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Found 5 papers, 1 papers with code
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data.
A Potts-Mixture Spatiotemporal Joint Model for Combined MEG and EEG Data
Our new model incorporates two major extensions: (i) We combine EEG and MEG data together and formulate a joint model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent states termed mesostates.
Applications
A Bayesian Group Sparse Multi-Task Regression Model for Imaging Genetics
Results: We develop a Bayesian hierarchical modeling formulation where the posterior mode corresponds to the estimator proposed by Wang et al. (Bioinformatics, 2012), and an approach that allows for full posterior inference including the construction of interval estimates for the regression parameters.
Regularization Parameter Selection for a Bayesian Multi-Level Group Lasso Regression Model with Application to Imaging Genomics
We demonstrate these problems through simulation examples and study an approximation to the marginal likelihood which sheds light on the cause of this problem.
High-Dimensional Classification for Brain Decoding
Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity.
