Pinned Repositories
HINT
MMM
BPMM
We proposed a novel graph-theoretic approaches for estimating a population of dynamic networks that are able to borrow information across multiple heterogeneous samples in an unsupervised manner and guided by covariate information. In particular, we develop a Bayesian product mixture model (BPMM) that imposes independent mixture priors at each time scan and uses covariates to model the mixture weights, which results in time-varying clusters of samples designed to pool information.
CCPD
Connectivity Change Point Detection (CCPD) is a fully automated two-stage approach which pools information across multiple subjects to estimate change points in functional connectivity, and subsequently estimates the brain networks within each state phase lying between consecutive change points.
DDT
siGGM
DICA
SparseBayesICA
dynaLOCUS_tutorial_data
Tutorial Data for Illustrating the dynaLOCUS Method
dynaLOCUS
Emory-CBIS's Repositories
Emory-CBIS/HINT
Emory-CBIS/MMM
Emory-CBIS/BPMM
We proposed a novel graph-theoretic approaches for estimating a population of dynamic networks that are able to borrow information across multiple heterogeneous samples in an unsupervised manner and guided by covariate information. In particular, we develop a Bayesian product mixture model (BPMM) that imposes independent mixture priors at each time scan and uses covariates to model the mixture weights, which results in time-varying clusters of samples designed to pool information.
Emory-CBIS/CCPD
Connectivity Change Point Detection (CCPD) is a fully automated two-stage approach which pools information across multiple subjects to estimate change points in functional connectivity, and subsequently estimates the brain networks within each state phase lying between consecutive change points.
Emory-CBIS/DDT
Emory-CBIS/DICA
Emory-CBIS/DPL-SVM
Emory-CBIS/dynaLOCUS
Emory-CBIS/dynaLOCUS_tutorial_data
Tutorial Data for Illustrating the dynaLOCUS Method
Emory-CBIS/siGGM
Emory-CBIS/SparseBayesICA