Tagged: Biomedical computing

Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain

We present an algorithm for blindly recovering constituent source spectra from magnetic resonance (MR) chemical shift imaging (CSI) of the human brain. The algorithm, which we call constrained nonnegative matrix factorization (cNMF), does not enforce independence or sparsity, instead only requiring the source and mixing matrices to be nonnegative. It is based on the nonnegative matrix factorization (NMF) algorithm, extending it to include a constraint on the positivity of the amplitudes of the recovered spectra. This constraint enables recovery of physically meaningful spectra even in the presence of noise that causes a significant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithm’s performance using /sup 31/P volumetric brain data, comparing the results with two different blind source separation methods: Bayesian spectral decomposition (BSD) and nonnegative sparse coding (NNSC). We then incorporate the cNMF algorithm into a hierarchical decomposition framework, showing that it can be used to recover tissue-specific spectra given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the hierarchical procedure on /sup 1/H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up.

Perceptual salience as novelty detection in cortical pinwheel space

We describe a filter-based model of orientation processing in primary visual cortex (V1) and demonstrate that novelty in cortical “pinwheel” space can be used as a measure of perceptual salience. In the model, novelty is computed as the negative log likelihood of a pinwheel’s activity relative to the population response. The population response is modeled using a mixture of Gaussians, enabling the representation of complex, multi-modal distributions. Hidden variables that are inferred in the mixture model can be viewed as grouping or “binding” pinwheels which have similar responses within the distribution space. Results are shown for several stimuli that illustrate well-known contextual effects related to perceptual salience, as well as results for a natural image.