Gist 5c928c271ee8dd212aef9236c69ed525

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Gist 5c928c271ee8dd212aef9236c69ed525

✓ Published0🌍 Public

Last edited May 3, 2016

Created on May 3, 2016

The example shows a PCA-based analysis of neuroimaging and clinical data from a multiple sclerosis dataset, displaying how imaging-derived features relate to demographic and clinical variables. It computes principal components from diffusion and magnetization transfer metrics, then explores correlations with clinical measures like EDSS and timed walk. The notebook uses pandas for data handling, scikit-learn's PCA and RandomForestRegressor for modeling, and statsmodels for statistical testing, with seaborn and matplotlib providing visualization support.

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