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cpm_tutorial

Python-based tutorial on Connectome-based Predictive Modeling (CPM), prepared for Georgetown Methods Lab, December 2019.

This repo contains functional connectivity data for n = 337 subjects in a variety of task conditions from the Human Connectome Project, as well as associated behavioral data. The Jupyter notebook cpm_tutorial.ipynb demonstrates how to use CPM to predict behavioral measures from whole-brain functional connectivity, with a focus on exploring how rest and different task states differentially predict trait variables.

If you use CPM in your research, please consider citing the following papers:

Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. (2015) Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18: 1664–1671. [link]

Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, Constable RT. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 12: 506-18. [link]

Enjoy!

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Python-based tutorial on Connectome-based Predictive Modeling (CPM), originally prepared for Georgetown Methods Lab.

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