Research Associate in Data Science and Computational Neuroscience, Ulster University

My research interests focus on the intersection between computer science, artificial intelligence, machine learning, data science, neuroinformatics, and neuroscience. The main aim uses computational modelling and simulation over multiple time-scales to understand the link between functional connectivity among the neural circuits and pathophysiology of brain disorders and diseases. Additionally, I am interested in the analysis and interpretation of complex biological interacting over multiple levels of the temporospatial organisation.

My recent works can be summarised by the following research projects:

Project: A novel computational framework for predicting the conversion from early stage mild cognitive impairment to Alzheimer’s disease.

Overview: The project indents to develop a computational framework to integrate and analyse heterogeneous large biological and clinical datasets of dementia to estimate the conversion from early stage mild cognitive impairment (MCI) to Alzheimer’s disease (AD) of an individual. 

Project: To explore the micro-circuitry of Dorsal raphe nucleus and investigate what role it plays in the pathophysiology of various psychiatric disorders.

Overview: I am working on a collaborative project with neuroscientists from the University of Oxford. I am using data science techniques to analyse and interpret experimental datasets recorded simultaneously from multiple brain areas (e.g., dorsal raphe, frontal cortex). The major aim of the project is to understand the complexity of a specific brain area (dorsal raphe) and its links to psychiatric disorders

Project: Exploration of neural circuit interaction between Prelimbic cortex and Dorsal raphe nucleus: A real-time network simulation study using SpiNNaker.

Overview: I’m working on the large-scale model (~60,000 neurons) of the interaction between brain regions (Dorsal Raphe Nucleus and Prelimbic Cortex), as these brain areas play a key role in the regulation of many cognitive functions. This project is in collaboration with colleagues from the SpiNNaker software team. The major goal of this study is to investigate the role of different synapses (e.g., serotonin, glutamate) in the generation of slow and fast oscillations.

 

Project: Analytical methods to analyse and interpret the complex neurobiological data.

 

Overview: I have developed analytical methods to analyse and interpret complex biological (in-vitro) data interacting at multiple levels (e.g., network). This includes analysis of time series data related to a bioluminescence intensity of distinctly localised cells in the specific brain area (e.g., Suprachiasmatic nucleus). This method is very convenient in comparing the period, amplitude and synchrony among the clock cells with or without drug applications or across wild-type (WT) and animals with altered clock function. Additionally, I have  analysed multiunit neuronal activity in brain slices for WT and specific genotype animals, across day and night, and also explored the complex drug effects (e.g., excitatory, inhibitory) on the electrical activity of a network. Both these analytical methods provide in-depth insight into the biophysical nature of clock cells and are very useful for experimentalists.

​​Project: Neural Circuit Modelling of the Orexin/Hypocretin System with Implications for Clinical Depression

Overview: The major focus of this work was to develop new computational models to study the interaction of the monoaminergic and orexin/hypocretin systems, as for many years the monoamines, and more recently, orexin, have been implicated in the etiology of depression. I have developed biologically plausible computational models with various levels of complexity to provide insights and predictions on the neural circuit functions of these systems. They include coarse-grained neuronal population, and spiking neuronal network type models. The computational models were developed by integrating in-vitro and in-vivo biological data from various experiments. These neural circuit models shed light on the complex relationship between orexin and serotonergic systems and contribute towards bridging the gap between neuronal activities and behaviour.

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