Objective quality assessment for precision functional MRI data.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Data Science

Background:

• Precision functional mapping (PFM) characterizes individual brain networks using fMRI.
• PFM requires high-quality, extensive fMRI data, but objective criteria for data sufficiency are lacking.
• Ensuring interpretable and replicable individual-level results from PFM is challenging.

Purpose of the Study:

• To introduce an objective measure, the network similarity index (NSI), for evaluating fMRI data quality for PFM.
• To establish criteria for data sufficiency and quality needed for PFM.
• To provide a framework for principled decisions regarding data collection and replication in precision fMRI.

Main Methods:

• Developed the network similarity index (NSI) to quantify functional connectivity (FC) pattern alignment with large-scale network structure.
• NSI assesses low-spatial-frequency organization and denoising fidelity.
• Accounted for individual variability in FC reliability.

Main Results:

• The NSI objectively measures the extent to which FC patterns support PFM.
• NSI scores correlate strongly with expert assessments of PFM data usability.
• The NSI framework provides models linking NSI values to PFM suitability.

Conclusions:

• The NSI offers an objective metric for fMRI data quality assessment in PFM.
• This framework aids in making informed decisions about data sufficiency and optimizing data collection.
• The NSI promotes replicability and interpretability in individual-level brain network research.