How Aging Influences Brain Criticality and Network Dynamics
Poster No:
933
Submission Type:
Abstract Submission
Authors:
Kaichao Wu1, Mauro Copelli2, Leonardo Gollo1,3
Institutions:
1Monash University, Melbourne, Victoria, 2Federal University of Pernambuco,, Recife, Pernambuco, 3Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Palma de Mallorca, Spain
First Author:
Co-Author(s):
Leonardo Gollo
Monash University|Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)
Melbourne, Victoria|Palma de Mallorca, Spain
Monash University|Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC)
Melbourne, Victoria|Palma de Mallorca, Spain
Introduction:
Brain criticality refers to the hypothesized optimal balance between order and chaos within neural networks[1,2]. Understanding how aging shifts brain criticality could provide insights into mechanisms underlying cognitive resilience and decline [4]. We propose a two-pathway framework for understanding changes in brain criticality with age. The normal aging pathway suggests a gradual shift toward a subcritical regime, reflecting reduced adaptability and dynamic range as aging progresses. In contrast, the pathological aging pathway describes a shift toward criticality or even a supercritical state as a result of pathological brain changes, such as lesion events, which lead to altered neural dynamics. These two pathways reconcile conflicting findings-some studies report a distance from criticality with age [4], while others suggest the brain moves closer to criticality during the aging process [5]. The framework implies that healthy aging maintains increased distance from criticality, while pathological aging demonstrates dynamic shifts toward supercritical states driven by neurodegenerative changes or lesions.
Methods:
We modeled the brain as a network of interconnected functional regions using Erdös–Rényi undirected random graphs to simulate neuronal connectivity. Functional regions were defined using the Desikan–Killiany–Tourville (DKT) atlas [6]. Interregional connectivity was estimated with diffusion MRI tractography to compute streamline counts between regions (Figure 1A). The whole-brain network model consisted of 84 functional regions, with each region modeled as a large network of excitable neurons. The connectivity profile is visualized in Figure 1B. Neurons were modeled to spike either through propagation from a spiking neighbor with a fixed probability P or in response to external stimulation modeled as a Poisson process with a rate r =10⁻⁵ [4]. To simulate healthy aging-characterized by reduced neuronal density and synaptic loss-we progressively removed nodes and connections from the network (Figure 1C). Conversely, pathological aging was simulated by introducing lesion-like disruptions modeled as stochastic processes targeting nodes at random intervals and locations, leading to increased P (Figure 1D). The network branching ratio [7] and the largest eigenvalues of the brain connectivity matrix [8] were used to assess network dynamics, we used two primary metrics: Criticality was defined by σ = 1 or λ = 1: networks with σ or λ < 1 are in a subcritical state where network activity decays over time, while networks with σ or λ > 1 are in a supercritical state where network activity grows over time. These metrics allowed us to capture shifts in network dynamics associated with aging and lesion-like disruptions.
Results:
Our simulation results (Figure 2) support the presence of both proposed criticality pathways. In the normal aging pathway, we observed a gradual shift toward a subcritical regime with age. This suggests a decline indynamic range and adaptability, consistent with cognitive changes typically seen in healthy elderly populations. Conversely, the pathological aging pathway exhibited initial shifts toward subcriticality, signaling early neural vulnerability. However, over time, the brain displayed compensatory adaptations, leading to either a return to criticality or even a transition into supercritical states in response to lesion events or pathological changes.
Conclusions:
The proposed two pathways suggest that changes in criticality could serve as a valuable biomarker for monitoring cognitive health, identifying early signs of neural dysfunction, and evaluating compensatory responses to lesions or disease. Furthermore, these findings underscore the importance of multi-modal neuroimaging techniques-such as fMRI, EEG, and MEG-to explore how criticality changes manifest across individuals with differing health trajectories. These imaging modalities off er unique insights into the temporaland spatial evolution of criticality with aging.
Lifespan Development:
Aging 1
Lifespan Development Other 2
Modeling and Analysis Methods:
Methods Development
Keywords:
Aging
Computational Neuroscience
Other - Network Dynamics; Criticality
1|2Indicates the priority used for review
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