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Since the discovery of functional connectivity in fMRI data (i.e., temporal correlations between spatially distinct regions of the brain) there has been a considerable amount of work in this field. One important focus has been on the analysis of brain connectivity using the concept of networks instead of regions. Approximately ten years ago, two important(More)
The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the(More)
—We derive a class of algorithms for independent component analysis (ICA) based on maximum likelihood (ML) estimation and perform stability analysis of natural gradient ML ICA with and without the constraint for unitary demixing matrix. In the process, we demonstrate how Wirtinger calculus facilitates derivations, and most importantly, performing(More)
We introduce non-negative matrix factorization with orthog-onality constraints (NMF-OC) for detection of a target spectrum in a given set of Raman spectra data. An orthogonal-ity measure is defined and two different orthogonality constraints are imposed on the standard NMF to incorporate prior information into the estimation and hence to facilitate the(More)
A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall(More)
Smith and colleagues recently presented a temporal independent component analysis (tICA) decomposition of resting-state functional MRI data. Compared to the widely used spatial ICA (sICA), tICA better allows for a brain region to engage in multiple, independent interactions with other regions and will potentially offer new insights into brain function.
In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features(More)
Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent(More)
In this work, we propose a structured approach to compare common and distinct features of two multidimensional datasets using a combination of canonical correlation analysis (CCA) and independent component analysis (ICA). We develop formulations of information theoretic criteria to determine the dimension of the subspaces for common and distinct features of(More)
—The generalized Gaussian distribution (GGD) provides a flexible and suitable tool for data modeling and simulation, however the characterization of the complex-valued GGD, in particular generation of samples from a complex GGD have not been well defined in the literature. In this correspondence, we provide a thorough presentation of the complex valued GGD(More)