OSCR

Olfactory bulb-cortex oscillations encode perceived odor intensity rather than concentration.

Code ↔ Paper

9 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 9 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] § Materials and methods › Signal processing and analysis › Source connectivity. ↔ EffectiveConnectivity.m, the whole file · a weak match · score 0.73 · Granger causality, Effective connectivity, DPSS, tapered, smoothing, Fourier
  2. [2] § Materials and methods › Signal processing and analysis › Source time-course reconstruction. ↔ forward_model.m, lines 115–136 · score 0.72 · Finite Element, SimBio, skull, dipoles, scalp, segmented
  3. [3] § Materials and methods › Signal processing and analysis › Source time-course reconstruction. ↔ forward_model.m, lines 218–266 · score 0.68 · MNI space, source model, resolution, FieldTrip, dipoles, MRI
  4. [4] § Materials and methods › Signal processing and analysis › Burst analysis. ↔ bursts_lin_models.m, lines 1–84 · score 0.61 · gamma bursts, linear models, behavioral, variable, valence, 100 Hz
  5. [5] § Materials and methods › Signal processing and analysis › Source time-course reconstruction. ↔ SL20_inverse_calc.m, lines 35–82 · score 0.58 · inverse problem, eLORETA, FieldTrip, dipoles, space, cortex
  6. [6] § Materials and methods › Signal processing and analysis › Phase-amplitude coupling. ↔ plot_ML_2.m, lines 58–95 · score 0.55 · Monte Carlo, coherence spectrum, cluster correction, variable, valence, PC
  7. [7] § Materials and methods › Signal processing and analysis › Support Vector Machine classification. ↔ OB_PC_ML_COH.m, lines 17–104 · score 0.55 · OB PC, searchlight, binned, neighbors, SVM, classifier
  8. [8] § Materials and methods › Signal processing and analysis › Phase-amplitude coupling. ↔ bursts_lin_models.m, lines 151–235 · score 0.52 · WCM cluster correction, AUC, variable, valence, model, gamma
  9. [9] § Materials and methods › Signal processing and analysis › Support Vector Machine classification. ↔ cluster_and_linear_model.m, lines 204–286 · score 0.50 · WCM algorithm, shuffled, Vector, clusters, concentration, PC

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 317 lines · 12 KB · no license · 2 matches

