Gist acc818edc967714bcbb49613956eb4cb

Gist 9678802

This interactive visualization simulates MRI brain volume data from two scanner sites, comparing healthy controls and multiple sclerosis patients across age and disease severity. It applies linear calibration transformations between sites and tests statistical models, including OLS for controls and robust linear regression for patients. Built with Vue and WebGL, the notebook uses Python with matplotlib, seaborn, and pandas to generate and analyze the synthetic datasets, displaying distributions and regression fits interactively.

77% match

Gist 237fa77ce0984eaedbfd

A Vue-based visualization of brain MRI segmentation data, this example loads anatomical and labeled brain volumes using nibabel and renders them with nipy's OrthoSlicer. The notebook applies a threshold to isolate specific cortical regions, remaps label values, and displays the result as a colored overlay on the anatomical scan using an HSV colormap, producing a single axial slice visualization of segmented brain structures.

76% match

Gist b67c03f2dc7fc6d38bf4b732214d6589

An interactive 3D volume rendering of structural MRI brain data, created with IPyvolume and its pylab interface. The notebook loads NIfTI images via nibabel, first displaying a defaced scan with translucent opacity levels, then a full T1-weighted image from OSF. The visualization uses `p3.figure` to configure camera properties, hide axes and box, and set coordinate limits, with `p3.volshow` rendering the volume. Users can rotate and inspect the brain in three dimensions through the Jupyter widget.

76% match

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 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 156e9d479d1f72e7656463843af8fd6d

This notebook demonstrates how to convert Mindboggle neuroimaging outputs into a format compatible with Mindcontrol, a web-based visualization tool. It shows the process of generating structured entries that link brain segmentation results, T1-weighted MRI images, and associated metrics for each subject. The code uses Python libraries including pandas for handling demographic data, nipype for JSON file management, and custom functions to extract file paths and create metadata entries. The visualization approach relies on organizing subject-specific data into JSON-ready dictionaries that Mindcontrol can consume for interactive display.

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.

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.