Skip to content

fd301/GraphPredictiveModels

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 

GraphPredictiveModels

Use predictive models and ensembles to statistically estimate similarities and differences between cohorts of brain networks.

Sparse Canonical Correlation Analysis (sCCA) is used as a bi-directional predictive model of brain connectomes (typically structural brain connectomes and functional brain connectomes). The sCCA biconvex criterion implemented in PMA R toolbox has been modified based on randomised Lasso principle to allow identification of the most relevant connections based on ensemble/bootstrapping principles. Therefore, relevant structural and functional connections that play an important role to the prediction are identified along with a probability score.

Identification.R projects functional connectivity matrices into an approximate tanget space on the Riemannian manifold, which allows to constrain prediction to Symmetric Positive Definite Matrices (SPD).


Related Publications

About

Use predictive models and ensembles to estimate statistical similarities and differences between cohorts of brain networks

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published