Tagged: Biomedical engineering

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.

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

The Bilinear Brain: Towards Subject‐Invariant Analysis

A major challenge in single-trial electroencephalography (EEG) analysis and Brain Computer Interfacing (BCI) is the so called, inter-subject/inter-session variability: (i.e large variability in measurements obtained during different recording sessions). This variability restricts the number of samples available for single-trial analysis to a limited number that can be obtained during a single session. Here we propose a novel method that distinguishes between subject-invariant features and subject-specific features, based on a bilinear formulation. The method allows for one to combine multiple recording of EEG to estimate the subject-invariant parameters, hence addressing the issue of inter-subject variability, while reducing the complexity of estimation for the subject-specific parameters. The method is demonstrated on 34 datasets from two different experimental paradigms: Perception categorization task and Rapid Serial Visual Presentation (RSVP) task. We show significant improvements in classification performance over state-of-the-art methods. Further, our method extracts neurological components never before reported on the RSVP thus demonstrating the ability of our method to extract novel neural signatures from the data.

Do We See Before We Look?

We investigated neural correlates of target detection in the electroencephalogram (EEG) during a free viewing search task and analyzed signals locked to saccadic events. Subjects performed a search task for multiple random scenes while we simultaneously recorded 64 channels of EEG and tracked subjects eye position. For each subject we identified target saccades (TS) and distractor saccades (DS). We sampled the sets of TS and DS saccades such that they were equalized/matched for saccade direction and duration, ensuring that no information in the saccade properties themselves was discriminating for their type. We aligned EEG to the saccade onset and used logistic regression (LR), in the space of the 64 electrodes, to identify activity discriminating a TS from a DS on a single-trial basis. We found significant discriminating activity in the EEG both before and after the saccade. We also saw substantial reduction in discriminating activity when the saccade was executed. We conclude that we can identify neural signatures of detection both before and after the saccade, indicating that subjects anticipate the target before the last saccade, which serves to foveate and confirm the target identity.

Analysis of a gain control model of V1: Is the goal redundancy reduction?

In this paper we analyze a popular divisive normalization model of V1 with respect to the relationship between its underlying coding strategy and the extraclassical physiological responses of its constituent modeled neurons. Specifically we are interested in whether the optimization goal of redundancy reduction naturally leads to reasonable neural responses, including reasonable distributions of responses. The model is trained on an ensemble of natural images and tested using sinusoidal drifting gratings, with metrics such as suppression index and contrast dependent receptive field growth compared to the objective function values for a sample of neurons. We find that even though the divisive normalization model can produce “typical” neurons that agree with some neurophysiology data, distributions across samples do not agree with experimental data. Our results suggest that redundancy reduction itself is not necessarily causal of the observed extraclassical receptive field phenomena, and that additional optimization dimensions and/or biological constraints must be considered.

Spatio-temporal linear discrimination for inferring task difficulty from EEG

We present a spatio-temporal linear discrimination method for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required i.e. the algorithm requires no knowledge about the timing and/or spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel and then spatially integrates the channels for each channel-specific onset time. The algorithm can be seen as learning discrimination trajectories defined within the space of EEG channels. We demonstrate the method for detecting auditory evoked neural activity and discrimination of task difficulty in a complex visual-auditory environment

Recovery of metabolomic spectral sources using non-negative matrix factorization

1H magnetic resonance spectra (MRS) of biofluids contain rich biochemical information about the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, such data have been shown to provide information for disease diagnosis as well as the effects of potential therapeutics. In this paper we describe a novel approach, using non-negative matrix factorization (NMF), for rapidly identifying metabolically meaningful spectral patterns in1H MRS. We show that the intensities of these identified spectral patterns can be related to the onset of, and recovery from, toxicity in both a time-related and dose-related fashion. These patterns can be seen as a new type of biomarker for the biological effect under study. We demonstrate, using k-means clustering, that the recovered patterns can be used to characterize the metabolic status of the animal during the experiment.

