Skip to main content
100%

Gist 39800f8b9a6191fff320

✓ Published0🌍 Public
Aakeshavan
Last edited Feb 6, 2016
Created on Feb 6, 2016

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.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 1ac505c846b09faa7bc4

This visualization demonstrates the variability of FreeSurfer-derived brain structure measurements across different MRI scanner models and sites. It plots the volume values for anatomical regions like the lateral ventricle and cerebellum, revealing distribution patterns and potential batch effects. Built with a Vue framework, the notebook uses pandas for data manipulation, matplotlib and seaborn for statistical plotting, and processes a CSV file containing subject-level neuroimaging metrics. The display emphasizes comparative analysis of brain region volumes across scanning conditions.

Aakeshavan
87% match
Loading thumbnail…

Gist 42b954f71c1668a26572

This example visualizes longitudinal Freesurfer brain imaging data, showing how volumes of cerebrospinal fluid, white matter, gray matter, and whole brain, along with mean cortical thickness, change across time points and scanner types. The code loads CSV tables into pandas DataFrames, aggregates specific anatomical regions into tissue categories, and merges hemisphere thickness values. It uses matplotlib, seaborn, and statsmodels for plotting and statistical modeling, with the data prepared through pandas operations before visualization.

Aakeshavan
84% match
Loading thumbnail…

Gist 593724ef136bade2d45a

This example visualizes longitudinal FreeSurfer brain imaging data, showing how CSF, white matter, gray matter, and cortical thickness change over time and across different scanners. The code uses pandas to aggregate tissue volumes from the asegstats tables, merges thickness measurements from left and right hemisphere parcellations, and displays the resulting dataframe. Seaborn and matplotlib inline plotting are imported for potential statistical visualization, while statsmodels formulas are available for regression analysis of the tissue metrics.

Aakeshavan
83% match
Loading thumbnail…

Gist 9540056

This visualization simulates multi-site MRI data, modeling brain volume across two sites for control and patient groups, and compares three regression strategies for combining site data, including a linear calibration transformation. It shows how model predictions and confidence intervals vary across age and disease metrics. Built in a Jupyter notebook, the code uses NumPy for data generation, statsmodels for ordinary least squares and robust linear model fitting, and matplotlib with prettyplotlib for plotting the results.

Aakeshavan
83% match
Loading thumbnail…

Gist d90eaeea718cdd2db085

This visualization presents the variance of brain volume estimates from Freesurfer 5.3.0 across multiple MRI sites and scanners, using data from a multicenter study of traveling subjects. It shows how T1-weighted scans from different sites compare in terms of acquisition counts and subsequent volume metrics. The example uses Vue.js with WebGL rendering for interactive display. The underlying analysis relies on pandas for data manipulation, NumPy for numerical operations, and Matplotlib with Seaborn styling, as seen in the notebook’s code cells that load demographic, NIfTI, and Freesurfer CSV files.

Aakeshavan
83% match
Loading thumbnail…

Gist 9674955

This example simulates multi-site MRI data to test how brain volume relates to age and disease severity across two scanner sites, comparing calibration strategies for cross-site analysis. It uses a Jupyter notebook with Python, plotting the simulated relationships through matplotlib and seaborn, and applies ordinary least squares and robust linear models via the statsmodels API. The notebook defines governing equations with parameters like scanner scaling and offset, then generates synthetic control and patient cohorts to evaluate how site effects and EDSS scores influence brain volume estimates.

Aakeshavan
82% match
Loading thumbnail…

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.

Aakeshavan
82% match
Loading thumbnail…

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.

Aakeshavan
81% match