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Updated: May 21, 2025

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Computational memory capacity predicts aging and cognitive decline.

Mite Mijalkov1, Ludvig Storm2, Blanca Zufiria-Gerbolés3

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|March 21, 2025
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Summary
This summary is machine-generated.

Computational memory capacity, assessed using reservoir computing on brain networks, robustly tracks aging. This brain network marker correlates with cognitive function and brain integrity, offering new insights into aging and related disorders.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Aging

Background:

  • Memory decline is a common aspect of aging.
  • Traditional cognitive tests are used to assess memory.
  • Brain network architecture is an underutilized metric for memory assessment.

Purpose of the Study:

  • To evaluate the linear memory capacities of brain networks using reservoir computing.
  • To determine if computational memory capacity is a marker of aging.
  • To explore the relationship between computational memory capacity, brain function, and cognitive performance.

Main Methods:

  • Utilized reservoir computing, a recurrent neural network paradigm.
  • Extracted neural-network reservoirs from brain anatomical connectivity data.
  • Analyzed data from a lifespan cohort (n=636) and replicated in an independent cohort (n=226).

Main Results:

  • Computational memory capacity serves as a robust marker of aging.
  • This capacity is associated with resting-state functional activity, white matter integrity, and locus coeruleus signal intensity.
  • Computational memory capacity correlates with cognitive performance.

Conclusions:

  • Reservoir computing provides a novel method to assess brain network memory capacity.
  • Computational memory capacity links brain network integrity and function to cognitive aging.
  • This approach offers new avenues for studying aging and age-related neurological disorders.