Gist d8aa62e81e12e25d45a3

Gist 560eb7758354bb52ada6

This notebook analyzes MRI volumetric reliability and power for a multisite study, showing how between-site reliability and study size affect the ability to detect genetic effects on brain volumes. It uses statsmodels and NumPy for statistical calculations, pandas for data tables, and matplotlib with pylab inline mode to render reliability metrics and power curves. The visualization includes tables of intraclass correlation coefficients and power curve figures for varying site numbers, effect sizes, and reliability thresholds.

81% match

Gist 2a60ff0d906a45ea03f0

This example visualizes statistical power analysis for multisite neuroimaging studies, plotting curves that show how power varies with different parameters like effect size, sample size, and number of sites. The notebook uses Python with `%pylab inline`, seaborn for styling, and custom functions from `euro.stats` such as `power_solver` and `get_power` to compute power values. It also integrates R via `rpy2` for statistical functions, and uses pandas DataFrames and NumPy arrays to handle data and generate the curves.

81% match

Gist 8e62843a6ffa1dfd7293

This visualization shows how brain tissue volumes (CSF, WM, GM, and total brain) change across multiple time points and scanner types, with each subject’s measurements pivoted into a comparative table. The code uses pandas for data wrangling, including `pivot_table` and column aggregation, and relies on `seaborn` and matplotlib’s `%pylab inline` for plotting. Data comes from SIENAX report files parsed into a DataFrame, then normalized to cubic centimeters. The final table displays time point and scanner metadata alongside the computed tissue volumes.

Gist 0c28ed2b6d7235146517

This example visualizes intraclass correlations for within- and between-site reliability of brain volume estimates from Freesurfer and SIENAX across multicenter MS study scans. It presents a pandas DataFrame table of scanning parameters and reliability metrics for nine scanners at six sites. The code uses IPython’s `%pylab inline` for interactive plotting, pandas for tabular data handling, and nipype’s `loadpkl` to load the analysis results. The visualization is built in a Jupyter notebook with Vue integration, showing how heterogeneous acquisition protocols affect measurement consistency.

Gist 39800f8b9a6191fff320

This visualization demonstrates the relationship between brain scan variables and subject metadata across a multi-site study. It uses pandas for data loading, seaborn for statistical plotting, and matplotlib for inline rendering within a Jupyter notebook. The code reads a FreeSurfer CSV dataset, applies LeaveOneOut cross-validation from scikit-learn, and fits models with statsmodels formula API. It displays sample rows of the dataframe to show the structure of the neuroimaging data.

Gist 9557217

This example simulates brain-volume measurements for healthy controls and multiple-sclerosis patients across two scan protocols, using synthetic data generated with NumPy and Statsmodels. It shows how measurement noise and protocol-related biases affect the relationship between age, disability scores, and brain volume. The visualization displays the simulated distributions and calibration results, with matplotlib and prettyplotlib rendering static plots. The code relies on inline plotting via `%pylab` and custom utility functions for distribution and calibration analysis, though no interactive or WebGL output is present in the provided notebook cells.

Gist 439dddffaa8ff758dc07

The visualization compares brain structure volumes across subjects before and after a software upgrade, using pandas to load, merge, and reshape CSV data from cortical and subcortical parcellation files. It calculates coefficients of variation per subject and region, then computes percentage volume changes relative to a reference measurement, isolating specific regions of interest for display. The code relies on pandas operations such as `pd.melt`, `pivot_table`, and `groupby` with lambda functions to structure the data for plotting.

79% match

Gist d29f14977a2b747e9354

This interactive slideshow presents a statistical analysis of MRI volumetric reliability and power across non-standardized multisite studies, focusing on multiple sclerosis research. It shows how between-site reliability varies and demonstrates that aggregating data from 7–12 sites enables detection of small disease effects. The notebook uses Python with statsmodels, NumPy, and IPython.display, rendering inline matplotlib plots via the `%pylab` magic command. The WebGL-based Vue interface displays the notebook cells as slide-style content, with tables and figures illustrating ICC values, power curves, and calibration results for gray and white matter volumes.