Loza Carlos A, Okun Michael S, Príncipe José C
Department of Mathematics, Universidad San Francisco de Quito, Quito, Ecuador.
Department of Neurology and Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.
Front Syst Neurosci. 2017 Dec 18;11:95. doi: 10.3389/fnsys.2017.00095. eCollection 2017.
Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response to external stimuli or internal physiological processes. We propose a novel generative statistical model of a single EEP channel, where the collected signal is regarded as the noisy addition of reoccurring, multi-frequency phasic events over time. One of the main advantages of the proposed framework is the exceptional temporal resolution in the time location of the EEP phasic events, e.g., up to the sampling period utilized in the data collection. Therefore, this allows for the first time a description of neuromodulation in EEPs as a Marked Point Process (MPP), represented by their amplitude, center frequency, duration, and time of occurrence. The generative model for the multi-frequency phasic events exploits sparseness and involves a shift-invariant implementation of the clustering technique known as k-means. The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP. Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time. The framework is validated using two publicly available datasets: the DREAMS sleep spindles database and one of the Brain-Computer Interface (BCI) competition datasets. The results achieve benchmark performance and provide novel quantitative descriptions based on power, event rates and timing in order to assess behavioral correlates beyond the classical power spectrum-based analysis. This opens the possibility for a unifying point process framework of multiscale brain activity where simultaneous recordings of EEP and the underlying single neuron spike activity can be integrated and regarded as marked and simple point processes, respectively.
神经调制是细胞外电位（EEP）的重要组成部分，如脑电图（EEG）、皮质电图（ECoG）和局部场电位（LFP）。这种时空组织的多频瞬态（相位）活动反映了神经元群体在响应外部刺激或内部生理过程时的多尺度时空同步。我们提出了一种新颖的单个EEP通道生成统计模型，其中收集到的信号被视为随时间重复出现的多频相位事件的噪声叠加。所提出框架的主要优点之一是在EEP相位事件的时间定位方面具有出色的时间分辨率，例如，高达数据收集所使用的采样周期。因此，这首次允许将EEPs中的神经调制描述为标记点过程（MPP），由其幅度、中心频率、持续时间和发生时间表示。多频相位事件的生成模型利用稀疏性，并涉及称为k均值的聚类技术的平移不变实现。成本函数包含基于相关熵的鲁棒估计组件，以减轻EEP中固有噪声引起的异常值。最后，可以将背景EEP活动明确建模为收集信号的非稀疏分量，以进一步改善多频相位事件在时间上的描绘。该框架使用两个公开可用的数据集进行了验证：DREAMS睡眠纺锤波数据库和脑机接口（BCI）竞赛数据集之一。结果达到了基准性能，并基于功率、事件发生率和时间提供了新颖的定量描述，以便在基于经典功率谱的分析之外评估行为相关性。这为多尺度脑活动的统一点过程框架开辟了可能性，其中EEP的同步记录和潜在的单个神经元尖峰活动可以分别整合并视为标记点过程和简单点过程。
