OSCR

Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI.

Code ↔ Paper

11 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 11 matches
  1. [1] § Methods › Data processing › Cell extraction and validation ↔ camri_code/preprocess/preprocess_neural.py, lines 115–168 · score 0.90 · gSiz, gSig, min_corr, min_pnr, ring, decay
  2. [2] § Methods › Data processing › Support vector machines and recursive feature elimination ↔ camri_code/functions/decoder_distance_rfe.m, lines 295–420 · score 0.82 · recursive feature elimination, SVM RFE, lowest ranking, ranking criteria, subset, trained
  3. [3] § Results › Neuronal responses exhibit diametric activation patterns depending on their distance to the local vasculature ↔ camri_code/scripts/run_figure_3.m, lines 61–164 · score 0.74 · distance histogram, Euclidean distance, SVM RFE, ranking criteria, proximity, decoding
  4. [4] § Results › Neuronal responses exhibit diametric activation patterns depending on their distance to the local vasculature ↔ camri_code/scripts/run_suppl_figure_6.m, lines 61–131 · score 0.73 · distance histogram, Euclidean distance, SVM RFE, ranking criteria, proximity, decoding
  5. [5] § Results › Neuronal responses exhibit diametric activation patterns depending on their distance to the local vasculature ↔ camri_code/scripts/run_figure_3.m, lines 61–164 · score 0.73 · Euclidean distance, annotated vascular, weight elimination, ranking criteria, location, lowest
  6. [6] § Results › Neuronal responses exhibit diametric activation patterns depending on their distance to the local vasculature ↔ camri_code/scripts/run_suppl_figure_6.m, lines 61–131 · score 0.73 · Euclidean distance, annotated vascular, weight elimination, ranking criteria, location, lowest
  7. [7] § Methods › Data processing › Vascular activity derived from microscopy ↔ camri_code/preprocess/process_vascular.m, lines 48–99 · score 0.64 · Gaussian filter, motion corrected, Frangi, ROI, videos, vessel
  8. [8] § Methods › Data processing › Support vector machines and recursive feature elimination ↔ camri_code/functions/decoder_distance_rfe.m, lines 295–420 · score 0.61 · weight elimination, ranking criteria, decoder, lowest, recursive, machines
  9. [9] § Methods › Data processing › Annotation of the vascular structure and neuron-vascular proximity ↔ camri_code/scripts/process_data.m, lines 3–26 · score 0.60 · 100–150 Hz, vessel patterns, 100 Hz, filter, microscopic
  10. [10] § Results › From local population measurements to large-scale recordings of brain connectomes ↔ camri_code/scripts/run_figure_4.m, lines 61–123 · score 0.55 · Magnitude map, cross validated, ipsi, contra, NRMSE, shuffled
  11. [11] § Results › From local population measurements to large-scale recordings of brain connectomes ↔ camri_code/scripts/run_suppl_figure_8.m, lines 142–263 · score 0.53 · Magnitude map, cross validated, ipsi, contra, NRMSE, shuffled

