Gist 8dd27e910a1deefdc332bbac3224b4ca

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.

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Gist fb72ad147c42cdb900ded0f4b433b0f5

This visualization evaluates lesion segmentation results by plotting false positives (FP) against false negatives (FN) for each exam. It retrieves subject data from a MongoDB collection, filters entries reviewed by a specific clinician, and aggregates counts of FP and FN annotations into a pandas DataFrame. The notebook uses matplotlib through `%pylab inline` for plotting, with a custom `remove_crap` function to clean chart spines and ticks. Data comes from the `subjects` collection in a local MongoDB instance.

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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.

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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.

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Gist d4b2a37f3ec9a90469c8

This visualization simulates longitudinal brain-imaging measurements across multiple subjects and time points, showing how percentile values (e.g., MTR_25) vary and are modeled with ordinary least squares regression per subject. It first computes subject means, average standard deviations, and true beta coefficients from the input CSV, then generates synthetic data for three time points using normal distributions. The code relies on pandas for data manipulation, statsmodels formula API for OLS fitting, numpy for random sampling, and seaborn for plotting.

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.

Gist 935ce0911aeed011e75e

This visualization presents a power analysis for a multisite MRI study, showing how the number of sites and between-site reliability affect the ability to detect genetic effects on neuroanatomical volumes. It displays power curves and tables that illustrate the minimum detectable effect sizes and required sample sizes across varying reliability levels. The example uses Vue for the interface and WebGL for rendering, with data derived from the accompanying Jupyter notebook that employs statsmodels and numpy for statistical calculations. The visualization emphasizes the impact of non-standardized imaging protocols on statistical power.

Gist 5dd4cc899220d90a2606a1f3376b5f6f

This visualization demonstrates signal-to-noise ratio (SNR) calculation from ADNI phantom MRI data using a Jupyter notebook. It loads NIfTI images with nibabel, applies Otsu thresholding and histogram-based binning to segment structures, then labels connected components with scipy.ndimage. The workflow includes FOV validation, quaternion-based RAS coordinate transformation, and voxel volume checks using custom functions like `bin_data` and `clean_labels`, with matplotlib and prettyplotlib used for plotting. The notebook processes phantom scans to identify regions meeting required volume thresholds.