Computational memory capacity predicts aging and cognitive decline

Mite Mijalkov (), Ludvig Storm, Blanca Zufiria-Gerbolés, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe () and Joana B. Pereira ()
Additional contact information
Mite Mijalkov: Karolinska Institutet
Ludvig Storm: Goteborg University
Blanca Zufiria-Gerbolés: Karolinska Institutet
Dániel Veréb: Karolinska Institutet
Zhilei Xu: Karolinska Institutet
Anna Canal-Garcia: Karolinska Institutet
Jiawei Sun: Karolinska Institutet
Yu-Wei Chang: Goteborg University
Hang Zhao: Goteborg University
Emiliano Gómez-Ruiz: Goteborg University
Massimiliano Passaretti: Karolinska Institutet
Sara Garcia-Ptacek: Karolinska Institutet
Miia Kivipelto: Karolinska Institutet
Per Svenningsson: Karolinska Institutet
Henrik Zetterberg: the Sahlgrenska Academy at the University of Gothenburg
Heidi Jacobs: Maastricht University
Kathy Lüdge: Weimarer Straße 25
Daniel Brunner: CNRS
Bernhard Mehlig: Goteborg University
Giovanni Volpe: Goteborg University
Joana B. Pereira: Karolinska Institutet

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-57995-0

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