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Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Co-registration of functionally defined neurons › nVoke to Zeiss registration ↔ routine/coregistration.py, lines 51–124 · score 0.66 · gradient descent, exhaustive, optimization, correlation, filter, transformation
  2. [2] § Materials and methods › Linking neural activity to behavior and delineating by cell-type ↔ 02.specific_event_responses.py, lines 111–123 · score 0.50 · sankey diagram, aggression, social

Paper

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

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The authors' code

Python · 140 lines · 4.1 KB · no license · 1 match

  1. import copy
  2. import re
  3. from collections.abc import Iterable
  5. import cv2
  6. import numpy as np
  7. import pandas as pd
  8. import SimpleITK as sitk
  9. from scipy.ndimage import gaussian_filter
  11. from .utilities import normalize
  14. def process_temp(
  15. im: np.ndarray, dn_sigma=5, back_sigma=50, blk_wnd=(11, 11), q_thres=None
  16. ):
  17. if not isinstance(blk_wnd, Iterable):
  18. blk_wnd = (blk_wnd, blk_wnd)
  19. im_ps = gaussian_filter(im, dn_sigma)
  20. im_ps = remove_background(im_ps, back_sigma)
  21. krn = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, blk_wnd)
  22. im_ps = cv2.morphologyEx(im_ps, cv2.MORPH_BLACKHAT, krn)
  23. if q_thres is not None:
  24. q = np.quantile(im_ps, q_thres)
  25. im_ps[im_ps < q] = im_ps.min()
  26. return normalize(im_ps)
  29. def remove_background(im, back_sigma):
  30. back = gaussian_filter(im, back_sigma)
  31. return im - back
  34. def apply_tx(
  35. fm: np.ndarray, tx: sitk.Transform, fill: float = 0, ref: np.ndarray = None
  36. ):
  37. if ref is None:
  38. ref = fm
  39. else:
  40. ref = sitk.GetImageFromArray(ref)
  41. fm = sitk.GetImageFromArray(fm)
  42. fm = sitk.Resample(fm, ref, tx, sitk.sitkLinear, fill)
  43. return sitk.GetArrayFromImage(fm)
  46. def it_callback(reg, param_dict):
  47. param = reg.GetOptimizerPosition()
  48. param_dict[param] = reg.GetMetricValue()
  51. def est_sim(
  52. src: np.ndarray,
  53. dst: np.ndarray,
  54. exhaustive: bool,
  55. trans_init=None,
  56. src_ma=None,
  57. dst_ma=None,
  58. lr: float = 0.5,
  59. niter: int = 1000,
  60. scal_init=1.9,
  61. scal_stp=1e-2,
  62. scal_nstp=3,
  63. ang_stp=np.deg2rad(1),
  64. ang_nstp=3,
  65. trans_stp=(1.0, 1.0),
  66. trans_nstp=(3, 3),
  67. ):
  68. src = sitk.GetImageFromArray(src.astype(np.float32))
  69. dst = sitk.GetImageFromArray(dst.astype(np.float32))
  70. reg = sitk.ImageRegistrationMethod()
  71. if src_ma is not None:
  72. reg.SetMetricMovingMask(sitk.GetImageFromArray(src_ma.astype(np.uint8)))
  73. if dst_ma is not None:
  74. reg.SetMetricFixedMask(sitk.GetImageFromArray(dst_ma.astype(np.uint8)))
  75. if trans_init is None:
  76. trans_init = sitk.CenteredTransformInitializer(
  77. dst,
  78. src,
  79. sitk.Similarity2DTransform(1 / scal_init),
  80. sitk.CenteredTransformInitializerFilter.GEOMETRY,
  81. )
  82. reg.SetInitialTransform(trans_init)
  83. reg.SetMetricAsCorrelation()
  84. reg.SetInterpolator(sitk.sitkLinear)
  85. # reg.SetOptimizerAsRegularStepGradientDescent(
  86. # learningRate=lr,
  87. # minStep=1e-7,
  88. # numberOfIterations=niter,
  89. # )
  90. if exhaustive:
  91. if not isinstance(trans_stp, Iterable):
  92. trans_stp = (trans_stp, trans_stp)
  93. if not isinstance(trans_nstp, Iterable):
  94. trans_nstp = (trans_nstp, trans_nstp)
  95. reg.SetOptimizerAsExhaustive(
  96. [scal_nstp, ang_nstp, trans_nstp[0], trans_nstp[1]]
  97. )
  98. reg.SetOptimizerScales(
  99. [
  100. 1 / scal_init - 1 / (scal_init + scal_stp),
  101. ang_stp,
  102. trans_stp[0],
  103. trans_stp[1],
  104. ]
  105. )
  106. else:
  107. reg.SetOptimizerAsGradientDescent(learningRate=lr, numberOfIterations=niter)
  108. reg.SetOptimizerScalesFromPhysicalShift()
  109. param_dict = dict()
  110. reg.AddCommand(sitk.sitkIterationEvent, lambda: it_callback(reg, param_dict))
  111. tx = reg.Execute(dst, src).Downcast()
  112. param_df = (
  113. pd.Series(param_dict)
  114. .reset_index(name="metric")
  115. .rename(
  116. columns={
  117. "level_0": "scale",
  118. "level_1": "angle",
  119. "level_2": "transX",
  120. "level_3": "transY",
  121. }
  122. )
  123. )
  124. return tx, param_df
  127. def estimate_tranform(src, dst, **kwargs):
  128. tx_exh, param_exh = est_sim(src, dst, exhaustive=True, **kwargs)
  129. tx_gd, param_gd = est_sim(
  130. src, dst, exhaustive=False, trans_init=copy.deepcopy(tx_exh), **kwargs
  131. )
  132. param_exh["stage"] = "exhaustive"
  133. param_gd["stage"] = "gradient"
  134. return tx_gd, tx_exh, pd.concat([param_exh, param_gd], ignore_index=True)
  137. def thres_roi(roi, th):
  138. nzvals = roi[roi > 0]
  139. thres = np.quantile(nzvals, th)
  140. return np.where(roi > thres, roi, 0)

