Using Gaussian graphical models as the basis for functional connectivity, we propose new models and test statistics to detect whether subject covariates ...
Population Inference for Node Level Differences in Functional Connectivity
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Given a population of subjects where functional connectivity varies across individuals, we investigate methods to test whether covariates predict a subject's ...
Population Inference for Node Level Differences in Multi-subject Functional Connectivity · Manjari Narayan, Genevera I. Allen · Published in International ...
Our approach emphasizes the need to account for errors in estimating subject level networks when conducting inference at the population level. Using simulations ...
Using Gaussian graphical models as the basis for functional connectivity, we propose new models and test statistics to detect whether subject covariates ...
We review the statistical methods proposed to compare brain connectomes. We classified the different methods into two scales of analysis.
Brain connectivity analysis aims to understand how spatially distinct regions of the brain interact with each other. The use of networks to model whole-brain ...
Consequently, many multi-subject fcMRI studies first estimate functional connectivity for every subject and then assuming these subject networks are fixed ...
We proposed a multi-GCN based GAN (MGCN-GAN) to infer individual SC based on corresponding FC by automatically learning the complex associations between ...
Oct 21, 2020 · We demonstrate how multilayer network embeddings can be used to visualize, cluster, and classify functional regions of the brain for these individuals.