Publications

The pathophysiology of dystonia is still poorly understood. We used diffusion tensor imaging to screen for white matter abnormalities in regions between the basal ganglia and the thalamus in cervical and hand dystonia patients. All patients exhibited an abnormal hemispheric asymmetry in a focal region between the pallidum and the thalamus. This asymmetry was absent 4 weeks after the same patients were treated with intramuscular botulinum toxin injections. These findings represent a new systems-level abnormality in dystonia, which may lead to new insights about the pathophysiology of movement disorders. More generally, these findings demonstrate central nervous system changes following peripheral reductions in muscle activity. This raises the possibility that we have observed activity-dependent white matter plasticity in the adult human brain.

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

A segmentation application prototype of the volume of the left ventricle for Magnetic Resonance Imaging is being developed. The foundation for this work is given by modeling possible radial deformations of the epicardium and endocardium contours by means of a Markov Random Field over which the most probable configuration is estimated. The field makes use of a Bayesian approach based on a priori terms which impose smoothness along the coupled contours and likelihood terms which gather information provided by the images about the areas where the contours are supposed to be. The parameters of the field are estimated on a supervised basis.

The interaction between beta-cyclodextrin (CD) and gemini surfactant of the type alkyl-alpha,omega-bis(dodecyldimethylammonium bromide) with different spacer lengths of 2, 8, and 10 carbons has been investigated by means of electric conductivity (EC) and proton self-diffusion NMR at 298 K. The formation of a 2:1 (CD:gemini) complex in a two-step mechanism is observed with the first association constant (K(11)) higher than the second one (K(21)), but both relatively small in comparison with single C(12)-tailed surfactant. The value of the association constants increased with spacer length both for the first and second associated CD, which indicates that the available space on the gemini molecule is important. The magnitudes of the association constant both for the first and second complexation are discussed. The first association constant is small (when compared with the homologous single-chain surfactant) due to hydrophobic interaction between the hydrocarbon tails within the gemini molecule, while the second association constant shows no cooperativity and its magnitude is discussed in terms of steric constrains.

This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems.

We describe an MRI-based system for topological analysis followed by measurements of topographic features for the human cerebral cortex that takes as its starting point volumetric segmentation data. This permits interoperation between volume-based and surface-based topographic analysis and extends the functionality of many existing segmentation schemes. We demonstrate the utility of these operations in individual as well as to group analysis. The methodology integrates analyses of cortical segmentation data generated by manual and semi-automated volumetric morphometry routines (such as the program cardviews) with the procedures of the FreeSurfer program to generate a cortical ribbon of the cerebrum and perform cortical topographic measurements (including thickness, surface area and curvature) in individual subjects as well as in subject populations. This system allows the computation of topographical cortical measurements for segmentation data generated from manual and semi-automated volumetric sources other than FreeSurfer. These measurements can be regionally specific and integrated with systems of cortical parcellation that subdivides the neocortex into gyral-based parcellation units (PUs). This system of topographical analysis of the cerebral cortex is consistent with current views of cortical development and neural systems organization of the human and non-human primate brain.

BACKGROUND/PURPOSE: Despite its potential for visualizing white matter fiber tracts in vivo, diffusion tensor tractography has found only limited applications in clinical research in which specific anatomic connections between distant regions need to be evaluated. We introduce a robust method for fiber clustering that guides the separation of anatomically distinct fiber tracts and enables further estimation of anatomic connectivity between distant brain regions. METHODS: Line scanning diffusion tensor images (LSDTI) were acquired on a 1.5T magnet. Regions of interest for several anatomically distinct fiber tracts were manually drawn; then, white matter tractography was performed by using the Runge-Kutta method to interpolate paths (fiber traces) following the major directions of diffusion, in which traces were seeded only within the defined regions of interest. Next, a fully automatic procedure was applied to fiber traces, grouping them according to a pairwise similarity function that takes into account the shapes of the fibers and their spatial locations. RESULTS: We demonstrated the ability of the clustering algorithm to separate several fiber tracts which are otherwise difficult to define (left and right fornix, uncinate fasciculus and inferior occipitofrontal fasciculus, and corpus callosum fibers). CONCLUSION: This method successfully delineates fiber tracts that can be further analyzed for clinical research purposes. Hypotheses regarding specific fiber connections and their abnormalities in various neuropsychiatric disorders can now be tested.

Practitioners’ decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.

The cytosolic fraction of Vigna radiata contains a 17-kD protein that binds plant hormones from the cytokinin group, such as zeatin. Using recombinant protein and isothermal titration calorimetry as well as fluorescence measurements coupled with ligand displacement, we have reexamined the K(d) values and show them to range from approximately 10(-6) M (for 4PU30) to 10(-4) M (for zeatin) for 1:1 stoichiometry complexes. In addition, we have crystallized this cytokinin-specific binding protein (Vr CSBP) in complex with zeatin and refined the structure to 1.2 A resolution. Structurally, Vr CSBP is similar to plant pathogenesis-related class 10 (PR-10) proteins, despite low sequence identity (

Group data analysis in brainstem neuroimaging is predicated on accurate co-registration of anatomy. As the brainstem is comprised of many functionally heterogeneous nuclei densely situated adjacent to one another, relatively small errors in co-registration can manifest in increased variance or decreased sensitivity (or significance) in detecting activations. We have devised a 2-stage automated, reference mask guided registration technique (Automated Brainstem Co-registration, or ABC) for improved brainstem co-registration. Our approach utilized a brainstem mask dataset to weight an automated co-registration cost function. Our method was validated through measurement of RMS error at 12 manually defined landmarks. These landmarks were also used as guides for a secondary manual co-registration option, intended for outlier individuals that may not adequately co-register with our automated method. Our methodology was tested on 10 healthy human subjects and compared to traditional co-registration techniques (Talairach transform and automated affine transform to the MNI-152 template). We found that ABC had a significantly lower mean RMS error (1.22 +/- 0.39 mm) than Talairach transform (2.88 +/- 1.22 mm, mu +/- sigma) and the global affine (3.26 +/- 0.81 mm) method. Improved accuracy was also found for our manual-landmark-guided option (1.51 +/- 0.43 mm). Visualizing individual brainstem borders demonstrated more consistent and uniform overlap for ABC compared to traditional global co-registration techniques. Improved robustness (lower susceptibility to outliers) was demonstrated with ABC through lower inter-subject RMS error variance compared with traditional co-registration methods. The use of easily available and validated tools (AFNI and FSL) for this method should ease adoption by other investigators interested in brainstem data group analysis.