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In this assignment, we’ll walk you through analyzing some brain graphs by creating a program that will learn the difference between graphs of different phenotypes

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Graph Analysis

Applications and Discovery on Brain Networks - REVISED

Introduction

You may be wondering what happens after all of the time and money is spent on creating a connectome. In this assignment, we’ll walk you through analyzing some brain graphs by creating a program that will learn the difference between graphs of different phenotypes (attributes of an organism). So for example, you could learn which brain networks are likely to belong to a male or female subject.

Assignment

Please create a 1-2 page submission that covers each of the following 6 criteria. You should also include any code that you use to generate your results with your submission (e.g., a copy of your colab notebook.

Criteria 0 (20 points) Scienceinthe.cloud is not being actively maintained.  That’s a bummer for reproducible science. Instead we’ll investigate another very successful reproducible neuroscience project. Visit http://thunder-project.org/ and run the live demo. This should launch a live jupyter notebook in your browser using binder. Exe- cute the tutorials and report on what you’ve learned (how did it go, was it reproducible, easy to follow). Make at least 3 suggestions for improvement and 3 things that worked well. Please also read their paper (Open source tools for large-scale neuroscience) for context here: https://www.ncbi.nlm.nih.gov/pubmed/25982977)

  • Criteria 1 (25 points) compute and visualize mean connectomes for all of the data

Criteria 2 (25 points) compute and visualize class conditional connectomes (e.g., male and female subpopulations)

  • Criteria 3 (30 points) compute 5 different covariates related to graphs

Criteria 4 (20 points) interpret meaning of covariates, with some hypothesis connect- ing to neuroscience

  • Criteria 5 (30 points) build classifier and obtain classification score at 70% or higher

  • Total 150 points

Instructions for Analysis Environment

To greatly simplify this assignment, we’ve created a Jupyter notebook in Google Colab with detailed instructions; the original instructions requested you to set up a Docker-backed python environment on your local computer. This will instead run self-contained browser. If you are interested in learning more, please get in touch! Go here to get started:

github.com/wrgr/graph-explorer/blob/master/graph_explorer_2020.ipynb

Click on the colab link to launch a copy of the notebook in your own space.

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