coregistration.py at commit ccd94d8, no license · at the source

Overview

Authors: Mary L Phillips1,2, Nicolai T Urban1, Taddeo Salemi1, Zhe Dong3, Ryohei Yasuda1
ORCID iDs: Mary L Phillips, Nicolai T Urban, Ryohei Yasuda
  1. Max Planck Florida Institute for Neuroscience Jupiter United States
  2. ZEISS Research Microscopy Solutions White Plains United States
  3. MetaCell Boston United States
Institutions: Max Planck Florida Institute for Neuroscience (United States); ZEISS Research Microscopy Solutions White Plains United States
Journal: eLife, volume 15, article RP110277
Dates: published online 12 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.110277 · PMID 42117453 · PMCID PMC13167110 · OpenAlex W7131885036
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), systems (subfield)
Methods: Statistics, Preprocessing, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Miniscope, confocal microscopy, social behavior, multiplexing, Mouse
MeSH: Behavior, Animal*, Neurons*, Optical Imaging*, Animals, Mice, Microscopy, Confocal (* major topic)
Journal subjects: Neuroscience
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute of Neurological Disorders and Stroke (R35NS068410); National Institutes of Health (F32MH120872)
Citations: cited by 1 paper (Europe PMC); 29 references in the paper
Research resources: pCAG-mTagBFP2 RRID:Addgene_122373, CMV-mCyRFP2-CREB RRID:Addgene_137003, mVenus RRID:Addgene_198192, mTurquoise2 RRID:Addgene_198196, pcDNA3-mNeptune2.5 RRID:Addgene_51310, mT-Sapphire-C1 RRID:Addgene_54545, mOrange2-N1 RRID:Addgene_54568, FusionRed-pBAD RRID:Addgene_54677, pmScarlet-H_C1 RRID:Addgene_85043, B6.Cg-Tg(Camk2a-tTA)1Mmay/DboJ mice RRID:IMSR_JAX:007004, B6.DBA-Tg(tetO-GCaMP6s)2Niell/J mice RRID:IMSR_JAX:024742, Matlab RRID:SCR_001622, nVoke2 RRID:SCR_023028, JAABA RRID:SCR_027430

