Robert S. Vorburger
Postdoctoral Research Fellow
Cognitive Neuroscience Division, Department of Neurology
Columbia University College of Physicians & Surgeons
630 West 168th Street
New York, NY 10032
- Diffusion Metrics as a Correlate of Aging and of Age-Associated Cognitive Functions: My current research effort focuses on the evaluation of different diffusion metrics to investigate age related changes in the neuroanatomy of the brain. Abnormalities in the brain’s white matter have recently received increasing attention as potentially being central aspects of the pathogenic cascade in Alzheimer’s disease related dementia. The fractional anisotropy value, a diffusion-weighted magnetic resonance imaging parameter describing the anisotropic spatial diffusion distribution, has been proposed as an early biomarker for amnestic mild cognitive impairment, which purportedly represents a transitional stage between normal aging and Alzheimer’s disease. However, the tremendous potential of different diffusion weighted imaging techniques, such as diffusion tensor imaging or diffusion kurtosis imaging, is by far not explored exhaustively. I am most interested in the following questions: Which diffusion metrics are most strongly correlated with aging and cognition among older adults? Which diffusion metrics best predict changes in cognitive functioning?
- Diffusion Tensor Imaging and Probabilistic Tractography: Quantitative tractography allows in a first step to segment specific fiber bundles in the white matter and in a second step to analyze the diffusion characteristics along these trajectories. I have developed a new tractography algorithm, called BootGraph. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution. Moreover, the BootGraph can be applied to common diffusion tensor imaging data sets without further modifications and shows a high repeatability. Thus, it is very well suited for longitudinal studies and meta-studies.
Vorburger R. S., Reischauer C., and Boesiger P. (2013). BootGraph: Probabilistic Fiber Tractography Using Bootstrap Algorithms and Graph Theory. NeuroImage, 66C, 426-435.
Vorburger R. S., Reischauer C., Dikaiou K., and Boesiger P. (2012). In-Vivo Precision of Bootstrap Algorithms Applied to DTI Data. Journal of Magnetic Resonance Imaging, 36(4), 979-986.
Reischauer C., Gutzeit A., Vorburger R. S., Froehlich J. M., Binkert C. A., and Boesiger P. (2012). Optimizing the Functional Diffusion Map Using Monte Carlo Simulations. Journal of Magnetic Resonance Imaging, 36(4):1002-1009.