Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

Cognitive decline is characterized by substantial inter-individual variability across the lifespan, even among clinically healthy adults. However, it is unclear how structural and functional brain organization jointly relate to differences in cognitive performance within the typical populations. We considered a multimodal brain age framework by predicting for each subject its age from either structural MRI or resting-state functional MRI, after training on the 1000BRAINS cohort (901 participants, 18-85 years). Following, a structural and a functional brain age gap markers were defined as the deviations with respect to the real chronological age. We then used the markers to characterize the interplay between structure and function in shaping cognitive performances. Our findings reveal that the functional marker is as important to predict global cognitive performance as the structural marker. The most accurate prediction was found for global cognition, executive functions and episodic memory, followed by working memory, attention and language. In particular, the structural marker was contributing to all domains, except for working memory, whereas the functional marker had the highest contribution. Overall, structural and functional brain age models together provide a meaningful subject-level summary of brain information for predicting cognition, supporting a multidimensional view of typical aging.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-26
DOI
https://doi.org/10.64898/2026.10.20.760445
Primary Topic
Functional Brain Connectivity Studies
Type
preprint

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preprint

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

Andrea Brovelli, Annachiara Crocetta, Nóra Bittner, Svenja Caspers et al.
bioRxiv (Cold Spring Harbor Laboratory)
Functional Brain Connectivity Studies
preprint

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

Andrea Brovelli, Annachiara Crocetta, Nóra Bittner, Svenja Caspers, Franco Cauda, Matthieu Gilson, Marta Morandini
preprint en

Abstract

Cognitive decline is characterized by substantial inter-individual variability across the lifespan, even among clinically healthy adults. However, it is unclear how structural and functional brain organization jointly relate to differences in cognitive performance within the typical populations. We considered a multimodal brain age framework by predicting for each subject its age from either structural MRI or resting-state functional MRI, after training on the 1000BRAINS cohort (901 participants, 18-85 years). Following, a structural and a functional brain age gap markers were defined as the deviations with respect to the real chronological age. We then used the markers to characterize the interplay between structure and function in shaping cognitive performances. Our findings reveal that the functional marker is as important to predict global cognitive performance as the structural marker. The most accurate prediction was found for global cognition, executive functions and episodic memory, followed by working memory, attention and language. In particular, the structural marker was contributing to all domains, except for working memory, whereas the functional marker had the highest contribution. Overall, structural and functional brain age models together provide a meaningful subject-level summary of brain information for predicting cognition, supporting a multidimensional view of typical aging.

bioRxiv (Cold Spring Harbor Laboratory)
Centre National de la Recherche Scientifique (FR), Forschungszentrum Jülich (DE), Aix-Marseille Université (FR), Düsseldorf University Hospital (DE), Institut de Neurosciences de la Timone (FR), Heinrich Heine University Düsseldorf (DE), University of Turin (IT)
Aix-Marseille Université, European Commission, Agence Nationale de la Recherche, HORIZON EUROPE Framework Programme
Functional Brain Connectivity Studies
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