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Magnetic resonance identification tags for ultra-flexible electrodes.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Results › MRI characterization and channel mapping ↔ mri/Figure_3B.ipynb, lines 110–130 · score 0.61 · Gaussian curve, Gaussian fits, curve centers, sigma, islands, MRI
  2. [2] § Methods › IONP relaxivity ↔ mri/Suppl_Figure_9.ipynb, lines 6–78 · score 0.56 · relaxation curves, decay, relaxivity, signal, segmented, echo
  3. [3] § Methods › MRID analysis ↔ mri/Figure_3B.ipynb, lines 110–130 · score 0.56 · Gaussian curve, Gaussian centers, heatmap, fitted, coronal, island

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Jupyter notebook · 147 lines · 4.3 KB · no license · 2 matches

  1. # %%
  2. root="./"
  3. %run utils/imports.ipynb
  5. # %% [markdown]
  6. # # QUAD-zone pattern
  8. # %%
  9. animal_path = "./data/"
  10. filename="t2star_mge_raw.nii.gz"
  11. filenameseg="t2star_mge_segmentation.nii.gz"
  12. filenameanat="t2star_mge_anat.nii.gz"
  14. _, data=read_data(os.path.join(root, animal_path, filename))
  15. _, segmentation=read_data(os.path.join(root, animal_path, filenameseg))
  16. _, anat=read_data(os.path.join(root, animal_path, filenameanat))
  17. filenamelabels="labels.txt"
  18. labelsdf=read_labels(os.path.join(root, animal_path, filenamelabels))
  20. roi_name="quad"
  21. img_slice=3
  23. num_echos=8
  24. # quad_areas=np.flip([175,1075,2170,7118])
  25. quad_areas=np.array([7118, 2170, 1075, 175])
  26. quad_interseg=np.array([750, 755, 605])
  27. quad_lengths=np.array([670, 300, 295, 295])
  28. quad_densities=quad_areas/quad_lengths
  30. roi_areas=labelsdf["Labels"][labelsdf["Anatomical Regions"].str.contains(roi_name)]
  32. basestructs=["cortex", "cc", "striatum", "ventrical"]
  33. quadDictCC,_,_=find_roi(data, segmentation, anat, basestructs, labelsdf, ["quad"], num_echos)
  36. # %%
  37. dataDict = quadDictCC
  39. writer = pd.ExcelWriter('Figure_3B-detected_pixels.xlsx', engine = 'xlsxwriter')
  41. for echo in range(num_echos):
  42. dict1={}
  43. tmp_x = np.array([])
  44. tmp_y = np.array([])
  45. for j, roi in enumerate(dataDict.keys()):
  46. for i, island in enumerate(dataDict[roi].keys()):
  47. for k, region in enumerate(dataDict[roi][island].keys()):
  48. indeces=dataDict[roi][island][region][echo]["index"]
  49. if indeces:
  50. tmp_x = np.append(tmp_x, np.array(indeces)[:,1])
  51. tmp_y = np.append(tmp_y, np.array(indeces)[:,0])
  53. dict1["Significant Pixels x-indeces"] = tmp_x
  54. dict1["Significant Pixels y-indeces"] = tmp_y
  55. df = pd.DataFrame(data=dict1)
  56. df.to_excel(writer, sheet_name = "Echo-time "+str(echo))
  58. writer.close()
  61. # %%
