Investigating the Contribution of Molecular‐Enriched Functional Connectivity to Brain‐Age Analysis

Abstract

Brain‐age prediction from neuroimaging data provides a proxy of biological aging, yet most models rely on structural magnetic resonance imaging (MRI), a modality that captures macroanatomy but offers limited biological specificity. We tested whether integrating molecular‐enriched functional connectivity (FC) from resting‐state functional MRI (rs‐fMRI) data improves brain‐age prediction and biological explainability. We analyzed MRI data of 2120 healthy adults (1243/877 F/M; 18–90 years) from three public datasets. Molecular‐enriched connectivity maps were derived with Receptor‐Enriched Analysis of functional Connectivity by Targets (REACT) using receptor‐density templates for the dopamine (DAT), norepinephrine (NET), and serotonin (SERT) transporter systems. Support vector regression models were applied to predict chronological age from molecular‐enriched FC, structural morphometry, or both combined. The effect of multi‐site variability was mitigated via ComBat harmonization with and without Empirical Bayes pooling. We additionally conducted a common‐parcellation analysis to assess the impact of differing parcellations between modalities. Single‐transporter molecular‐enriched FC explained up to 51% of age variance. The most predictive transporter varied by dataset, with DAT dominating in the harmonized and common‐parcellation settings. Combining the three molecular‐enriched maps consistently improved prediction over any single map and increased explained variance up to 64%. Structural morphometry remained the strongest single modality overall. In the merged multi‐site cohort using a common parcellation, adding transporter‐enriched FC to structural features yielded a small but consistent reduction in prediction error (mean absolute error (MAE) from 6.02 to 5.81 years), supporting limited complementarity between the two modalities. Residual‐level paired comparisons across repeated cross‐validation confirmed that this improvement is statistically reliable but modest in magnitude. In contrast, when different parcellations were applied, incorporating molecular‐enriched FC into brain age prediction resulted in a 2% higher MAE compared to structural morphometry alone, suggesting that parcellation mismatch may obscure the functional contributions. In conclusion, molecular‐enriched FC is a feasible and biologically informative extension to brain‐age modeling; however, its added predictive value over structural morphometry was modest and depended on harmonization and atlas alignment.