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September 14, 2026International Journal of Neural Systems

Long-Range Temporal Correlations in Brain-state Dynamics via a Multivariate One-hot Approach

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Authors

FZFilippo ZappasodiPCPierpaolo Croce

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Overview

Computational analysis reveals condition-dependent temporal correlations in resting-state EEG microstates, indicating that structured state lifetimes drive multi-timescale neural dynamics.

Key Points

  • Introduce a representation-invariant one-hot detrended fluctuation analysis framework to assess long-range temporal correlations in discrete brain-state sequences under different resting conditions.
  • Formulated a one-hot detrended fluctuation analysis (DFA) approach to evaluate recurrence scaling in symbolic brain-state sequences independently of embedding geometry.
  • Analyzed electroencephalographic (EEG) microstate sequences recorded during eyes-open (EO) and eyes-closed (EC) resting states.
  • Implemented surrogate data manipulations, including microstate duration equalization, duration randomization, and transition-order shuffling, to isolate the structural drivers of temporal scaling.
  • Identified heterogeneous scaling exponents across individual microstates, with a widespread elevation of scaling exponents in the eyes-closed compared to the eyes-open condition.
  • Demonstrated that disrupting state duration distributions markedly decreases Hurst exponents, whereas transition-order shuffling produces comparatively small effects on overall scaling magnitude.
  • Showed that transition ordering selectively modulates the contrast between eyes-open and eyes-closed dynamics, with combined surrogate operations exerting additive effects.

Cite This Study

Zappasodi et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b2f70926e14a848b19e1https://doi.org/10.1142/s0129065727500262
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