Thesis Project Form

The project focuses on investigating the modulation of neural dynamics and voluntary breathing patterns induced by the psychedelic compound psilocybin. Using high-density physiological sensors in mice, the research aims to identify specific correlations between autonomous respiratory rhythms and brain-wide neural activity during different behavioural states. Understanding these interactions is crucial for the field of neuropharmacology and systems neuroscience, as it provides insights into how pharmacological interventions alter the fundamental coupling between autonomic functions and central nervous system dynamics.

The general objectives and activities are:
1. Designing and optimizing hardware-software interface for the synchronized acquisition of neural and respiratory signals (e.g. surgical implant and hardware synchronization of intracranial thermistors for monitoring respiratory signals with electrophysiological probes).
2. Implementation of an automated pipelines for signal pre-processing including digital filtering, and artifact removal across multi-channel streams.
3. Execution of cross-domain data integration, merging real-time experimental streams with large-scale datasets into a unified analytical framework.
4. Development of computational models to identify and quantify the relationships between autonomous respiratory rhythms and psychedelic-induced neural dynamics.
5. Performing multivariate statistical analysis to classify physiological responses across different behavioural states (e.g. using PCA to decode dynamics during rest vs. movement VS psychedelics state).

The training objectives include:
1. Closed-loop system development – mastering the latency-critical integration of real-time physiological feedback with experimental control hardware.
2. Laboratory hardware integration - acquiring technical proficiency in the configuration, calibration, and troubleshooting of laboratory hardware chains (sensors, thermistors and amplifiers).
3. Advanced Digital Signal Processing (DSP) - learning to implement time-frequency analysis and phase-detection algorithms for respiratory and high-density neural data.
4. Computational modeling of biological systems with multimodal datasets using MATLAB.
5. Electrophysiological data management - learning the specialized data formats (i.e. nwb) and signal integrity verification in large-scale recordings.