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Computational memory capacity predicts aging and cognitive decline

Author

Listed:
  • 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
    Theme Inflammation and Aging. Aging Brain Theme. Karolinska University Hospital)

  • Miia Kivipelto

    (Karolinska Institutet
    University of Eastern Finland)

  • Per Svenningsson

    (Karolinska Institutet)

  • Henrik Zetterberg

    (the Sahlgrenska Academy at the University of Gothenburg
    Sahlgrenska University Hospital
    Queen Square
    UK Dementia Research Institute at UCL)

  • Heidi Jacobs

    (Maastricht University
    Massachusetts General Hospital)

  • Kathy Lüdge

    (Weimarer Straße 25)

  • Daniel Brunner

    (CNRS)

  • Bernhard Mehlig

    (Goteborg University)

  • Giovanni Volpe

    (Goteborg University)

  • Joana B. Pereira

    (Karolinska Institutet)

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.

Suggested Citation

  • 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 Gar, 2025. "Computational memory capacity predicts aging and cognitive decline," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57995-0
    DOI: 10.1038/s41467-025-57995-0
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    References listed on IDEAS

    as
    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    2. Kathy Y. Liu & Rogier A. Kievit & Kamen A. Tsvetanov & Matthew J. Betts & Emrah Düzel & James B. Rowe & Robert Howard & Dorothea Hämmerer, 2020. "Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Denise C. Park & Sara B. Festini, 2017. "Theories of Memory and Aging: A Look at the Past and a Glimpse of the Future," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 72(1), pages 82-90.
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