Korea university BSD License Yes Brain Signal Processing Lab. NITRC Iterative dual-regression with sparse prior MATLAB Jong-Hwan Lee Iterative Dual-Regression with Sparse Prior (IDRwSP) is aimed to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method). An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. The proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization. See the reference paper. Kim YH, Kim J, Lee JH., Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data., Neuroimage. 2012. 2013-12-26 4 - Beta v20131226 Iterative dual-regression with sparse prior MR, BSD License, Application, 4 - Beta, MATLAB, NIfTI-1 http://www.nitrc.org/projects/iterdrwsp/, http://www.nitrc.org/projects/iterdrwsp/ jhlee.jonghwanlee@gmail.com