The concept of oscillations is fundamental in the field of neural data analysis. Oscillations are thought to reflect brain states in different cognitive or physiological states and are essential to higher-order features of neural dynamics, such as functional connectivity or long-range temporal correlations. Neuronal oscillations are typically measured using power spectral methods that quantify signal amplitude but not the rhythmicity or 'oscillatoriness' per se.Here we present a new method, the phase-autocorrelation function (pACF), for quantifying rhythmicity directly. We applied pACF to data from human intracerebral stereoelectroencephalography (SEEG, N=64) and magnetoencephalography (MEG, N=57) and discovered a detailed cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. In contrast to power-spectrum based clustering, we discovered that the SEEG and MEG alpha band does not form a single cluster, but rather three subclusters with distinct anatomy. Additionally, we observed that single-frequency and multi-frequency parcels are organized differently in terms of anatomy.Our findings demonstrate the functional significance of rhythmicity, as it is a prerequisite for long-range synchronization in resting-state networks and has a significant positive correlation with alpha and beta bands. It has been demonstrated that rhythmicity can be dynamically modulated during event-related processing with higher spectral resolution, in contrast to previously established methods. Additionally, the pACF approach has been extended to measure the 'burstiness' of oscillatory processes, allowing for the characterization of regions with stable and bursty oscillations.These findings suggest that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.