  62. plot_all_roi(data[:,:,img_slice,:], quadDictCC, roi_name, slice_orientation="coronal", savepath="", color="g", savefigs=False, dpi=1000)
  64. # %%
  65. num_echos=data.shape[-1]
  66. heatmaps=np.zeros((len(roi_areas),segmentation.shape[0], segmentation.shape[1]))
  68. r=[2, 1, 1, 1]
  69. for i, roi in enumerate(roi_areas):
  70. heatmaps[i]=segment_relaxation(data[:,:,img_slice,:], segmentation[:,:,img_slice], anat[:,:,img_slice], basestructs,labelsdf, roi, te=[4.0, 4.09], r=r[i])
  73. # # create a colormap object
  74. cmap = plt.cm.get_cmap("jet").copy()
  75. cmap.set_under('white', alpha=0)
  77. # # set colourbar map
  78. cmap_args = dict(cmap=cmap, vmin=6)
  80. plt.imshow(data[:,:,img_slice,0], cmap='gray')
  81. plt.imshow(np.sum(heatmaps, axis=0), **cmap_args, alpha=0.5)
  82. plt.colorbar()
  83. plt.savefig("./Figure_3B-heatmap_r=1sqr.pdf", dpi=1000)
  84. plt.show()
  86. # %%
  87. islands, x_range, y_range = np.shape(heatmaps)
  89. writer = pd.ExcelWriter('Figure_3B-heatmap.xlsx', engine = 'xlsxwriter')
  91. for i in range(islands):
  92. dict1={}
  93. x_idx = []
  94. y_idx = []
  95. contrast = []
  96. for x in range(x_range):
  97. for y in range(y_range):
  98. x_idx.append(x)
  99. y_idx.append(y)
  100. contrast.append(heatmaps[i,x,y])
  102. dict1["x-indeces"] = x_idx
  103. dict1["y-indeces"] = y_idx
  104. dict1["MRI Contrast"] = contrast
  105. df = pd.DataFrame(data=dict1)
  106. df.to_excel(writer, sheet_name = "Island "+str(4-i))
  108. writer.close()
  110. # %% [markdown]
  111. # ## 2D Gaussian fit
  113. # %%
  114. fixed_img = data[:,:,img_slice,0]
  115. px_size = 136
  116. coronalFlag = True
  117. gaussian_centers, gaussAmp, gaussSig, popt = find_gaussian_centers(heatmaps, fixed_img, px_size, coronal=coronalFlag)
  119. islands, x_range, y_range = np.shape(heatmaps)
  121. writer = pd.ExcelWriter('Figure_3B-gaussian_fit.xlsx', engine = 'xlsxwriter')
  122. dict1={}
  123. dict1["Gaussian curve center x"] = gaussian_centers[:,0]
  124. dict1["Gaussian curve center y"] = gaussian_centers[:,1]
  125. dict1["Gaussian curve amplitude"] = gaussAmp
  126. dict1["Gaussian curve sigma"] = gaussSig
  127. df = pd.DataFrame(data=dict1)
  128. df.to_excel(writer, sheet_name = "Gaussian Fit")
  130. writer.close()
  132. # %% [markdown]
  133. # ## Barcodes
  135. # %%
  136. px_size = 136
  138. gaussian_centers_3d=combined_gaussian_centers(gaussian_centers,
  139. contrast_intensities_coronal = gaussAmp,
  140. savepath="")
  142. barcode, ticks, tickLabels = gen_barcode_mrid(gaussSig*2*px_size, get_dist(gaussian_centers_3d, px_size))
  144. plt.figure(figsize=(10,5))
  145. plt.imshow(barcode, cmap='gray')
  146. plt.xticks(ticks=ticks, labels=tickLabels)
  147. plt.show()

