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 the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (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.

神经元振荡通常使用量化信号幅度的功率谱方法进行分析，但不能量化节律性或“振荡性”本身。在这里，我们引入了一种新方法，即相位自相关函数（pACF），用于直接量化节律性。我们将 pACF 应用于人类脑内立体脑电图 (SEEG) 和脑磁图 (MEG) 数据，并揭示了单频和多频神经元振荡节律性的光谱和解剖学细粒度皮质结构。为了证明节律性的功能意义，我们发现它是静息状态网络中远程同步的先决条件，并且在事件相关处理过程中进行动态调制。我们还扩展了 pACF 方法来测量振荡过程的“突发性”，并描述了具有稳定和突发振荡的区域。这些发现表明节律性与振幅是双重可分离的，并且构成神经元振荡的功能相关和动态特征。