OSCR

Neural sequences underlying directed turning in Caenorhabditis elegans.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Decoding postreversal turn direction from SAAV activity › Model architecture ↔ model.py, lines 7–37 · score 0.75 · GRUCell, layer normalization, dropout, hidden, sigmoid, linear
  2. [2] § Results › Neurons in the head-steering circuit encode sensory information and turning directions ↔ make_WB_struct.m, lines 79–158 · score 0.65 · octanol reversals, octanol encounters, Head curvature, velocity, NGM, counterparts
  3. [3] § Results › Neurons in the head-steering circuit encode sensory information and turning directions ↔ DecodingMain.ipynb, lines 45–79 · score 0.57 · scored activity, head swing, reversal end, decoding, SAAV, Dorsal
  4. [4] § Results › C. elegans directs the angles of its reorientations to improve its bearing during olfactory navigation ↔ make_WB_struct.m, lines 79–158 · score 0.56 · octanol encounter, head curvature, GCaMP, NGM, body, worms
  5. [5] § Methods › Decoding postreversal turn direction from SAAV activity ↔ DecodingMain.ipynb, lines 137–174 · score 0.53 · head swing, reversal endings, decoding, SAAV, dorsal, activity

Paper

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

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The authors' code

MATLAB · 158 lines · 7.8 KB · MIT · 2 matches

  1. %% Takes h5 files and neuron IDs generated by whole brain confocal recording and puts them in Matlab structure
  3. % Goal for this eventually is to get a data structure with behavior and
  4. % neuron trace info for each condition. This will then be used to analyze
  5. % their behavior. So first want to save data in a strucutre, then make
  6. % other scripts to analyze the output.
  7. clear
  8. conditionName = ['octanol'];
  9. h5FileNames =["2023-07-01-01-data.h5"...
  10. "2023-07-01-09-data.h5"...
  11. "2023-06-24-28-data.h5"...
  12. "2023-06-24-02-data.h5"...
  13. "2023-06-24-11-data.h5"...
  14. "2023-07-12-01-data.h5"...
  15. "2023-07-13-01-data.h5"...
  16. "2023-08-07-08-data.h5"...
  17. "2023-08-07-16-data.h5"...
  18. "2023-08-18-18-data.h5"...
  19. "2023-08-19-01-data.h5"...
  20. "2023-08-23-09-data.h5"...
  21. "2023-08-31-03-data.h5"...
  22. "2023-09-01-01-data.h5"...
  23. "2023-09-02-10-data.h5"...
  24. "2023-10-15-18-data.h5"...
  25. "2023-08-24-03-data.h5"...
  26. "2023-08-23-02-data.h5"...
  27. "2023-07-16-02-data.h5"...
  28. "2023-07-13-17-data.h5"...
  29. "2023-07-13-09-data.h5"...
  30. "2023-07-01-30-data.h5"...
  31. "2023-07-01-23-data.h5"...
  32. "2023-06-24-19-data.h5"...
  33. "2023-07-08-06-data.h5"...
  34. "2024-04-14-06-data.h5"...
  35. "2024-04-15-03-data.h5"...
  36. "2024-04-21-05-data.h5"...
  37. "2024-04-25-07-data.h5"...
  38. "2024-05-02-04-data.h5"...
  39. "2024-05-02-13-data.h5"...
  40. "2024-05-09-13-data.h5"...
  41. ];
  42. % list out all the h5 files you want to analyze need the h5 files and corresponding neuron IDs in the same directory /location, and it should be open
  45. % ---------- do not change below this line ------------%
  47. nFiles = length(h5FileNames);
  49. wbData = struct();
  50. for f = 1:nFiles % each row of chxData corresponds to a video file
  51. name = h5FileNames(f);
  52. wbData(f).Name = name; % save the name of the video
  53. wbData(f).velocity = h5read(name,'/behavior/velocity');
  54. wbData(f).reversal_vec = double(h5read(name,'/behavior/reversal_vec'));
  55. wbData(f).reversal_events = double(h5read(name,'/behavior/reversal_events'));
  56. wbData(f).head_angle = double(h5read(name,'/behavior/head_angle'));
  57. wbData(f).gcamp_trace = h5read(name, '/gcamp/trace_array');
  58. wbData(f).gcamp_Fmean = h5read(name, '/gcamp/traces_array_F_Fmean');
  59. wbData(f).worm_angle = double(h5read(name,'/behavior/worm_angle'));
  61. getName = erase(name,"data.h5");
  62. txtName = getName + 'neuron-id.txt';
  63. if isfile(txtName) == 1 % if we have cell IDs now
  64. neuronIDs = readcell(txtName); % get all neuroPal cell IDs into a cell
  65. wbData(f).gcamp_wIDs = cell(length(neuronIDs),2); % make an empty cell array to fill in
  66. wbData(f).gcamp_wIDs_Fmean = cell(length(neuronIDs),2); % repeat for F mean
  67. for g = 1:size(wbData(f).gcamp_trace,1)
  68. wbData(f).gcamp_wIDs{g,2} = wbData(f).gcamp_trace(g,:); % fill in cell 2 with neuron activity
  69. wbData(f).gcamp_wIDs_Fmean{g,2} = wbData(f).gcamp_Fmean(g,:); % same as for F mean
  70. end
  72. for n = 1:length(neuronIDs)
  73. rowIdx = neuronIDs{n,1};
