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

Overview

1. Department of Information Engineering, University of Padua, Padua, Italy
2. Institute of Psychiatry, Psychology & Neuroscience, Department of Neuroimaging, King's College London, London, UK

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• humanconnectome.org/study/hcp-lifespan-aging, at Human Connectome Project; found in “Data Availability Statement”

Data Availability Statement

All neuroimaging data used in this study are publicly accessible. T1w and rs‐fMRI scans from Cam‐CAN are available at https://camcan‐archive.mrc‐cbu.cam.ac.uk/dataaccess/ (https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/) (Shafto et al. 2014; Taylor et al. 2017). HCP‐Aging data can be downloaded from https://www.humanconnectome.org/study/hcp‐lifespan‐aging (https://www.humanconnectome.org/study/hcp-lifespan-aging) (Bookheimer et al. 2019; Harms et al. 2018). NKI‐RS data are provided at https://fcon_1000.projects.nitrc.org/indi/enhanced/ (Nooner et al. 2012; Tobe et al. 2022). Preprocessing pipelines relied exclusively on publicly available BIDS‐Apps: functional MRI Preprocessing (fMRIPrep) (v23.2.2, https://fmriprep.org/en/23.2.2/), XCP‐D (v0.7.4, https://xcp‐d.readthedocs.io/en/0.7.4/ (https://xcp-d.readthedocs.io/en/0.7.4/)), and MRIQC (v24.1.0, https://mriqc.readthedocs.io/en/latest/) or common neuroimaging tools as FSL (v7.3.2, https://fsl.fmrib.ox.ac.uk/fsl/docs/). REACT was implemented with the open‐source react‐fmri package, available at https://github.com/ottaviadipasquale/react‐fmri (https://github.com/ottaviadipasquale/react-fmri). Custom scripts for brain‐age modeling (SVR) and feature harmonization with ComBat were written for local execution and are available upon reasonable request from the corresponding author under a standard data‐use agreement.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 14 MeSH terms, 4 funders, 103 references.

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This paper

Pinamonti, M., Moretto, M., Sammassimo, V., Castellaro, M., & Veronese, M. (2026). Investigating the Contribution of Molecular-Enriched Functional Connectivity to Brain-Age Analysis. Human brain mapping, 47(13), e70627. https://doi.org/10.1002/hbm.70627

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