Improved Methods for Single Subject FMRI Analysis

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DESCRIPTION (provided by applicant): Mental illness is a great burden for the affected individual and economically costly for society. The annual cost of mental disorders has been estimated to be $150 billion, increasing every year, and this total does not include more than three million people receiving disability benefits due to mental disorders. It is imperative that we prioritize research efforts focused on understanding brain function in order to improve diagnostic strategies and discover more effective therapies. Functional Magnetic Resonance Imaging (fMRI) is a powerful tool to visualize and measure typical and atypical cognitive processing. However, many important cognitive processing systems, such as those associated with memory, language, emotion and executive control, only produce small BOLD signals and thus measurements are noisy and have low statistical confidence. Hence, fMRI has not been readily adopted for clinical diagnosis of individual patients. I propose to develop greatly improved methods to suppress the noise sources in fMRI data in order to transform fMRI from a research tool about populations to a consistent and accurate diagnostic tool to study individual cognitive functions. Using the strategy that every noise suppression algorithm must perform well to reliably detect single trial fMRI BOLD signals, I developed visualization methods to "see" deeply into fMRI data to evaluate the quality of the data at every step of fMRI data processing. The preliminary studies indicate that there are clear opportunities to improve fMRI image analysis techniques. The proposed research will first develop and test methods to improve suppression of errors from motion and physiological fluctuations. Then it will translate this research by combining these techniques with pattern recognition to characterize individual cognitive activation patterns in typical and atypical populations. My quantitative science expertise is in image processing, algorithm design, and pattern recognition. The research directly supports my interdisciplinary career development with hands-on experience in experiment planning, fMRI scanner operation, neuroscience coursework, and new software methods for application to severely brain disordered populations. In particular, the subjects for this research will include important clinical psychiatric populations with disorders such as fragile X syndrome, Turner syndrome, autism, Williams syndrome, depression, and bipolar disorder, so that all newly developed methods can be immediately put into practice.