  1. %{
  2. This script reproduces the source reconstruction in the paper entitled
  3. " Odor intensity coding in the human olfactory system "
  4. Author: Frans Nordén, Irene Zanettin, Artin Arshamian, Mikael Lundqvist and Johan N. Lundstrom
  5. %}
  7. %Inspired from https://www.fieldtriptoolbox.org/tutorial/headmodel_eeg_fem/
  8. %and https://www.fieldtriptoolbox.org/workshop/ohbm2018/forward/
  10. sub = 1;
  11. path = ['data/',num2str(sub),'/'];
  12. %
  13. %Load subject MRI
  14. mri = ft_read_mri(fullfile('\T1',['SL20_0',num2str(sub),'.nii']), 'dataformat','nifti');
  16. %We work in acpc coordinate which has the origin at the same location as
  17. %MNI & SPM (https://www.fieldtriptoolbox.org/faq/acpc/).
  18. cfg = [];
  19. cfg.method = 'interactive';
  20. cfg.coordsys = 'acpc';
  21. mri_acpc = ft_volumerealign(cfg,mri);
  23. cfg = [];
  24. cfg.resolution = 1;
  25. cfg.dim = [256 256 256];
  26. mri_resliced = ft_volumereslice(cfg, mri_acpc);
  28. cfg = [];
  29. ft_sourceplot(cfg, mri_acpc);
  30. save(fullfile('data\',[num2str(sub)],'mri_resliced.mat'), 'mri_resliced')
  31. %%
  32. % Prepare for freesurfer
  33. output_path='data';
  35. % We need to transform from raw voxels to spm coordinates
  36. transform_vox2spm = mri_resliced.transform;
  37. save(fullfile(output_path,num2str(sub),'transform_vox2spm.mat'),'transform_vox2spm');
  38. %
  41. % Then use ft_volumewrite to save the MRI as a "mgz" file that Freesurfer will read.
  43. % If you run this yourself, remember to change the path and filename. Here the filename is "sub02" with filetype "mgz".
  45. cfg = [];
  46. cfg.filename = fullfile(output_path,num2str(sub),num2str(sub));
  47. %cfg.filename = ;
  48. cfg.filetype = 'mgh';
  49. cfg.parameter = 'anatomy';
  50. ft_volumewrite(cfg, mri_resliced);
  52. % Save brainmask for Freesurfer
  54. %Save mask
  55. mri_file = fullfile(output_path,num2str(sub),[num2str(sub),'.mgh']);
  56. mri = ft_read_mri(mri_file);
  57. mri.coordsys = 'spm';
  61. cfg = [];
  62. cfg.output = 'brain';
  63. seg = ft_volumesegment(cfg, mri);
  64. mri.anatomy = mri.anatomy.*double(seg.brain);
  68. cfg = [];
  69. %cfg.filename = convertStringsToChars(strcat(num2str(sub),'mask'));
  70. cfg.filename = fullfile(output_path,num2str(sub),[num2str(sub),'_mask.mgh']);
  71. cfg.filetype = 'mgh';
  72. cfg.parameter = 'anatomy';
  73. ft_volumewrite(cfg, mri);
  76. %%
  77. %Segment into different areas. threshold could be changed if segmentation
  78. %looks weird
  79. cfg = [];
  80. %cfg.skullthreshold = .3;
  81. %cfg.scalpthreshold = .3; %
  82. %cfg.brainthreshold = .1; %
  83. cfg.output = {'csf','gray','scalp','skull','white'};
  84. mri_segmented_5_compartment = ft_volumesegment(cfg, mri_resliced); %USES the SPM toolbox to statistically approximate the 5 compartments based on T1 scan
  86. %%
  88. cfg = [];
  89. cfg.funparameter = 'skull';
  90. %cfg.funcolormap = white(6); % distinct color per tissue (air is included)
  91. cfg.location = 'center';
  92. %cfg.atlas = seg_i; % the segmentation can also be used as atlas
  93. ft_sourceplot(cfg, mri_segmented_5_compartment);
  95. cfg = [];
  96. cfg.funparameter = 'csf';
  97. %cfg.funcolormap = white(6); % distinct color per tissue (air is included)
  98. cfg.location = 'center';
  99. %cfg.atlas = seg_i; % the segmentation can also be used as atlas
  100. ft_sourceplot(cfg, mri_segmented_5_compartment);
  102. cfg = [];
  103. cfg.funparameter = 'white';
  104. %cfg.funcolormap = white(6); % distinct color per tissue (air is included)
  105. cfg.location = 'center';
  106. %cfg.atlas = seg_i; % the segmentation can also be used as atlas
  107. ft_sourceplot(cfg, mri_segmented_5_compartment);
  109. cfg = [];
  110. cfg.funparameter = 'gray';
  111. %cfg.funcolormap = white(6); % distinct color per tissue (air is included)
  112. cfg.location = 'center';
  113. %cfg.atlas = seg_i; % the segmentation can also be used as atlas
  114. ft_sourceplot(cfg, mri_segmented_5_compartment);
  115. %%
  116. %Create a mesh of small hexahedrons that makes up the whole brain
  117. %(discretising). Each hexahedron belongs to one brain segmentation.
  118. cfg = [];
  119. cfg.shift = 0.3; % shifts the orientation of the hexahedrons to improve the geometrical properties. 0.3 recommended by fieldtrip
