Faculty Bibliography

In this paper we present a robust method for segmenting and tracking cardiac contours and tags in 4D cardiac MRI tagged images via spatio-temporal propagation. Our method is based on two main techniques: the Metamorphs segmentation for robust boundary estimation, and the tunable Gabor filter bank for tagging lines enhancement, removal and myocardium tracking. We have developed a prototype system based on the integration of these two techniques, and achieved efficient, robust segmentation and tracking with minimal human interaction

In this paper, we present a novel method for automatically extracting the tagging sheets in tagged cardiac MR images, and tracking their displacement during the heart cycle, using a tunable 3D Gabor filter bank. Tagged MRI is a non-invasive technique for the study of myocardial deformation. We design the 3D Gabor filter bank based on the geometric characteristics of the tagging sheets. The tunable parameters of the Gabor filter bank are used to adapt to the myocardium deformation. The whole 3D image dataset is convolved with each Gabor filter in the filter bank, in the Fourier domain. Then we impose a set of deformable meshes onto the extracted tagging sheets and track them over time. Dynamic estimation of the filter parameters and the mesh internal smoothness are used to help the tracking. Some very encouraging results are shown

The development of gene array techniques to quantify expression levels of dozens to thousands of genes simultaneously within selected tissue samples from control and diseased brain has enabled researchers to generate expression profiles of vulnerable neuronal populations in several neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, schizophrenia, multiple sclerosis, and Creutzfeld-Jakob disease. Intriguingly, gene expression analysis reveals that vulnerable brain regions in many of these diseases share putative pathogenetic alterations in common classes of genes, including decrements in synaptic transcript levels and increments in immune response transcripts. Thus, gene expression profiles of diseased neuronal populations may reveal mechanistic clues to the molecular pathogenesis underlying various neurological diseases and aid in identifying potential therapeutic targets. This chapter will review how regional and single cell gene array technologies have advanced our understanding of the genetics of human neurological disease.

There are now a number of studies that suggest that cholesterol might regulate the processing of the amyloid precursor protein to form the neurotoxic peptide Abeta. This research has opened the possibility that cholesterol-lowering drugs might be efficacious as anti-Abeta drugs for use in Alzheimer's disease. The use of HMG-CoA reductase inhibitors (commonly called statins) in vitro and in vivo has proven them to be Abeta-lowering agents, however, the mechanism of action of these drugs is not yet known. One possible mechanism is that they reduce Abeta levels indirectly by reducing cholesterol in the central nervous system (CNS). In this study, we administered three different statins (simvastatin, lovastatin, and atorvastatin) to nontransgenic mice. We found that all three compounds had similar effects on Abeta, reducing both Abeta40 and Abeta42. The statins decreased beta-cleaved C-terminal fragment (CTF) although having no effect on alpha-CTF levels. However, the drugs did not have a similar effect on cholesterol in the CNS. Only lovastatin significantly reduced total cholesterol in isolated plasma membranes. As cholesterol is not distributed evenly in the plasma membrane, we examined bilayer distribution of cholesterol and found that all three statins caused CNS cholesterol to translocate from the cytofacial leaflet to the exofacial leaflet. This data suggests that cholesterol distribution and not total cholesterol levels may be important to Abeta production in the CNS

Several lines of evidence have illuminated the fundamental developmental principles involved in establishing and implementing pattern formation in the mammalian neocortex. A recent study has sought to unravel the underlying genetic control of cortex patterning by elucidating the transcriptional profile of discrete neocortical regions

Galanin (GAL)-containing fibers enlarge and hyperinnervate remaining cholinergic basal forebrain (CBF) neurons within the anterior nucleus basalis (NB) in late-stage Alzheimer's disease (AD). Whether GAL hypertrophy occurs in the CBF in the prodromal or early stages of AD remains unknown. The present study used GAL immunohistochemistry and an unbiased semiquantitative scoring method to evaluate GAL innervation in the anterior NB of subjects clinically diagnosed as having no cognitive impairment, mild cognitive impairment or early-stage (mild/moderate) AD. There was no difference in GAL fiber staining within the anterior NB across the three clinical groups examined. Furthermore, GAL fiber innervation was not correlated with the number of NB neurons expressing the nerve growth factor receptors p75(NTR) or TrkA or with cortical choline acetyltransferase activity in the same cases. Single-cell gene expression analysis demonstrated that cholinergic NB neurons express mRNA for the GAL receptors GALR1, GALR2 and GALR3, yet the levels of these mRNAs were unchanged across the three diagnostic groups. These observations indicate that GAL hypertrophy within the anterior NB subfield is a late-stage AD response, which may play a role in regulating the cholinergic tone of remaining basocortical projection neurons.

In this paper we present an accurate cardiac boundary tracking method for 2D tagged MRI time sequences. This method naturally integrates the motion and the static local appearance features and generates accurate boundary criteria via a boosting approach. We extend the conventional Adaboost classifier into a posterior probability form, which can be embedded in a particle filtering-based shape tracking framework. To make the tracking process more robust and faster, we use a PCA subspace shape representation to constrain the shape variation and lower the dimensionality. We also learn two shape-dynamic models for systole and diastole separately, to predict the shape evolution. Our tracking method incorporates the static appearance, the motion appearance, the shape constraints, and the dynamic prediction in a unified way. The proposed method has been implemented on 50 tagged MRI sequences. The experimental results show the accuracy and robustness of our approach