Spatio-temporal representations and analysis of brain function from fmri

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Classically, 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 pre-selected regions in the human brain. However, it is widely acknowledged that cognition recruits the entire brain and that the underlying mental processes are fundamentally spatio-temporal in nature. 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 thesis, I present new paradigms and an accompanying suite of tools to facilitate a time–resolved exploration of mental processes as captured by fMRI. The first part of the thesis describes a method for visualizing the metabolic activity recorded in the data and a method for studying the timing differences in the recruitment of the different functional modules during a task. In the next part a state-space formalism is used to model the brain transitioning through a sequence of mental states as it solves a task, enabling study of the spatial distribution of activity along with its temporal structure. Efficient algorithms for estimating the parameters, state-sequence and the hemodynamic behavior of the brain have been developed. In addition to revealing the mental patterns of an individual subject, such a generative model enables comparing mental processes between subjects in their entirety, not just as spatial activation maps. The methods developed here were applied to fMRI studies for developmental disorders such as dyslexia and dyscalculia (i.e. math learning disability) and for visuo-spatial working memory. I show the types of inferences possible with these methods in analyzing and differentiating mental capabilities and the neuro-scientific conclusions that they provide.