Histological and diffusion-weighted magnetic resonance imaging data from normal and degenerated optic nerve and chiasm of the rat.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to infer microstructural characteristics of tissue, particularly in cerebral white matter. Histological validation of the metrics derived from dMRI methods are needed to fully characterize their ability to capture biologically-relevant histological features non-invasively. The data described here were used to correlate metrics derived from dMRI and quantitative histology in an animal model of axonal degeneration ("Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration" [1]).

Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration.

Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter.

Reconstruction of local frequencies for recovering the unwrapped phase in optical interferometry.

In optics, when interferograms or digital holograms are recorded and their phase is recovered, it is common to obtain a wrapped phase with some errors, noise and artifacts such as singularities due to the non linearities of the demodulation process. This paper shows how to reconstruct the frequency field of the wrapped phase by using adaptive Gabor filters. Gabor filters are Gaussian quadrature filters tuned in at a certain frequency.

Characterization of the Sensorimotor Rhythm in 4-Month-Old Infants Born at Term and Premature.

The sensorimotor rhythm (SMR) is an electroencephalographic rhythm associated with motor and cognitive development observed in the central brain regions during wakefulness in the absence of movement, and it reacts contralaterally to generalized and hemibody movements. The purpose of this work was to characterize the SMR of 4-month-old infants, born either healthy at term or prematurely with periventricular leukomalacia (PVL). Two groups of infants were formed: healthy and premature with PVL.

Estimation of individual axon bundle properties by a Multi-Resolution Discrete-Search method.

A stable, accurate and robust-to-noise method for the estimation of the intra-voxel bundle-wise diffusion properties for diffusion-weighted magnetic resonance imaging is presented. The proposed method overcomes some of the limitations of most of the multi-fiber algorithms in the literature and extends them to estimate the diffusion profiles, improving the estimation of the intra-voxel geometry at challenging microstructure configurations, that is to say: relatively small crossing angles, different voxel-wise anisotropic diffusion profiles and low SNR. The proposed methodology is based on four key novel ideas: (i) A Multi-Resolution Discrete-Search determines the orientation of the fiber bundles accurately and naturally constrains the sparsity on the recovered solutions; (ii) the determination of the number of fiber bundles using the F-test combined with a Rician bias correction; (iii) a Simultaneous Denoising and Fitting procedure that exploits the spatial redundancy of the axon bundles to achieve robustness with respect to noise; and (iv) a general framework for the estimation of the axial and radial diffusivity parameters independently for each voxel.

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison.

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g.

A maximum linear separation criterion for the analysis of neurophysiological data.

In this paper we propose an approach for the extraction of features that differentiate two populations or two experimental conditions in a neurophysiological experiment. These features consist of summarizing variables defined as total activity (e.g.

Event-related EEG oscillations to semantically unrelated words in normal and learning disabled children.

Learning disabilities (LD) are one of the most frequent problems for elementary school-aged children. In this paper, event-related EEG oscillations to semantically related and unrelated pairs of words were studied in a group of 18 children with LD not otherwise specified (LD-NOS) and in 16 children with normal academic achievement. We propose that EEG oscillations may be different in LD NOS children versus normal control children that may explain some of the deficits observed in the LD-NOS group.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology--specifically, morphological erosions and dilations--that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level.

Region-based current-source reconstruction for the inverse EEG problem.

This paper presents a new method for the reconstruction of current sources for the electroencephalography (EEG) inverse problem, which produces reconstructed sources, which are confined to a few anatomical regions. The method is based on a partition of the gray matter into a set of regions, and in the construction of a simple linear model for the potential produced by feasible source configurations inside each one of these regions. The proposed method computes the solution in two stages: in the first one, a subset of active regions is found so that the combined potentials produced by sources inside them approximate the measured potential data.

Plasmacytoid dendritic cells regulate autoreactive B cell activation via soluble factors and in a cell-to-cell contact manner.

Plasmacytoid dendritic cells (pDCs) are specialized type I IFN producers, which play an important role in pathogenesis of autoimmune disorders. Dysregulated autoreactive B cell activation is a hallmark in most autoimmune diseases. This study was undertaken to investigate interactions between pDCs and autoreactive B cells.

Time-frequency-topographic analysis of induced power and synchrony of EEG signals during a Go/No-Go task.

Induced changes in electroencephalographic power and synchrony between pairs of electrodes were assessed during the Go/No-Go task in 15 young adults. Processes common to both conditions, such as attention, activation of working memory, letter identification, and discrimination processes were characterized by increased power and synchrony in the following frequency ranges: delta band (inhibition of the non-relevant stimuli), theta band (activation of working memory), and low alpha band in occipital regions immediately after the stimulus (withhold or control of the execution of a response), and decreased power in the high alpha band from 300 to 700 ms. However, the most important findings were those specific for each condition.

Bayesian segmentation of range images of polyhedral objects using entropy-controlled quadratic Markov measure field models.

We present a method based on Bayesian estimation with prior Markov random field models for segmentation of range images of polyhedral objects. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well as an improved method for localization of the boundaries between regions. The performance of the method compares favorably with other state-of-the-art procedures when evaluated using a standard benchmark.

Targeting of antigens to B cells augments antigen-specific T-cell responses and breaks immune tolerance to tumor-associated antigen MUC1.

