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

Journal of Neuroscience, vol. 46, pp. e0295262026

Abstract

Contemporary electrophysiology experiments often involve massively parallel recordings of neuronal activity using multi-electrode arrays. While researchers have been aware of artifacts arising from electric cross-talk between channels in setups for such recordings, systematic and quantitative assessment of the effects of those artifacts on the data quality has never been reported. Here we present, based on examination of electrophysiology recordings from multiple laboratories, that multi-electrode recordings of spiking activity commonly contain extremely precise (at the data sampling resolution) spike coincidences far above the chance level. The recordings analyzed here were obtained from two rhesus macaques ( Macaca mulatta ; one male, one female) and one male mouse (C57BL/6J). We derive, through modeling of the electric cross-talk, a systematic relation between the amount of such hyper-synchronous events (HSEs) in channel pairs and the correlation between the raw signals of those channels in the multi-unit activity frequency range (500–7,500 Hz). We show that whitening the band-pass filtered raw signals removes the above chance HSEs; strongly suggesting they originate from linear mixing of signals. Whitening should therefore be performed prior to spike sorting and any further analysis of precise spike correlation, otherwise analysis results may be considerably affected.

Authors 7

1. Jonas Oberste-Frielinghaus corresponding Aachen

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre

   RWTH Aachen University

2. Aitor Morales-Gregorio

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre

3. Simon Essink Aachen

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre

   RWTH Aachen University

4. Alexander Kleinjohann Aachen

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre

   RWTH Aachen University

5. Frédéric V. Barthélemy

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany

   Institute for Advanced Simulation (IAS-6), Jülich Research Centre

6. Alexa Riehle

   Centre National de la Recherche Scientifique · Aix-Marseille Université · Institut de Neurosciences de la Timone

   Affiliation as printed

   Institut de Neurosciences de la Timone (INT), Aix-Marseille Université, CNRS, Marseille, France

7. Thomas G. Brochier

   Centre National de la Recherche Scientifique · Aix-Marseille Université · Institut de Neurosciences de la Timone

   Affiliation as printed

   Institut de Neurosciences de la Timone (INT), Aix-Marseille Université, CNRS, Marseille, France