Abstract

Head-mounted miniscopes have enabled functional fluorescence imaging in freely moving animals. However, current technology is limited to recording at most two spectrally distinct fluorophores, severely restricting the number of identifiable cell types. Here, we introduce multiplexed neuronal imaging (Neuroplex), a pipeline combining miniscope Ca2+ recordings with in vivo multiplexed confocal spectral imaging to distinguish nine projection-defined neuronal subtypes through the same GRIN lens. By co-registering defined neurons with fluorophore-specific spectral fingerprints via linear unmixing, we link projection-defined identities to behaviorally relevant neuronal activity. This approach overcomes spectral constraints of miniscopes, enabling circuit-level dissection of behavior in single animals.

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 2 matches between paragraphs and lines of code.

Neurocipher/PythonPipeline

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ccd94d881aa5a456b81ab4f601a780878766f151, 11 December 2025
Languages: Python (12)
Size: 332 files, 12 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (11 files), xarray (9 files), pandas (8 files), OpenCV (4 files), SciPy (4 files), Matplotlib (3 files), SimpleITK (3 files), Plotly (1 file), scikit-learn (1 file), tifffile (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/Neurocipher/PythonPipeline

MetaCell/Zeiss-Data-Science

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: a21b69c599cacdb45b3f6dd414fc215b53f947f7, 22 July 2025
Languages: Python (13)
Size: 1,051 files, 13 scripts
Software Heritage: not archived
Found in: “Code and data availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (12 files), pandas (11 files), xarray (10 files), Plotly (9 files), SciPy (7 files), Matplotlib (5 files), seaborn (4 files), scikit-learn (3 files), statsmodels (2 files), OpenCV (1 file), tifffile (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
14 files
At the source: github.com/MetaCell/Zeiss-Data-Science

The paper's code and data availability statement is in the Data section.

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;
  • 25 scripts, each with its path and the digest of its content;
  • 2 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

Datasets cited

Data availability

All raw data are available at https://doi.org/10.5281/zenodo.17915226.

The following dataset was generated:

YasudaR PhillipsM UrbanN SalemiT DongZ 2025Dataset for: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animalsZenodo10.5281/zenodo.17915226PMC1316711042117453

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

Code and data availability

Raw and processed data: https://bit.ly/NeuroplexData.

Code and data utilized in Neuroplex multispectral detection: GitHub - Neurocipher/PythonPipeline: Python pipeline for Neurocipher (https://github.com/Neurocipher/PythonPipeline) (copy archived at Neurocipher, 2025).

Code and data utilized in calcium/behavior correlation: https://github.com/MetaCell/Zeiss-Data-Science (copy archived at MetaCell, 2025).

A detailed tutorial on these processes can be found via this repository: https://zeiss.tourial.com/dc/MultiColorInVivoImaging.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 6 MeSH terms, 2 funders, 26 references, 14 RRIDs.

Cite

This paper

Phillips, M. L., Urban, N. T., Salemi, T., Dong, Z., & Yasuda, R. (2026). Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals. eLife, 15, RP110277. https://doi.org/10.7554/elife.110277

BibTeX

@article{phillips2026functional,
author = {Phillips, Mary L and Urban, Nicolai T and Salemi, Taddeo and Dong, Zhe and Yasuda, Ryohei},
title = {{Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals}},
journal = {eLife},
year = {2026},
month = may,
volume = {15},
pages = {RP110277},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.110277},
url = {https://doi.org/10.7554/elife.110277},
pmid = {42117453},
pmcid = {PMC13167110}
}

RIS

TY - JOUR
AU - Phillips, Mary L
AU - Urban, Nicolai T
AU - Salemi, Taddeo
AU - Dong, Zhe
AU - Yasuda, Ryohei
TI - Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/05/12
VL - 15
SP - RP110277
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.110277
UR - https://doi.org/10.7554/elife.110277
LA - en
ER -

CSL-JSON

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"id": "10.7554/elife.110277",
"type": "article-journal",
"title": "Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals",
"container-title": "eLife",
"author": [
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"family": "Phillips",
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"container-title-short": "Elife",
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"DOI": "10.7554/elife.110277",
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"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.110277",
"language": "en",
"issued": {
"date-parts": [
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}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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