P324 Brain Criticality Trajectories in Aging: From Cognitive Slowing to Hyperexcitability
Kaichao Wu*1,Leonardo L. Gollo1,2
1Brain Networks and Modelling Laboratory, The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia
2Institute for Cross-Disciplinary Physics and Complex Systems, IFISC (UIB-CSIC), Palma de Mallorca, Campus University de les Illes Baleares, Spain.
*Email: kaichao.wu@monash.edu
Introduction
Brain criticality—the dynamic balance between stability and flexibility in neural activity—is a fundamental property that supports efficient information processing, adaptability, and cognitive function [1-3]. However, how aging influences brain criticality remains a subject of debate, with conflicting findings in the literature [4,5]. Some studies suggest that normal aging shifts neural dynamics toward a subcritical state characterized by reduced neural variability and cognitive slowing [6]. In contrast, others propose that aging may lead to supercritical dynamics, increasing the risk of hyperexcitability and instability[7].
Methods
To reconcile these opposing views, we developed a whole brain neuronal network model that simulates aging as a combination of two processes: healthy aging, which gradually prunes network connections at a steady rate(Figure 1A), and pathological aging, which introduces random lesions that locally alter regional excitability(Figure 1B). This model enables us to track how the distance to criticality(Figure 1C), estimated from temporal correlation length(intrinsic timescales), evolves over time. We find that healthy aging drives the system toward subcriticality, while pathological aging progressively pushes the system toward supercriticality due to lesion accumulation and compensatory excitability changes(Figure 1D).
Results
Our results reveal two distinct trajectories of criticality in aging. In normal aging, where no major disruptions occur, neural dynamics gradually shift toward subcriticality, aligning with empirical findings of diminished neural variability and cognitive slowing in older adults [5]. Conversely, in pathological aging, an initial decline in criticality due to network degradation is followed by a shift toward supercriticality, potentially contributing to hyperexcitable states observed in neurodegenerative diseases.
Discussion
These findings offer a theoretical framework that reconciles previously conflicting results, demonstrating that normal and pathological aging follow distinct criticality trajectories. By identifying key mechanisms underlying these transitions, our model provides insights into early detection of neurodegenerative diseases and highlights potential interventions aimed at preserving critical neural dynamics in aging populations.
Figure 1. Figure 1. Brain criticality trajectories in Aging. (A) Brain network connectivity (K) reduces with normal aging. (B) For pathological aging, excitability within localized brain regions increases. (C) The neuronal network modeling indicates two distinct trajectories for normal and pathological aging. (D) The relationship between intrinsic timescales and criticality.
Acknowledgements
This work was supported by the Australian Research Council (ARC), Future Fellowship (FT200100942), the Rebecca L. Cooper Foundation (PG2019402), the Ramón y Cajal Fellowship (RYC2022-035106-I) from FSE/Agencia Estatal de Investigación (AEI), Spanish Ministry of Science and Innovation, and the María de Maeztu Program for units of Excellence in R&D, grant CEX2021-001164-M.
References
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6.Wu, K., et al. (2025). https://doi.org/10.1038/s42003-025-07517-x.
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