Ying Guo


Independent Component Analysis (ICA)

Independent component analysis (ICA) is the most commonly used computational tool for identifying and characterizing underlying brain functional networks. One of the challenging research topics in ICA is how to perform group ICA for multi-subject imaging studies. Our research on group ICA methodology focused on development of probabilistic group ICA framework for estimating brain functional networks based on multi-subject fMRI data. We have proposed general temporal-concatenation group ICA (TC-GICA) models that can accommodate different types of between-subject variability in temporal responses and model wide variety of neural signals under different experimental tasks (Guo and Pagnoni, 2008; Guo, Biometrics, 2011).  In addition to TC-GICA, we have developed a novel hierarchical group ICA method to formally model subject-specific effects in both temporal and spatial domains in fMRI data (Guo and Tang, 2013).  Furthermore, we have recently developed a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks.  Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We plan to release a matlab toolbox for hc-ICA in 2016.

 

References
  • Shi, R. and Guo, Y (2016+). Modeling Covariate Effects in Group Independent Component Analysis with Application to Functional Magnetic Resonance Imaging. Annals of Applied Statistics. (Accepted). An earlier version of the paper was selected for Best Student Paper Award, American Statistical Association (ASA) Statistics in Imaging Section.
  • Guo, Y and Tang, Li. (2013). A hierarchical probabilistic model for group independent component analysis in fMRI studies, Biometrics, doi: 10.1111/biom.12068.
  • Guo Y (2011). A general probabilistic model for group independent component analysis and its estimation methods. Biometrics.  67(4): 1532-1542.
  • Guo Y and Pagnoni G (2008). A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage 42: 1078-1093.   Listed in ScienceDirect’s Top 25 hottest articles of NeuroImage between July-Sep. 2008.
  • Guo Y (2008). Group Independent Component Analysis of Multi-subject fMRI data: Connections and Distinctions between Two Methods.  IEEE Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics v2: 748-752.


Agreement Methods
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. My 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, I 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.


Agreement Methods for Multi-scale and High Dimensional Data:  My colleagues Dr. Limin Peng, Ruosha Li, Amita Manatunga and I have developed new statistical methods that extend the existing agreement paradigm to handle multiple scale (continuous/ordinal) scenarios (Peng, Li, Guo and Manatunga, JASA, 2011). Recently, I have started working 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.


References
  • Guo Y Li R, Peng L and Manatunga AK. (2013). A new agreement measures based on survival processes, Biometrics, doi: 10.1111/biom.12063.
  • Peng L, Li R, Guo Y, and Manatunga AK.(2011). Assessing Broad Sense Agreement between Ordinal and Continuous Measurements. Journal of American Statistician Association. 106: 1592-1601. Selected as JASA Featured Article.
  • Guo Yand Manatunga AK (2010).  A note on assessing agreement for frailty models. Statistics and Probability Letters.  80: 527-533.
  • Guo Y and Manatunga AK (2009). Measuring agreement of multivariate discrete survival times using a modified weighted kappa coefficient. Biometrics, 65(1):125-34.
  • Guo Y and Manatunga AK (2007). Nonparametric estimation of the concordance correlation coefficient under univariate censoring. Biometrics, 63(1): 164-172.
  • Guo Y and Manatunga AK (2005).  Modeling the agreement of discrete bivariate survival times using kappa coefficient.  Lifetime Data Analysis, 11(3): 309-332


 
Imaging-based Prediction Methods

In recent years, there has been a strong interest in the neuroimaging community to utilize information in imaging data to predict individual disease status and treatment response. I developed two statistical prediction methods based on brain images. First, I have developed a prediction method based on a Bayesian hierarchical model for forecasting future neural activity based on a subject’s baseline brain images and other individual characteristics (Guo, Bowman and Kilts, 2008), which can potentially help select optimal treatment plans for individual patients. Secondly, I proposed a weighted cluster kernel PCA model for predicting subjects’ cognitive state using brain images (Guo, 2010).


References





Neuroimaging and Psychiatric Studies

As a founding member of the Center for Biomedical Imaging Statistics (CBIS) at Emory University, I have collaborated with imaging scientists at Emory including  Dr. Clinton Kilts, Giuseppe Pagnoni, Helen Mayberg and Opal Ousley from the Department of Psychiatry and Behavioral Sciences. I also collaborated with Dr. Dominique Musselman and her team on depression-related psychiatric studies.

 
References