B cells are antibody (Ab)-secreting cells as well as potent antigen (Ag)-presenting cells that prime T-cell activation, which evokes great interest in their use for vaccine development. Here, we targeted ovalbumin (OVA) to B cells via CD19 and found that a single low dose of anti-CD19-OVA conjugates, but not isotype mAb-OVA, stimulated augmented CD4 and CD8 T-cell proliferation and expansion. Administration of TLR9 agonist CpG could significantly enhance long-term T-cell survival.

Entropy-controlled quadratic markov measure field models for efficient image segmentation.

We present a new Markov random field (MRF) based model for parametric image segmentation. Instead of directly computing a label map, our method computes the probability that the observed data at each pixel is generated by a particular intensity model. Prior information about segmentation smoothness and low entropy of the probability distribution maps is codified in the form of a MRF with quadratic potentials so that the optimal estimator is obtained by solving a quadratic cost function with linear constraints.

Combined yeast {beta}-glucan and antitumor monoclonal antibody therapy requires C5a-mediated neutrophil chemotaxis via regulation of decay-accelerating factor CD55.

Administration of a combination of yeast-derived beta-glucan with antitumor monoclonal antibodies (mAb) has significant therapeutic efficacy in a variety of syngeneic murine tumor models. We have now tested this strategy using human carcinomas implanted in immunocompromised severe combined immunodeficient mice. Combined immunotherapy was therapeutically effective in vivo against NCI-H23 human non-small-cell lung carcinomas, but this modality was surprisingly ineffective against SKOV-3 human ovarian carcinomas.

Robust nonrigid multimodal image registration using local frequency maps.

Automatic multi-modal image registration is central to numerous tasks in medical imaging today and has a vast range of applications e.g., image guidance, atlas construction, etc.

Exploration of event-induced EEG phase synchronization patterns in cognitive tasks using a time-frequency-topography visualization system.

In this paper, we present a method for the study of synchronization patterns measured from EEG scalp potentials in psychophysiological experiments. This method is based on various techniques: a time-frequency decomposition using sinusoidal filters which improve phase accuracy for low frequencies, a Bayesian approach for the estimation of significant synchrony changes, and a time-frequency-topography visualization technique which allows for easy exploration and provides detailed insights of a particular experiment. Particularly, we focus on in-phase synchrony using an instantaneous phase-lock measure.

Yeast beta-glucan amplifies phagocyte killing of iC3b-opsonized tumor cells via complement receptor 3-Syk-phosphatidylinositol 3-kinase pathway.

Anti-tumor mAbs hold promise for cancer therapy, but are relatively inefficient. Therefore, there is a need for agents that might amplify the effectiveness of these mAbs. One such agent is beta-glucan, a polysaccharide produced by fungi, yeast, and grains, but not mammalian cells.

Toll-like receptor engagement stimulates anti-snRNP autoreactive B cells for activation.

Autoreactive B cells are the source of pathogenic autoantibodies (autoAb) in systemic lupus erythematosus (SLE). Previous studies have demonstrated that anti-small nuclear ribonucleoprotein particles (snRNP) B cells from normal background mice tolerize T cells in the periphery and do not secrete autoAb. In this study, we examined whether these anti-snRNP B cells can be activated for autoAb production by the engagement of Toll-like receptors (TLR).

Beta-glucan enhances complement-mediated hematopoietic recovery after bone marrow injury.

Myelotoxic injury in the bone marrow (BM) as a consequence of total body irradiation (TBI) or granulocyte colony-stimulating factor (G-CSF) mobilization results in the deposition of iC3b on BM stroma (stroma-iC3b). In the present study, we have examined how stroma-iC3b interacts with hematopoietic progenitor cells (HPCs) and the role of complement (C) and complement receptor 3 (CR3) in BM injury/repair. We demonstrate here that stroma-iC3b tethers HPCs via the inserted (I) domain of HPC complement receptor 3 (CR3, CD11b/CD18, Mac-1).

Exploratory EEG data analysis for psychophysiological experiments.

A method for the exploratory analysis of electroencephalographic (EEG) data for neurophysiological experiments is presented. It is based on a time-frequency decomposition of the EEG time series, which is measured by several electrodes in the scalp surface, and includes the computation of a statistic that measures the deviations of the log-power with respect to the pre-stimulus average; the computation of a significance index for these deviations; a new type of display (the time-frequency-topography plot) for the visualization of these indices, and the segmentation of the time-frequency plane into regions with uniform activation patterns. As a particular example, an experiment to study EEG changes during figure and word categorization is analyzed in detail.

Regularized quadrature and phase tracking from a single closed-fringe interferogram.

A new sequential phase demodulator based on a regularized quadrature and phase tracker system (RQPT) is applied to demodulate two-dimensional fringe patterns. This RQPT system tracks the fringe pattern's quadrature and phase in a sequential way by following the path of the fringes. To make the RQPT system more robust to noise, the modulating phase around a small neighborhood is modeled as a plane and the quadrature of the signal is estimated simultaneously with the fringe's modulating phase.

Half-quadratic cost functions for phase unwrapping.

We present a generic regularized formulation, based on robust half-quadratic regularization, for unwrapping noisy and discontinuous wrapped phase maps. Two cases are presented: the convex case and the nonconvex case. The unwrapped phase with the convex formulation is unique and robust to noise; however, the convex function solution deteriorates as a result of real discontinuities in phase maps.