Abstract
BackgroundWith 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 methodIn 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. ResultsPLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals’ ongoing behavior. ConclusionsPLC 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.
Highlights
The dimensionality of neuroscience measurements has increased drastically over the past few decades (Stevenson and Kording (2011); Danielle and Sporns (2017)), meaning that neuroscientists are capable of recording simultaneous activity of dozens, hundreds, and even tens of thousands of neurons
All experiments were approved by the Centrale Commissie Dierproeven (CCD), and the surgeries and experiments were conducted according to approved indications of the local Radboud University Medical Centre animal welfare body (Approval number 2016–0079)
Custom-designed and self-made electrode arrays were constructed to target three different regions of the mouse brain
Summary
The dimensionality of neuroscience measurements has increased drastically over the past few decades (Stevenson and Kording (2011); Danielle and Sporns (2017)), meaning that neuroscientists are capable of recording simultaneous activity of dozens, hundreds, and even tens of thousands of neurons. Larger and richer datasets provide new opportunities for hypothesis-testing and exploratory discovery, and challenges in conceptualizing and characterizing the multivariate signals. Most traditional data analysis methods in neuroscience are univari ate or mass-univariate, such as spike counts (in single-unit studies) or spectral power (in LFP studies). 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. 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. Conclusions: PLC adds to an important growing literature on methods for characterizing dynamics in highdimensional datasets, using a smaller number of parameters. The method is applicable to other kinds of datasets, such as EEG or MEG
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