Objective quality assessment for precision functional MRI data
Year: 2026
Type: article
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 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.
Source: Neuron
Authors: 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 M. Buchanan, Nolan Williams, Mahendra T. Bhati, Jacqueline Clauss, Benjamin Zebley, Lindsay W. Victoria, Jonathan D. Power, Logan Grosenick, Faith M. Gunning, Conor Liston
Institutions: Weill Cornell Medicine, Washington University in St. Louis, Harvard University, Northwestern University, Leibniz Institute for Resilience Research, University Medical Center of the Johannes Gutenberg University Mainz, Maastricht University, Basque Center on Cognition, Brain and Language, Centre for Addiction and Mental Health, University of Toronto, University of British Columbia, University of California San Diego, University of Minnesota, Stanford Medicine, Stanford University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $10,400
Awards: G1976989629, Positive valence systems engagement during social reward exposure psychotherapy for mid- and late-life depression, G5142318511, Targeting salience network dysfunction in depression with individualized brain network mapping and TMS, Precision Functional Mapping in Treatment-Resistant Depression, Efficacy of biomarker-guided rTMS for treatment resistant depression
OpenAlex ID: W7165530968