Uncovering vulnerable connections in the aging brain using reservoir computing

Mite Mijalkov; Blanca Zufiria Gerboles; Daniel Vereb; Kathy Lüdge; Daniel Brunner; Giovanni Volpe; Joana B. Pereira

doi:10.1117/12.2677364

28 September 2023 Uncovering vulnerable connections in the aging brain using reservoir computing

Mite Mijalkov, Blanca Zufiria Gerboles, Daniel Vereb, Kathy Lüdge, Daniel Brunner, Giovanni Volpe, Joana B. Pereira

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265508 (2023) https://doi.org/10.1117/12.2677364
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

We used reservoir computing to explore the changes in the connectivity patterns of whole-brain anatomical networks derived by diffusion-weighted imaging, and their impact on cognition during aging. The networks showed optimal performance at small densities. This performance decreased with increasing density, with the rate of decrease being strongly associated with age and performance on behavioural tasks measuring cognitive function. This suggests that a network core of anatomical hubs is crucial for optimal functioning, while weaker connections are more susceptible to aging effects. This study highlights the potential utility of reservoir computing in understanding age-related changes in cognitive function.

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Citation Download Citation

Mite Mijalkov, Blanca Zufiria Gerboles, Daniel Vereb, Kathy Lüdge, Daniel Brunner, Giovanni Volpe, and Joana B. Pereira "Uncovering vulnerable connections in the aging brain using reservoir computing", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265508 (28 September 2023); https://doi.org/10.1117/12.2677364

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KEYWORDS

Brain

Reservoir computing

Anatomy

Biological imaging

Cognition

Machine learning

Reflection

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