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Automating the Human Connectome Project's Temporal ICA Pipeline.

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    This study introduces an automated pipeline for temporal independent component analysis (tICA) to effectively remove noise from functional magnetic resonance imaging (fMRI) data. The new method simplifies artifact removal, improving the analysis of neural activity in brain imaging studies.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Biomedical Signal Processing

    Background:

    • Functional magnetic resonance imaging (fMRI) data contain significant noise and artifacts, obscuring true neural activity.
    • Temporal independent component analysis (tICA) can denoise fMRI data but requires manual, complex steps for dimensionality selection and artifact classification.
    • Lack of automated pipelines hinders widespread adoption and reproducibility of tICA for fMRI analysis.

    Approach:

    • Developed a nine-step, fully automated tICA pipeline for denoising fMRI data, integrated with existing HCP pipelines.
    • Implemented automated group spatial ICA (sICA) dimensionality selection using Wishart distribution fitting.
    • Created a hierarchical classifier with handcrafted and self-supervised features to distinguish artifactual from signal components in tICA decompositions.

    Key Points:

    • The automated pipeline successfully removes global artifacts from fMRI data after sICA+FIX cleaning and MSMAll alignment.
    • Automated dimensionality selection for MIGP data aligns with previous manual estimates and demonstrates high reproducibility.
    • The tICA classifier achieves >0.98 PR-AUC, correctly classifying >95% of tICA-represented variance across diverse HCP datasets.

    Conclusions:

    • The automated tICA pipeline offers a user-friendly and powerful tool for neuroimaging researchers.
    • This advancement significantly improves the efficiency and reliability of artifact removal in fMRI analysis.
    • The pipeline enhances the ability to accurately study neural activity by reducing noise and artifacts in fMRI data.