Dottorato in NEUROSCIENZE DEL COMPORTAMENTO

Titolo della tesi: Characterization of the Hippocampal Network State Space during Wakefulness and Sleep

Neuronal oscillations are ubiquitous, phylogenetically preserved and are crucial for neuronal communication. Oscillations in a given brain region are a result of superposition of various inputs from its upstream and downstream areas as well as the activity of itslocal population. Oscillations occurring simultaneously in different brain areas, support the ongoing perception, information processing and behavioral demands. Network oscillations occurring simultaneously, at any given time and in a given brain area, represents a physiological unit (“atom”) of a thought. Thus, a thought consists of various network oscillations occurring simultaneously across multiple brain areas. As the brain traverses from one thought to another, the underlying physiological representation in terms of simultaneously occurring network oscillations also changes, thereby supporting the seamless train of thoughts as well as the continuous perceptual experience. In this study, I have developed an analytical method to study the network oscillations simultaneously by constructing a network state space of hippocampal oscillations. This state space compactly represents the simultaneously ongoing oscillatory processes in hippocampus during sleep and wakefulness. Each state on this state space represents a combination of hippocampal oscillations occurring simultaneously at that time. The network state space, thus, provides a framework to study the contribution of hippocampal oscillations to various global brain states such as sleep and wakefulness. In order to study how various hippocampal oscillations are simultaneously organized, their interrelationships, their temporal progression and their influence of hippocampal population and behavior, I have characterized various properties of the network state space. I found that during wakefulness, the hippocampal oscillations are restricted to a subspace of the network state space, whereas during sleep, the hippocampal oscillations occupy a relatively wider area on the network state space. This finding suggests the presence of functional constraints on the hippocampal network possibly due to its inherent architecture, anatomy and environmental stimuli and by extension represents a constraint on the number of unique states that an animal can physiologically enter during wakefulness as compared to sleep. I then characterized how various oscillations co-occur on the state space and found the state dependent coupling of hippocampal oscillations. In particular, during NREM sleep, the hippocampus is dominated by oscillations in the theta band and gamma band in addition to delta and spindle band (by definition) whereas during REM, the hippocampal exhibits varying amount of activity in various gamma bands and spindle bands in addition to theta (by definition). This organization was different during wakefulness with strong correlation between medium and fast gamma bands and relatively weaker correlations oscillations in theta, spindle and slow gamma bands. Lastly, delta oscillations were negatively correlated with the rest during wakefulness. These findings suggest the state dependent composition of thoughts as evident from state dependent coupling of hippocampal oscillations. In order to study how the hippocampal network transitions on the network state space, I have characterized state transitions during sleep and wakefulness. This allowed to examine the probable future network states to which the hippocampal network oscillations might transition. I found that the probability with which state transition occurs is altered after exploration. This allows the network to perform state transitions in a different manner during post exploration sleep and suggests that the hippocampal network oscillations and its state transitions are plastic in nature. I then characterized the speed with which the hippocampal oscillations cover the network state space. I found that during wakefulness, the speed of coverage is significantly reduced as compared to sleep. I also found a sharp reduction in coverage speed when the network transition from NREM to REM. This suggests the stabilization of network on the state space during transition to REM. This stabilization during REM was associated with increase in power of medium and fast gamma oscillations but not in slow gamma oscillations. This suggests the dichotomous nature of hippocampal gamma oscillations during REM sleep and points towards possible distinct origins and/or regulation of hippocampal gamma oscillations. Lastly, I demonstrate two applications of the network state space. First, I utilized network state space as a canvas to map the activity of hippocampal neurons. I found that cells that have distinct firing patterns during exploration in arena have distinct firing maps on the network state space. Secondly, I characterized and found the alterations in organization of hippocampal oscillations in neuroligin 3 knock-out mice, an animal model of autism.