Gist f143713edafac5aa5c62

Gist 5f27eb42f27c58d522f1210fd3659f06

This visualization displays brain imaging results for a single subject, comparing fractional anisotropy (APM), mean diffusivity (MD), and B0 maps before and after a procedure. It automatically locates the world coordinates of the maximum APM difference and generates six static PNG plots, each showing a statistical map overlaid on a pre-APM background. The code uses Nilearn’s `plot_stat_map` and `plot_anat` functions with a jet colormap and black background, alongside NumPy for coordinate transforms and Matplotlib for figure styling.

78% match

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.

76% 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.

75% match

Gist 51ee3c0f179008ff4a15

This visualization demonstrates white-stripe normalization of a T1-weighted brain MRI, overlaying a colored white-stripe mask on the normalized anatomical image. The notebook imports nipy’s `plot_map` for rendering, and uses scikit-image’s `threshold_otsu` to mask background voxels. It processes a control subject’s N4-corrected, rigidly aligned image via an R script (WhiteStripe package) called through rpy2, then visualizes the mask at the stripe’s centroid coordinates derived from the affine transform. The rendering uses a HSV colormap for the stripe overlay.

73% match

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.

Gist 5ba4f4c088bc7690ffc7cc7417736203

The visualization displays resting-state fMRI data across seven brain networks, showing how functional connectivity patterns are organized in the brain. It uses a force-directed layout to position nodes representing brain regions, with edges indicating connectivity strength between them. The code relies on numpy and scipy for data processing, while the rendering pipeline employs matplotlib for generating the network plot, with pylab enabling interactive plotting in a Jupyter notebook environment.

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 d8aa62e81e12e25d45a3

This visualization presents statistical calculations for brain imaging data, comparing reliability metrics across different analysis methods. The notebook uses Python with rpy2 to bridge R and Python, calling the `psych` package for intraclass correlation coefficients and `statsmodels` for additional statistical modeling. It generates plots with matplotlib and seaborn, applying custom chart-cleaning functions to remove unnecessary axes and gridlines. The code also leverages pandas for data manipulation and NumPy for z-score normalization, with output rendered inline through IPython’s `%pylab` magic.