An Unsupervised Approach to Extracellular Electrophysiology with Biologically Validated Cell Type Classification

Abstract

Large-scale extracellular recording techniques represent a major advance in interrogating the structure and function of neuronal circuits. However, the field lacks methods that can resolve cell type identity in a principled way, while simultaneously scaling to thousands of neurons. Here, I introduce a pipeline for the analysis of large-scale recordings of in vitro cortical activity that not only allows for the detection of spikes produced by single neurons (spike sorting), but also allows for the reliable distinction between genetically determined cell types by utilizing viral and optogenetic strategies to provide a ground-truth validation. Importantly, this novel methodology tightly integrates the analysis pipeline to an experimental protocol allowing for the dynamical probing of distinct cell types while simultaneously recording from large populations. The novelty of the proposed approach is to combine a stream of analysis, and experimental techniques in an end-to-end fashion as follows. First, individual spike waveforms are fitted by spline interpolation to estimate their half-amplitude and peak-to-peak durations. These values are then entered in a principal component analysis with k-means clustering to identify uncorrelated signals from single channels on the array. Optimal separability of clusters is assessed by linear discriminant analysis. Finally, each channel's source location is identified using spatiotemporal characteristics of spike waveforms across the array. I validated this pipeline using activity monitored from mouse prefrontal cortex in vitro slices with an array of 4,096 closely-spaced channels, in conjunction with optogenetic, viral and pharmacological strategies. I show an effective distinction of regular-spiking excitatory neurons from genetically validated fast-spiking inhibitory interneurons using measures of action potential waveform, Fano factor, and spatially-dependent correlations. In sum, the proposed approach allows for a comprehensive characterization of neuronal activity obtained from different cell types in high-density multielectrode recordings. This provides a scalable approach to investigate the interplay between distinct cell types in microcircuits of the brain.