Publications by Year: 2006

White matter hyperintense lesions on T2-weighted images are associated with late-life depression. Little work has been carried out examining differences in lesion location between elderly individuals with and without depression. In contrast to previous studies examining total brain white matter lesion volume, this study examined lobar differences in white matter lesion volumes derived from brain magnetic resonance imaging. This study examined 49 subjects with a DSM-IV diagnosis of major depression and 50 comparison subjects without depression. All participants were age 60 years or older. White matter lesion volumes were measured in each hemisphere using a semiautomated segmentation process and localized to lobar regions using a lobar atlas created for this sample using the imaging tools provided by the Biomedical Informatics Research Network (BIRN). The lobar lesion volumes were compared against depression status. After controlling for age and hypertension, subjects with depression exhibited significantly greater total white matter lesion volume in both hemispheres and in both frontal lobes than did control subjects. Although a similar trend was observed in the parietal lobes, the difference did not reach a level of statistical significance. Models of the temporal and occipital lobes were not statistically significant. Older individuals with depression have greater white matter disease than healthy controls, predominantly in the frontal lobes. These changes are thought to disrupt neural circuits involved in mood regulation, thus increasing the risk of developing depression.

Semantic priming refers to a reduction in the reaction time to identify or make a judgment about a stimulus that has been immediately preceded by a semantically related word or picture and is thought to result from a partial overlap in the semantic associates of the two words. A semantic priming lexical decision task using spoken words was presented in event-related fMRI and behavioral paradigms. Word pairs varied in terms of semantic relatedness and the connectivity between associates. Thirteen right-handed subjects underwent fMRI imaging and 10 additional subjects were tested in a behavioral version of the semantic priming task. It was hypothesized priming would be greatest, reaction time fastest, and cortical activation reduced the most for related word pairs of high connectivity, followed by related word pairs of low connectivity, and then by unrelated word pairs. Behavioral and fMRI results confirmed these predictions. fMRI activity located primarily in bilateral posterior superior and middle temporal regions showed modulation by connectivity and relatedness. The results suggest that these regions are involved in semantic processing.

INTRODUCTION: Mitral Valve (MV) 3D structural data can be easily obtained using standard transesophageal echocardiography (TEE) devices but quantitative pre- and intraoperative volume analysis of the MV is presently not feasible in the cardiac operation room (OR). Finite element method (FEM) modelling is necessary to carry out precise and individual volume analysis and in the future will form the basis for simulation of cardiac interventions. METHOD: With the present retrospective pilot study we describe a method to transfer MV geometric data to 3D Slicer 2 software, an open-source medical visualization and analysis software package. A newly developed software program (ROIExtract) allowed selection of a region-of-interest (ROI) from the TEE data and data transformation for use in 3D Slicer. FEM models for quantitative volumetric studies were generated. RESULTS: ROI selection permitted the visualization and calculations required to create a sequence of volume rendered models of the MV allowing time-based visualization of regional deformation. Quantitation of tissue volume, especially important in myxomatous degeneration can be carried out. Rendered volumes are shown in 3D as well as in time-resolved 4D animations. CONCLUSION: The visualization of the segmented MV may significantly enhance clinical interpretation. This method provides an infrastructure for the study of image guided assessment of clinical findings and surgical planning. For complete pre- and intraoperative 3D MV FEM analysis, three input elements are necessary: 1. time-gated, reality-based structural information, 2. continuous MV pressure and 3. instantaneous tissue elastance. The present process makes the first of these elements available. Volume defect analysis is essential to fully understand functional and geometrical dysfunction of but not limited to the valve. 3D Slicer was used for semi-automatic valve border detection and volume-rendering of clinical 3D echocardiographic data. FEM based models were also calculated. METHOD: A Philips/HP Sonos 5500 ultrasound device stores volume data as time-resolved 4D volume data sets. Data sets for three subjects were used. Since 3D Slicer does not process time-resolved data sets, we employed a standard movie maker to animate the individual time-based models and visualizations. Calculation time and model size were minimized. Pressures were also easily available. We speculate that calculation of instantaneous elastance may be possible using instantaneous pressure values and tissue deformation data derived from the animated FEM.

Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.

A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.

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.