Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

Abstract

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal

“…In addition, electrical interference, cable and head movement, broken cables, etc. within the recording setup could all result in spurious spike events [46]. All of these technical glitches originate from sources other than neurons, and can produce artefacts that look very similar to spikes of neurons, and can be recorded on several channels simultaneously.…”

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Nevertheless, computational costs escalate almost exponentially with channel numbers, and the number of wires that send signals forward would also grow, except for methods employing controllable switches to attach recording sites with single wires (Lee et al, 2021 ). Increasing channel density and adopting a divide-and-conquer processing strategy (Chen et al, 2021 ) allows for effortless detection of duplicated or overlapping spikes (Larionov et al, 2019 ; Chou et al, 2021 ; Dehnen et al, 2021 ) and provides more detailed spatial information on action potential sources (Rácz et al, 2020 ). Meanwhile, by increasing the number of spikes sorted out, false positive or negative detections become an imaginable source of errors: a small quantity of mistakenly identified spikes perturb firing rate or interspike interval values (Chiarion and Mesin, 2021 ).…”

Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

“…One artifact linked to cross-talk is extremely precise (at the data sampling resolution, e.g. ms) spike synchrony (Yu et al, 2009; Torre et al, 2016; Dehnen et al, 2021; Chen et al, 2022). Spiking activity is known to be coordinated on the timescales of a few milliseconds (König et al, 1995; Riehle et al, 1997; Butts et al, 2007; Grün, 2009), but no observation of sub-millisecond coordination has been reported, nor are there any known mechanisms that enable sub-millisecond synchronization.…”

“…Some studies remove suspicious spikes from further analysis either during the spike sorting process (Musial et al, 2002; Dann et al, 2016) or post-hoc (Torre et al, 2016; Dehnen et al, 2021). Other studies remove entire channels or neurons instead (Churchland et al, 2006; Yu et al, 2009; Snyder et al, 2021; Chen et al, 2022; Semedo et al, 2022; Morales-Gregorio et al, 2023).…”

“…Extremely precise (within ms) synchronous spiking events have been linked to cross-talk artifacts (Yu et al, 2009; Torre et al, 2013; Dehnen et al, 2021; Chen et al, 2022), since they occur well above the expectation from chance coincidences of spikes. Spiking activity is known to be coordinated on the timescales of a few milliseconds (König et al, 1995; Riehle et al, 1997; Butts et al, 2007; Grün, 2009), but no observation of sub-millisecond coordination has ever been reported, nor are there any known mechanisms that would enable sub-millisecond synchronization.…”