Title: Using neuroimaging to investigate the effect of expertise in rapid perceptual decision making
Description: Although we rarely think about our everyday cognition as skilled cognition --- because it comes naturally and all of us possess it --- we are all experts in mastering our everyday environment. This expertise may be manifested in mundane or everyday tasks like discerning familiar faces from strangers, or for some people, in more complex situations such as determining whether to swing at a 95mph fastball. The athlete's brain offers a good opportunity for studying neuroplasticity and perceptual expertise because athletes participate in long term training and practice, often starting very early in childhood, and continuing throughout their entire careers. The goal of this dissertation is to investigate the effect of expertise on brain network dynamics during perceptual decision making tasks using techniques for multimodal data fusion. Specifically, we design novel stimuli and methods of combining simultaneously collected electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to investigate brain networks involved in the split-second decisions faced by baseball batters.

Using single-trial analysis with experts in baseball, we find the neural correlates of expertise in baseball pitch recognition in both the temporal domain (EEG) and spatial domain (fMRI). We find that experts in baseball pitch recognition exhibit larger activations in early visual prediction networks as well as motor planning areas which aid in the experts superior behavioral performance. In this dissertation, we also focus on leveraging the complementary strengths of the two neuroimaging modalities (EEG, fMRI) to create novel fusion techniques that can provide richer network dynamics than by either modality separately. We design a novel encoding model to fuse EEG and fMRI to provide unprecedented spatio-temporal resolution of a perceptual decision in the human brain. On top of the methodologies for EEG-fMRI fusion, we show that motion correction hardware can be implemented to significantly improve signal-to-noise for fMRI acquisition by reducing motion artifacts which highly contaminate simultaneous EEG-fMRI data.

These tools taken together provide researchers with another dimension---temporal ordering of brain activations--- to probe behavioral, psychological, or even compromised states during perceptual decision making.