{"version":"1.0","type":"rich","title":"Gist dd6562218ed80aaca786","description":"This example demonstrates a spectral clustering workflow for neuroimaging data, using scikit-learn’s `SpectralClustering` to segment thalamus voxels into distinct clusters based on correlation matrices. It processes fMRI time-series data via Nipype pipelines, nibabel for image I/O, and matplotlib with `%pylab inline` for visualization. The notebook shows how clustering results are saved as NIfTI files, with parameters like subject ID, region of interest, and cluster count varied across runs.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-dd6562218ed80aaca786?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}