Univariate and Dynamic Multivariate Analysis of Functional MRI Data
Type of Degreedissertation
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Functional magnetic resonance imaging (fMRI) has been increasingly used in the past decade for inferring brain function in both healthy and clinical populations. In this dissertation, we demonstrate fMRI data analysis strategies using both univariate and multivariate models. We demonstrate the utility of mass univariate general linear regression models in a novel context wherein we analyzed fMRI data of conscious dogs to investigate their olfactory system at the cognitive level. We investigated the modulation of olfactory related activity in the canine brain as a function of odorant concentration and dogs’ consciousness state (lightly anesthetized or fully conscious). Besides, we conducted experiments with zinc nanoparticles added to the odorant to investigate their enhancement effect on olfactory perception, with pure odorant and odorant + gold nanoparticles as control conditions. The major finding was that olfactory bulb and piriform lobes were commonly activated in both awake and anesthetized dogs, while the frontal cortex and cerebellum were activated mainly in conscious dogs. Comparison of responses to low and high odorant concentrations showed differences in both the intensity and spatial extent of activation in the olfactory bulb, piriform lobes, cerebellum, and frontal cortex. Zinc nanoparticles conspicuously enhanced activations of olfactory bulb and olfactory cortex for anesthetized dogs and frontal areas for conscious dogs, compared to pure odorant and odorant + gold nanoparticles. On the other hand, multivariate regression models of fMRI data were employed to investigate connectivity between different regions of the brain. We used human resting state fMRI data to calculate four types of connectivities: static & dynamic functional connectivity (FC), and static & dynamic effective connectivity (EC). For dynamic connectivities, we used adaptive evolutionary clustering algorithm to cluster brain network states, correlated FCs with behavioral metrics, and linked EC network patterns to real world functionalities. We found several major patterns of brains states alternating with each other in a quasi-stable way, featuring the default mode network (DMN) in FC patterns and temporal-parietal-frontal interactions in EC patterns. Also, connectivity dynamics were testified to have more power in explaining behavior as compared to the static counterparts. Finally, connectivity dynamics were compared with connectivity statics with an application to classify patients with post-traumatic stress disorder (PTSD) and healthy individuals. We found that the temporal variability of connectivities had more sensitivity in predicting the diagnostic label of a novel subject than connectivity statics.