Discovering rhythmicity of neuronal oscillations

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.