The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity.
The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity.
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DOI：
10.1371/journal.pone.0153154
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Khodakhah K
中科院分区：
文献类型：
--
作者：
Keller CJ;Chen C;Lado FA;Khodakhah K
到目前为止，单个神经元记录仍然是监测神经元群体活动的金标准。由于获得单个神经元的记录并不总是可能的，高频或“多单位活动”(MUA)经常被用作替代。虽然MUA记录可以监测大量神经元的活动，但它们不能识别特定的神经元亚型，对这些亚型的了解通常对理解电生理过程至关重要。在这里，我们探索了特定神经元类型的单个单位波形的先验知识是否足以允许使用MUA来监测和区分单个神经元类型的不同活动。我们使用实验和建模的方法来确定MUA的组成部分是否可以监测小鼠背侧纹状体中的中棘神经元(MSN)和快速放电中间神经元(FSIS)。我们证明，当记录到良好隔离的尖峰时，频率大于100赫兹的MUA与单个单位尖峰相关，高度依赖于每种神经元类型的波形，并准确地反映了每个神经元的时序和频谱特征。然而，在没有良好隔离的尖峰(大多数MUA记录中的标准)的情况下，MUA通常不包含足够的信息来准确预测MSN和FSIS各自的种群活动。因此，即使在MUA可靠地预测特定局部神经元集合的时刻到时刻活动的理想条件下，了解潜在神经元群体的尖峰波形也是必要的，但还不够。
To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or ‘multiunit activity’ (MUA) is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings), the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.