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...
In recent years, the number of imaging studies on the structure and function of human brains has grown significantly. Most of these studies recruit a number of subjects for brain scans and apply various statistical methods to assess the association patterns between brain locations and human behavior as well as neurological disease or dysfunction. However, the limited sample sizes in many studies may lead to low statistical power. At the same time, results from different literatures on similar topics could sometimes be inconsistent...
We have developed several methods that combine modalities of neuroimaging data, namely fMRI and DTI data, to study the relationship between brain structure and function and to investigate the connectivity disruption pathways that characterize certain brain diseases. Resting-state and task-related brain activity, measured by fMRI, reflects the functional connectivity (FC) or associations between different brain regions. Diffusion tensor imaging (DTI), which enables the reconstruction and probabilistic quantification of major fiber tracts in the brain, provides structural connectivity (SC) information that may improve our understanding of FC...
Brain Networks and Connectivity
Recently, network analyses for fMRI data have emerged that characterize the functional relationships between brain regions. A typical neuroimaging network analysis involves defining brain regions (nodes), quantifying a measure of association between all pairs of brain regions (edges) to produce a connectivity matrix, thresholding these associations to obtain a more sparse connectivity matrix, and then calculating summary statistics that...
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. We developed two statistical prediction methods based on brain images. First, 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...
Scott Liang and Yujie Zhao have been admitted into PhD programs starting in Fall 2018. Scott will join the PhD program in Statistics at Rice University. Yujie Zhao will join the PhD Program at The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences.
Congratulations to Dr. Ying Guo for being selected as a Fellow of the American Statistical Association.
Congratulations to Zae Higgins who has been selected to participate in the Building Future Faculty program at North Carolina State University.
Congratulations to Yikai Wang who was named as the first place winner of the ASA Statistics in Imaging Section 2018 Student Paper Competition.
Congratulations to Yikai Wang whose paper "A novel hierarchical independent component modeling framework with application to longitudinal fMRI studies" has been selected as one of the winners of 2018 ENAR Distinguished Student Paper Awards.
Dr. Suprateek Kundu was invited to give a presentation on "Introduction to Graphical Models and Application to Brain Networks" for the ASA GA Chapter webinar series.
Dr. Ben Risk was invited to give an oral presentation on "Impacts of multiband acceleration factors on sensitivity and specificity" at OHBM 2017 conference.
Zae Higgins was one of the recipients of the Student Paper Award of ASA Statistics in Imaging Section Annual Conference in 2017.
Yikai Wang was one of the winners of the GA Chapter Student Poster Competition that was held during the 5th Workshop on Biostatistics and Bioinformatics at Georgia State University in May 2017.
Zae Higgins has been selected as one of the student paper winners of the Annual Conference for Statistical Methods in Imaging that is co-sponsored by ASA Section on Statistics in Imaging and University of Pittsburgh in June 2017.
Subhadip Pal joins the Department of Bioinformatics and Biostatistics at the University of Louisville as a tenure-track assistant professor in January, 2017.
Ying Guo and Phebe Kemmer taught the short course “ Statistical Methods for Brain Network Analysis Using Multimodal Imaging Data” at International Biometrics Society (ENAR) conference held at Washington DC in March 2017.
Zae Higgins receives a diversity supplement award (PI: Ying Guo) from National Institute of Mental Health for training and research in statistical methods for multimodality imaging data, June, 2016.
Kemmer PB successfully defended her dissertation in July, 2016 and joined the Neuroscience Division of Ely Lilly and Company as Research Scientist.
Ying Guo received Honorable Mention in 2016 Teaching Award of Department of Biostatistics and Bioinformatics at Emory for teaching the course of “Advanced Topics in Neuroimaging Statistics”.
Junhan Fan is admitted to the PhD program in Biostatistics at the University of Waterloo in September, 2016.
Jin Ming is admitted to the PhD program of the Department Biostatistics and Bioinformatics at Emory University in September, 2016.
Zae Higgins has been appointed as an Emory University ORDER Scholar for the 2016/2017 academic year.
New publication inn Frontier's of Neuroscience: Wang Y, Kang J, Kemmer PB and Guo Y (2016) An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. more...
Ran Shi successfully defended his dissertation on 3/30/2016
Ran Shi received 2016 ENAR distinguished student paper award.
Tian Dai successfully defended her dissertation in Feb 2016.
Tian Dai's paper “Predictability of Brain Functional Connectivity in Resting-state fMRI Data Using a Bayesian Hierarchical Model” was selected as one of two runner-up prize winners of ASA Statistics in Imaging Section Student Paper Competition for JSM 2016.
Phebe Kemmer was Student Paper Competition Winner of the 2015 ‘Statistical Methods in Imaging Workshop’ co-sponsored by ASA Statistics in Imaging Section and University of Michigan.
Phebe Kemmer won the Clint Miller Best Poster Award and Boyd Harsbarger Travel Award of 2015 Southern Regional Council on Statistics (SRCoS) Summer Research Conference.
Phebe Kemmer won the Best Emory Student Poster Award of 2015 Georgia Statistics Day.
Josh Lukemire, Honorable mention in the Student Poster competition at the 2015 Georgia Statistics Day.
Ran Shi's paper "Modeling Covariate Effects in Group Independent Component Analysis With Applications to Functional Magnetic Resonance Imaging " was selected as one of the two top-place winners of Student Paper Award by Section on Imaging Statistics of ASA 2015 (declined)
Ran Shi's paper won student paper award by section on Baysian Statistics of ASA 2015
Yikai Wang, nominated for the Shepard Award for Master thesis in RSPH. 2015
Zae Higgins, Honorable Mention of Ford Foundation Fellowship 2015
Ying Guo was elected as 2015 Program Chair-Elect of Section on Imaging Statistics of ASA
Phebe Kemmer was the 2014 winner of Michael H. Kutner Distinguished Doctoral Student Award in our department
Dr. Ying Guo and Jian Kang received an R01 award from NIMH for the project" Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data"