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Protocol for longitudinal two-photon calcium imaging and holographic optogenetic manipulation to investigate memory in mice.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Step-by-step method details › Imaging data processing pipeline ↔ CalciumDataProcessing/Preprocessing/MotionCorrection/DriftCorrection.m, lines 85–120 · score 0.59 · motion correction, imaging stacks, raw
  2. [2] § Before you begin ↔ BehavioralDataProcessing/FigS1E_SimulationTtestCpr.m, the whole file · a weak match · score 0.55 · Remote memory, memory retrieved, behaviorally, manipulation

Paper

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

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The authors' code

MATLAB · 120 lines · 4.1 KB · GPL-3.0 · 1 match

  1. clc; clear;
  3. addpath("E:\Data Analysis\2P Imaging\Programs\Fiji.app\scripts");
  4. addpath('D:\Git\FindUSN\CalciumDataProcessing\Preprocessing\MotionCorrection');
  6. % Select the root folder to process
  7. rootFolder = uigetdir(pwd, 'Select Root Folder to Process');
  8. if rootFolder == 0
  9. error('Folder selection canceled.');
  10. end
  11. fprintf('Selected folder: %s\n', rootFolder);
  13. % Start MIJ once
  14. Miji(false);
  16. % Use a queue to hold folders to process (start with root)
  17. foldersToProcess = {rootFolder};
  18. processedFolders = {}; % To avoid repeated processing if needed
  20. while ~isempty(foldersToProcess)
  21. currentFolder = foldersToProcess{1};
  22. foldersToProcess(1) = []; % Dequeue
  24. % Check if folder contains any .raw files
  25. rawFiles = dir(fullfile(currentFolder, '*.raw'));
  26. hasRaw = ~isempty(rawFiles);
  28. % Check if folder contains Image.tif (skip if yes)
  29. tifFiles = dir(fullfile(currentFolder, 'Image.tif'));
  30. hasTif = ~isempty(tifFiles);
  32. if hasRaw && ~hasTif
  33. fprintf('Processing folder: %s\n', currentFolder);
  35. % Use the first .raw file found
  36. rawFileName = rawFiles(1).name;
  37. rawFilePath = fullfile(currentFolder, rawFileName);
  39. % Open raw file in MIJ
  40. openRawInMIJ(rawFilePath);
  42. % Get reference frame (first frame)
  43. img = uint8(MIJ.getCurrentImage);
  44. ref = img(:,:,1);
  46. % Save Reference.tif in current folder
  47. imwrite(ref, fullfile(currentFolder, 'Reference.tif'));
  49. % Load Reference.tif back for registration
  50. imref = mijread(fullfile(currentFolder, 'Reference.tif'));
  52. % Perform motion correction and processing
  53. performMotionCorrection(rawFilePath, currentFolder);
  55. % Clear variables before next iteration
  56. clear img ref imref;
  57. end
  59. % Enqueue subfolders to process
  60. subItems = dir(currentFolder);
  61. % Remove '.' and '..' and files (keep folders only)
  62. subFolders = subItems([subItems.isdir] & ~ismember({subItems.name}, {'.','..'}));
  64. for i = 1:length(subFolders)
  65. foldersToProcess{end+1} = fullfile(currentFolder, subFolders(i).name);
  66. end
  67. end
  69. % Exit MIJ once after all processing
  70. MIJ.exit;
  72. disp('All folders processed.');
  74. %% --- Subfunctions ---
  76. function openRawInMIJ(rawPath)
  77. % Opens raw file in MIJ with fixed parameters (modify if needed)
  78. [folder, fileName, ext] = fileparts(rawPath);
  79. fprintf('Opening raw file in MIJ: %s\n', rawPath);
  80. MIJ.run('Raw...', ['open=', fileName, ext, ' image=[16-bit Unsigned] width=512 height=512 offset=0 number=2800 gap=0 little-endian']);
  81. MIJ.run('Size...', 'width=256 height=256 depth=2800 constrain average interpolation=None');
  82. MIJ.run('8-bit');
  83. end
  85. function performMotionCorrection(rawFilePath, currentFolder)
  86. % Perform motion correction steps in MIJ
  88. [~, rawFileName, ext] = fileparts(rawFilePath);
  90. % Open raw file again for registration
  91. MIJ.run('Raw...', ['open=', rawFileName, ext, ' image=[16-bit Unsigned] width=512 height=512 offset=0 number=2800 gap=0 little-endian']);
  92. MIJ.run('Size...', 'width=256 height=256 depth=2800 constrain average interpolation=None');
  93. MIJ.run('8-bit');
  95. % Concatenate all open images
  96. MIJ.run('Concatenate...', 'all_open title=[Corrected]');
  98. % Run StackReg rigid body registration
  99. MIJ.run('StackReg Ming', 'transformation=[Rigid Body]');
  101. % Get corrected image stack
  102. temp = uint8(MIJ.getCurrentImage);
  104. % Max intensity projection
  105. MIJ.run('Z Project...', 'projection=[Max Intensity]');
  106. AVG = uint8(MIJ.getCurrentImage);
  108. % Close all open images in MIJ
  109. MIJ.run('Close All');
  111. % Save processed images in current folder
  112. IMG = temp(:,:,2:end); % skip first image (reference)
  113. imwrite(IMG(:,:,1), fullfile(currentFolder, 'Image.tif'));
  114. for k = 2:size(IMG,3)
  115. imwrite(IMG(:,:,k), fullfile(currentFolder, 'Image.tif'), 'WriteMode', 'append');
  116. end
  117. imwrite(AVG, fullfile(currentFolder, 'Average.tif'));
  119. fprintf('Motion correction done for folder: %s\n', currentFolder);
  120. end

