Predictability of temporal network dynamics in normal ageing and brain pathology

TL;DR

This study models brain activity as a high-dimensional network to analyze its short-term predictability, revealing differences in brain dynamics between healthy aging and neurological diseases like Parkinson's and Alzheimer's.

Contribution

It introduces a novel non-parametric method to quantify the predictability of brain network trajectories and applies it to distinguish between healthy aging and neurodegenerative conditions.

Findings

Predictability varies with scale across groups.

Healthy aging differs from Parkinson's disease in predictability.

Pathological groups show distinct predictability patterns.

Abstract

Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurological and psychiatric pathologies. Here we model the full brain's electroencephalographic activity as a high-dimensional functional network performing a trajectory in a latent graph phase space. This approach allows us to investigate the orbital stability of brain's activity and in particular its short-term predictability. We do this by constructing a non-parametric statistic quantifying the expansion of initially close functional network trajectories. We apply the method to cohorts of healthy ageing individuals, and patients previously diagnosed with Parkinson's or Alzheimer's disease. Results not only characterise brain dynamics from a new angle, but further show that functional network predictability varies in a marked scale-dependent way across healthy controls and patient groups. The path towards both pathologies is markedly different. Furthermore, healthy ageing's predictability appears to strongly differ from that of Parkinson's disease, but much less from that of patients with Alzheimer's disease.