Publications

We examined semantic vs. associational influences on word priming in schizophrenia. Tested on three occasions, subjects made speeded lexical decisions to three kinds of prime-word relationships: semantic-only (e.g., Deer-Pony), associated-only (e.g., Bee-Honey), or semantic-and-associated (e.g., Doctor-Nurse). Controls showed greater priming of words related via two relationships (semantic-and-associated) than for words related only semantically.. However, patients showed greater priming for associated-only words than for words related only semantically. Schizophrenic patients may show an associational bias, restricting semantic integration and contributing to their disturbed thinking.

PURPOSE: The purpose of this study was to determine the range of fixed trajectory curvilinear distraction devices required to correct a variety of severe mandibular deformities. MATERIALS AND METHODS: Preoperative computed tomography (CT) scans from 18 patients with mandibular deformities were imported into a CT-based software program (Osteoplan). Three-dimensional virtual models of the individual skulls were made with landmarks to track movements. An ideal treatment plan was created for each patient. Upper and lower boundaries for the dimensions of curvilinear distractors were established based on manufacturing and geometric constraints. Then, anatomically acceptable distractor attachment points were identified on the models using proximal and distal grids. Treatment plans were simulated for a series of distractors with varying radii of curvature, elongations (arc-length of device), and placements along the grids. The outcomes using these distractors were compared with the ideal treatment plans. Discrepancies were quantified in millimeters by comparing landmarks in the simulated versus ideal movements. RESULTS: Approximately 400,000 simulated 3-dimensional movements, based on the distractor parameters and variations in placement were computationally evaluated for the 18 cases. It was determined that, by varying distractor placement, a family of 5 distractors, with 3, 5, 7, and 10 cm radii of curvature and a straight-line device, could be used to treat all 18 cases to within 1.8 mm of error. CONCLUSIONS: The results of this study indicate that a family of 5 curvilinear distractors may suffice to treat a broad range of mandibular deformities.

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.

The use of surgical simulator systems for education and preoperative planning is likely to increase in the future. A natural course of development of these systems is to incorporate patient specific anatomical models. This step requires some kind of segmentation process in which the different anatomical parts are extracted. Anatomical datasets are, however, usually very large and manual processing would be too demanding. Hence, automatic, or semi-automatic, methods to handle this step are required. The framework presented in this paper uses nonrigid registration, based on the morphon method, to automatically segment the hip anatomy and generate models for a hip surgery simulator system.

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.