Background

With the growing size and richness of neuroscience datasets in terms of dimension, volume, and resolution, identifying spatiotemporal patterns in those datasets is increasingly important. Multivariate dimension-reduction methods are particularly adept at addressing these challenges.

New method

In this paper, we propose a novel method, which we refer to as Principal Louvain Clustering (PLC), to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential (LFP) recordings in awake behaving mice. Data were recorded from prefrontal cortex, hippocampus, and parietal cortex in eleven mice while they explored novel and familiar environments.

Results

PLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals’ ongoing behavior.

Conclusions

PLC adds to an important growing literature on methods for characterizing dynamics in high-dimensional datasets, using a smaller number of parameters. The method is also applicable to other kinds of datasets, such as EEG or MEG.

中文翻译：

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通过 Principal Louvain Clustering 表征神经相空间轨迹

背景

随着神经科学数据集在维度、体积和分辨率方面的规模和丰富性不断增加，识别这些数据集中的时空模式变得越来越重要。多元降维方法特别擅长解决这些挑战。

新方法

在本文中，我们提出了一种新方法，我们将其称为 Principal Louvain Clustering (PLC)，基于跨多站点局部场势 (LFP) 的光谱动力学的时变轨迹来识别低维数据子空间中的簇清醒行为小鼠的录音。在 11 只小鼠探索新奇和熟悉的环境时，它们的前额叶皮层、海马和顶叶皮层的数据被记录下来。

结果

PLC 识别的子空间和簇在动物之间显示出高度的一致性，并受动物持续行为的调节。

结论

PLC 增加了越来越多的重要文献，这些文献使用较少数量的参数来表征高维数据集中的动力学。该方法也适用于其他类型的数据集，例如 EEG 或 MEG。