Evidence map›Paper›PMID 42049714›Full record

ArticleNature communications2026

Magnetic resonance identification tags for ultra-flexible electrodes.

Eminhan Özil, Peter Gombkoto, Athina Apostolelli, Tansel Baran Yasar, Angeliki D Vavladeli, Markus Marks, Manabu Rohr-Fukuma, Wolfger von der Behrens, Mehmet Fatih Yanik

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Eminhan Özil *Neurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0009-0006-0759-8617
Peter Gombkoto *Neurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-8393-173X
Athina ApostolelliNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-8061-1703
Tansel Baran YasarNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-4424-0172
Angeliki D VavladeliNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.
Markus MarksNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-8016-1637
Manabu Rohr-FukumaNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0009-0003-2546-3677
Wolfger von der BehrensNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-6883-0236
Mehmet Fatih YanikNeurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland. yanik@ethz.ch.ORCID http://orcid.org/0000-0002-8963-2893

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 818179
6 · The paper itself

Abstract

Ultra-flexible electrodes, due to their superior biocompatibility, are likely to lead the future of neuroprosthetics. However, identifying the precise positions of implanted high-density ultra-flexible electrodes in the brain for accurately assigning neural signals to specific structures remains a major challenge. To address this, we developed magnetic resonance identification (MRID)-tags. Each ultra-flexible electrode bundle carries an MRID-tag with unique barcode patterns visible in MRI (MRI-barcodes) for identification of the bundle. Individual bars in MRI-barcodes allow an accurate 3D reconstruction of the ultra-flexible electrode bundle's trajectory in the brain and determine the anatomical positions of individual electrodes. We generate the MRI-barcodes by patterning superparamagnetic iron-oxide nanoparticles into electrode fibers (10 µm

Indexed as

Electrodes, ImplantedMagnetic Resonance ImagingAnimalsHippocampusMaleRats

Identifiers

PMID42049714
PMCPMC13324162

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.