  74. wbData(f).gcamp_wIDs{rowIdx,1} = neuronIDs{n,2}; % fill in cell 1 with neuron ID, when known
  75. % wbData(f).gcamp_wIDs_Fmean{rowIdx,1} = neuronIDs{n,2}; % fill in cell 1 with neuron ID, when known
  76. end
  77. end % if we don't have IDs yet. just proceed
  79. % get a variable that gives head curvature with + being dorsal and - as ventral
  80. flipped_datasets = append("2023-06-09-01 ","2023-06-09-10 ", "2023-06-24-28 ", "2023-07-01-01 ", ...
  81. "2023-07-07-18 ","2023-07-12-01 ","2023-07-28-04 ", "2023-08-07-08 ", "2023-06-24-11 ", "2023-08-22-08 ", ...
  82. "2023-08-25-02 ", "2023-09-01-01 ","2023-09-15-01 ", "2023-09-15-08 ","2023-08-19-01 ", "2023-09-02-10", ...
  83. "2023-08-24-03 ", "2023-08-23-02 ","2023-08-23-09 ", "2023-08-07-08 ", "2023-07-16-02 ", "2023-07-13-17 ", "2023-07-13-09 ", ...
  84. "2023-07-01-30 ", "2024-04-15-03 ", "2024-04-25-07 ", "2024-05-02-13 "); % datasets with theta_pos_is_ventral = true
  85. checkName = erase(name,"-data.h5");
  86. if contains(flipped_datasets, checkName) == 1 % if ventral is currently positive
  87. wbData(f).dv = -h5read(name,'/behavior/head_angle'); % make ventral negative
  88. else % if it's one already in the right order
  89. wbData(f).dv = h5read(name,'/behavior/head_angle');
  90. end
  92. octName = getName + 'octanol.xlsx';
  93. if isfile(octName) == 1 % if it's an octanol encounter video
  94. octEncounters = readmatrix(octName);
  95. wbData(f).octanol_events = octEncounters; % this is loops where it went on/off the octanol
  96. wbData(f).octanol_idx = zeros(1,size(wbData(f).velocity,1));
  97. for i = 1:size(octEncounters, 1)
  98. wbData(f).octanol_idx(octEncounters(i, 1):octEncounters(i, 2)) = 1; % this is a 0/1 matrix where 1 is on octanol
  99. end
  100. wbData(f).nonoctanol_reversal_events = [];
  101. wbData(f).octanol_reversal_events = [];
  103. for r = 1:length(wbData(f).reversal_events)
  104. is_oct = sum(wbData(f).octanol_idx(wbData(f).reversal_events(r,1):wbData(f).reversal_events(r,2)));
  105. if is_oct >= 1 % if the worm is on octanol for any part of this reversal
  106. wbData(f).octanol_reversal_events = [wbData(f).octanol_reversal_events; wbData(f).reversal_events(r,:)];
  107. elseif is_oct == 0 % if the worm is NOT on octanol for any part of this reversal
  108. wbData(f).nonoctanol_reversal_events = [wbData(f).nonoctanol_reversal_events; wbData(f).reversal_events(r,:)];
  109. end
  110. end
  112. wbData(f).avoid_events = []; % all reversals where the animal successfully avoids octanol
  114. for r = 1:length(wbData(f).reversal_events)
  115. if wbData(f).octanol_idx(wbData(f).reversal_events(r,1)) == 1 && wbData(f).octanol_idx(wbData(f).reversal_events(r,2)) == 0
  116. % if the worm starts the reversal on octanol and ends the reversal on NGM
  117. wbData(f).avoid_events = [wbData(f).avoid_events; wbData(f).reversal_events(r,:)];
  118. end
  119. end
  121. wbData(f).not_avoid_events = []; % all reversals where the animal revereses and stays on octanol
  123. for u = 1:length(wbData(f).reversal_events)
  124. if wbData(f).octanol_idx(wbData(f).reversal_events(u,1)) == 1 && wbData(f).octanol_idx(wbData(f).reversal_events(u,2)) == 1
  125. % if the worm starts the reversal on octanol and ends the reversal on octanol
  126. wbData(f).not_avoid_events = [wbData(f).not_avoid_events; wbData(f).reversal_events(u,:)];
  127. end
  128. end
  129. else % if it's not an octanol encounter
  130. wbData(f).octanol_idx = zeros(1,length(wbData(f).velocity));
  131. wbData(f).nonoctanol_reversal_events = wbData(f).reversal_events; % all reversals are non octanol reversals
  132. end
  133. % other things you can add here: /behavior /angular_velocity, body_angle,
  134. % body_angle_absoloute, body_angle_all, head_angle, pumping,
  135. % worm_curvature
  136. % /gcamp : idx_splits, match_org_to_skip, match_skip_to_org,
  137. % trace_array_original (not Z score), traces_array_F_F20,
  138. % traces_array_F_Fmean
  140. end
  142. % Save the WB data file (with a new name if it already exists)
  143. savename = strcat(conditionName,'.wbData.mat');
  144. counter = 1;
  145. while true
  146. if exist(savename,"file") % don't overwrite
  147. savename = strcat(conditionName,'_version',num2str(counter),'.wbData.mat');
  148. counter = counter+1;
  149. else
  150. try
  151. save(savename,'wbData')
  152. catch
  153. warning('Error saving as a v7 file, probably due to large size. Trying v7.3.')
  154. save(savename,'wbData','-v7.3')
  155. end
  156. break;
  157. end
  158. end

