Data from: Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

  • Vladislav Myrov (Creator)
  • Felix Siebenhühner (Creator)
  • Joonas Juvonen (Creator)
  • Gabriele Arnulfo (Creator)
  • Satu Palva (Creator)
  • Matias Palva (Creator)

Dataset

Description

Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
Date made available15 Mar 2024
PublisherZenodo

Dataset Licences

  • CC0-1.0

Cite this