Gist f89f4fd152dcae710273

100%

Gist f89f4fd152dcae710273

✓ Published0🌍 Public

Last edited Oct 21, 2014

Created on Oct 21, 2014

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.

AI-generated description

Similar vizzes

Loading thumbnail…

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.

Aakeshavan

90% match

Loading thumbnail…

Gist 64d00f92b66ebdebeffb

This example visualizes functional connectivity between brain regions by computing and displaying Pearson correlation matrices from resting-state fMRI time series. The notebook loads parcellated time-course data for multiple subjects, maps region names from summary statistics files, and uses pandas `DataFrame.corr()` to generate correlation matrices. It then renders the matrices as tabular heatmaps with Matplotlib’s inline plotting, showing pairwise connectivity strengths across cortical and subcortical regions.

Aakeshavan

87% match

Loading thumbnail…

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.

Aakeshavan

84% match

Loading thumbnail…

Gist 67e34573cadb6e725c2b

This visualization displays resting-state functional connectivity among 18 subdivisions of the left and right precentral motor cortex, rendered as a circular connectome with node angles arranged by hemisphere. It shows both inflated brain surface labels color-coded by subdivision and a connectivity circle plotting pairwise correlations from CSV connectome data. The code uses PySurfer’s `Brain` for surface rendering, MNE’s `plot_connectivity_circle` with `circular_layout` for the network diagram, and seaborn’s `blend_palette` to generate node and edge colormaps, with Matplotlib’s `GridSpec` composing the multi-panel figure.

Aakeshavan

80% match

Loading thumbnail…

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.

Aakeshavan

80% match

Loading thumbnail…

Gist fd5980c016748347a3ed

This visualization displays correlation plots of spinal cord measurements from a multiple sclerosis dataset, including metrics like cord area, gray matter, and white matter at different spinal levels, alongside clinical scores (EDSS). The analysis uses pandas to load the data and seaborn to generate the plots, with a white background, Set1 color palette, and talk-context styling. Dark red and dark blue colors distinguish the plots, which examine relationships between structural measurements and disability scores.

Aakeshavan

78% 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

78% 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

77% match