Objective quality assessment for precision functional MRI data.
| Publication Type | Academic Article |
| Authors | Lynch C, Chang M, Elbau I, Gordon E, Laumann T, Du J, Ladwig Z, Lueckel M, Perez D, Summerville I, Chou J, Johnson M, Ho C, Manfredi N, Nilchian P, Solomonov N, Goldwaser E, Ng T, Moia S, Caballero-Gaudes C, Downar J, Vila-Rodriguez F, Gregory E, Daskalakis Z, Blumberger D, Kay K, Buchanan D, Williams N, Bhati M, Clauss J, Zebley B, Victoria L, Power J, Grosenick L, Gunning F, Liston C |
| Journal | Neuron |
| Date Published | 06/22/2026 |
| ISSN | 1097-4199 |
| Abstract | Precision functional mapping (PFM) enables the individual-level characterization of brain network organization but requires substantially more and higher-quality fMRI data than is standard. Despite the growing use of PFM, the objective criteria for data sufficiency and the 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 express the large-scale network structure required for PFM. The NSI captures low-spatial-frequency, coherent network organization and denoising fidelity, and it aligns closely with blinded expert assessments of PFM usability. The NSI also accounts for the variability in the rate at which FC becomes reliable across individuals. This NeuroResource provides an open source framework for NSI-based data quality evaluation and models linking NSI values with expert-judged PFM suitability. This framework can inform expected returns from additional data collection, thus enabling principled decisions about data sufficiency and replication in precision fMRI research. |
| DOI | 10.1016/j.neuron.2026.05.020 |
| PubMed ID | 42330956 |
| PubMed Central ID | PMC13288760 |