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...
PhD student Phebe Kemmer won the Clint Miller best poster award at this year's SRCOS Summer Research Conference.
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"