Paper

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The authors' code

MATLAB · 420 lines · 13 KB · no license · 2 matches

  1. function [results] = decoder_distance_rfe( ...
  2. neural_data, mri_data, p, exclude)
  4. %% Checks
  5. % Check if fields are equal
  6. fields_mri = fieldnames(mri_data);
  7. fields_mri(strcmp(fields_mri, 'results')) = [];
  9. fields_neural = fieldnames(neural_data);
  10. fields_neural(strcmp(fields_neural, 'results')) = [];
  12. if ~isequal(sort(fields_mri), sort(fields_neural))
  13. fprintf('Fields of modalities are not equal...')
  14. else
  15. fields = fields_neural;
  16. end
  18. % Check start times
  19. start_times = p.start_times/1000;
  20. end_times = p.end_times/1000;
  22. %% MRI
  24. % Arrange parameters
  25. mri_fields = fieldnames(p.plot.mri);
  26. for i = 1:size(mri_fields, 1)
  27. field = mri_fields{i};
  28. p_mri.plot.(field) = p.plot.mri.(field);
  29. end
  31. % Get session data
  32. [mri_avg_traces, mri_avg_ts, ...
  33. mri_avg_subject_idx, mri_avg_channel_idx] = ...
  34. get_session(mri_data, p_mri, exclude);
  36. % Adjust timestamps for plotting
  37. mri_avg_ts = median(mri_avg_ts, 1, 'omitmissing');
  38. mri_avg_ts = mri_avg_ts/1000;
  40. %% Single cells (individual)
  42. % Arrange parameters
  43. cells_ind_fields = fieldnames(p.plot.cells_ind);
  44. for i = 1:size(cells_ind_fields, 1)
  45. field = cells_ind_fields{i};
  46. p_cells_ind.plot.(field) = p.plot.cells_ind.(field);
  47. end
  49. % Get session data
  50. [cells_ind_traces, cells_ind_ts, ...
  51. cells_ind_subject_idx, cells_ind_channel_idx] = ...
  52. get_session(neural_data, p_cells_ind, exclude);
  54. % Adjust timestamps for plotting
  55. cells_ind_ts = median(cells_ind_ts, 1, 'omitmissing');
  56. cells_ind_ts = cells_ind_ts/1000;
  58. %% Converge data
  60. if size(cells_ind_ts, 2) >= size(mri_avg_ts, 2)
  61. if ~any(cells_ind_ts(1:size(mri_avg_ts, 2)) ~= mri_avg_ts)
  62. ts = cells_ind_ts(1, 1:size(mri_avg_ts, 2));
  63. cells_ind_traces = cells_ind_traces(:, 1:size(mri_avg_ts, 2));
  64. else
  65. fprintf('Timestamps do not overlap...\n')
  66. return
  67. end
  68. elseif size(mri_avg_ts, 2) >= size(cells_ind_ts, 2)
  69. if ~any(cells_ind_ts ~= mri_avg_ts(1:size(cells_ind_ts, 2)))
  70. ts = mri_avg_ts(1, 1:size(cells_ind_ts, 2));
  71. mri_avg_traces = mri_avg_traces(:, 1:size(cells_ind_ts, 2));
  72. else
  73. fprintf('Timestamps do not overlap...\n')
  74. return
  75. end
  76. elseif size(mri_avg_ts, 2) == size(cells_ind_ts, 2)
  77. if ~any(cells_ind_ts ~= mri_avg_ts(1:size(cells_ind_ts, 2)))
  78. ts = cells_ind_ts(1, 1:size(mri_avg_ts, 2));
  79. else
  80. fprintf('Timestamps do not overlap...\n')
  81. return
  82. end
  83. end
  85. %% Checks
  87. % Check if subjects are the same for both modalities
  88. if unique(mri_avg_subject_idx) ~= unique(cells_ind_subject_idx)
  89. fprintf('Subjects are not equal between modalities...\n')
  90. return
  91. else
  92. subject_idx = unique(mri_avg_subject_idx);
  93. end
  95. %% Process traces
  97. % Adjust data range
  98. idx = ts(1, :) >= p.plot.decoder_timerange(1) & ...
  99. ts(1, :) < p.plot.decoder_timerange(2);
  100. ts = ts(1, idx);
  101. mri_avg_traces = mri_avg_traces(:, idx);
  102. cells_ind_traces = cells_ind_traces(:, idx);
  104. % remove NaN values between trials
  105. idx_nan = isnan(mean(cells_ind_traces)); % find the NaN values
  106. idx_nan = medfilt1(double(idx_nan));
  107. idx_nan = logical(idx_nan);
  109. ts(idx_nan) = [];
  110. mri_avg_traces(:, idx_nan) = [];
  111. cells_ind_traces(:, idx_nan) = [];
  113. % Remove first and last 2 columns due to NaNs
  114. ts([1 2 end-1 end]) = [];
  115. mri_avg_traces(:, [1 2 end-1 end]) = [];
  116. cells_ind_traces(:, [1 2 end-1 end]) = [];
  118. % Linear extrapolate over the cell traces to remove NaNs
  119. for iChannels = 1:size(cells_ind_traces, 1)
  120. target_trace = cells_ind_traces(iChannels, :);
  121. nanx = isnan(target_trace);
  122. t = 1:numel(target_trace);
