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

Myrov, Vladislav1; Siebenhühner, Felix2; Juvonen, Joonas J1; Arnulfo, Gabriele3; Palva, Satu4; Palva, Matias1

Published Mar 15, 2024 on Dryad. https://doi.org/10.5061/dryad.rbnzs7hhf

Data files

Mar 15, 2024 version files 10 GB

Abstract

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.