Estimation of phase in EEG rhythms for real-time applications.

OBJECTIVE: Estimating the ongoing phase of oscillations in electroencephalography (EEG) recordings is an important aspect of understanding brain function, as well as for the development of phase-dependent closed-loop real-time systems that deliver stimuli. Such stimuli may take the form of direct brain stimulation (for example transcranial magnetic stimulation), or sensory stimuli (for example presentation of an auditory stimulus). We identify two linked problems related to estimating the phase of EEG rhythms with a specific focus on the alpha-band: 1) when the signal after a specific stimulus is unknown (real-time case), or 2) when it is corrupted by the presence of the stimulus itself (offline analysis). We propose methods to estimate the phase at the presentation time of these stimuli. APPROACH: Machine learning methods are used to learn the causal mapping from an unprocessed EEG recording to a phase estimate generated with a non-causal signal processing chain. This mapping is then used to predict the phase causally where non-causal methods are inappropriate. MAIN RESULTS: We demonstrate the ability of these machine learning methods to estimate instantaneous phase from an EEG signal subjected to very minor pre-processing with higher accuracy than commonly used signal-processing methods. SIGNIFICANCE: Neural oscillations have been implicated in a wide variety of sensory, cognitive and motor functions. The instantaneous phase of these rhythms may reflect specific processes of computation which can be acted upon if they can be estimated with sufficient accuracy. Such brain-state dependent paradigms are of increasing medical and scientific interest.

In vivo snapshot hyperspectral image analysis of age-related macular degeneration.

Drusen, the hallmark lesions of age related macular degeneration (AMD), are biochemically heterogeneous and the identification of their biochemical distribution is key to the understanding of AMD. Yet the challenges are to develop imaging technology and analytics, which respect the physical generation of the hyperspectral signal in the presence of noise, artifacts, and multiple mixed sources while maximally exploiting the full data dimensionality to uncover clinically relevant spectral signatures. This paper reports on the statistical analysis of hyperspectral signatures of drusen and anatomical regions of interest using snapshot hyperspectral imaging and non-negative matrix factorization (NMF). We propose physical meaningful priors as initialization schemes to NMF for finding low-rank decompositions that capture the underlying physiology of drusen and the macular pigment. Preliminary results show that snapshot hyperspectral imaging in combination with NMF is able to detect biochemically meaningful components of drusen and the macular pigment. To our knowledge, this is the first reported demonstration in vivo of the separate absorbance peaks for lutein and zeaxanthin in macular pigment.

Mosaicking and enhancement of slit lamp biomicroscopic fundus images.

AIMS: To process video slit lamp biomicroscopic fundus image sequences in order to generate wide field, high quality fundus image montages which might be suitable for photodocumentation. METHODS: Slit lamp biomicroscopic fundus examination was performed on human volunteers with a contact or non-contact lens. A stock, charge coupled device camera permitted image capture and storage of the image sequence at 30 frames per second. Acquisition time was approximately 30 seconds. Individual slit lamp biomicroscope fundus image frames were aligned and blended with custom developed software. RESULTS: The developed algorithms allowed for highly accurate alignment and blending of partially overlapping slit lamp biomicroscopic fundus images to generate a seamless, high quality, wide field montage. CONCLUSIONS: Video image acquisition and processing algorithms allow for mosaicking and enhancement of slit lamp biomicroscopic fundus images. The improved quality and wide field of view may confer suitability for inexpensive, real time photodocumentation of disc and macular abnormalities.

Coarse-grain parallel computing for very large scale neural simulations in the NEXUS simulation environment.

We describe a neural simulator designed for simulating very large scale models of cortical architectures. This simulator, NEXUS, uses coarse-grain parallel computing by distributing computation and data onto multiple conventional workstations connected via a local area network. Coarse-grain parallel computing offers natural advantages in simulating functionally segregated neural processes. We partition a complete model into modules with locally dense connections--a module may represent a cortical area, column, layer, or functional entity. Asynchronous data communications among workstations are established through the Network File System, which, together with the implicit modularity, decreases communications overhead, and increases overall performance. Coarse-grain parallelism also benefits from the standardization of conventional workstations and LAN, including portability between generations and vendors.

Intermediate-level visual representations and the construction of surface perception.

Abstract Visual processing has often been divided into three stages-early, intermediate, and high level vision, which roughly correspond to the sensation, perception, and cognition of the visual world. In this paper, we present a network-based model of intermediate-level vision that focuses on how surfaces might be represented in visual cortex. We propose a mechanism for representing surfaces through the establishment of "ownership"-a selective binding of contours and regions. The representation of ownership provides a central locus for visual integration. Our simulations show the ability to segment real and illusory images in a manner consistent with human perception. In addition, through ownership, other processes such as depth, transparency, and surface completion can interact with one another to organize an image into a perceptual scene.

Maternal serum alpha-fetoprotein screening. III. Pitfalls in evaluating black gravid women.

Maternal serum alpha-fetoprotein levels are higher in black women. Misinterpretation of maternal serum alpha-fetoprotein screening results can subject black gravid women to unnecessary invasive diagnostic procedures and their calculable risks. Screening errors for black women can result from the use of normative data bases established with maternal serum samples drawn from other racial groups or the use of such data bases in conjunction with a published correction factor. Because the incidence of open neural tube defects is lower for blacks than for others, excessive false positive results for blacks (estimated to be 8817 to 28,215 cases annually) would be a pernicious misapplication of maternal serum alpha-fetoprotein screening. We address the problem outlined above and recommend independently developed, valid, normative data bases.