Figure_3B.ipynb at commit 71706d2, no license · at the source

Overview

Authors: Eminhan Özil1,2, Peter Gombkoto1,2, Athina Apostolelli1,3, Tansel Baran Yasar1, Angeliki D Vavladeli1,2,4,5,6, Markus Marks1,7,8, Manabu Rohr-Fukuma1,9,10, Wolfger von der Behrens1,2, Mehmet Fatih Yanik1,2
ORCID iDs: Eminhan Özil, Peter Gombkoto, Athina Apostolelli, Tansel Baran Yasar, Markus Marks, Manabu Rohr-Fukuma, Wolfger von der Behrens, Mehmet Fatih Yanik
  1. Neurotechnology Group, Institute of Neuroinformatics, Department of Information Technology and Electrical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
  2. Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
  3. Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
  4. Brain Research Institute, University of Zurich, Zurich, Switzerland
  5. University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland
  6. Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland
  7. Division of Computing and Mathematical Sciences, Caltech, Pasadena, CA USA
  8. Division of Engineering and Applied Science, Caltech, Pasadena, CA USA
  9. Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
  10. Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
Institutions: University of Zurich (Switzerland); ETH Zurich (Switzerland); Institute of Neuroinformatics (Switzerland); Sainsbury Wellcome Centre (United Kingdom); University College London (United Kingdom); Center for Microscopy and Image Analysis (Switzerland); California Institute of Technology (United States); University Hospital Zurich (Switzerland)
Journal: Nature communications, volume 17, issue 1, article 5725
Dates: received 17 April 2025; accepted 25 March 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71887-x · PMID 42049714 · PMCID PMC13324162 · OpenAlex W4410146547
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-6406139/v1
Status: code verified
Categories: structural MRI / diffusion (modality), rat (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Neuroscience, Biomedical engineering, Nanoparticles, Magnetic devices, Implants
MeSH: Electrodes, Implanted*, Magnetic Resonance Imaging*, Animals, Hippocampus, Male, Rats (* major topic)
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: Swiss National Science Foundation (198739, 211760); EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (818179)
Citations: cited by 1 paper (Europe PMC); 55 references in the paper

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 µm2) with dot-matrix nanoparticle coating technique. We chronically tested MRID-tagged ultra-flexible electrodes in vivo in the dorsal hippocampus of freely-moving rats, where distinct electrophysiological landmarks validated our electrode localization results. We were able to localize individual electrodes with a mean accuracy of 95 μm. MRID-tagged ultra-flexible electrodes demonstrated high long-term recording stability with mean single-unit signal-to-noise ratios as high as 20.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Zenodo 18917094

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

Neurotechnology-at-ETH-Zurich/MRID-code

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 71706d2d79a84fe7380f1d3b87e8db5cff9e1808, 9 May 2026
Languages: Jupyter (24), MATLAB (11), Python (1)
Size: 70 files, 36 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 24 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), Matplotlib (2 files), SciPy (2 files), tifffile (2 files), OpenCV (1 file), pandas (1 file), scikit-image (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
11 files
At the source: github.com/Neurotechnology-at-ETH-Zurich/MRID-code

Code availability

All custom code and preprocessed data used in this manuscript are available at the GitHub repository https://github.com/Neurotechnology-at-ETH-Zurich/MRID-code and on Zenodo 10.5281/zenodo.1891709455.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All data supporting the findings of this study are available within the article and its supplementary files. Additional data is deposited to the Zenodo repository at: 10.5281/zenodo.1891709455. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding author. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 6 MeSH terms, 2 funders, 49 references.

Cite

This paper

Özil, E., Gombkoto, P., Apostolelli, A., Yasar, T. B., Vavladeli, A. D., Marks, M., Rohr-Fukuma, M., von der Behrens, W., & Yanik, M. F. (2026). Magnetic resonance identification tags for ultra-flexible electrodes. Nature communications, 17(1), 5725. https://doi.org/10.1038/s41467-026-71887-x

BibTeX

@article{ozil2026magnetic,
author = {Özil, Eminhan and Gombkoto, Peter and Apostolelli, Athina and Yasar, Tansel Baran and Vavladeli, Angeliki D and Marks, Markus and Rohr-Fukuma, Manabu and von der Behrens, Wolfger and Yanik, Mehmet Fatih},
title = {{Magnetic resonance identification tags for ultra-flexible electrodes}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5725},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71887-x},
url = {https://doi.org/10.1038/s41467-026-71887-x},
pmid = {42049714},
pmcid = {PMC13324162}
}

RIS

TY - JOUR
AU - Özil, Eminhan
AU - Gombkoto, Peter
AU - Apostolelli, Athina
AU - Yasar, Tansel Baran
AU - Vavladeli, Angeliki D
AU - Marks, Markus
AU - Rohr-Fukuma, Manabu
AU - von der Behrens, Wolfger
AU - Yanik, Mehmet Fatih
TI - Magnetic resonance identification tags for ultra-flexible electrodes
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/28
VL - 17
IS - 1
SP - 5725
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71887-x
UR - https://doi.org/10.1038/s41467-026-71887-x
LA - en
ER -

CSL-JSON

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