UC Santa Barbara

Brain systems span a wide range of spatial and temporal scales, from individual neurons firing every few milliseconds to whole-brain connectivity changing over many years. Network models offer a mathematical framework for describing, predicting, and controlling a diverse array of brain-related phenomena. Regardless of the particular system of study, it is possible to characterize the range of observed variability in system behavior over time by calculating topological and statistical metrics from inferred networks. In this thesis, tools from network science, computational neuroscience, and statistics are applied to data sets spanning several systems and spatiotemporal scales to characterize patterns of variability in brain activity and behavior within and between individuals over time.

First, electrophysiological activity of dissociated rat hippocampal neurons in vitro is analyzed to reveal the emergence and persistence of stereotyped activity patterns over time in the absence of external stimuli. These discoveries motivate further use of in vitro hippocampal cultures as model systems for studying developmental phenomena related to learning and memory.

Next, fruit fly behavior is modeled using tools from computational ethology and statistics to define and compare the syntax of grooming, a common behavior in Drosophila. We identify duration dependence in syntax for the first time, suggesting that central pattern generators contribute to organizing behavior in a partially sensory-independent manner.

We also identify inter- and intra-species variation in grooming syntax, but do not observe a significant correspondence between genetic heterogeneity and grooming variability. We also examine optogenetically-stimulated flies and find variability in grooming responses despite careful control of sensory presentation. Taken together, these results demonstrate that genetic differences can produce variation in grooming behavior, but that sensory experiences and stochastic effects contribute more strongly than genetics to the natural range of expressed grooming phenotypes.

Finally, we characterize the stability and changes in human brain network connectivity over the course of a complete menstrual cycle in a ``dense sampling" experimental paradigm. We identify stable communities that persist for the majority of the menstrual cycle. However, we also discover a correspondence between sex hormones and transient large-scale brain network reorganization, implicating hormones in shaping network topology.

Despite the differences in systems and phenomena of interest in this thesis, the work contained within is unified by a concern for variability in brain activity and behavior. Across contexts, we demonstrate the utility of network models in describing changes in complex systems over time.