DriftCorrection.m at commit 43b3ef0, under GPL-3.0 · at the source

Overview

Authors: Huaiqin Guo1, He Yang1, Feng Su2, Yuzhi Zhu1, Qingyi Tang1, Rui Cao3, Qiao Wang3, Siyi Tong1, Shaoli Wang1, Chen Zhang4,5,6, Wei Lu1,7,8
  1. Department of Neurosurgery, Huashan Hospital, Institute for Translational Brain Research, State Key Laboratory of Brain Function and Disorders, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
  2. National Biomedical Imaging Center, College of Future Technology, Peking University, Beijing, China
  3. School of Information Science and Engineering and Yau Shing-Tung Center, Southeast University, Nanjing 210096, China
  4. School of Basic Medical Sciences, Laboratory of Organ Synergy and Intelligent Regenerative Manufacturing, Capital Medical University, Beijing 100069, China
  5. College of Basic Medicine, Inner Mongolia Medical University, Hohhot 010110, China
  6. State Key Laboratory of Translational Medicine and Innovative Drug Development, Nanjing, Jiangsu 210000, China
  7. MOE Innovative Center for New Drug Development of Immune Inflammatory Diseases, Fudan University, Shanghai, China
  8. MOE Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, Jiangsu Province 210096, China
Institutions: Fudan University (China); Huashan Hospital (China); Frontiers Center for Brain Science of the Ministry of Education (China); Peking University (China); Southeast University (China); Shanghai Medical College of Fudan University (China); Capital Medical University (China); Inner Mongolia Medical University (China)
Journal: STAR protocols, volume 7, issue 3, article 104678
Dates: published online 2 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.xpro.2026.104678 · PMID 42391006 · PMCID PMC13352386 · OpenAlex W7167031753
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism)
Methods: fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Microscopy, Cognitive Neuroscience, Behavior
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain-like Intelligence Technology; National Science and Technology Major Project (2021ZD0203502); National Natural Science Foundation of China (National Science Foundation of China) (T2394531, 32200835); Ministry of Science and Technology of the People's Republic of China (2021YFA1101302); China Postdoctoral Science Foundation (2021M700847, 2024T170168)
Citations: not cited yet (Europe PMC); 22 references in the paper
Research resources: MATLAB R2021b RRID:SCR_001622, GraphPad Prism 9.3.0 RRID:SCR_002798, ImageJ 1.47v RRID:SCR_003070, pClamp10.1 RRID:SCR_01132

Abstract

Here, we present a protocol for longitudinal two-photon calcium imaging and holographic optogenetic manipulation to investigate memory in mice. We describe chronic cranial window implantation, stereotaxic viral delivery, and head-fixed contextual fear conditioning synchronized with imaging acquisition and optical stimulation of identified neurons. We further detail procedures for image data processing pipelines for cross-session registration and longitudinal tracking of the same neuronal population. This protocol enables simultaneous neuronal activity recording, behavioral monitoring, and causal interrogation of neuronal ensembles.

