Rimehaug Atle E, Dale Anders M, Arkhipov Anton, Einevoll Gaute T
Department of Informatics, University of Oslo, Oslo, Norway.
Department of Neuroscience, University of California San Diego, San Diego, California, United States of America.
PLoS Comput Biol. 2024 Dec 12;20(12):e1011830. doi: 10.1371/journal.pcbi.1011830. eCollection 2024 Dec.
The local field potential (LFP), the low-frequency part of the extracellular potential, reflects transmembrane currents in the vicinity of the recording electrode. Thought mainly to stem from currents caused by synaptic input, it provides information about neural activity complementary to that of spikes, the output of neurons. However, the many neural sources contributing to the LFP, and likewise the derived current source density (CSD), can often make it challenging to interpret. Efforts to improve its interpretability have included the application of statistical decomposition tools like principal component analysis (PCA) and independent component analysis (ICA) to disentangle the contributions from different neural sources. However, their underlying assumptions of, respectively, orthogonality and statistical independence are not always valid for the various processes or pathways generating LFP. Here, we expand upon and validate a decomposition algorithm named Laminar Population Analysis (LPA), which is based on physiological rather than statistical assumptions. LPA utilizes the multiunit activity (MUA) and LFP jointly to uncover the contributions of different populations to the LFP. To perform the validation of LPA, we used data simulated with the large-scale, biophysically detailed model of mouse V1 developed by the Allen Institute. We find that LPA can identify laminar positions within V1 and the temporal profiles of laminar population firing rates from the MUA. We also find that LPA can estimate the salient current sinks and sources generated by feedforward input from the lateral geniculate nucleus (LGN), recurrent activity in V1, and feedback input from the lateromedial (LM) area of visual cortex. LPA identifies and distinguishes these contributions with a greater accuracy than the alternative statistical decomposition methods, PCA and ICA. The contributions from different cortical layers within V1 could however not be robustly separated and identified with LPA. This is likely due to substantial synchrony in population firing rates across layers, which may be reduced with other stimulus protocols in the future. Lastly, we also demonstrate the application of LPA on experimentally recorded MUA and LFP from 24 animals in the publicly available Visual Coding dataset. Our results suggest that LPA can be used both as a method to estimate positions of laminar populations and to uncover salient features in LFP/CSD contributions from different populations.
局部场电位（LFP）是细胞外电位的低频部分，反映了记录电极附近的跨膜电流。它主要被认为源于突触输入引起的电流，提供了与神经元输出——尖峰互补的神经活动信息。然而，许多对LFP有贡献的神经源，同样还有导出的电流源密度（CSD），常常使得对其进行解释具有挑战性。为提高其可解释性所做的努力包括应用主成分分析（PCA）和独立成分分析（ICA）等统计分解工具来厘清不同神经源的贡献。然而，它们分别基于的正交性和统计独立性假设对于产生LFP的各种过程或通路并不总是成立的。在此，我们扩展并验证了一种名为层状群体分析（LPA）的分解算法，该算法基于生理学而非统计学假设。LPA联合利用多单元活动（MUA）和LFP来揭示不同群体对LFP的贡献。为了对LPA进行验证，我们使用了由艾伦脑科学研究所开发的大规模、生物物理细节丰富的小鼠V1模型模拟的数据。我们发现LPA能够从MUA中识别出V1内的层状位置以及层状群体放电率的时间分布。我们还发现LPA能够估计由外侧膝状体核（LGN）的前馈输入、V1中的循环活动以及视觉皮层的外侧内侧（LM）区域的反馈输入所产生的显著电流汇和电流源。与主成分分析（PCA）和独立成分分析（ICA）等替代统计分解方法相比，LPA能更准确地识别和区分这些贡献。然而，V1内不同皮层层的贡献无法通过LPA进行稳健的分离和识别。这可能是由于各层群体放电率存在大量同步性，未来通过其他刺激方案可能会降低这种同步性。最后，我们还展示了LPA在公开可用的视觉编码数据集中对24只动物的实验记录的MUA和LFP上的应用。我们的结果表明，LPA既可以用作估计层状群体位置的方法，也可以用于揭示不同群体对LFP/CSD贡献中的显著特征。
