Here is a little bit about us:
I have been a brain imaging methods researcher since 1990 and was fortunate enough to be a graduate student in the right place (The Medical College of Wisconsin and working with fellow graduate student, Eric Wong) at the right time (just before the inception of fMRI). Thanks to Eric’s construction of a local head gradient / rf coil setup and his writing of a bare bones echo planar imaging (EPI) pulse sequence, we published some early fMRI results in 1992. Ever since then, I have been working on fMRI methods development.
I did a post doc a Massachusetts General Hospital in 1994 – 1996, then briefly came back to MCW as an assistant professor. In 1999, I became Chief of the Section on Functional Imaging Methods (SFIM) and Director of the Functional MRI Facility (FMRIF) at the National Institutes of Health in Bethesda, MD. I am also Director of the Center for Neuroimaging and Neuromodulation, and have started Machine Learning and Data Sharing teams in FMRIF, as I feel that both of these areas will play a central role in the future of neuroimaging. Lastly, I am Editor-In-Chief of the journal, NeuroImage.
I continue to work to advance brain image acquisition and processing methods – specifically those related to fMRI. I’m interested not only in squeezing every possible bit of information from the MRI signal towards understanding the brain, but also effectively applying fMRI to individuals in a clinical setting.
My twitter feed is @fMRI_today.
All views & posts are my own.
In 1991 I received a PhD in Biophysics from the Medical College of Wisconsin, working in the MRI lab of the enigmatic Dr. James Hyde, building gradient coils and pulse sequences. During grad school I was lucky to run into Peter Bandettini, who pulled me into the very early days of fMRI. In 1995 I moved to UCSD where I focused on perfusion imaging for both clinical and fMRI applications for the next 15+ years.
I am now jumping into brain science after 2 decades of mostly MRI pulse sequence development. My motivation for this shift was something like:
- fMRI has been cranking along for 20+ years and we (MRI people) haven’t done much to accelerate brain science other that a rather generic push toward higher spatial and temporal resolution. What more can or should we do?
- Given advances in MRI technology (like realizing that we are not confined to living in k-space), image reconstruction (or more generally, information extraction), and machine learning, can we rethink fMRI to more specifically generate and process data to make a quantum leap along the path to ‘understanding the brain’.
- Which brings us to the question of what it means to ‘understand the brain’. My naive assumption was that this would be a well defined thing, but as you probably know, it’s not.
- Now I’m backing my way into brain science with the hopes of starting by helping to discuss what might be a good working definition of what it means to ‘understand the brain’, and from there further hope that I can help to cook up some tools that can accelerate progress towards that goal.