Disrupted temporal structure of the M/EEG meta-states sequencing in Alzheimer's disease

The characterization of dynamic functional connectivity (dFC) patterns, known as meta-states, which activate in the brain during rest provides valuable insights into the underlying organization of time-varying neural activity. High temporal resolution neurophysiological techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), enable the analysis of fast-evolving meta-state activation sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt dFC between different brain regions; however, their impact on the temporal organization of meta-state activation sequences remains unclear. The aim of this study was to confirm the existence of recurrent brain meta-state activations during the resting-state and to investigate how this recurrent structure is affected by MCI and AD. To fulfill this goal, the autocorrelation of the meta-state temporal activation sequence was computed from 60-second M/EEG resting-state signals; afterwards, different metrics were calculated to quantify its properties. Three databases were considered in the analysis to evaluate the generalizability of the findings across different acquisition sites and neurophysiological modalities: EEG1 (38 controls, 64 MCI, and 74 AD), EEG2 (38 controls, 38 MCI, and 70 AD), and MEG (52 controls, 32 MCI, and 52 AD). The results provide evidence of an underlying cyclical structure in meta-state activation sequences, which becomes progressively disrupted in MCI and AD, leading to a decrease in recurrence and an increase in randomness. These findings suggest that the loss of temporal organization in meta-state dynamics may be indicative of neurodegenerative conditions.