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Gist dd6562218ed80aaca786

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Aakeshavan
Last edited Jun 26, 2014
Created on Jun 26, 2014

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.

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