Depression is a major psychological and neurobiological disorder in society, with a set of diversified symptoms and a complex pathophysiology. Understanding the causes and mechanisms of depression represents a major research challenge, due to the involvement of complex neural circuitry, various neurotransmitters, hormones, intracellular pathways and genetic factors. Neurobiologically realistic computational models allow us to integrate various available data types in a systemic way, account for underlying neural mechanisms, and make predictions of yet to be discovered biological parameters. This thesis focuses on the interaction of the monoaminergic and orexin/hypocretin systems, as for many years monoamines (serotonin and norepinephrine), and more recently, orexin, have been implicated in the etiology of depression. In particular, a major emphasis is on orexin-monoamine interactions. To assist in understanding the functional connectivity of monoaminergic and orexin systems, the work in this thesis develops and studies biologically based computational models at different levels of complexity .. Initially, hybrid firing-rate type models are presented. The simulation results show that baseline values found in different studies can coexist. Moreover, the influence of orexin's extracellular concentration dynamics has been explored in the circuitry, and correlations between the neuromodulator levels and neural firing activities are predicted. The influence of specific drugs (selective serotonin reuptake inhibitor antidepressants, orexin antagonist) has also been simulated in the neural circuit models. These studies are then followed by the development of a more detailed microcircuit model involving the orexin-serotonin system, where the direct and indirect interactions between the orexin and serotonergic neurons are investigated. In particular, autoreceptors are implemented, the local inhibitory GABAergic intemeurons are included into the model to mediate indirect interactions, and the influence of hypothesised connections and the time scales of their receptor types are studied. The model simulations demonstrate that the microcircuit is more stable if the connection from serotonin neurons to GABAergic neurons local to the orexin neurons is sufficiently strongly excitatory. It is also found that orexin receptors on local GABAergic neurons are not important to the system, and that the latter is resilient to 'knockout' of orexin neurons. Moreover, the connectivity timescales do not affect the steady state of the system but have significant influences on its transient behaviour. Finally, based on known electrophysiological properties, orexin and serotonergic spiking neuronal models are developed. More importantly, biologically realistic serotonin and orexin receptor induced currents are developed for the first time, allowing inter-neuronal interactions in a spiking neuronal network model. The model faithfully replicates the known spiking behaviour of the neurons, including the current-frequency curves. The simulation results also showed that the baseline activity and concentration levels found in different experiments can co-exist, just as in the previous more coarse-grained hybrid firing-rate model. Moreover, hypothesized model parameters of orexin concentration-vs-conductance curve (e.g. shift and slope factors) can significantly affect the network behaviour when their values are less than that of control. In addition, by simulating the 5-HTIA agonist effect, the model suggests that higher doses can lead to more synchronized slow oscillations of spiking activity among the 5-HT neuronal population. Overall, the neural circuit models developed at various levels shed light on the complex relationship between orexin and serotonergic systems, and contribute to bridging the gap between neuronal activities and behaviour.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:602385 |
| Date | January 2014 |
| Creators | Joshi, Alok |
| Publisher | Ulster University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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