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

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.

神经元振荡（neuronal oscillations）的常规分析多采用功率谱方法，此类方法仅可量化信号幅度，无法直接反映节律性或振荡本身的固有特性。本文提出一种全新分析范式——相位自相关函数（phase-autocorrelation function, pACF），用于直接量化神经元振荡的节律性。我们将该方法应用于人类颅内立体脑电图（stereo-electroencephalography, SEEG）与脑磁图（magnetoencephalography, MEG）数据，揭示了单频及多频神经元振荡的节律性在频谱维度与解剖层面的精细皮层架构。为验证节律性的功能意义，研究发现节律性是静息态网络实现长程同步的必要前提，且在事件相关加工过程中呈现动态调节特征。此外，我们将pACF拓展至振荡过程的爆发性（burstiness）测量，并刻画了存在稳定振荡与爆发性振荡的脑区特征。上述研究结果表明，节律性与信号幅度呈双分离特性，是神经元振荡一项具备功能相关性且动态变化的关键特征。