Objective Quality Assessment for Precision Functional MRI Data

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Brain Network Analysis

Background:

• Precision functional mapping (PFM) offers individualized brain network insights but demands high-quality fMRI data.
• Current criteria for assessing data sufficiency and quality for PFM are not well-defined, hindering reproducibility.
• Lack of objective metrics complicates the interpretation and replication of individual-level fMRI findings.

Purpose of the Study:

• Introduce an objective measure, the Network Similarity Index (NSI), to evaluate fMRI data quality for PFM.
• Provide a framework for NSI-based data quality assessment and PFM suitability.
• Guide decisions on data sufficiency and optimize data collection strategies in precision fMRI research.

Main Methods:

• Developed the Network Similarity Index (NSI) to quantify the integrity of large-scale network organization in individual fMRI datasets.
• Assessed NSI's alignment with expert evaluations of PFM usability and its ability to account for individual variability in functional connectivity reliability.
• Created an open-source framework for NSI implementation and developed models linking NSI values to PFM suitability.

Main Results:

• NSI objectively measures the extent to which functional connectivity patterns reflect the network structure essential for PFM.
• NSI demonstrates strong agreement with blinded expert assessments of PFM data quality.
• The NSI framework accounts for individual differences in the reliability of functional connectivity.

Conclusions:

• The NSI provides an objective, quantitative metric for evaluating fMRI data quality for precision functional mapping.
• This framework facilitates principled decisions regarding data sufficiency and optimizes data collection for reproducible individual-level brain network research.
• The open-source NSI tool enhances the reliability and interpretability of precision fMRI studies.