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Gist d18d5df2cd9e4f2adf27

✓ Published0🌍 Public
Aakeshavan
Last edited Aug 19, 2015
Created on Aug 10, 2015

This notebook analyzes longitudinal brain-imaging data by parsing SIENA reports from multiple scanner types (GE, Ph, Si) across time points. It shows summary statistics of percentage brain volume change (PBVC), including mean, standard deviation, and root mean squared deviation, grouped by scanner in a Pandas DataFrame. The code uses glob to collect report files, pandas for data aggregation, and NumPy for norm calculations. It also gathers associated GIF/PNG images for a quality-check webpage, though the visualization itself is primarily tabular statistical output.

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