Computer Science and Engineering
State-space models for Analyzing fMRI data
Traditionally, the analysis of functional Magnetic Resonance Imaging (fMRI) has focused either on the creation of static maps localizing the metabolic fingerprints of neural processes or on studying their temporal evolution in a few preselected regions in the human brain. By neglecting either the temporal dimension or the spatial entirety of brain function, such methods must necessarily compromise on extracting and representing all the information contained in the data. In this talk, I will describe new paradigms to facilitate a time–resolved exploration of mental processes as captured by fMRI. I will especially describe a state-space formalism that is used to model the brain transitioning through a sequence of mental states as it solves a task. Such a formalism will enable the study of the spatial distribution of activity along with its temporal structure. In addition to revealing the mental patterns of an individual subject, the proposed generative model enables the comparison of mental processes between subjects in their entirety, not just as spatial activation maps. I also illustrate how the developed methods were applied to fMRI studies for developmental disorders such as dyslexia and dyscalculia (i.e. math learning disability). This was completed in collaboration with Firdaus Janoos and Istavan Morocz of the Harvard Medical School.