David R. Haynor

An anatomically comprehensive atlas of the adult human brain transcriptome

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.

PET-CT image registration in the chest using free-form deformations

We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.

Validating clustering for gene expression data

MOTIVATION Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to apply a clustering algorithm to the data from all but one experimental condition. The remaining condition is used to assess the predictive power of the resulting clusters-meaningful clusters should exhibit less variation in the remaining condition than clusters formed by chance. RESULTS We successfully applied our methodology to compare six clustering algorithms on four gene expression data sets. We found our quantitative measures of cluster quality to be positively correlated with external standards of cluster quality.

A multiple active contour model for cardiac boundary detection on echocardiographic sequences

Tracing of left-ventricular epicardial and endocardial borders on echocardiographic sequences is essential for quantification of cardiac function. The authors designed a method based on an extension of active contour models to detect both epicardial and endocardial borders on short-axis cardiac sequences spanning the entire cardiac cycle. They validated the results by comparing the computer-generated boundaries to the boundaries manually outlined by four expert observers on 44 clinical data sets. The mean boundary distance between the computer-generated boundaries and the manually outlined boundaries was 2.80 mm (sigma=1.28 mm) for the epicardium and 3.61 (sigma=1.68 mm) for the endocardium. These distances were comparable to interobserver distances, which had a mean of 3.79 mm (sigma=1.53 mm) for epicardial borders and 2.67 mm (sigma=0.88 mm) for endocardial borders. The correlation coefficient between the areas enclosed by the computer-generated boundaries and the average manually outlined boundaries was 0.95 for epicardium and 0.91 for endocardium. The algorithm is fairly insensitive to the choice of the initial curve. Thus, the authors have developed an effective and robust algorithm to extract left-ventricular boundaries from echocardiographic sequences.

Partial volume tissue classification of multichannel magnetic resonance images-a mixel model

A single volume element (voxel) in a medical image may be composed of a mixture of multiple tissue types. The authors call voxels which contain multiple tissue classes mixels. A statistical mixel image model based on Markov random field (MRF) theory and an algorithm for the classification of mixels are presented. The authors concentrate on the classification of multichannel magnetic resonance (MR) images of the brain although the algorithm has other applications. The authors also present a method for compensating for the gray-level variation of MR images between different slices, which is primarily caused by the inhomogeneity of the RF field produced by the imaging coil.

A controlled study of repetitive transcranial magnetic stimulation in medication-resistant major depression.

BACKGROUND Repetitive transcranial magnetic stimulation (TMS) as a treatment for depression has shown statistically significant effects, but the clinical significance of these effects has been questioned. METHODS Patients with medication-resistant depression were randomized to receive 15 sessions of active or sham repetitive TMS delivered to the left dorsolateral prefrontal cortex at 110% the estimated prefrontal cortex threshold. Each session consisted of 32 trains of 10 Hz repetitive TMS delivered in 5-second trains. The primary end point was treatment response defined as a >or=50% decrease in Hamilton Depression Rating Scale (HDRS) score at both 1 and 2 weeks following the final repetitive TMS treatment. Remission was defined as a HDRS score < 8. RESULTS The response rate for the TMS group was 30.6% (11/35), significantly (p = .008) greater than the 6.1% (2/33) rate in the sham group. The remission rate for the TMS group was 20% (7/35), significantly (p = .033) greater than the 3% (1/33) rate in the sham group. The HDRS scores showed a significantly (p < .002) greater decrease over time in the TMS group compared with the sham group. CONCLUSIONS Transcranial magnetic stimulation can produce statistically and clinically significant antidepressant effects in patients with medication-resistant major depression.

Three-Year Incidence of Low Back Pain in an Initially Asymptomatic Cohort : Clinical and Imaging Risk Factors

Study Design. Prospective cohort study of randomly selected Veterans Affairs out-patients without baseline low back pain (LBP). Objective. To determine predictors of new LBP as well as the 3-year incidence of magnetic resonance imaging (MRI) findings. Summary of Background Data. Few prospective studies have examined clinical and anatomic risk factors for the development of LBP, or the incidence of new imaging findings and their relationship to back pain onset. Methods. We randomly selected 148 Veterans Affairs out-patients (aged 35 to 70) without LBP in the past 4 months. We compared baseline and 3-year lumbar spine MRI. Using data collected every 4 months, we developed a prediction model of back pain-free survival. Results. After 3 years, 131 subjects were contacted, and 123 had repeat MRI. The 3-year incidence of pain was 67% (88 of 131). Depression had the largest hazard ratio (2.3, 95% CI = 1.2–4.4) of any baseline predictor of inci-dent back pain. Among baseline imaging findings, central spinal stenosis and nerve root contact had the highest, though nonsignificant, hazard ratios. We did not find an association between new LBP and type 1 endplate changes, disc degeneration, annular tears, or facet degeneration. The incidence of new MRI findings was low, with the most common new finding being disc signal loss in 11 (9%) subjects. All five subjects with new disc extrusions and all four subjects with new nerve root impingement had new pain. Conclusion. Depression is an important predictor of new LBP, with MRI findings likely less important. New imaging findings have a low incidence; disc extrusions and nerve root contact may be the most important of these findings.

Nonrigid multimodality image registration

We have designed, implemented, and validated an algorithm capable of 3D PET-CT registration in the chest, using mutual information as a similarity criterion. Inherent differences in the imaging protocols produce significant non-linear motion between the two acquisitions. To recover this motion, local deformations modeled with cubic B-splines are incorporated into the transformation. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, the deformation model allows for closed-form expressions of the criterion and its gradient. A limited-memory quasi-Newton optimization package is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the algorithm, 27 scans from patients involved in routine lung cancer screening were used in a validation study. The registrations were assessed visually by two observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0-6mm range and the average computation time is 100 minutes.

Magnetic Resonance Imaging Signal Changes in Denervated Muscles after Peripheral Nerve Injury

The evaluation of peripheral nerve disorders has traditionally relied on a clinical history, physical examination, and electrodiagnostic studies. Recent studies have used magnetic resonance imaging (MRI) to evaluate a variety of both nerve and muscle disorders. In this article, we describe the use of MRI, using short-tau inversion recovery (STIR) sequences, to evaluate muscle signal characteristics in a variety of peripheral nerve disorders. A total of 32 patients were studied, and 12 representative cases are discussed in detail. Increased STIR signal in muscle was seen in cases of severe axonotmetic injuries involving the transection of axons producing severe denervation changes on electromyography. The increased STIR signal in denervated muscles was seen as early as 4 days after the onset of clinical symptoms, which is significantly earlier than changes detected on electromyography. The MRI signal changes were reversible when the recovery of motor function occurred as a result of further muscle innervation. In cases of neurapraxic nerve injuries, characterized by conduction block without axonal loss, the STIR signal in muscle was normal. These findings show that MRI using STIR sequences provides a panoramic visual representation of denervated muscles useful in localizing and grading the severity of peripheral nerve injury secondary to either disease or trauma. MRI using STIR sequences may therefore play an important role in the prediction of clinical outcome and the formulation of appropriate therapy early after peripheral nerve injury.

Edge-guided boundary delineation in prostate ultrasound images

Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease. Here, a new paradigm for guided edge delineation is described, a which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient-specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, the authors found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers. The use of edge guidance for boundary delineation can also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques.