Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
Brain Topogr. 2019 Nov;32(6):1020-1034. doi: 10.1007/s10548-019-00745-5. Epub 2019 Nov 21.
Electrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral 'bursts' or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.
神经元活动的电生理记录显示其频域功率谱中存在自发和任务依赖的变化。这些变化通常被解释为潜在振荡幅度的调制。然而，这忽略了潜在瞬态谱“爆发”或事件的可能性，其动态可以通过多种方式映射到试验平均谱功率的变化。在这种新兴的观点下，一个关键的挑战是以一种有原则的方式进行突发检测，即表征单试瞬态谱事件。在这里，我们描述了如何使用隐马尔可夫模型（HMM）来操作和估计瞬态谱事件。HMM 克服了突发检测中标准幅度阈值方法的许多限制；它能够同时检测具有不同频谱内容的不同类型的突发，而无需预先定义感兴趣的频带，并且在很大程度上不需要预先指定阈值。我们描述了如何使用 HMM 进行突发检测，并说明了它在模拟数据上的好处。最后，我们将该方法应用于经验数据，以在任务-MEG 数据集中检测多种突发类型，并说明了如何计算突发指标，例如突发持续时间的任务诱发时程。
