Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

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 short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources

Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm3) diffusion tensor imaging of the entire brain applicable in a clinical context

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS: PART-I - Special Section on Blind Signal Processing and Its Applications

Blind signal processing (BSP) is currently one of the most exciting areas of research in statistical signal processing, unsupervised machine learning, neural networks, information theory, and exploratory data analysis. It has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the World Wide Web, audio, video, and sensor signals. Because BSP is an interdisciplinary research area, the combination of ideas from the above disciplines is a developing avenue of research. The aim of this Special Section is to offer an opportunity to link these techniques in different areas and to find effectiveways of solving this problem. The Special Section constitutes a vehicle whereby researchers can present new studies of BSP, thus paving the way for future developments in the field.We received 20 submissions for consideration. After the review process, we selected the following eight papers for publication that span the approaches identified above. These are complex blind source extraction from noisy mixtures using second order statistics by Javidi et al.; complex independent component analysis by entropy bound minimization by Li et al.; real-time independent vector analysis for convolutive blind source separation by Kim; a nonnegative blind source separation model for binary test data by Schachtner et al.; a matrix pseudoinversion lemma and its application to block-based adaptive blind deconvolution for MIMO systems by Kohno et al.; colored subspace analysis: dimension reduction based on a signal’s autocorrelation structure by Theis; blind adaptive equalization of MIMO systems: new recursive algorithms and convergence analysis by Radenkovic et al.; and noise estimation using mean square cross prediction error for speech enhancement by Wang et al. We hope that this Special Section will stimulate interest in the challenging area of BSP, and look forward to seeing an increasing body of high-quality research aligned to this idea. We would like to express our gratitude to the authors of the papers in this special section, and also to the more than 60 reviewers who helped us evaluate the submissions