1024-channel electrophysiological recordings in macaque V1 and V4 during resting state

Xing Chen*, Aitor Morales-Gregorio, Julia Sprenger, Alexander Kleinjohann, Shashwat Sridhar, Sacha J. van Albada, Sonja Grün, Pieter R. Roelfsema

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Co-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined resting state activity throughout the brain using MRI scans, or observed local co-variations in activity by recording from a small number of electrodes. We carried out electrophysiological recordings from over a thousand chronically implanted electrodes in the visual cortex of non-human primates, yielding a resting state dataset with unprecedentedly high channel counts and spatiotemporal resolution. Such signals could be used to observe brain waves across larger regions of cortex, offering a temporally detailed picture of brain activity. In this paper, we provide the dataset, describe the raw and processed data formats and data acquisition methods, and indicate how the data can be used to yield new insights into the ‘background’ activity that influences the processing of visual information in our brain.

Original languageEnglish
Article number77
Pages (from-to)1-16
Number of pages16
JournalScientific Data
Volume9
DOIs
Publication statusPublished - 11 Mar 2022

Bibliographical note

Funding Information:
We thank Kor Brandsma and Anneke Ditewig for technical support; Matthew Self and Feng Wang for assistance during surgeries; Chris Klink for help with technical validation; John van Veldhuizen, Stephen Super, Joop Bos, Joost Brand, and Ruud van der Blom, for help with mechanical engineering; Cyril Voisard (Medicoat) for biocompatible coating of the implants; Florian Solzbacher, Marcus Gerhardt, Nick Halper, Stephen Hou, Rob Franklin, Saman Hagh-Gooie, Kian Torab, Sherman Wiebe, Charles Dryden, Vinh Ngo, William Yang, Greg Palis, Mike Gruenhagen, and others at Blackrock Microsystems for scientific and technical collaborations; and Sebastian Lehmann for assistance with graphic design. Funding: This work was supported by NWO (STW grant number P15-42 ‘NESTOR’ and Crossover grant number 17619 ‘INTENSE’), the European Union FP7 (ERC grant number 339490 ‘Cortic_al_gorithms’), the European Union Horizon 2020 Framework Programme for Research and Innovation under the Framework Partnership (HBP FPA agreement number 650003), the H2020 Framework Programme for Research and Innovation (Human Brain Project SGA2 grant number 785907, and HBP SGA3 grant number 945539), the European Union Horizon 2020 Future and Emerging Technologies (FET Open grant number 899287 ‘NeuraViPeR'), and the Deutsche Forschungsgemeinschaft (German Research Foundation, grant number 368482240/ RTG 2416).

Publisher Copyright:
© 2022, The Author(s).

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