make_WB_struct.m, under MIT · at the source

Overview

Authors: Talya S Kramer1,2, Flossie K Wan1, Sarah M Pugliese1, Adam A Atanas1, Sreeparna Pradhan1, Alex W Hiser1, Lillie M Godinez1, Jinyue Luo1, Eric Bueno1, Thomas Felt1, Steven W Flavell1
ORCID iDs: Talya S Kramer, Flossie K Wan, Sreeparna Pradhan, Lillie M Godinez, Eric Bueno, Steven W Flavell
  1. Howard Hughes Medical Institute, Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA USA
  2. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA USA
Institutions: Howard Hughes Medical Institute (United States); Massachusetts Institute of Technology (United States)
Journal: Nature neuroscience, volume 29, issue 6, pages 1408-1424
Dates: received 4 December 2024; accepted 5 March 2026; published online 10 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02257-5 · PMID 41963559 · PMCID PMC13246447 · OpenAlex W7153222014
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: C. elegans (organism), systems (subfield)
Methods: Statistics, Machine learning, Evoked potentials, fMRI & imaging
Keywords: Sensorimotor processing, Neural circuits
MeSH: Brain*, Neurons*, Spatial Navigation*, Animals, Animals, Genetically Modified, Caenorhabditis elegans, Smell, Tyramine (* major topic)
Topic: Genetics, Aging, and Longevity in Model Organisms (Aging, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS131457); Alfred P. Sloan Foundation (research fellowship); Howard Hughes Medical Institute (HHMI) (investigator award); National Science Foundation (NSF) (1845663); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (NS131457); McKnight Foundation (scholar award)
Citations: cited by 8 papers (Europe PMC); 75 references in the paper

Abstract

Complex behaviors, such as navigation, rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select appropriate motor sequences remains poorly understood. Here we characterize the architecture of neural circuits that control Caenorhabditis elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to the spatial distribution of attractive and aversive olfactory cues, anticipate upcoming turn directions and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to link sensory cues to motor actions.

