Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

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 method, the phase-autocorrelation functon(pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data to quantify rhythmicity and uncovered a spectrally and anatomically fine-grained cortical architecture of single- and multi-frequency neuronal oscillations. We also extended the pACF approach to measure "burstiness" of oscillatory processes and characterized regions with stable and bursty oscillations. We found that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.