Gist fd5980c016748347a3ed

Gist 1e296721b61bae725016

This visualization displays functional connectivity between brain regions as a circular node-link diagram, with edges colored by PLS regression coefficients relating resting-state fMRI correlations to disability scores (EDSS) in multiple sclerosis patients. The code converts correlation values to z-scores via Fisher transform, then applies PLSRegression to extract predictive components. Using MNE-Python’s `circular_layout` and `plot_connectivity_circle`, it arranges region names around a ring, styles nodes with seaborn color palettes, and encodes edge weights through a custom colormap with a colorbar. The underlying data is loaded from a CSV of freesurfer parcellations and connectivity metrics.

80% match

Gist f89f4fd152dcae710273

This visualization displays the correlation between resting-state fMRI time series across selected brain regions, represented as a matrix of pairwise connectivity values. It computes Pearson correlations from parcellated signal data, then organizes the resulting edge strengths into a tabular format per subject. The code uses pandas for data manipulation, numpy for numerical operations, and seaborn for potential statistical plotting, with region names drawn from FreeSurfer cortical and subcortical parcellations.

78% match

Gist cd42289402eda15e23c7

This visualization shows correlations between brain regions from resting-state fMRI data, focusing on subcortical and cortical areas implicated in Parkinson’s disease. It displays pairwise connectivity values as a correlation matrix, computed from FreeSurfer parcellation time series. The code uses Python with pandas for data handling, seaborn for statistical plotting, and matplotlib’s inline mode for rendering. It also employs itertools to generate all region pairs, and NumPy’s genfromtxt for reading the input files, with clinical follow-up data integrated by visit type.

77% match

Gist 9556811

This example simulates brain volume and disability data for healthy controls and multiple sclerosis patients across two sites, generating synthetic datasets with site-specific EDSS distributions. It uses Python with statsmodels and matplotlib via prettyplotlib to plot the simulated distributions, including scatterplots and regression fits that illustrate relationships between age, brain volume, and EDSS scores. The visualization is part of a Vue-based gallery, but the core computation and plotting rely on numpy and statsmodels for statistical simulation and visualization.

77% 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 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.

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.

Gist e5affea97473980182b59a9d4efb945e

This example visualizes the relationship between brain connectivity measures and disability in multiple sclerosis patients. It fits ordinary least squares regression models predicting the EDSS score from diffusion imaging metrics and demographic covariates across each brain connection, then displays the resulting p-values and R-squared values in a table. The code uses pandas for data handling, statsmodels' `ols` API for the regression models, and seaborn with pylab inline for plotting.