Mapping and Modeling Age-Related Changes in Intrinsic Neural Timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Normal aging, often associated with cognitive deficits, involves progressive neuronal and synaptic loss, shaping brain structure and dynamics. The impact of these structural changes on temporal integration coding in the aged brain remains largely unknown. To address this gap, we mapped differences in intrinsic timescales and gray matter volume (GMV) within brain regions using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on a visual discrimination task was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related changes in GMV and intrinsic timescales, we propose an age-dependent model of spiking neuron networks. In younger subjects, brain regions were modeled in a near-critical branching regime, while in elderly subjects, regions had fewer neurons and synapses, pushing the dynamics toward a more subcritical regime. The empirical results were reproduced by the model: Neuronal networks representing brain regions in young subjects exhibited longer intrinsic timescales due to critical slowing down. These findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing future interventions for cognitive decline.