PREreviews de “Objective Quality Assessment for Precision Functional MRI Data”

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Objective Quality Assessment for Precision Functional MRI Data

por Charles J. Lynch, Megan Chang, Immanuel Elbau, Evan M. Gordon, Timothy O. Laumann, Jingnan Du, Zach Ladwig, Maximilian Lueckel, Diana C. Perez, Indira Summerville, Jolene Chou, Megan Johnson, Claire Ho, Nicola Manfredi, Parsa Nilchian, Nili Solomonov, Eric Goldwaser, Tommy Ng, Stefano Moia, Cesar Caballero-Gaudes, Jonathan Downar, Fidel Vila-Rodriguez, Elizabeth Gregory, Zafiris J. Daskalakis, Daniel M. Blumberger, Kendrick Kay, Derrick Buchanan, Nolan Williams, Mahendra T. Bhati, Jacqueline Clauss, Benjamin Zebley, Lindsay W. Victoria, Jonathan D. Power, Logan Grosenick, Faith M. Gunning y Conor Liston

Publicado: 11 de febrero de 2026
Servidor: bioRxiv
DOI: 10.64898/2026.02.10.704857

Resumen

Precision functional mapping (PFM) enables individual-level characterization of brain network organization but requires substantially more and higher-quality fMRI data than is standard. Despite its growing use, objective criteria for data sufficiency and quality needed to ensure interpretable and replicable individual-level results remain unclear. Here, we introduce the Network Similarity Index (NSI), an objective measure of the extent to which functional connectivity (FC) patterns in an individual dataset express the large-scale network structure required for PFM. NSI captures the integrity of low-spatial-frequency, coherent network organization and denoising fidelity, and aligns closely with blinded expert assessments of PFM usability. NSI also accounts for variability in the rate at which FC becomes reliable across individuals. Here, we provide an open-source framework for NSI-based data quality evaluation and models for linking NSI values with expert-judged PFM suitability. This framework can also inform expected returns from additional data collection, enabling principled decisions about data sufficiency and replication in precision fMRI research.

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PREreviews de Objective Quality Assessment for Precision Functional MRI Data

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Competing interests