Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Institute for Neural Computation, University of California San Diego, San Diego, California.
J Neurophysiol. 2021 Apr 1;125(4):1408-1424. doi: 10.1152/jn.00633.2020. Epub 2021 Mar 10.
Extracellular recordings of brain voltage signals have many uses, including the identification of spikes and the characterization of brain states via analysis of local field potential (LFP) or EEG recordings. Though the factors underlying the generation of these signals are time varying and complex, their analysis may be facilitated by an understanding of their statistical properties. To this end, we analyzed the voltage distributions of high-pass extracellular recordings from a variety of structures, including cortex, thalamus, and hippocampus, in monkeys, cats, and rodents. We additionally investigated LFP signals in these recordings as well as human EEG signals obtained during different sleep stages. In all cases, the distributions were accurately described by a Gaussian within ±1.5 standard deviations from zero. Outside these limits, voltages tended to be distributed exponentially, that is, they fell off linearly on log-linear frequency plots, with variable heights and slopes. A possible explanation for this is that sporadically and independently occurring events with individual Gaussian size distributions can sum to produce approximately exponential distributions. For the high-pass recordings, a second explanation results from a model of the noisy behavior of ion channels that produce action potentials via Hodgkin-Huxley kinetics. The distributions produced by this model, relative to the averaged potential, were also Gaussian with approximately exponential flanks. The model also predicted time-varying noise distributions during action potentials, which were observed in the extracellular spike signals. These findings suggest a principled method for detecting spikes in high-pass recordings and transient events in LFP and EEG signals. We show that the voltage distributions in brain recordings, including high-pass extracellular recordings, the LFP, and human EEG, are accurately described by a Gaussian within ±1.5 standard deviations from zero, with heavy, exponential tails outside these limits. This offers a principled way of setting event detection thresholds in high-pass recordings. It also offers a means for identifying event-like, transient signals in LFP and EEG recordings which may correlate with other neural phenomena.
脑电压信号的细胞外记录有多种用途，包括通过局部场电位 (LFP) 或脑电图记录分析来识别尖峰和表征脑状态。虽然产生这些信号的因素是时变和复杂的，但通过了解其统计特性，它们的分析可能会得到促进。为此，我们分析了猴子、猫和啮齿动物的各种结构（包括皮层、丘脑和海马体）的高通细胞外记录的电压分布。我们还研究了这些记录中的 LFP 信号以及在不同睡眠阶段获得的人类 EEG 信号。在所有情况下，分布在零的±1.5 个标准差内都可以被高斯函数准确描述。在这些范围之外，电压往往呈指数分布，即在对数线性频率图上呈线性下降，具有可变的高度和斜率。一种可能的解释是，具有个体高斯尺寸分布的偶发且独立发生的事件可以相加产生近似指数分布。对于高通记录，第二个解释来自产生动作电位的离子通道噪声行为的模型，该模型通过 Hodgkin-Huxley 动力学产生动作电位。相对于平均电位，该模型产生的分布也是高斯分布，具有近似指数分布的侧翼。该模型还预测了动作电位期间时变噪声分布，这些分布在细胞外尖峰信号中观察到。这些发现为在高通记录和 LFP 和 EEG 信号中检测尖峰和瞬态事件提供了一种有原则的方法。我们表明，脑记录中的电压分布，包括高通细胞外记录、LFP 和人类 EEG，在零的±1.5 个标准差内可以被高斯函数准确描述，在这些范围之外则有较重的指数分布尾部。这为在高通记录中设置事件检测阈值提供了一种有原则的方法。它还提供了一种识别 LFP 和 EEG 记录中可能与其他神经现象相关的类事件、瞬态信号的方法。
