Characterizing variability in resting-state functional magnetic resonance imaging (rsfMRI) metrics: a normative modeling framework.
Authors
Affiliations (3)
Affiliations (3)
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada; Imaging Research Centre, St. Joseph's Healthcare, Hamilton, ON, Canada.
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada; Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada.
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada; Imaging Research Centre, St. Joseph's Healthcare, Hamilton, ON, Canada; Department of Kinesiology, McMaster University, Hamilton, ON, Canada; Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada; Department of Medical Imaging, McMaster University, Hamilton, ON, Canada. Electronic address: [email protected].
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 remains comparatively limited relative to case-control studies and has been applied almost exclusively to functional connectivity. 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. Aggregating five public datasets yielded a sample of 1,978 participants aged 10-30 years. For each of 110 grey matter regions, we computed amplitude of low-frequency fluctuations (ALFF), fractional ALFF (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, head motion parameters, 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 regionally: while the median of each distribution stayed bounded across regions, the spread was more variable. All four metrics showed predominantly negative slopes with age, indicating a decline over the age window. This work provides a normative reference across four rsfMRI metrics capturing distinct BOLD signal features, complementing the case-control paradigm and supporting individual-level inference.