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High-density, Identified Cell Recordings from Motor Cortex of Awake Behaving Macaques using Multiple 1024-channel CMOS Probes

Lookup NU author(s): Dr Anne BakerORCiD, Professor Stuart BakerORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Objective/Background: Recording bioelectric signals from large populations of single neurons in the primate brain remains challenging. Chronic implants offer limited coverage (∼100 channels) and sample fixed cortical regions, while acutely inserted electrodes allow broader access via multiple daily penetrations. We aimed to develop a CMOS-based probe with high electrode-channel density, and optimized procedures for acute large-scale single-unit recordings in behaving monkeys. Methods: We designed a novel single-shank SiNAPS CMOS probe for acute recordings in monkeys with additional integrated multiplexing circuits to reduce output lines. A multi-probe system enables synchronous sampling at 20 kHz/channel from two SINAPS-NHP probes during repeated insertions into the motor cortex of behaving macaques. We developed methods to identify neurons via antidromic activation. Results: The probe (10.7 mm × 158 μm × 50 μm) samples neural activity from 1,024 electrodes (14 × 14 μm2, 30 μm pitch) arranged in four columns and reaches an electrode-channel density of 304.4 channels/mm2. A pilot hole facilitates dural penetration, and optimized insertion procedures allow recordings from diverse cortical sites. Some neurons were identified as pyramidal tract cells projecting to the spinal cord. Conclusion: Each probe enables monitoring of intracortical areas of 7.75 × 0.1 mm2, detecting hundreds of single neurons per session, and reaches deep regions such as the anterior bank of the central sulcus, rich in corticospinal cells. Significance: This technology and methods unlock routine acute recordings from 2,048 channels with single-neuron resolution and cell-type identification, advancing the neurophysiological toolkit for primate research.

Publication metadata

Author(s): Angotzi GN, Baker AME, Vincenzi M, Orban G, Ribeiro JF, Tenorio V, Berdondini L, Baker SN

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Biomedical Engineering

Year: 2026

Pages: Epub ahead of print

Online publication date: 18/05/2026

Acceptance date: 02/04/2018

Date deposited: 01/06/2026

ISSN (print): 0018-9294

ISSN (electronic): 1558-2531

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TBME.2026.3694160

DOI: 10.1109/TBME.2026.3694160

PubMed id: 42149759

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