Cholinergic Modulation of Network Activity and Applications in Sleep, Memory and Anesthesia

In the nervous system, neurons work in tandem with a full range of complex signaling chemicals known as neuromodulators which tune neuron function to fit different behavioral tasks by varying temporal firing of the neurons. The aim of this dissertation is to use biophysically-based in-silico modeling to study how acetylcholine (ACh), one of the major neuromodulatory molecules in the brain, through its effect on cellular firing behavior, can affect brain function. As ACh modulates, among others, m-type voltage gated potassium currents through muscarinic receptors, neurons change their firing behavior in response to extracellular input. These changes are exhibited in both the average neuron firing rate as well as differences in phase relationships between coupled neurons. Our modeling results focus on elucidating how these cellular-level changes lead to modulation of network dynamics that can influence brain network functions. First, we investigated the influence of ACh on neuron firing behavior and its network-wide implications in the transition between rate and phase coding of information. We used direct current input as a proxy for the effects of external stimuli on the network and found that for high ACh conditions, increased neural gain causes a dispersion of firing rates in response to the different magnitudes of these inputs. Additionally, ACh-induced increased neural responsiveness to input allowed neurons to persist in firing to maintain a representation in frequency space (rate coding). Alternatively, in low ACh conditions, phase coding was promoted through reduced frequency spread, increased neural resonance, and augmented propensity for synchronization. Next, we analyzed how ACh-induced changes in firing behavior can contribute to the formation and consolidation of memories during non-rapid eye movement (NREM) sleep. Combining reduced neuronal network models and analysis of in vivo recordings, we tested the hypothesis that ACh-induced neuromodulatory changes during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide temporal firing patterns, with the temporal order of neuronal firing dependent on their intrinsic mean firing rate during wake. We found, in both reduced models and in vivo recordings from mouse hippocampus, that the temporal order of firing among neurons during NREM sleep initially reflects their relative firing rates during prior wake. We also showed that learning-dependent reordering of sequential firing in the hippocampus during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network, similarly as has been reported in multiple brain circuits across periods of sleep. Finally, we investigated changes in electrophysiological activity associated with anesthesia and showed that differences in synaptic transmission properties can emulate the observed alteration of neural firing patterns observed during states of anesthesia. We then proposed how these effects can be ameliorated by ACh-induced changes to the muscarinic receptor-based potassium currents. Specifically, we showed that increasing the influence of the muscarinic-mediated ACh effects under simulated anesthesia leads to an increase in firing rate and neural interaction measures, showing a population level reversal of anesthesia-induced changes in activity. We found that the simulated ACh reversal restored neurons’ spiking activity, functional connectivity, as well as other measures of pairwise and population interactions.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/176587/1/eniwbola_1.pd

Modeling cortical synaptic effects of anesthesia and their cholinergic reversal.

General anesthetics work through a variety of molecular mechanisms while resulting in the common end point of sedation and loss of consciousness. Generally, the administration of common anesthetics induces reduction in synaptic excitation while promoting synaptic inhibition. Exogenous modulation of the anesthetics' synaptic effects can help determine the neuronal pathways involved in anesthesia. For example, both animal and human studies have shown that exogenously induced increases in acetylcholine in the brain can elicit wakeful-like behavior despite the continued presence of the anesthetic. However, the underlying mechanisms of anesthesia reversal at the cellular level have not been investigated. Here we apply a computational model of a network of excitatory and inhibitory neurons to simulate the network-wide effects of anesthesia, due to changes in synaptic inhibition and excitation, and their reversal by cholinergic activation through muscarinic receptors. We use a differential evolution algorithm to fit model parameters to match measures of spiking activity, neuronal connectivity, and network dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that facilitating muscarinic receptor effects of acetylcholine on top of anesthetic-induced synaptic changes predicts the reversal of anesthetic suppression of neurons' spiking activity, functional connectivity, as well as pairwise and population interactions. Thus, our model predicts a specific neuronal mechanism for the cholinergic reversal of anesthesia consistent with experimental behavioral observations

Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation.

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons' firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories