ArticlebioRxiv : the preprint server for biology2025
Investigating the Contribution of Molecular-Enriched Functional Connectivity to Brain-Age Analysis.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 2,120 healthy adults (1,243/877 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%. In the merged multi-site cohort using a common parcellation, augmenting structural information with transporter-enriched FC reduced mean absolute error (MAE) from 6.02 to 5.81 years, supporting complementarity of the two modalities. 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, enhancing prediction and interpretability with respect to neurotransmitter systems.
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