Parse MRIQC and fMRIPrep outputs to generate subject exclusion lists.
npx skills add https://github.com/yibeichan/claude-skills --skill neuroimaging-qc
Please help me install this Agent Skill. Skill: neuroimaging-qc Source: https://github.com/yibeichan/claude-skills/tree/main/skills/neuroimaging-qc Command: npx skills add https://github.com/yibeichan/claude-skills --skill neuroimaging-qc
This Skill provides a principled framework for interpreting QC metrics across neuroimaging pipelines to guide subject inclusion decisions and report reproducible quality criteria.
Start by loading MRIQC and fMRIPrep outputs, mapping key metrics to predefined thresholds, and generating a subject-level exclusion list with justification.
High-intent search queries and answers about installing and using this skill.
You can determine subject exclusion criteria by parsing MRIQC and fMRIPrep outputs to map QC metrics like FD and tsnr to predefined thresholds, generating a reproducible subject-level exclusion list with justifications.
The best way to generate a reproducible QC report is to interpret QC metrics across neuroimaging pipelines, apply standardized thresholds, and document exclusion summaries and methods sections to accompany your publications.
Yes, you can apply standardized thresholds to structural MRI and EEG/MEG data quality workflows by mapping Image Quality Metrics to predefined limits and assessing data quality across multiple neuroimaging modalities.
You set fMRI data exclusion thresholds by loading pipeline outputs, mapping key metrics like framewise displacement and tsnr to predefined limits, and producing an exclusion list detailing which subjects fail these criteria.
You need pandas and numpy to parse and process neuroimaging QC metrics, while matplotlib is used to visualize data quality distributions and support your exclusion decisions.
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