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BIBE
2008
IEEE
137views Bioinformatics» more  BIBE 2008»
13 years 11 months ago
A sparse variational Bayesian approach for fMRI data analysis
— The aim of this work is to propose a new approach for the determination of the design matrix in fMRI experiments. The design matrix embodies all available knowledge about exper...
Vangelis P. Oikonomou, Evanthia E. Tripoliti, Dimi...
TMI
2010
182views more  TMI 2010»
13 years 3 months ago
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data
Abstract—We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated ob...
Seyoung Kim, Padhraic Smyth, Hal S. Stern
MICCAI
2006
Springer
14 years 5 months ago
A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data
Traditional techniques for statistical fMRI analysis are often based on thresholding of individual voxel values or averaging voxel values over a region of interest. In this paper w...
Hal S. Stern, Padhraic Smyth, Seyoung Kim
ISBI
2008
IEEE
14 years 5 months ago
Inferring brain dynamics using granger causality on fMRI data
Here we present a scalable method to compute the structure of causal links over large scale dynamical systems that achieves high efficiency in discovering actual functional connec...
Guillermo A. Cecchi, Rahul Garg, A. Ravishankar Ra...
IPMI
2011
Springer
12 years 8 months ago
Generalized Sparse Regularization with Application to fMRI Brain Decoding
Many current medical image analysis problems involve learning thousands or even millions of model parameters from extremely few samples. Employing sparse models provides an effecti...
Bernard Ng, Rafeef Abugharbieh