Computational memory capacity predicts aging and cognitive decline

Mite Mijalkov; Ludvig Storm; Blanca Zufiria-Gerboles; Daniel Vereb; Zhilei Xu; Anna Canal-Garcia; Jiawei Sun; Yu-Wei Chang; Hang Zhao; Emiliano Gomez-Ruiz; Massimiliano Passaretti; Sara Garcia-Ptacek; Miia Kivipelto; Per Svenningsson; Henrik Zetterberg; Heidi Jacobs; Kathy Luedge; Daniel Brunner; Bernhard Mehlig; Giovanni Volpe; Joana B. Pereira

doi:10.1038/s41467-025-57995-0

Computational memory capacity predicts aging and cognitive decline

Mite Mijalkov^*, Ludvig Storm, Blanca Zufiria-Gerboles, Daniel Vereb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gomez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Luedge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe^*Joana B. Pereira^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

Original language	English
Article number	2748
Number of pages	14
Journal	Nature Communications
Volume	16
Issue number	1
DOIs	https://doi.org/10.1038/s41467-025-57995-0
Publication status	Published - 20 Mar 2025

Keywords

AGE
ANTERIOR CINGULATE
CONNECTIVITY
FMRI
FUNCTIONAL ARCHITECTURE
HUMAN BRAIN
HUMAN CONNECTOME
NETWORKS

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Mijalkov, M., Storm, L., Zufiria-Gerboles, B., Vereb, D., Xu, Z., Canal-Garcia, A., Sun, J., Chang, Y.-W., Zhao, H., Gomez-Ruiz, E., Passaretti, M., Garcia-Ptacek, S., Kivipelto, M., Svenningsson, P., Zetterberg, H., Jacobs, H., Luedge, K., Brunner, D., Mehlig, B., ... Pereira, J. B. (2025). Computational memory capacity predicts aging and cognitive decline. Nature Communications, 16(1), Article 2748. https://doi.org/10.1038/s41467-025-57995-0

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abstract = "Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.",

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author = "Mite Mijalkov and Ludvig Storm and Blanca Zufiria-Gerboles and Daniel Vereb and Zhilei Xu and Anna Canal-Garcia and Jiawei Sun and Yu-Wei Chang and Hang Zhao and Emiliano Gomez-Ruiz and Massimiliano Passaretti and Sara Garcia-Ptacek and Miia Kivipelto and Per Svenningsson and Henrik Zetterberg and Heidi Jacobs and Kathy Luedge and Daniel Brunner and Bernhard Mehlig and Giovanni Volpe and Pereira, {Joana B.}",

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doi = "10.1038/s41467-025-57995-0",

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Mijalkov, M, Storm, L, Zufiria-Gerboles, B, Vereb, D, Xu, Z, Canal-Garcia, A, Sun, J, Chang, Y-W, Zhao, H, Gomez-Ruiz, E, Passaretti, M, Garcia-Ptacek, S, Kivipelto, M, Svenningsson, P, Zetterberg, H, Jacobs, H, Luedge, K, Brunner, D, Mehlig, B, Volpe, G & Pereira, JB 2025, 'Computational memory capacity predicts aging and cognitive decline', Nature Communications, vol. 16, no. 1, 2748. https://doi.org/10.1038/s41467-025-57995-0

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T1 - Computational memory capacity predicts aging and cognitive decline

AU - Mijalkov, Mite

AU - Storm, Ludvig

AU - Zufiria-Gerboles, Blanca

AU - Vereb, Daniel

AU - Xu, Zhilei

AU - Canal-Garcia, Anna

AU - Sun, Jiawei

AU - Chang, Yu-Wei

AU - Zhao, Hang

AU - Gomez-Ruiz, Emiliano

AU - Passaretti, Massimiliano

AU - Garcia-Ptacek, Sara

AU - Kivipelto, Miia

AU - Svenningsson, Per

AU - Zetterberg, Henrik

AU - Jacobs, Heidi

AU - Luedge, Kathy

AU - Brunner, Daniel

AU - Mehlig, Bernhard

AU - Volpe, Giovanni

AU - Pereira, Joana B.

PY - 2025/3/20

Y1 - 2025/3/20

N2 - Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

AB - Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

KW - AGE

KW - ANTERIOR CINGULATE

KW - CONNECTIVITY

KW - FMRI

KW - FUNCTIONAL ARCHITECTURE

KW - HUMAN BRAIN

KW - HUMAN CONNECTOME

KW - NETWORKS

U2 - 10.1038/s41467-025-57995-0

DO - 10.1038/s41467-025-57995-0

M3 - Article

SN - 2041-1723

VL - 16

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 2748

ER -

Computational memory capacity predicts aging and cognitive decline

Abstract

Keywords

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