Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease

Josephine Cruzat, Ruben Herzog, Pavel Prado, Yonatan Sanz-Perl, Raul Gonzalez-Gomez, Sebastian Moguilner, Morten L. Kringelbach, Gustavo Deco, Enzo Tagliazucchi, Agustín Ibañez

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels.

Original languageEnglish
Pages (from-to)1643-1656
Number of pages14
JournalJournal of Neuroscience
Volume43
Issue number9
DOIs
StatePublished - 1 Mar 2023
Externally publishedYes

Keywords

  • Alzheimer's disease
  • EEG
  • dynamic networks
  • fMRI
  • irreversibility dynamics
  • machine learning

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