Pages that link to "Item:Q5739290"
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The following pages link to A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data (Q5739290):
Displaying 9 items.
- A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments (Q641064) (← links)
- Estimating large covariance matrix with network topology for high-dimensional biomedical data (Q1663109) (← links)
- Detecting and testing altered brain connectivity networks with \(k\)-partite network topology (Q2008002) (← links)
- Bayesian modeling of the structural connectome for studying Alzheimer's disease (Q2281223) (← links)
- Bayesian inference of high-dimensional, cluster-structured ordinary differential equation models with applications to brain connectivity studies (Q2404447) (← links)
- Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors (Q5120655) (← links)
- A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development (Q5881117) (← links)
- Detection of sparse differential dependent functional brain connectivity (Q6189826) (← links)
- Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration (Q6627539) (← links)
