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Rapid cortical mapping with cross-participant encoding models.

Jerry Tang, Alexander G Huth

    Biorxiv : the Preprint Server for Biology
    |April 3, 2026
    PubMed
    Summary
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    This study introduces a rapid cortical mapping method using cross-participant functional MRI models. This approach significantly reduces data needs for accurate brain mapping, potentially enabling new clinical neuroimaging applications.

    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Cognitive Neuroscience

    Background:

    • Voxelwise encoding models using functional MRI (fMRI) provide detailed cortical organization maps.
    • Current fMRI methods require extensive participant data, limiting clinical use.

    Purpose of the Study:

    • To introduce a cross-participant modeling framework for rapid cortical mapping.
    • To evaluate the efficacy of this framework for linguistic, non-linguistic semantic, and auditory mapping.

    Main Methods:

    • Trained voxelwise encoding models on reference participant data.
    • Transferred models to new participants via brain response alignment using minimal stimuli.
    • Evaluated model performance against within-participant models.

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    Main Results:

    • Cross-participant models showed superior selectivity and prediction accuracy compared to within-participant models with limited data.
    • Model performance improved with increased reference participant data and number of participants.
    • Demonstrated effective mapping across linguistic, non-linguistic semantic, and auditory domains.

    Conclusions:

    • Cross-participant modeling substantially reduces data requirements for accurate cortical mapping.
    • This framework holds promise for advancing clinical applications of functional neuroimaging.