Functional, clustered, feedforward, and mesoscale brain networks

The brain is a network spanning multiple scales from subcellular to macroscopic. In this thesis I present four projects studying brain networks at different levels of abstraction. The first involves determining a functional connectivity network based on neural spike trains and using a graph theoretical method to cluster groups of neurons into putative cell assemblies. In the second project I model neural networks at a microscopic level. Using diferent clustered wiring schemes, I show that almost identical spatiotemporal activity patterns can be observed, demonstrating that there is a broad neuro-architectural basis to attain structured spatiotemporal dynamics. Remarkably, irrespective of the precise topological mechanism, this behavior can be predicted by examining the spectral properties of the synaptic weight matrix. The third project introduces, via two circuit architectures, a new paradigm for feedforward processing in which inhibitory neurons have the complex and pivotal role in governing information flow in cortical network models. Finally, I analyze axonal projections in sleep deprived mice using data collected as part of the Allen Institute's Mesoscopic Connectivity Atlas. After normalizing for experimental variability, the results indicate there is no single explanatory difference in the mesoscale network between control and sleep deprived mice. Using machine learning techniques, however, animal classification could be done at levels significantly above chance. This reveals that intricate changes in connectivity do occur due to chronic sleep deprivation.

A perfect day for zebrafish : neuromodulation of sleep in a diurnal vertebrate

Every day, we shift among various states of sleep and arousal to meet the many demands of our bodies and environment. A central puzzle in neurobiology is how the brain controls these behavioral states, which are essential to an animal's well-being and survival. Mammalian models have predominated sleep and arousal research, although in the past decade, invertebrate models have made significant contributions to our understanding of the genetic underpinnings of behavioral states. More recently, the zebrafish (Danio rerio), a diurnal vertebrate, has emerged as a promising model system for sleep and arousal research.

In this thesis, I describe two studies on sleep/arousal pathways that I conducted using zebrafish, and I discuss how the findings can be combined in future projects to advance our understanding of vertebrate sleep/arousal pathways. In the first study, I discovered a neuropeptide that regulates zebrafish sleep and arousal as a result of a large-scale effort to identify molecules that regulate behavioral states. Taking advantage of facile zebrafish genetics, I constructed mutants for the three known receptors of this peptide and identified the one receptor that exclusively mediates the observed behavioral effects. I further show that the peptide exerts its behavioral effects independently of signaling at a key module of a neuroendocrine signaling pathway. This finding contradicts the hypothesis put forth in mammalian systems that the peptide acts through the classical neuroendocrine pathway; our data further generate new testable hypotheses for determining the central nervous system or alternative neuroendocrine pathways involved.

Second, I will present the development of a chemigenetic method to non-invasively manipulate neurons in the behaving zebrafish. I validated this technique by expressing and inducing the chemigenetic tool in a restricted population of sleep-regulating neurons in the zebrafish. As predicted by established models of this vertebrate sleep regulator, chemigenetic activation of these neurons induced hyperactivity, whereas chemigenetic ablation of these neurons induced increased sleep behavior. Given that light is a potent modulator of behavior in zebrafish, our proof-of-principle data provide a springboard for future studies of sleep/arousal and other light-dependent behaviors to interrogate genetically-defined populations of neurons independently of optogenetic tools.