Comparison of supervised and unsupervised linear methods for recovering task-relevant activity in EEG

In this paper we compare three linear methods, independent component analysis (ICA), common spatial patterns (CSP), and linear discrimination (LD) for recovering task relevant neural activity from high spatial density electroencephalography (EEG). Each linear method uses a different objective function to recover underlying source components by exploiting statistical structure across a large number of sensors. We test these methods using a dual-task event-related paradigm. While engaged in a primary task, subjects must detect infrequent changes in the visual display, which would be expected to evoke several well-known event-related potentials (ERPs), including the N2 and P3. We find that though each method utilizes a different objective function, they in fact yield similar components. We note that one advantage of the LD approach is that the recovered component is easily interpretable, namely it represents the component within a given time window which is most discriminating for the task, given a spatial integration of the sensors. Both ICA and CSP return multiple components, of which the most discriminating component may not be the first. Thus, for these methods, visual inspection or additional processing is required to determine the significance of these components for the task.

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.

Simulated optical imaging of orientation preference in a model of V1

Optical imaging studies have played an important role in mapping the orientation selectivity and ocular dominance of neurons across an extended area of primary visual cortex (V1). Such studies have produced images with a more or less smooth and regular spatial distribution of relevant neuronal response properties. This is in spite of the fact that results from electrophysiological recordings, though limited in their number and spatial distribution, show significant scatter/variability in the relevant response properties of nearby neurons. In this paper we present a simulation of the optical imaging experiments of ocular dominance and orientation selectivity using a computational model of the primary visual cortex. The simulations assume that the optical imaging signal is proportional to the averaged response of neighboring neurons. The model faithfully reproduces ocular dominance columns and orientation pinwheels in the presence of realistic scatter of single cell preferred responses. In addition,we find the simulated optical imaging of orientation pinwheels to be remarkably robust, with the pinwheel structure maintained up to an addition of degrees of random scatter in the orientation preference of single cells. Our results suggest that an optical imaging result does not necessarily, by itself, provide any obvious upperbound for the scatter of the underlying neuronal response properties on local scales.

Spatial signatures of visual object recognition events learned from single-trial analysis of EEG

In this paper we use linear discrimination for learning EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (63 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Each trial consists of 100 images, with a 50% chance of a single target being in a trial. Subjects are instructed to press a left mouse button at the end of the trial if they detected a target image, otherwise they are instructed to press the right button. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Analysis of discrimination results indicated a periodic fluctuation (time-localized oscillation) in A/sub z/ performance. Analysis of the EEG using the discrimination components learned at the peaks of the A/sub z/ fluctuations indicate 1) the presence of a positive evoked response, followed in time by a negative evoked response in strongly overlapping areas and 2) a component which is not correlated with the discriminator learned during the time-localized fluctuation. Results suggest that multiple signatures, varying over time, may exist for discriminating between target and distractor trials.

Detection, synthesis and compression in mammographic image analysis with a hierarchical image probability model

We develop a probability model over image spaces and demonstrate its broad utility in mammographic image analysis. The model employs a pyramid representation to factor images across scale and a tree-structured set of hidden variables to capture long-range spatial dependencies. This factoring makes the computation of the density functions local and tractable. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters are found with maximum likelihood estimation using the EM algorithm. The utility of the model is demonstrated for three applications; 1) detection of mammographic masses in computer-aided diagnosis 2) qualitative assessment of model structure through mammographic synthesis and 3) compression of mammographic regions of interest.

Texture discrimination and binding by a modified energy model

The model presented shows how textured regions can be discriminated and textured surface created by the visual cortex. The model addresses two major processes: texture segmentation and texture binding. Textures are detected by using a version of the energy model of J. R. Bergen and E. H. Adelson (1988) and J. R. Bergen and M. S. Landy (1991), which was modified to include ON and OFF center cells, and units selective for line endings. A novel neural mechanism is described for binding a texture pattern together. Simulation results demonstrated the ability of the networks to segment and bind a well-known texture pattern.

A neural network model of object segmentation and feature binding in visual cortex

The authors present neural network simulations of how the visual cortex may segment objects and bind attributes based on depth-from-occlusion. They briefly discuss one particular subprocess in the occlusion-based model most relevant to segmentation and binding: determination of the direction of figure. They propose that the model allows addressing a central issue in object recognition: how the visual system defines an object. In addition, the model was tested on illusory stimuli, with the network’s response indicating the existence of robust psychophysical properties in the system.