Electronic Thesis and Dissertation Repository

Degree

Doctor of Philosophy

Program

Neuroscience

Supervisor

Dr. L. Stan Leung

2nd Supervisor

Dr. Seyed M. Mirsattari

Joint Supervisor

Abstract

The mammalian brain is composed of functional networks operating at different spatial and temporal scales — characterized by patterns of interconnections linking sensory, motor, and cognitive systems. Assessment of brain connectivity has revealed that the structure and dynamics of large-scale network organization are altered in multiple disease states suggesting their use as diagnostic or prognostic indicators. Further investigation into the underlying mechanisms, organization, and alteration of large-scale brain networks requires homologous animal models that would allow neurophysiological recordings and experimental manipulations. My current dissertation presents a comprehensive assessment and comparison of rat, macaque, and human brain networks based on evaluation of intrinsic low-frequency fluctuations of the blood oxygen-level-dependent (BOLD) fMRI signal. The signal fluctuations, recorded in the absence of any task paradigm, have been shown to reflect anatomical connectivity and are presumed to be a hemodynamic manifestation of slow fluctuations in neuronal activity. Importantly, the technique circumvents many practical limitations of other methodologies and can be compared directly between multiple species. Networks of all species were found underlying multiple levels of sensory, motor, and cognitive processing. Remarkable homologous functional connectivity was found across all species, however network complexity was dramatically increased in primate compared to rodent species. Spontaneous temporal dynamics of the resting-state networks were also preserved across species. The results demonstrate that rats and macaques share remarkable homologous network organization with humans, thereby providing strong support for their use as an animal model in the study of normal and abnormal brain connectivity as well as aiding the interpretation of electrophysiological recordings within the context of large-scale brain networks.


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