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In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These...
In this paper, we propose an effective technique to analyze task-based functional connectivity across multiple subjects for functional magnetic resonance imaging (fMRI) data. Instead of applying the assumption of group-independence or multiset correlation maximization, an alternative approach is adopted based on a combined framework of sparse dictionary learning (SDL) and multi-set canonical correlation...
This paper addresses the problem of scanner induced low frequency drift estimation in order to improve the significance of functional magnetic resonance imaging (fMRI) data for statistical analysis. A novel technique is presented to estimate the drift parameters using a sparse general linear model (sGLM) framework. The fMRI signal is modeled as a linear mixture of several signals such as low frequency...
A principal component analysis (PCA) based dictionary initialization approach accompanied by a computationally efficient dictionary learning algorithm for statistical analysis of functional magnetic resonance imaging (fMRI) is proposed. It replaces a singular value decomposition (SVD) computation with an approximate solution to obtain a local minima for a given initial dictionary. The K-SVD has been...
In this paper a novel framework that combines data-driven methods is proposed for functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. The basic idea is to overcome the shortcomings of compressed sensing based data-driven method by incorporating canonical correlation analysis (CCA) to extract a more meaningful temporal profile that is based solely on underlying brain...
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