  120. cfg.method = 'hexahedral'; %A geometric form with 6 faces (hexa)
  121. mesh_fem = ft_prepare_mesh(cfg,mri_segmented_5_compartment);
  122. save(fullfile('data\',[num2str(sub)],'mesh_fem.mat'), 'mesh_fem')
  124. %Here we start the FEM with simbio where we want to solve Au=bn
  125. %where A_ij = int{(\sig\grad h_i, \grad h_j)dx} Summing up the change at
  126. %specific locations from the discretised normalised Lagrange functions
  127. %and b_i = int{(\grad j^p)h_i dx j here represents a dipole
  128. %Here the Lagrange functions h_i(x) are "hat functions"(unit normalised) defined on the
  129. %finite element mesh.
  130. %prepare_headmodel calculates the stiffness matrix A
  131. cfg = [];
  132. cfg.method = 'simbio';
  133. cfg.conductivity = [1.79,0.33,0.43, 0.01, 0.14];
  134. cfg.tissuelabel = {'csf','gray','scalp', 'skull','white'};
  135. headmodel_fem_eeg = ft_prepare_headmodel(cfg, mesh_fem);
  136. save(fullfile('data\',[num2str(sub)],'headmodel_fem_eeg.mat'), 'headmodel_fem_eeg')
  138. %% Fit electrodes. Important to check the plots to see what fit is
  139. cfg = [];
  140. ft_sourceplot(cfg, mri_resliced);
  141. %%
  142. fid_mri.elecpos(1,:) = [0,77,-29]; % location of the nose
  143. fid_mri.elecpos(2,:) = [-110,-3,-58]; % location of the left ear
  144. fid_mri.elecpos(3,:) = [110,-3,-58]; % location of the right ear
  145. fid_mri.unit = 'mm'; % use the same units as those in mri
  146. fid_mri.label = {'FidNz', 'FidT9', 'FidT10'};
  148. % reasonable
  149. neuronavigation_path = fullfile('SL20_Neuronavigation',['SL20_',num2str(sub),'.txt']);
  150. elec = ft_read_sens(neuronavigation_path);
  151. elec_mm = ft_convert_units(elec,'mm');
  153. %
  154. %figure
  155. %hold on
  156. %ft_plot_mesh(mesh_fem,'surfaceonly','yes','vertexcolor','none','edgecolor','none','facecolor',[0.5 0.5 0.5],'facealpha',0.1)
  157. %camlight
  158. %ft_plot_sens(fid_mri, 'edgecolor', 'k','facealpha',0.1,'edgealpha',0.5);
  159. %ft_plot_sens(elec_mm,'facecolor','red');
  161. %first we fit based on fiducial points
  162. cfg = [];
  163. cfg.method = 'fiducial';
  164. cfg.elec = elec_mm; % the electrodes we want to align
  165. cfg.template = fid_mri; % the fiducial points extracted above
  166. cfg.fiducial = {'FidNz', 'FidT9', 'FidT10'};
  167. elec_aligned = ft_electroderealign(cfg,elec_mm);
  168. figure
  169. hold on
  170. ft_plot_mesh(mesh_fem,'surfaceonly','yes','vertexcolor','none','edgecolor','none','facecolor',[0.5 0.5 0.5],'facealpha',.5)
  171. camlight
  173. ft_plot_sens(elec_aligned,'facecolor','red','label','label');
  174. %%
  175. %then we project the electrode to the head surface
  176. cfg = [];
  177. cfg.method = 'project'; % onto scalp surface
  178. cfg.headshape = mesh_fem; % scalp surface
  180. elec_realigned = ft_electroderealign(cfg, elec_aligned);
  181. %
  182. figure
  183. hold on
  184. ft_plot_mesh(mesh_fem,'surfaceonly','yes','vertexcolor','none','edgecolor','none','facecolor',[0.5 0.5 0.5],'facealpha',.5)
  185. camlight
  187. ft_plot_sens(elec_realigned,'facecolor','red','label','label');
  188. elec_aligned = elec_realigned;
  189. %
  190. save(fullfile('data\',[num2str(sub)],['elec_fit.mat']),'elec_aligned','-v7.3');
  195. %% Prepare transfer matrix for computing leadfields
  196. %Here we calculate b with the transfer matrix approch (Wolters, 2004)
  197. %Uses the stifness matrix, the mesh geomerty and the electrodes as input to
  198. %compute the transfer matrix from the dipoles to the scalp positions
  199. %The transfer matrix is an o ptimisation method to solve the equations which
  200. %makes solving the system 100 times faster by only using the actual eeg
  201. %positions
  202. %We use the modified parallellised version
  203. for i_sub = 1:46
  204. load(fullfile('data',num2str(i_sub), 'elec_fit.mat'))
  205. load(fullfile('data',num2str(i_sub), 'headmodel_fem_eeg.mat'))
  206. [headmodel_fem_eeg_tr, elec] = ft_prepare_vol_sens_par(headmodel_fem_eeg, elec_aligned);
  207. save(fullfile('data',num2str(i_sub),'headmodel_fem_eeg_tr_norm.mat'),'headmodel_fem_eeg_tr','-v7.3');
  208. save(fullfile('data',num2str(i_sub),'elec_headmodel_norm.mat'),'elec','-v7.3');
  209. end
  211. % load(fullfile('data',num2str(sub), 'elec_fit.mat'))
  212. % load(fullfile('data',num2str(sub), 'headmodel_fem_eeg.mat'))
  213. % [headmodel_fem_eeg_tr, elec] = ft_prepare_vol_sens_par(headmodel_fem_eeg, elec_aligned);
