Ying Guo

My research has been funded by federal agencies and Emory University.  Listed below are several grants that have contributed to my methodological research efforts.


 

Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data
Funded by: National Institutes of Health (NIMH), R01
PI: Ying Guo, PhD. and Jian Kang, Ph.D.
Funding Period: 09/25/2014-07/31/2018

This project seeks to develop novel statistical independent component analysis (ICA) methods for integrating multi-dimensional data (multimodal imaging/genetics/behavior) to enhance understanding of mechanisms and treatment response of mental disorders.

 

 

 

Method Development of Agreement Measures and Applications in Mental Health 
Funded by: National Institutes of Health (NIMH), R01

PI: Ying Guo/A.K. Manatunga/Limin Peng
Funding Period: 09/01/2013-08/3/2017

 

This proposal aims to develop new statistical methods to investigate the alignment between traditional behavior/clinical outcomes and neuroimaging biomarkers and also to assess agreement and calibrate images from multi-center neuroimaging studies.

 

 

Statistical methods for group independent component analysis for multi-subject functional magnetic resonance imaging data
Funded by: Emory University Research Committee/ACTSI
PI: Ying Guo
Funding Period: 06/30/2009-06/30/2010

 

The objective of this grant is to develop new statistical methods for group independent component analysis (ICA) to estimate subject-specific spatial source signals and to establish formal statistical testing framework for between-group comparisons in spatial domain.

 


Analytic Methods for Determining Multimodal Biomarkers for Parkinson's Disease

Funded by: National Institutes of Health (NIMH), R01
Role: Co-Investigator (PI: F. DuBois Bowman)
Funding Period:
10/01/12-09/30/15


This project is funded as a part of the NINDS Parkinson's Disease Biomarker Program. We are pursuing two avenues that may reveal early-stage PD biomarkers. First, we are combining datasets from different imaging modalities [including neuromelanin magnetic resonance imaging (NM-MRI) of the locus coeruleus and the substantia nigra, chemical shift imaging (CSI), diffusion tensor imaging (DTI), and resting-state functional MRI], cerebrospinal fluid (CSF) analytes, genotype information, and numerous clinical variables and developing new statistical methods to identify multimodal PD biomarkers from these massive datasets. Secondly, we will consider a Kaiser Permanente clinical database with nearly 250,000 subscribers in Georgia and attempt to determine prior diagnoses, lab results, and medication histories that are risk factors for the subsequent development of PD.




Analytic Methods for Functional Neuroimaging Data
Funded by:
National Institutes of Health (NIMH), R01
Role: Co-Investigator (PI: F. DuBois Bowman)
Funding Period:
7/15/2007-6/30/2011

The purpose of this grant is to develop novel statistical methods for 1) predicting patterns of distributed neural processing following a treatment intervention, 2) determing the likelihood of response to treatment on an individual basis, using neural processing information, and 3) developing an improved spatio-temporal modeling framework for analyzing functional neuroimaging data to identify prominent functional connections in brain activity and to identify task-related increases (decreases) in brain activity.

Method Development of Agreement Measures and Applications in Mental Health 
Funded by:
National Institutes of Health (NIMH), R01
Role: Co-PI (PI: A.K. Manatunga)
Funding Period: 
04/01/2008-03/31/2012


This proposal is designed to improve analytic methods for mental health research by developing new methodology, incorporating existing methodology and by targeting this effort toward important scientific mental health studies.  These developments will directly benefit mental health research, but they are ubiquitous enough to be generally useful contributions to statistical practice.