Matzner Ayala, Bar-Gad Izhar
The Leslie & Susan Goldschmied (Gonda) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
PLoS Comput Biol. 2015 Apr 24;11(4):e1004252. doi: 10.1371/journal.pcbi.1004252. eCollection 2015 Apr.
Estimation of the power spectrum is a common method for identifying oscillatory changes in neuronal activity. However, the stochastic nature of neuronal activity leads to severe biases in the estimation of these oscillations in single unit spike trains. Different biological and experimental factors cause the spike train to differentially reflect its underlying oscillatory rate function. We analyzed the effect of factors, such as the mean firing rate and the recording duration, on the detectability of oscillations and their significance, and tested these theoretical results on experimental data recorded in Parkinsonian non-human primates. The effect of these factors is dramatic, such that in some conditions, the detection of existing oscillations is impossible. Moreover, these biases impede the comparison of oscillations across brain regions, neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. We introduce a novel objective measure, the "modulation index", which overcomes these biases, and enables reliable detection of oscillations from spike trains and a direct estimation of the oscillation magnitude. The modulation index detects a high percentage of oscillations over a wide range of parameters, compared to classical spectral analysis methods, and enables an unbiased comparison between spike trains recorded from different neurons and using different experimental protocols.
功率谱估计是识别神经元活动振荡变化的常用方法。然而，神经元活动的随机性导致在估计单个神经元放电序列中的这些振荡时产生严重偏差。不同的生物学和实验因素会使放电序列以不同方式反映其潜在的振荡频率函数。我们分析了诸如平均放电率和记录时长等因素对振荡可检测性及其显著性的影响，并在帕金森病非人灵长类动物记录的实验数据上检验了这些理论结果。这些因素的影响非常显著，以至于在某些情况下，无法检测到现有的振荡。此外，这些偏差阻碍了跨脑区、神经元类型、行为状态以及具有不同潜在参数的单独记录之间的振荡比较，不可避免地导致对实验结果的严重误解。我们引入了一种新的客观测量方法，即“调制指数”，它克服了这些偏差，能够可靠地从放电序列中检测振荡并直接估计振荡幅度。与经典频谱分析方法相比，调制指数在广泛的参数范围内能检测到高比例的振荡，并能够对从不同神经元记录的以及使用不同实验方案的放电序列进行无偏比较。