For complete details on the use and execution of this protocol, please refer to Wang et al.1

Reproduced under the paper's license (CC BY-NC), 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.

Zenodo 16830880

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (24 files), Matplotlib (6 files), SciPy (5 files), Statistics and Machine Learning Toolbox (4 files), seaborn (4 files), pandas (2 files), PyTorch (2 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
114 files
At the source: doi.org/10.5281/zenodo.16830880

ariadneunsworth/findusn

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 43b3ef039fb1b34c373d2799671ffbbec3546602, 13 August 2025
Languages: MATLAB (73), Python (36), Jupyter (3)
Size: 163 files, 112 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (CalciumDataProcessing/Preprocessing/SpikeInference/Deconvolution/requirements.txt, CalciumDataProcessing/Preprocessing/SpikeInference/DeepSpike/requirements.txt), 13 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (24 files), Matplotlib (6 files), SciPy (5 files), Statistics and Machine Learning Toolbox (4 files), seaborn (4 files), pandas (2 files), PyTorch (2 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
114 files
At the source: github.com/AriadneUnsworth/FindUSN/tree/v0.1.0

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;
  • 224 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

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

Data and code availability

• All data necessary to understand and assess the conclusions of the manuscript are presented in the main text and the supplementary materials. Raw calcium data will be shared by the lead contact upon request. • All original code has been deposited at Zenodo (https://doi.org/10.5281/zenodo.16830880) and is publicly available as of the date of publication. • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 5 funders, 22 references, 4 RRIDs.

Cite

This paper

Guo, H., Yang, H., Su, F., Zhu, Y., Tang, Q., Cao, R., Wang, Q., Tong, S., Wang, S., Zhang, C., & Lu, W. (2026). Protocol for longitudinal two-photon calcium imaging and holographic optogenetic manipulation to investigate memory in mice. STAR protocols, 7(3), 104678. https://doi.org/10.1016/j.xpro.2026.104678

BibTeX

@article{guo2026protocol,
author = {Guo, Huaiqin and Yang, He and Su, Feng and Zhu, Yuzhi and Tang, Qingyi and Cao, Rui and Wang, Qiao and Tong, Siyi and Wang, Shaoli and Zhang, Chen and Lu, Wei},
title = {{Protocol for longitudinal two-photon calcium imaging and holographic optogenetic manipulation to investigate memory in mice}},
journal = {STAR protocols},
year = {2026},
month = jul,
volume = {7},
number = {3},
pages = {104678},
publisher = {Elsevier},
issn = {2666-1667},
doi = {10.1016/j.xpro.2026.104678},
url = {https://doi.org/10.1016/j.xpro.2026.104678},
pmid = {42391006},
pmcid = {PMC13352386}
}

RIS

TY - JOUR
AU - Guo, Huaiqin
AU - Yang, He
AU - Su, Feng
AU - Zhu, Yuzhi
AU - Tang, Qingyi
AU - Cao, Rui
AU - Wang, Qiao
AU - Tong, Siyi
AU - Wang, Shaoli
AU - Zhang, Chen
AU - Lu, Wei
TI - Protocol for longitudinal two-photon calcium imaging and holographic optogenetic manipulation to investigate memory in mice
T2 - STAR protocols
J2 - STAR Protoc
PY - 2026
DA - 2026/07/02
VL - 7
IS - 3
SP - 104678
SN - 2666-1667
PB - Elsevier
DO - 10.1016/j.xpro.2026.104678
UR - https://doi.org/10.1016/j.xpro.2026.104678
LA - en
ER -

CSL-JSON

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