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 5 matches between paragraphs and lines of code.

flavell-lab/SAAV_Decoding

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: c7e7e18a84cde5dc02df9d63492293b38402eb2c, 24 February 2026
Languages: Python (5), Jupyter (1)
Size: 15 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: JAX (5 files), NumPy (2 files), Matplotlib (1 file), pandas (1 file), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
8 files
At the source: github.com/flavell-lab/SAAV_Decoding

flavell-lab/preprocess_videos

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: d4eaa181c1a4feafd3e40bce1c032f8b2fcab945, 24 February 2026
Languages: Python (5), Shell (1)
Size: 9 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: JAX (3 files), tifffile (3 files), NumPy (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
8 files
At the source: github.com/flavell-lab/preprocess_videos

flavell-lab/head_detector_unet2d

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: a91b162f1fd97ed0e5e33da0b66e137b8036d301, 24 February 2026
Languages: Python (16), Jupyter (7)
Size: 38 files, 23 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (environment.yaml, unet2d/setup.py), 7 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (11 files), NumPy (8 files), Matplotlib (6 files), h5py (4 files), Plots.jl (3 files), pandas (2 files), Pillow (1 file), SciPy (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
25 files
At the source: github.com/flavell-lab/head_detector_unet2d

Zenodo 12611760

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (2)
Size: 2 files, 2 scripts
Software Heritage: not checked
Found in: the Zenodo software companion of the Dryad dataset
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
2 files
At the source: zenodo.org/records/12611760

Code availability

Original code used to decode upcoming D/V turn direction from SAAV activity is available on GitHub: https://github.com/flavell-lab/SAAV_Decoding.

The Python code used for post hoc binning of pixel intensities is available on GitHub: https://github.com/flavell-lab/preprocess_videos.

The code for retraining and evaluation of the ANTSUN model performance is available on GitHub: https://github.com/flavell-lab/head_detector_unet2d.

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 37 scripts, each with its path and the digest of its content;
  • 5 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 brain-wide imaging data from this study is available at the following Dryad link: 10.5061/dryad.8sf7m0cz2.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 8 MeSH terms, 6 funders, 71 references.

Cite

This paper

Kramer, T. S., Wan, F. K., Pugliese, S. M., Atanas, A. A., Pradhan, S., Hiser, A. W., Godinez, L. M., Luo, J., Bueno, E., Felt, T., & Flavell, S. W. (2026). Neural sequences underlying directed turning in Caenorhabditis elegans. Nature neuroscience, 29(6), 1408-1424. https://doi.org/10.1038/s41593-026-02257-5

BibTeX

@article{kramer2026neural,
author = {Kramer, Talya S and Wan, Flossie K and Pugliese, Sarah M and Atanas, Adam A and Pradhan, Sreeparna and Hiser, Alex W and Godinez, Lillie M and Luo, Jinyue and Bueno, Eric and Felt, Thomas and Flavell, Steven W},
title = {{Neural sequences underlying directed turning in Caenorhabditis elegans}},
journal = {Nature neuroscience},
year = {2026},
month = apr,
volume = {29},
number = {6},
pages = {1408--1424},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02257-5},
url = {https://doi.org/10.1038/s41593-026-02257-5},
pmid = {41963559},
pmcid = {PMC13246447}
}

RIS

TY - JOUR
AU - Kramer, Talya S
AU - Wan, Flossie K
AU - Pugliese, Sarah M
AU - Atanas, Adam A
AU - Pradhan, Sreeparna
AU - Hiser, Alex W
AU - Godinez, Lillie M
AU - Luo, Jinyue
AU - Bueno, Eric
AU - Felt, Thomas
AU - Flavell, Steven W
TI - Neural sequences underlying directed turning in Caenorhabditis elegans
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/04/10
VL - 29
IS - 6
SP - 1408
EP - 1424
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02257-5
UR - https://doi.org/10.1038/s41593-026-02257-5
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Neural sequences underlying directed turning in Caenorhabditis elegans",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Kramer",
"given": "Talya S"
},
{
"family": "Wan",
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},
{
"family": "Pugliese",
"given": "Sarah M"
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{
"family": "Atanas",
"given": "Adam A"
},
{
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{
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{
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{
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}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "6",
"page": "1408-1424",
"DOI": "10.1038/s41593-026-02257-5",
"PMID": "41963559",
"PMCID": "PMC13246447",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
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"language": "en",
"issued": {
"date-parts": [
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