Gist 64d00f92b66ebdebeffb

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.

87% 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.

81% match

Gist 5075781b66de8d9e3eb0d804ce180a58

This visualization displays a brain connectivity network as a node-link diagram, where each node represents a cortical region labeled with Desikan-Killiany atlas terminology (e.g., lh-bankssts, rh-precentral) and sized by its import count. Edges connect regions based on structural connections, with the layout revealing clusters of functionally related brain areas. The visualization uses a hierarchical naming convention to organize regions by hemisphere and lobe, with connection strengths implied by the size values shown in the JSON data. The interactive graph allows exploration of the brain's structural connectivity patterns across 68 cortical regions.

Gist 441d9e9e69dfd1c303a0c0b76719a9cb

This example visualizes brain connectivity by computing a streamline connectivity matrix from diffusion tractography data. The notebook uses DIPY’s `utils.connectivity_matrix` to map streamlines to cortical and deep gray matter regions, then writes the resulting metric matrices to CSV files. It loads track-vis streamlines and FreeSurfer-labeled anatomical images with nibabel, applies a voxel-to-world affine transformation, and reduces labels to relevant gray matter regions. The code also demonstrates how to filter streamlines for specific functional regions using custom region maps, and relies on FSL and FreeSurfer tools for image processing.

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.

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.

Gist 65e3c6b08f24fc0f33141f71ce37e702

This example visualizes the relationship between imaging-derived connectivity metrics and clinical measures in multiple sclerosis patients by computing global efficiency from weighted brain graphs. The notebook applies PCA to combine multiple imaging features per connection, then calculates efficiency values for each subject using the Brain Connectivity Toolbox (bct.efficiency_wei). The resulting efficiency dataframe is merged with demographic and clinical data using pandas, with Seaborn and matplotlib available for plotting, though the visualization output itself is not displayed.

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.