Thesis Project Form

The project is motivated by the need to better understand the collective dynamics governing neural communication in healthy and pathological brain states. By combining high-density electrophysiological recordings with neuronal avalanche analysis and criticality theory, the study aims to identify quantitative biomarkers capable of characterising alterations in large-scale neural activity associated with neurological disorders. The application domain includes computational neuroscience, neural signal processing, and biomarker extraction from in vivo electrophysiological recordings.

The project involves analysing Neuropixels recordings from freely moving mice, looking at single-unit activities across 384 simultaneous channels. The goal is to investigate the critical dynamics of neural signatures during various behaviours and brain states, particularly in the context of neurological disorders such as temporal lobe epilepsy, focal cortical dysplasia, schizophrenia, and dementia. It focuses on the analysis of neuronal avalanche dynamics in order to understand the discrepancies in neural communication between a healthy brain, in this case a mouse brain, and a brain affected by pathological conditions. To characterise the underlying neural state, several neuronal avalanche statistics are employed, including the branching parameter, shape collapse analysis, and other criticality-related metrics.

How to perform in vivo experiments, how to analyze the neural data, and how to compute biomarkers of interest from Electrophysiological recordings from HD-MEA in vivo. All neural data analysis was performed in MATLAB using additional toolboxes such as Kilosort and mrestimator in Python.