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Temporal irreversibility of large-scale brain dynamics in Alzheimer's disease

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Deco,  Gustavo
Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain;
Department of Information and Communication Technologies, Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain;
Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra, Barcelona, Spain;
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
School of Psychological Sciences, Monash University, Melbourne, Australia;

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Citation

Cruzat, J., Herzog, R., Prado, P., Sanz-Perl, Y., Gonzalez-Gomez, R., Moguilner, S., et al. (2023). Temporal irreversibility of large-scale brain dynamics in Alzheimer's disease. The Journal of Neuroscience, 43(9), 1643-1656. doi:10.1523/JNEUROSCI.1312-22.2022.


Cite as: https://hdl.handle.net/21.11116/0000-000C-8E53-7
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 towards 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 (HC). We quantified the level of irreversibility and, thus, proximity to non-equilibrium dynamics by comparing forward and backward timeseries 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 grey matter volume in HC. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from controls. 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.