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Gist 65e3c6b08f24fc0f33141f71ce37e702

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Aakeshavan
Last edited May 3, 2016
Created on May 3, 2016

This example visualizes the relationship between imaging-derived connectivity metrics and clinical measures in multiple sclerosis patients by computing global efficiency from weighted brain graphs. The notebook applies PCA to combine multiple imaging features per connection, then calculates efficiency values for each subject using the Brain Connectivity Toolbox (bct.efficiency_wei). The resulting efficiency dataframe is merged with demographic and clinical data using pandas, with Seaborn and matplotlib available for plotting, though the visualization output itself is not displayed.

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