This year I just finished my undergraduate studies in neuroscience and I am now starting a master degree this summer at the Douglas institute. For my master, I will be investigating functional connectivity in a mouse model of neuropsychiatric disorder, the model of maternal immune activation (MIA), and this based on resting-state fMRI scans acquired in mice under anesthesia. The MIA model consists in triggering an immune response in pregnant dams during gestation, which has been shown to reproduce various phenotypic features of schizophrenia and autism in the offsprings throughout their development.
To study functional connectivity in this model, I will need to develop appropriate protocols for scan acquisition, anesthesia induction and maintenance, and in particular learn how to appropriately conduct pre-processing of fMRI data and subsequent analysis of functional connectivity.
At brainhack school, I intend to learn to apply fMRI preprocessing pipelines to resting-state fMRI datasets and conduct functional connectivity analysis. More specifically, this includes:
- Learn to use and build containers using Docker and run containerized applications with Singularity on Compute Canada
- Learning to implement NIAK, understand its documentation, and then maybe learn Nipype as well for more a more flexible access to preprocessing tools
- Learn the good practice of QC procedures
- Learn Nilearn for data visualization, manipulation of fMRI data in python, and conducting functional connectivity analysis
- Learn to conduct independent component analysis (ICA) and seed-based correlation analysis to map the default mode network on a standard rs-fMRI dataset
- If I have the time, try to conduct a similar analysis on an online dataset of fMRI data from a mouse study