Connectivity Based Characterization of Brain Function in Humans and Dogs
Type of DegreePhD Dissertation
Electrical and Computer Engineering
MetadataShow full item record
Functional magnetic resonance imaging (fMRI) has been widely used to infer brain function in both healthy and clinical populations. Here, we propose novel approaches for connectivity based characterization in both humans and dogs. In humans, these approaches have been applied for characterizing brain network alterations in Alzheimer’s disease (AD) patients. AD, which affects millions of elderly worldwide, is a neurodegenerative disorder with a long pre-morbid period such as mild cognitive impairment (MCI). Brain declines, both functional and structural, are inevitable with age. However, determining how and when the trajectories begin to deviate from healthy elderly individuals is a crucial step to effectively slow down the progression of the disease. Using resting-state fMRI, we first estimated Betweenness Centrality (BC) and a novel nodal characterization approach called Middlemen Power (MP) from directed network that characterize information flow. The directed network were derived from the following populations: Normal Control (NC), Early MCI (EMCI), Late MCI (LMCI) and AD. Our results demonstrate that MP detected more brain regions that progressively deteriorated from NC to EMCI to LMCI to AD, as compared to BC in directed networks. Also, BC did not identify a single node from undirected networks that significantly deteriorated. This demonstrates the MP may represent a more sensitive analytic tool for characterizing biomarkers in both directed and undirected networks. Most connectivity analyses have reported distributed decreases as well as increases in causal relationships among brain regions in MCI and AD. However, it is difficult to interpret these connectivity results because traditionally, our knowledge of brain function is anchored on regions and not connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. Two foci were identified, locus coeruleus (LC) in the brain stem and right orbitofrontal cortex (OFC). Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD. Functional brain connectivity based on resting state fMRI has been shown to be correlated with human personality and behavior. In the third study, we sought to know whether capabilities and traits in dogs can be predicted from their resting state connectivity as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score. Functional scans and behavioral measures were acquired at three different time points (TPs). We hypothesized that the correlation between resting state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2), and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting state FC features that can predict the success of training, dogs at TP1 were divided into successful group and failure group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in the successful group compared to failure group and whose connectivity strength at TP1 predicted whether a given dog was eventually successful in becoming a detector dog. A second flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior. Our findings suggest that upon replication and refinement, fMRI-based resting state brain connectivity may assist in choosing dogs that are more easily trainable for performing detection tasks.