How the brain changes its shape
My research program is focused on how the brain changes its shape. For this we use both mouse models as well as human subjects combined with imaging and advanced image processing techniques to understand subtle changes in neuroanatomy. Some particular examples include:
- MRI of learning and memory: Research using human imaging, including the famous study of the hippocampus of London Taxi drivers, has indicated that experience and expertise is reflected on local structures of the brain. In order to understand these effects better we are training mice on different mazes, scanning their brains using high resolution Magnetic Resonance Images, and have found that we can grow differing regions of the brain depending on the training paradigm used. These studies, augmented by complimentary imaging techniques to understand corresponding cellular events, are continuing in order to allow us to understand how the brain changes with learning and experience.
- The effect of specialized training on the human brain: There is increasing evidence that subtle variations in our brains reflect past experience. A particularly potent modifier of brain shape appears to be learning a specialized skill, such as a musical instrument, ballet, etc. This part of my research program will use imaging to attempt to delineate precisely how such learning changes our brains, and relate these findings back to work in the mouse (described above) in order to ascertain more precise cellular and genetic contributions.
- Methods of analyzing neuroanatomy: The research projects outlined above depend on precise automated measurements of brain shape from MRI. We use a combination of image registration techniques, tissue classification and deformable models to obtain these measurements. Ongoing research will thus also focus on improving these methods as well as understanding precisely what they can and cannot tell us. Image processing will further be augmented by traditional as well as novel statistics so as to identify reliable signals in our data.