Skip to main content
100%

Gist f28ea71aa261d47cf5277019bc65887e

✓ Published0🌍 Public
Aakeshavan
Last edited Jul 11, 2016
Created on Jul 11, 2016

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.

AI-generated description

Similar vizzes

Loading thumbnail…

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.

Aakeshavan
75% match
Loading thumbnail…

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.

Aakeshavan
75% match
Loading thumbnail…

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.

Aakeshavan
74% match
Loading thumbnail…

Gist 6857a0243ad24b95924f

This example uses pandas and Matplotlib within an IPython notebook to aggregate and compare FreeSurfer brain segmentation statistics across multiple CSV files. It processes volume, area, thickness, and aseg measurements, grouping them by subject ID and averaging values to produce normalized and unnormalized output tables. The code merges separate CSV files, collapses left/right hemisphere labels into combined metrics, and then saves the resulting aggregated data for each measurement type.

Aakeshavan
73% match
Loading thumbnail…

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.

Aakeshavan
72% match
Loading thumbnail…

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.

Aakeshavan
71% match
Loading thumbnail…

Gist 593724ef136bade2d45a

This example visualizes longitudinal FreeSurfer brain imaging data, showing how CSF, white matter, gray matter, and cortical thickness change over time and across different scanners. The code uses pandas to aggregate tissue volumes from the asegstats tables, merges thickness measurements from left and right hemisphere parcellations, and displays the resulting dataframe. Seaborn and matplotlib inline plotting are imported for potential statistical visualization, while statsmodels formulas are available for regression analysis of the tissue metrics.

Aakeshavan
71% match
Loading thumbnail…

Gist 225ed5c6d9fcd6b9b720

This notebook analyzes FreeSurfer metrics from a brewery database, focusing on EUROTRIP study subjects. It melts the dataframe to iterate over variables and visualizes scanner and institution distributions using pandas and matplotlib’s inline plotting. The code filters by study tag, extracts sequence identifiers from subject IDs, and uses statsmodels for potential statistical modeling. It demonstrates a data-cleaning and exploratory workflow for neuroimaging quality metrics.

Aakeshavan
71% match