  214. % save(fullfile('data',num2str(sub),'headmodel_fem_eeg_tr_norm.mat'),'headmodel_fem_eeg_tr','-v7.3');
  215. % save(fullfile('data',num2str(sub),'elec_headmodel_norm.mat'),'elec','-v7.3');
  218. %% Extract sourcemodel and calculate leadfields
  219. % First create a template source model in MNI space
  220. % NOTE: the path to the template file is user-specific
  221. ftpath = 'path_to_your_fieldtrip_version'; % this is the path to FieldTrip
  222. template = ft_read_mri(fullfile(ftpath, '/external/spm8/templates/T1.nii'));
  223. template.coordsys = 'spm'; % so that FieldTrip knows how to interpret the coordinate system
  225. % segment the template brain and construct a volume conduction model (i.e. head model):
  226. % this is needed to describe the boundary that define which dipole locations are 'inside' the brain.
  227. cfg = [];
  228. template_seg = ft_volumesegment(cfg, template);
  230. cfg = [];
  231. cfg.method = 'singleshell';
  232. template_headmodel = ft_prepare_headmodel(cfg, template_seg);
  233. template_headmodel = ft_convert_units(template_headmodel, 'cm'); % Convert the vol to cm, because the CTF convenction is to express everything in cm.
  235. % construct the dipole grid in the template brain coordinates
  236. % the negative inwardshift means an outward shift of the brain surface for inside/outside detection
  237. cfg = [];
  238. cfg.resolution = 1;
  239. cfg.tight = 'yes';
  240. cfg.inwardshift = -1.5;
  241. % cfg.inwardshift = -15;
  242. cfg.headmodel = template_headmodel;
  243. template_grid = ft_prepare_sourcemodel(cfg);
  245. %add the OB and PC coordinates (in cm) to the template model
  246. Pir_left = [-2.2 0 -1.4];
  247. Pir_right = [2.2 .2 -1.2];
  248. Bulb_left = [-.3,4.1,-3];
  249. Bulb_right = [.3,4.1,-3];
  250. s.inside = true(4,1);
  251. s.pos = [
  252. Pir_left
  253. Pir_right
  254. Bulb_left
  255. Bulb_right
  256. ];
  259. template_grid.pos = [template_grid.pos;s.pos];
  260. template_grid.inside = [template_grid.inside;s.inside];
  261. % make a figure with the template head model and dipole grid
  262. figure
  263. hold on
  264. ft_plot_headmodel((template_headmodel), 'facecolor', 'cortex', 'edgecolor', 'none');alpha 0.5; camlight;
  265. ft_plot_mesh(template_grid.pos(template_grid.inside,:));
  266. ft_plot_mesh(s.pos(s.inside,:),'vertexcolor','red','vertexsize', 80);
  268. %%
  269. sub = 1;
  270. load(fullfile('data',num2str(sub),'mri_resliced.mat'))
  271. load(fullfile('data',num2str(sub),'headmodel_fem_eeg.mat'))
  272. % create the subject specific grid, using the template grid that has just been created
  273. cfg = [];
  274. cfg.warpmni = 'yes';
  275. cfg.template = template_grid;
  276. cfg.nonlinear = 'yes';
  277. cfg.mri = mri_resliced;
  278. cfg.unit ='mm';
  279. grid = ft_prepare_sourcemodel(cfg);
  281. %%
  282. cfg = [];
  283. ft_sourceplot(cfg, mri_resliced);
  285. sourcemodel = ft_read_headshape(fullfile('freesurfer_data\',[num2str(sub),'-oct-6-src.fif']), 'format', 'mne_source');
  287. sourcemodel = ft_convert_units(sourcemodel, 'mm');
  289. % check sourcemodel manually and change postion if necessary
  290. figure
  291. hold on
  292. ft_plot_headmodel(headmodel_fem_eeg, 'facecolor', 'cortex', 'edgecolor', 'none', 'facealpha', 0.4);
  293. ft_plot_mesh(sourcemodel.pos)
  295. %%
  296. sm.pos = sourcemodel.pos;
  297. s.inside = true(4,1); % it must be logical otherwise you will not get error but miscalculation
  298. s.pos = grid.pos(6121:end,:)
  299. sm.pos = [sm.pos;s.pos];
  300. sm.inside = true(numel(sm.pos(:,1)),1);
  301. save(fullfile('data',num2str(sub),'source_model.mat'),'sm')
  302. %%
  304. for i_sub = 1:46
  305. load(fullfile('data',num2str(i_sub), 'source_model.mat'))
  306. load(fullfile('data',num2str(i_sub), 'headmodel_fem_eeg_tr_norm.mat'))
  307. load(fullfile('data',num2str(i_sub), 'elec_headmodel_norm.mat'))
  308. cfg = [];
  309. cfg.sourcemodel = sm;
  310. cfg.unit = 'mm';
  311. cfg.headmodel = headmodel_fem_eeg_tr;
  312. cfg.elec = elec;
  313. cfg.reducerank = 3; %removes the weakest orientation
  314. leadfield_fem_eeg = ft_prepare_leadfield(cfg); %The lead field matrix relate
  315. path = ['data/',num2str(i_sub),'/'];
  316. save(fullfile(path,['leadfield_fem_eeg.mat']),'leadfield_fem_eeg','-v7.3');
  317. end

