This study evaluated objective criteria for data sufficiency and quality in precision functional mapping (PFM) fMRI, introducing the **Network Similarity Index (NSI)** — a measure of how well functional connectivity (FC) patterns express large-scale network structure needed for reliable individual-level brain mapping.
The NSI captures coherent, low-spatial-frequency network organization and denoising fidelity, and closely aligns with blinded expert assessments of PFM usability; it also accounts for individual variability in the rate at which FC becomes reliable over time.
The study does not report a specific sample size or external validation cohort; the NSI framework's generalizability across scanner platforms, acquisition protocols, and clinical populations is not yet established.
Use the open-source NSI framework to objectively evaluate whether fMRI datasets meet quality thresholds before conducting precision functional mapping studies. NSI-based models can guide decisions about whether additional data collection is needed to achieve interpretable, replicable individual-level results.
