AbstractThis Ph.D. thesis contributes towards the neural computational modelling and analysis, and functional connectivity analysis of the serotonergic system. The thesis starts with a concise review of the known neurobiological functions of the serotonergic system. Different experimental measurements of the system are described, with focus on electrophysiological, optogenetic and voltammetry recordings. Further, the neuronal signalling of serotonergic systems under reward and punishment tasks are described. This is followed by a brief review on computational modelling of the serotonergic system, with focus on mechanistic and biologically based models. Then, signal processing and data analytical approaches of electrophysiological data are discussed. While reviewing these, research questions are formulated regarding the serotonergic system. It is amply clear from the reviews that, it is not known whether neural circuits encompassing serotonergic neurons can be degenerate (i.e. different structures performing the same functions), and how population of serotonin neurons in the dorsal raphe nucleus interact with themselves and with the cortex.
Following the literature review are three original contributing chapters. In the first contributing chapter, biologically based mean-field network models of serotonergic and dopaminergic neural interactions under reward and punishment tasks are developed and simulated to evaluate the possibility of network structure degeneracy. Non-serotonergic and non-dopaminergic neural populations are considered to evaluate multiple possible indirect serotonin-dopamine connections. The modelling results reveal the possibility that serotonin-dopamine neural circuits can be degenerate, at least under reward/punishment tasks. In the next contributing chapter, the stability of these degenerate neural circuits under tonic and phasic activity modes are evaluated using dynamical systems theory. The analyses show that all the considered degenerate neural circuits are stable in both activity modes. In the third contributing chapter, signal processing and analysis are performed on electrophysiological data (neuronal spike trains and electrocorticography, ECoG) from experimental collaborators. In particular, analyses were conducted on ECoG activities in the frontal cortices and the visual cortex, and neuronal firing activities from the dorsal raphe nucleus (DRN), a main source of serotonergic neurons. Then coherence-based method is used to identify the functional connectivity among simultaneously recorded neurons in the DRN, and between the DRN neuronal activity and the ECoG activity. The coherence analyses show that interactions of the DRN neurons are generally weak and sparse, and that the slow-firing DRN neurons (putative serotonergic neurons) exhibit relatively stronger interactions with each other. Further, unlike the strong corticocortical ECoG interactions, the DRN neuronal to ECoG interactions are generally weak, and that slow, regular firing DRN (putative serotonergic) neurons have relatively stronger interactions with the right frontal ECoG activity. Finally, this thesis concludes with a discussion on all the chapters and proposed future work.
|Date of Award||Apr 2023|
|Sponsors||Research Challenge Fund|
|Supervisor||Girijesh Prasad (Supervisor) & Kongfatt Wong-Lin (Supervisor)|
- Neural circuit computational modelling
- Dynamical systems analysis
- Dorsal raphe nucleus DRN
- Ventral tegmental area VTA
- Reward and punishment
- Dorsal raphe nucleus
- Neuronal firing activity
- Cortical oscillations
- Cortico-subcortical coherence
- Neuronal spike correlation