimation and graphical models.  

1. Direct brain connectivity via partial correlations.  We have developed an efficient and reliable statistical method for estimating direct functional connectivity in large-scale brain networks via partial correlations (Wang et al., 2016).  Based on this work, we have released an R package DensParcorr.  

2. New Graphical Model Approaches.  We have developed novel graphical model approaches of brain network research including: (a) dynamic brain connectivity (Kundu et al., 2018, NeuroImage), (b) integrative analysis of brain networks by fusing functional MRI and diffusion MRI (Higgins et al., 2018, NeuroImage) and (c) joint analysis of multiple brain networks using both task-related and resting state fMRI data (Lukemire et al 2019+, JASA). 


Imaging-based Prediction Models

We have developed statistical models to predict subjects’ clinical outcomes and cognitive states based on brain images. The proposed methods contribute to enhancing the utility of neuroimaging techniques in clinical practice, particularly in developing personalized interventions for more effective treatment of mental disorders. 

1. A predictive model for forecasting future neural activation. This predictive model has potentials in helping select optimal treatment plans for individual patients in clinical practice. For example, it has been successfully applied in a neuroimaging study of schizophrenia to predict patients’ post-treatment neural activity after various treatments (Guo et al., Human Brain Mapping, 2008).

2. A weighted cluster kernel PCA model for predicting subjects’ cognitive states using brain images (Guo, 2010). The kernel PCA model improves prediction accuracy by capturing nonlinearity patterns in images and is robust to outlying subjects. 

3. A Bayesian hierarchical model to provide predictive information on disease progression- or treatment-related changes in brain connectivity. The proposed method improves the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common across subjects as well as unique individual-level connectivity features. Our predictive model has been applied to Alzheimer's Disease Neuroimaging Initiative (ADNI) data to predict longitudinal changes in neural circuits in Alzheimer patients (Dai and Guo,  NeuroImage, 2017).


Agreement Methods

1. Agreement Methods for Time-to-Event Data   Agreement studies have wide and important applications in biomedical research and clinical practices since multiple-raters/methods measurements commonly occur in such settings. One challenging topic in agreement studies is how to account for censored or truncated observations such as those observed in survival studies. Our work in this area includes: 1) development of one of the first nonparametric estimation methods for agreement measures in the presence of censoring (Guo and Manatunga, 2007 and 2009), 2) development of agreement methods to accommodate different types of survival outcomes including both discrete (Guo and Manatunga, 2005 and 2009) and continuous data (Guo and Manatunga, 2007 and 2010); 3) development of modeling tools in agreement studies that allow for modeling subjects’ covariate effects on the strength of agreement (Guo and Manatunga, 2005). Recently, we have developed a novel framework for assessing agreement based on survival processes (Guo et al., 2013). This new framework circumvents the need of estimating moment functions of survival times in the previous agreement methods and can be widely applied to survival studies with various study lengths. 

2. Agreement Methods for Multi-scale and High Dimensional Data   We have developed new statistical methods that extend the existing agreement paradigm to handle multiple scale (continuous/ordinal) scenarios. Additionally, we have worked on development of new agreement methodology to investigate the alignment between traditional behavior/clinical outcomes and neuroimaging biomarkers and to assess agreement between images acquired from multi-center neuroimaging studies.