Characterizing variability in resting-state functional magnetic resonance imaging (rsfMRI) metrics: a normative modeling framework

Abstract

Clinical adoption of new biomedical techniques depends on establishing reference values against which individual patients can be compared. In resting-state functional MRI (rsfMRI), most biomarker research has relied on the case-control paradigm, whose underlying assumptions are often invalid as diseases are frequently heterogeneous, limiting biomarker generalizability. Normative modeling offers a complementary alternative by characterizing individual deviations against a reference population. However, in rsfMRI, normative modeling has been applied almost exclusively to functional connectivity, with limited attention to age trajectories and sex effects. We address these gaps by developing a spatial normative model of four rsfMRI metrics that capture complementary features of the blood-oxygen-level-dependent (BOLD) signal across age and sex. Five publicly available datasets were aggregated to form a sample of 1,978 participants aged 10-30 years. Four metrics were computed for each of 110 grey matter regions: amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and Hurst exponent. A machine-learning model based on hierarchical Bayesian regression with a non-Gaussian likelihood was fitted per metric, modeling non-linear age effects, sex, and multi-site acquisition. Models were well calibrated across all four metrics, with fALFF showing the strongest predictive performance and Hurst exponent the weakest. Normative trajectories varied across brain regions for each metric, but on average, the median of each distribution remained bounded across regions, while the spread was more regionally variable. All four metrics showed predominantly negative slopes with age, indicating a decrease in each metric over the age window. This work provides a normative reference across four rsfMRI metrics that capture distinct features of the BOLD signal, complementing the case-control paradigm and supporting individual-level inference.