The objective of this study was to expand on a method previously established in the lab for predicting subcortical structures using functional magnetic resonance imaging (fMRI) data restricted to the cortical surface. Our goal is to enhance the utility of low cost, portable imaging modalities, such as functional near infrared spectroscopy (fNIRS), which is limited in signal penetration depth. Previous work in the lab successfully employed functional connectivity to predict ten resting state networks and six anatomically de fined structures from the outer 10 mm layer of cortex using resting state fMRI data. The novelty of this study was two-fold: we chose to predict the functionally de fined region fusiform face area (FFA), and we utilized the functional connectivity of both resting state and task activation. Right FFA was identi ed for 27 subjects using a general linear model of a functional localizer tasks, and the average time series were extracted from right FFA and used as training and testing labels in support vector regression (SVR) models. Both resting state and task data decoded activity in right FFA above chance, both within and between run types. Our method is not specific to resting state, potentially broadening the scope of research questions depth-limited techniques can address. We observed a similarity in our accuracy cross-validation to previous work in the lab. We characterized this relationship between prediction accuracy and spatial signal-to-noise (SNR). We found that this relationship varied between resting state and task, as well as the functionality of features included in SVR modeling. / Master of Science / We used functional magnetic resonance imaging (fMRI) to predict activity in a deep brain region based on activity along the brain surface. This would increase the type of brain function a person could study using alternative methods that are less costly and easier to use, but can only detect signals along the surface. We were able to use this method to predict the fusiform face are, a region in the brain that responds more strongly to face images than other types of images. We also found a relationship between the quality of spatial information in the brain and the accuracy of predictions. This relationship differed depending on the types of brain regions were used to build the models, as well as whether the subjects were performing a task or rest scan.

Synchronized oscillations of ensembles of neurons in the brain underlie human cognition and behaviors. Neuronal network oscillations can be described by the physics of coupled dynamical systems. This dissertation examines the dynamic network activities in two distinct neurocognitive networks, the salience network (SN) and the ventral temporal cortex-dorsolateral prefrontal cortex (VTC-DLPFC) network, during perceptual decision-making (PDM). The key nodes of the SN include the right anterior insula (rAI), left anterior insula (lAI), and dorsal anterior cingulate cortex (dACC) in the brain. When and how a sensory signal enters and organizes within the SN before reaching the central executive network including the prefrontal cortex has been a mystery. Second, prior studies also report that perception of visual objects (face and house) involves a network of the VTC—the fusiform face area (FFA) and para-hippocampal place area (PPA)—and the DLPFC. How sensory information enters and organizes within the VTC-DLPFC network is not well understood, in milliseconds time-scale of human’s perception and decision-making. We used clear and noisy face/house image categorization tasks and scalp electroencephalography (EEG) recordings to study the dynamics of these networks. We demonstrated that beta (13–30 Hz) oscillation bound the SN, became most active around 100 ms after the stimulus onset, the rAI acted as a main outflow hub within the SN, and the SN activities were negatively correlated with the difficult tasks. We also uncovered that the VTC-DLPFC network activities were mediated by beta (13-30 Hz) and gamma (30-100 Hz) oscillations. Beta activities were enhanced in the time frame 125-250 ms after stimulus onset, the VTC acted as main outflow hub, and network activities were negatively correlated with the difficult tasks. In contrast, gamma activities were elevated in the time frame 0-125 ms, the DLPFC acted as a main outflow hub, and network activities—specifically the FFA-PPA pair—were positively correlated with the difficult tasks. These findings significantly enhance our understanding of how sensory information enters and organizes within the SN and the VTC-DLPFC network, respectively in PDM.

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