Publications by Year: 2006

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.

OBJECTIVES: Gray and white matter volume deficits have been reported in a number of studies of children with attention-deficit/hyperactivity disorder (ADHD); however, there is a paucity of structural magnetic resonance imaging (MRI) studies of adults with ADHD. This structural MRI study used an a priori region of interest approach. METHODS: Twenty-four adults with DSM-IV ADHD and 18 healthy controls comparable on age, socioeconomic status, sex, handedness, education, IQ, and achievement test performance had an MRI on a 1.5T Siemens scanner. Cortical and sub-cortical gray and white matter were segmented. Image parcellation divided the neocortex into 48 gyral-based units per hemisphere. Based on a priori hypotheses we focused on prefrontal, anterior cingulate cortex (ACC) and overall gray matter volumes. General linear analyses of the volumes of brain regions, adjusting for age, sex, and total cerebral volumes, were used to compare groups. RESULTS: Relative to controls, ADHD adults had significantly smaller overall cortical gray matter, prefrontal and ACC volumes. CONCLUSIONS: Adults with ADHD have volume differences in brain regions in areas involved in attention and executive control. These data, largely consistent with studies of children, support the idea that adults with ADHD have a valid disorder with persistent biological features.

Three of 2000 diagnostic hysteroscopies revealed residual fetal bony fragments in women with abnormal uterine bleeding. Removal of bony fragments by hysteroscopy is associated with therapeutic success.

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was

In this article we address analytic challenges inherent in brain volumetrics (i.e., the study of volumes of brains and brain regions). It has sometimes been assumed in the literature that deviations in regional brain size in clinical samples are directly related to maldevelopment or pathogenesis. However, this assumption may be incorrect; such volume differences may, instead, be wholly or partly attributable to individual differences in overall dimension (e.g., for head, brain, or body size). What quantitative approaches can be used to take these factors into account? Here, we provide a review of volumetric and nonvolumetric adjustment factors. We consider three examples of common statistical methods by which one can adjust for the effects of body, head, or brain size on regional volumetric measures: the analysis of covariance, the proportion, and the residual approaches. While the nature of the adjustment will help dictate which method is most appropriate, the choice is context sensitive, guided by numerous considerations-chiefly the experimental hypotheses, but other factors as well (including characteristic features of the disorder and sample size). These issues come into play in logically framing the assessment of putative abnormalities in regional brain volumes.