Project for BrainHack School 2020
Team Contributors: Hannah Kiesow & Elise Douard
Introduction
Hi! I am a second year PhD student studying computational neuroscience, working in the lab of Dr. Danilo Bzdok at McGill University. My research interests are in social cognition, and I am extremely fascinated with social interaction. My lab focuses on big data in neuroscience using data-driven methods. For my current projects, I mainly use Bayesian probabilistic modelling to analyze my data, using the UK BioBank, one of the world's largest biomedical databases. However I am starting to branch into machine learning and am super eager to apply ML methods in neuroimaging. My experience so far has been working mostly with structural MRI images, but I would love to branch out and work with other neuroimaging modalities.
Looking forward to collaborating with you!
twitter: @hannahmaykiesow
Project Definition
Goals:
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I would ideally like to focus on using machine learning techniques. I would definitely like to focus on getting intuitions behind different ML techniques, and perhaps try out different ensemble methods for prediction
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I also would like to get to know the nilearn API more in depth, especially working with the different atlases for feature engineering.
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Lastly, I think visualization is an extremely important skill to invest in. That's why I want to explore other visualization tools using python such as bokeh or ptit prince.
Progress overview
- So far, I am learning Canonical Correlation Analysis (CCA) so that I can first create my features using two atlases, the Social Brain Atlas and the Harvard Oxford Atlas
Tools
- Nilearn (working with different atlases)
- Machine Learning (sklearn)
- Visualization (ptit prince, seaborn, bokeh)
Data
- UK Biobank data (~10,000 person release)
Deliverables
At the end of this project, we will have:
- a better understanding and intuition of several machine learning algorithms suitable for neuroimaging
- a deeper understanding of feature engineering via atlases
- hopefully made a pretty figure :D
Tools I learned during this project
- Canonical Correlation Analysis