A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

Abstract

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

SpikeInterface, a unified framework for spike sorting

“…Previous work to standardize the field has focused on developing open-source frameworks that make extracellular analysis and spike sorting more accessible ( Egert et al, 2002 ; Bonomini et al, 2005 ; Hazan et al, 2006 ; Garcia and Fourcaud-Trocmé, 2009 ; Goldberg et al, 2009 ; Bokil et al, 2010 ; Xq et al, 2011 ; Bologna et al, 2010 ; Oostenveld et al, 2011 ; Kwon et al, 2012 ; Mahmud et al, 2012 ; Bongard et al, 2014 ; Regalia et al, 2016 ; Zhang et al, 2017 ; Nasiotis et al, 2019a ). While useful tools in their own right, these frameworks only implement a limited suite of spike sorting technologies since their main focus is to provide entire extracellular analysis pipelines (spike trains, LFPs, EEG, and more).…”

Electrophysiological Analysis of Brain Organoids: Current Approaches and Advancements

“…Spike detection is typically threshold-based to detect spikes over baseline noise, and spike sorting often consists of cluster analysis to separate spikes based on waveform shape corresponding to individual neurons detected by the same electrode ( Hilgen et al, 2017 ). This is a crucial technique for three-dimensional analysis, especially when high spatial resolution and individual neuronal signal isolation is necessary (e.g., connectivity mapping, spike timing analysis) ( Regalia et al, 2016 ; Hilgen et al, 2017 ; Yger et al, 2018 ). Spike sorting can be computationally intensive for 2D analysis and may be much more difficult in 3D, as increased channels and spiking events increases computation exponentially ( Hilgen et al, 2017 ).…”

SpikeInterface, a unified framework for spike sorting

“…Regalia et al [61] developed a spike sorting framework with an intuitive MATLAB-based GUI. The spike sorting functionality implemented in this framework includes 4 feature extraction methods, 3 clustering methods, and 1 template matching classifier (O-Sort [67]).…”

“…Previous work to standardize the field has focused on developing open-source frameworks that make extracellular analysis and spike sorting more accessible [26,16,35,29,33,13,45,14,57,43,48,15,61,76,56]. While useful tools in their own right, these frameworks only implement a limited suite of spike sorting technologies since their main focus is to provide entire extracellular analysis pipelines (spike trains, LFPs, EEG, and more).…”