neuroscience-imaging

To compute a functional connectivity matrix from resting-state fMRI data, you load NIfTI files and apply brain parcellation atlases like Schaefer 200. This skill processes the extracted time series to generate a structured connectivity matrix for network analysis.

Yes, you can run GLM-based statistical activation mapping for task fMRI using nilearn. This skill fits general linear models to preprocessed NIfTI images to produce statistical activation maps for task-based paradigms.

The best way to visualize brain imaging results and connectivity heatmaps is using this skill's visualization workflows. It generates high-quality glass brains, statistical brain maps, and connectivity heatmaps suitable for research reporting.

Yes, you need nibabel and numpy to analyze structural and functional MRI data because they handle NIfTI file input and array operations. The skill also relies on scipy, pandas, matplotlib, and seaborn for statistical computation and visualization.

Yes, this neuroimaging analysis workflow supports BIDS-formatted datasets. It streamlines the loading and manipulation of BIDS-structured NIfTI brain images to prepare them for parcellation, connectivity modeling, and statistical mapping.

ICA resting-state network identification works by applying Independent Component Analysis to fMRI scans to isolate spatially independent brain networks. This skill uses nilearn to decompose functional data and identify coherent resting-state networks.