Gist 5f27eb42f27c58d522f1210fd3659f06

Gist 5c36e7318dd5c85aa743

This visualization displays a 16-slice mosaic of brain MRI scans across three orthogonal planes (x, y, z), with yellow contour lines outlining anatomical labels from a segmentation. The code loads NIfTI brain images and masks via nibabel, masks background data with NumPy masked arrays, and uses nipy’s `plot_anat` and `contour_map` to render slices with matplotlib subplots. It computes bounding boxes from the affine transformation to select meaningful cut coordinates, then arranges the slices in 4×4 grids per plane. The function supports optional contouring and saving each mosaic as a PNG image.

80% 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.

80% match

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.

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.

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 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.

Gist f143713edafac5aa5c62

This visualization presents the results of a two-sample t-test comparing brain activation between MDD and healthy control groups across multiple regions of interest. It displays thresholded z-statistic maps for different brain ROIs using sliced image views with color-coded activation patterns, where hot colors indicate one comparison direction and blues indicate the other. The implementation uses Python with NumPy and NiBabel for image processing and nipy's plot_map for visualization. The code loads NIfTI files, labels connected components above a threshold, and generates labeled anatomical slices.

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.