On Jan 20th, 2009, I joined the Medical Image Analysis Laboratory (MIALab) as a research assistant (RA) at the MIND institute at UNM. This position will transition to a 2-3 year postdoc upon the completion of my PhD this May.
Vince Calhoun, Edward Bedrick (my stat advisor), Jeremy Bockholt, and I compose the Biostatistics & NeuroInformatics Core (STATNI) on UNM’s Center of Biomedical Research Excellence (COBRE) grant funded by the National Center for Research Resources (NCRR), a part of the National Institutes of Health (NIH). STATNI serves as a centralized resource for biostatistical consulting for a number of scientific projects. My role will be in the development of statistical methods and programming numerical and statistical methods to address the aims of the projects. Specifically, the development of a Bayesian ICA model for fMRI data.
There are five aims to the project that will ultimately extend the ability to incorporate prior information to move beyond the semi-blind ICA approach. [from the project summary] First, we will extend our semi-blind ICA (sbICA) framework to provide a general framework for incorporating prior information from multiple spatial and temporal sources. In the second aim we will focus upon statistical inference and develop a framework for integrating the relevant functional components. In the third aim, we will validate the algorithms in aims 1 and 2, including using fMRI data collected on multiple days from a variety of paradigms. In this aim we develop a decision mechanism for selecting the best combination of methods given a particular problem. For the fourth aim, we will apply our methods to data collected during four well-studied paradigms in healthy controls and patients with schizophrenia. Our final aim involves the continuing development of our GIFT toolbox, and incorporation of the above algorithms, constraint selection mechanisms, and visual interfaces into the software. The successful completion of this research will provide a powerful set of tools for the research community to increase the sensitivity and specificity of BOLD analysis methods by drawing upon the strengths of both model-based and data-driven approaches. These tools will also provide a way to study the inter-relationship among functional networks in a flexible manner.
This is an ideal position for me because the modeling is similar to work I have done in my dissertation, I continue to work closely with my advisor, Ed, who I continue to learn so much from, I get to learn and work with Vince who has many ideas and is very prolific, and all of this in the hot area of fMRI. I also have family and friends in Albuquerque who I want to stay close with for a little longer and this position allows me to stay put.