Journal article
Objective Quality Assessment for Precision Functional MRI Data
Neuron (Cambridge, Mass.)
06/22/2026
DOI: 10.1016/j.neuron.2026.05.020
PMID: 42330956
Abstract
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 express the large-scale network structure required for PFM. NSI captures 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. 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, enabling principled decisions about data sufficiency and replication in precision fMRI research.
Lynch et al. introduce the Network Similarity Index (NSI), a tool for assessing whether individual fMRI datasets contain enough coherent brain network structure for precision mapping applications. This work provides an objective framework for evaluating data quality, guiding additional data collection, and improving confidence in individualized brain maps.
Details
- Title: Subtitle
- Objective Quality Assessment for Precision Functional MRI Data
- Creators
- Charles J. Lynch - Cornell UniversityMegan Chang - Cornell UniversityImmanuel Elbau - Cornell UniversityEvan M. Gordon - Washington University in St. LouisTimothy O. Laumann - Washington University in St. LouisJingnan Du - Harvard UniversityZach Ladwig - Northwestern UniversityMaximilian Lueckel - Johannes Gutenberg University MainzDiana C. Perez - Northwestern UniversityIndira Summerville - Cornell UniversityJolene Chou - Cornell UniversityMegan Johnson - Cornell UniversityClaire Ho - Cornell UniversityNicola Manfredi - Cornell UniversityParsa Nilchian - Cornell UniversityNili Solomonov - Cornell UniversityEric Goldwaser - Cornell UniversityTommy Ng - Cornell UniversityStefano Moia - Maastricht UniversityCesar Caballero-Gaudes - Basque Center on Cognition, Brain and LanguageJonathan Downar - Centre for Addiction and Mental HealthFidel Vila-Rodriguez - University of British ColumbiaElizabeth Gregory - University of British ColumbiaZafiris J. Daskalakis - University of California San DiegoDaniel M. Blumberger - Centre for Addiction and Mental HealthKendrick Kay - University of MinnesotaDerrick M. Buchanan - Stanford UniversityNolan Williams - Stanford UniversityMahendra T. Bhati - Stanford UniversityJacqueline Clauss - University of Maryland, BaltimoreBenjamin Zebley - Cornell UniversityLindsay W. Victoria - Cornell UniversityJonathan D. Power - Cornell UniversityLogan Grosenick - Cornell UniversityFaith M. Gunning - Cornell UniversityConor Liston - Cornell University
- Resource Type
- Journal article
- Publication Details
- Neuron (Cambridge, Mass.)
- DOI
- 10.1016/j.neuron.2026.05.020
- PMID
- 42330956
- NLM abbreviation
- Neuron
- ISSN
- 0896-6273
- eISSN
- 1097-4199
- Publisher
- Elsevier
- Language
- English
- Electronic publication date
- 06/22/2026
- Academic Unit
- Psychiatry
- Record Identifier
- 9985222825802771
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