Thesis title: 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.