The Role of rare long-range cortical connections in Alzheimer's Disease : Simulation and analysis

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the deterioration of cognitive function and alterations in brain dynamics. Recent studies suggest that disruptions in long-range connectivity and reductions in neural complexity—often described as a breakdown in “turbulence-like” dynamics—may serve as early biomarkers of the disease. This study explores these alterations by applying turbulence metrics to resting-state fMRI data from the Alzheimer’s Disease neuroimaging Initiative (ADNI). Cortical brain regions (360 out of an original 379 parcellated regions) were analyzed using Exponential Distance Rules (EDR), with and without long-range correction (LR), to assess the impact of distance-based and anatomically informed coupling. Turbulence metrics were computed across several values of the global coupling parameter λ, selecting λ = 0.18 based on prior literature indicating its relevance for capturing realistic brain dynamics. While no statistically significant group diAerences in turbulence were observed between healthy controls (HC), mild cognitive impairment (MCI), and AD subjects, this may be partly attributed to the limited sample size. Nevertheless, the findings highlight the conceptual promise of turbulence-informed models for understanding functional brain alterations in neurodegeneration and underscore the need for further investigation with larger cohorts