forward_model.m, no license · at the source

Overview

Authors: Frans Nordén1, Irene Zanettin1, Mikael Lundqvist1, Artin Arshamian1, Johan N. Lundström1,2,3
ORCID iDs: Frans Nordén
  1. Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
  2. Department of Otorhinolaryngology, Karolinska University Hospital, Stockholm, Sweden
  3. Monell Chemical Senses Center, Philadelphia, Pennsylvania, United States of America‌‌
Institutions: Karolinska Institutet (Sweden); Karolinska University Hospital (Sweden); Monell Chemical Senses Center (United States)
Journal: PLoS biology, volume 24, issue 5, article e3003810
Dates: received 17 November 2025; accepted 5 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003810 · PMID 42213694 · PMCID PMC13221048 · OpenAlex W7162753568
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions, Spectral & time-frequency, Connectivity, fMRI & imaging, Physiology & signal measures
MeSH: Odorants*, Olfactory Bulb*, Olfactory Perception*, Piriform Cortex*, Female, Humans, Male, Smell (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Psychology, Social Sciences, Anatomy, Brain, Piriform Cortex, Medicine and Health Sciences, Physiology, Sensory Physiology, Olfactory System, Sensory Systems, Olfactory Bulb, Physical Sciences, Materials Science, Materials, Odorants, Mathematics, Discrete Mathematics, Combinatorics, Permutation, Engineering and Technology, Signal Processing
Topic: Olfactory and Sensory Function Studies (Sensory Systems, Neuroscience), according to OpenAlex
Funding: Knut and Alice Wallenberg Foundation (KAW 2018.0152); European Research Council (101118977); Vetenskapsrådet (2024-01605_VR); Stockholms Universitet (SU FV-5.1.2-1035-15)
Citations: cited by 2 papers (Europe PMC); 52 references in the paper

Abstract

Perceived stimulus intensity is a core feature of sensory experience, yet how it emerges in the human olfactory system remains unknown. Here, we demonstrate that oscillatory dynamics in the human olfactory bulb (OB) and piriform cortex (PC) primarily encode subjective perceived intensity rather than physical concentration. Using noninvasive electrobulbogram recordings, we show that early gamma-band activity in the OB reflects bottom-up transmission of perceived intensity to the PC, which in turn sends top-down beta-band feedback that modulates OB activity via phase–amplitude coupling and transient beta bursts. This bidirectional communication supports a dynamic updating mechanism that maintains perceptual constancy across varying environmental odor concentrations. Our findings reveal a previously uncharacterized oscillatory framework for intensity coding in the human olfactory system, highlighting the primacy of perception over stimulus properties and offering a mechanistic basis for predictive processing in early sensory circuits.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

OSF t7hj2

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (20), C (1)
Size: 37 files, 21 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files), Statistics and Machine Learning Toolbox (4 files), CoSMoMVPA (2 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
13 files
At the source: doi.org/10.17605/OSF.IO/T7HJ2

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 9 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 anonymized data and scripts required to reproduce the results are available at https://doi.org/10.17605/OSF.IO/T7HJ2.

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, issue, pages, dates, 5 authors, 8 MeSH terms, 4 funders, 50 references.

