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A clinically integrated, frameless human Neuropixels workflow.

Hugo Layard Horsfall, Ahmed K Toma, Laurence Watkins

Medrxiv : the Preprint Server for Health Sciences

|June 4, 2026

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This summary is machine-generated.

This study outlines a reproducible method for human Neuropixels recordings in the UK, achieving clinical safety and significant neural data yield. The developed workflow enables high-density electrophysiological recordings in routine clinical settings.

Area of Science:

Neuroscience
Medical Technology
Clinical Research

Background:

High-density electrophysiological recording with Neuropixels probes offers single-unit resolution of human neural activity.
Integration into clinical environments and broader accessibility remain significant challenges, with limited prior recordings in the US.

Purpose of the Study:

To present a reproducible roadmap for human Neuropixels recordings within the United Kingdom's regulatory framework.
To establish and evaluate a frameless intraoperative workflow for clinical Neuropixels recordings.

Main Methods:

Utilized the IDEAL Stage 2a (Development) framework for workflow establishment.
Employed manufacturer-sterilized probes and a commercially available, clinical-grade setup for frameless intraoperative recording.

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Prospectively evaluated the workflow in six participants undergoing ventriculoperitoneal shunt surgery, assessing safety, timing, and data yield.

Main Results:

Achieved zero research-related adverse events, demonstrating clinical safety.
Maintained a procedural extension of approximately 30 minutes.
Successfully resolved technical barriers like neuronavigation interference and hardware instability through iterative mitigation.
Progressively increased single-unit yield to a maximum of 146 manually curated units.

Conclusions:

The developed workflow provides a reproducible and clinically safe method for human Neuropixels recordings in the UK.
This roadmap facilitates the integration of high-density electrophysiology into routine clinical environments.
The study demonstrates the feasibility of obtaining high-quality neural data in a nationally managed regulatory setting.