Quasicriticality explains variability of human neural dynamics across life span

TL;DR

This study applies the quasicriticality framework to analyze age-related changes in human neural dynamics using MEG data, revealing correlations with age and gender, and suggesting potential biomarkers for neurological health.

Contribution

It introduces the application of the quasicriticality framework to large-scale aging brain data, linking connectivity changes to cognitive vulnerability and health markers.

Findings

Age correlates with changes in brain connectivity and dynamics.

Increased vulnerability to distraction with age.

Potential for developing neurological health biomarkers.

Abstract

Ageing impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset -- the largest ageing cohort available -- in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to ageing, and increased vulnerability to distraction from irrelevant information. Our findings suggest a platform to develop biomarkers of neurological health.