Cite

This paper

Nordén, F., Zanettin, I., Lundqvist, M., Arshamian, A., & Lundström, J. N. (2026). Olfactory bulb-cortex oscillations encode perceived odor intensity rather than concentration. PLoS biology, 24(5), e3003810. https://doi.org/10.1371/journal.pbio.3003810

BibTeX

@article{norden2026olfactory,
author = {Nordén, Frans and Zanettin, Irene and Lundqvist, Mikael and Arshamian, Artin and Lundström, Johan N.},
title = {{Olfactory bulb-cortex oscillations encode perceived odor intensity rather than concentration}},
journal = {PLoS biology},
year = {2026},
month = may,
volume = {24},
number = {5},
pages = {e3003810},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003810},
url = {https://doi.org/10.1371/journal.pbio.3003810},
pmid = {42213694},
pmcid = {PMC13221048}
}

RIS

TY - JOUR
AU - Nordén, Frans
AU - Zanettin, Irene
AU - Lundqvist, Mikael
AU - Arshamian, Artin
AU - Lundström, Johan N.
TI - Olfactory bulb-cortex oscillations encode perceived odor intensity rather than concentration
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/05/29
VL - 24
IS - 5
SP - e3003810
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003810
UR - https://doi.org/10.1371/journal.pbio.3003810
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pbio.3003810",
"type": "article-journal",
"title": "Olfactory bulb-cortex oscillations encode perceived odor intensity rather than concentration",
"container-title": "PLoS biology",
"author": [
{
"family": "Nordén",
"given": "Frans"
},
{
"family": "Zanettin",
"given": "Irene"
},
{
"family": "Lundqvist",
"given": "Mikael"
},
{
"family": "Arshamian",
"given": "Artin"
},
{
"family": "Lundström",
"given": "Johan N."
}
],
"container-title-short": "PLoS Biol",
"volume": "24",
"issue": "5",
"page": "e3003810",
"DOI": "10.1371/journal.pbio.3003810",
"PMID": "42213694",
"PMCID": "PMC13221048",
"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003810",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
29
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1126/sciadv.aee1002 [code]
Theta oscillations are an organizational unit of odor processing in the olfactory bulb.
Journal: Science advances
In common: FieldTrip, Image Processing Toolbox, Statistics and Machine Learning Toolbox, 7 references
[2] doi:10.1162/imag.a.1226 [code]
A neuroscientist's guide to neural burst detection.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: FieldTrip, Statistics and Machine Learning Toolbox, 5 references
[3] doi:10.1038/s41593-026-02345-6 [code]
Human hippocampal ripples tune cortical responses based on predicted uncertainty.
Journal: Nature neuroscience
In common: FieldTrip, Image Processing Toolbox, Statistics and Machine Learning Toolbox, 4 references
[4] doi:10.7554/elife.108408 [code]
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.
Journal: eLife
In common: FieldTrip, Image Processing Toolbox, Statistics and Machine Learning Toolbox, 3 references
[5] doi:10.2147/opth.s590186 [code]
Differential Effects of Balanced and Imbalanced Binocular Stimulation on Visual Cortex Responses in Amblyopic Children.
Journal: Clinical ophthalmology (Auckland, N.Z.)
In common: CoSMoMVPA, FieldTrip, Image Processing Toolbox, 1 other tool
[6] doi:10.1016/j.isci.2026.115375 [code]
Bursts of regional cortical inhibition during smartphone use.
Journal: iScience
In common: Statistics and Machine Learning Toolbox, 4 references
[7] doi:10.1016/j.isci.2026.116458 [code]
Neural tracking of prosodic and statistical rhythms jointly supports artificial language learning.
Journal: iScience
In common: FieldTrip, Statistics and Machine Learning Toolbox, 3 references
[8] doi:10.1038/s41598-026-46149-x [code]
Dynamic neural representations of scene beauty are relatively unaffected by stimulus timing and task.
Journal: Scientific reports
In common: CoSMoMVPA, FieldTrip, Statistics and Machine Learning Toolbox, 1 reference
[9] doi:10.1038/s41467-026-73553-8 [code]
Universal rhythmic architecture uncovers two modes of neural dynamics.
Journal: Nature communications
In common: FieldTrip, Statistics and Machine Learning Toolbox, 3 references
[10] doi:10.1038/s41467-026-75359-0 [code]
Neural mechanisms of time-forward predictions for naturalistic auditory tone sequences.
Journal: Nature communications
In common: FieldTrip, Statistics and Machine Learning Toolbox, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.