Faculty Bibliography

The inferior olive (IO) has among the highest densities of neuronal gap junctions in the nervous system. These gap junctions are proposed to be the underlying mechanism for generating synchronous Purkinje cell complex spike (CS) activity. Gap junctions between neurons are formed mostly by connexin36 proteins. Thus, the connexin36 knockout (Cx36KO) mouse provides an opportunity to test whether gap junction coupling between IO neurons is the basis of CS synchrony. Multiple electrode recordings of crus 2 CSs were obtained from wildtype (Wt) and Cx36KO mice. Wts showed statistically significant levels of CS synchrony, with the same spatial distribution as has been reported for other species: high CS synchrony levels occurred mostly among Purkinje cells within the same parasagittally-oriented cortical strip. In contrast, in Cx36KOs, synchrony was at chance levels and had no preferential spatial orientation, supporting the gap junction hypothesis. CS firing rates for Cx36KOs were significantly lower than for Wts, suggesting that electrical coupling is an important determinant of IO excitability. Rhythmic CS activity was present in both Wt and Cx36KOs, suggesting that individual IO cells can act as intrinsic oscillators. In addition, the climbing fiber reflex was absent in the Cx36KOs, validating its use as a tool for assessing electrical coupling of IO neurons. Zebrin II staining and anterograde tracing showed that cerebellar cortical organization and the topography of the olivocerebellar projection are normal in the Cx36KO. Thus, the differences in CS activity between Wts and Cx36KOs likely reflect the loss of electrical coupling of IO cells

Ultrasound Myocardial Elastography (UME) and Tagged Magnetic Resonance Imaging (tMRI) are two imaging modalities that were developed in the recent years to quantitatively estimate the myocardial deformations. Tagged MRI is currently considered as the gold standard for myocardial strain mapping in vivo. However, despite the low SNR nature of ultrasound signals, echocardiography enjoys the widespread availability in the clinic, as well as its low cost and high temporal resolution. Comparing the strain estimation performances of the two techniques has been of great interests to the community. In order to assess the cardiac deformation across different imaging modalities, in this paper, we developed a semi-automatic intensity and gradient based registration framework that rigidly registers the 3D tagged MRIs with the 2D ultrasound images. Based on the two registered modalities, we conducted spatially and temporally more detailed quantitative strain comparison of the RF-based UME technique and tagged MRI. From the experimental results, we conclude that qualitatively the two modalities share similar overall trends. But error and variations in UME accumulate over time. Quantitatively tMRI is more robust and accurate than UME

In this paper, we introduce an adaptive model-based segmentation framework, in which edge and region information are integrated and used adaptively while a solid model deforms toward the object boundary. Our 3D segmentation method stems from Metamorphs deformable models. The main novelty of our work is in that, instead of performing segmentation in an entire 3D volume, we propose model-based segmentation in an adaptively changing subvolume of interest. The subvolume is determined based on appearance statistics of the evolving object model, and within the subvolume, more accurate and object-specific edge and region information can be obtained. This local and adaptive scheme for computing edges and object region information makes our segmentation solution more efficient and more robust to image noise, artifacts and intensity inhomogeneity. External forces for model deformation are derived in a variational framework that consists of both edge-based and region-based energy terms, taking into account the adaptively changing environment. We demonstrate the performance of our method through extensive experiments using cardiac MR and liver CT images

In this paper, we apply qualitative tools and quantitative analysis for the EEG recordings of 23 adult patients. Specifically, independent component analysis (ICA) was applied to separate independent source components, followed by spectrum analysis. In terms of quantitative EEG analysis, we use several complexity measures to evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were prejudged as brain death. For the first time, we report statistically significant differences of quantitative statistics in such a clinical study. The empirical results reported in this paper suggest some promising directions and valuable clues for clinical practice.