Gist 156e9d479d1f72e7656463843af8fd6d

Gist 4bbeb39fd0c727966cbbac7d8b41ebc1

This notebook demonstrates a neuroimaging workflow that processes anatomical T1 brain images from five subjects in the ABIDE dataset. It uses FSL’s BET to create brain masks, FAST to segment gray matter, white matter, and CSF, and nilearn’s plotting tools to overlay masks and probability maps on the original scans. The code builds a nipype workflow with DataGrabber, DataSink, IdentityInterface, and Function nodes, then runs it iteratively over all subjects and visualizes the workflow graphs.

76% match

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 f28ea71aa261d47cf5277019bc65887e

This example shows how to update a MongoDB database with neuroimaging metrics derived from FreeSurfer, using a JSON file as the data source. The notebook loads brain volume measurements and associated demographic metadata, then prepares them for database insertion. It uses pandas for data manipulation, nipype's `load_json` and `save_json` for file handling, and pymongo to interact with the MongoDB collection. The visualization highlights the structure of FreeSurfer-derived brain metrics alongside study-specific metadata fields.

Gist 59ae1a59826b5b7e09a35c922852cb2b

This example prepares and summarizes a dataset of brain MRI slice images. The notebook loads a metadata CSV into a pandas DataFrame and verifies that all referenced image files exist. It then groups the data by subject and slice direction, counting the number of slices for each binary label (0 or 1) with `groupby` and `apply`, before restructuring the result with `unstack`. The output is a cross-tabulated summary showing slice counts per subject, direction, and label.

Gist acc818edc967714bcbb49613956eb4cb

The dashboard displays MRI volumes alongside scalar summary metrics, linking clinical or behavioral data to brain images for quality control. Users select subjects by histogram brushing to view corresponding volume slices, annotate findings, and edit voxels directly in the browser. The web app uses Python-generated JSON configurations to define database entries and UI elements, with images referenced as pointers. The notebook embeds the live dashboard via an iframe pointing to a local server, demonstrating modular pipeline integration.

Gist f8ef22f4884653740625923059e1acfb

This example processes brain scan sprite images for use in a web-based visualization. It loads PNG files from a local directory, replaces pure black pixels with white, and then applies histogram equalization and autocontrast using Pillow’s `ImageOps` and `ImageEnhance`. The cleaned images are saved to a new path, with the code iterating over a list of subject IDs read from a text file via `np.genfromtxt`. It reports completion or errors for each subject, demonstrating a batch image-preprocessing pipeline for preparing neuroimaging data.

73% match

Gist 61f729d5fdcda29e57ebc8ec0b06652f

This example generates image tiles from 3D neuroimaging data, converting MRI volumes into 2D slice images along a specified axis. It loads NIfTI files with nibabel, reorients the data to IPL coordinates, and renders each slice as a grayscale PNG using matplotlib. The code also creates JSON manifests that record slice indices, mask information, and file paths, enabling tile-based visualization of brain scans.

73% match

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.