Individualized functional mapping of the human brain
Modern neuroimaging methods such as magnetic resonance imaging (fMRI) provide powerful tools for functional mapping of the human brain. However, common methods of neuroimaging are optimized to provide data at the group level and so provide little detailed information about functional maps in individuals. Therefore, functional imaging is rarely used for clinical applications. We have pioneered a new approach to neuroimaging that provides detailed functional maps of each individual’s brain, and under complex, naturalistic conditions. In this talk I will review our approach and explain how we are combining this novel framework with statistical models drawn from machine learning in order to obtain detailed functional maps within the constraints of the clinical setting. Efficient individualized functional mapping will provide new opportunities to use functional brain data for diagnosis, prognosis and monitoring of brain disorders and neurodegenerative diseases.
Jack Gallant is Chancellor’s Professor and Class of 1940 Chair at the University of California at Berkeley. He is affiliated with the deparments of Psychology and Electrical Engineering and Computer Science, along with the programs in Bioengineering, Biophysics, Neuroscience and Vision Science. He received his Ph.D. from Yale University and did post-doctoral work at the California Institute of Technology and Washington University Medical School. His research program focuses on computational modeling of human brain activity. Further information about ongoing work, links to talks and papers and links to an online interactive brain viewer can be found at the lab web page: <http://gallantlab.org>.