  123. cells_ind_traces(iChannels, nanx) = ...
  124. interp1(t(~nanx), target_trace(~nanx), t(nanx));
  125. end
  127. %% Calculate neurovascular distance metric
  129. subject_idx_dist = [];
  130. channel_idx_dist = [];
  131. dist_total = [];
  132. dist_total_rv = [];
  133. for iSubjects = 1:size(subject_idx, 1)
  135. % Determine subject
  136. target_subject = subject_idx{iSubjects};
  137. target_channel_idx = cells_ind_channel_idx( ...
  138. cells_ind_subject_idx == target_subject, :);
  140. % Get filters and vessel image
  141. try
  142. filters = neural_data.(target_subject).cellmaps;
  143. catch
  144. fprintf('%s: cellmaps unavailable...\nSkipping...\n', ...
  145. target_subject)
  146. continue
  147. end
  149. try
  150. vessels = neural_data.(target_subject).vessels_logical;
  151. catch
  152. fprintf('%s: vessel image unavailable...\nSkipping...\n', ...
  153. target_subject)
  154. continue
  155. end
  157. % Initialize
  158. dist_rv = [];
  159. dist_norm = [];
  160. coords_cell = [];
  161. coords_vessel = [];
  162. filter_map = zeros(size(filters, 1:2));
  163. for iCells = 1:size(filters, 3)
  165. target_filter = filters(:, :, iCells);
  167. % Create filter map
  168. filter_map = filter_map + target_filter;
  170. % Calculate minimal distance
  171. [rows, columns] = find(target_filter == max(target_filter(:)));
  172. rows = rows(1);
  173. columns = columns(1);
  174. x_cell = [rows, columns];
  175. coords_cell(end + 1, :) = x_cell;
  177. [rows, columns] = find(vessels == max(vessels(:)));
  178. y_vessel = [rows, columns];
  180. D = pdist2(x_cell, y_vessel, 'euclidean');
  181. [~, idx] = min(D);
  182. dist_rv(iCells, 1) = D(idx);
  183. coords_vessel(end + 1, :) = y_vessel(idx, :);
  184. end
  186. % Plot cellmaps
  187. fig = figure();
  188. h = gca;
  189. hold on
  190. imshow(((filter_map * 400) / 1.5 + ...
  191. neural_data.(target_subject).average_image) * 250, ...
  192. [])
  193. hold on
  194. for iDist = 1:size(coords_vessel, 1)
  196. scatter(coords_cell(:, 2), coords_cell(:, 1), 40, 'ro', ...
  197. 'MarkerFaceColor', 'r')
  198. scatter(coords_vessel(:, 2), coords_vessel(:, 1), 15, 'ro', ...
  199. 'MarkerFaceColor', 'r')
  201. plot([coords_cell(iDist, 2) coords_vessel(iDist, 2)], ...
  202. [coords_cell(iDist, 1) coords_vessel(iDist, 1)], '-r', ...
  203. 'LineWidth', 3)
  204. end
  206. set(fig, 'Position', get(0, 'Screensize'));
  208. % Set image variables
  209. set(fig, 'color', 'w');
  211. set(h, 'Layer', 'top')
  212. set(fig, 'color', 'none');
  213. set(h, 'color', 'none');
  215. set(gca, 'XTickLabel', [], 'YTickLabel', []);
  217. % Save to vector image
  218. exportgraphics(fig, fullfile(p.path, ...
  219. replace(p.plot.save_cellmap, "*", target_subject)),...
  220. 'ContentType', 'vector',...
  221. 'BackgroundColor', 'none', ...
  222. 'Resolution', 300)
  224. close
  226. % Normalize distance
  227. dist_norm = mat2gray(dist_rv);
  229. % Calcute real world distance
  230. dist_rv = dist_rv * p.cam.pixel_size;
  232. % Results
  233. subject_idx_dist = [subject_idx_dist; ...
  234. repmat(string(target_subject), size(dist_rv))];
  235. channel_idx_dist = [channel_idx_dist; ...
  236. target_channel_idx];
  237. dist_total = [dist_total; dist_norm];
  238. dist_total_rv = [dist_total_rv; dist_rv];
  240. end
  242. % Plot histogram
  243. fig = figure();
  244. h = gca;
  245. hold on
  247. histogram(dist_total_rv, p.plot.histo.bin_size, ...
  248. 'EdgeColor', [0 0 0], ...
  249. 'FaceColor', [0.6 0.6 0.6])
  251. % Set image variables
  252. set(fig, 'color', 'w');
  254. h.LineWidth = p.line_width_axis;
  256. set(h, 'units', 'centimeters', 'position', [1, 1, p.plot.histo.scale]);
  257. set(h, 'FontSize', p.font_size, 'FontName', p.font);
  258. h.YAxis.FontWeight = 'bold';
  259. h.XAxis.FontWeight = 'bold';
  260. h.YRuler.TickLabelGapOffset = p.y_offset;
  261. h.XRuler.TickLabelGapOffset = p.x_offset;
  263. set(h, 'Layer', 'top')
  264. set(fig, 'color', 'none');
  265. set(h, 'color', 'none');
  267. % Save to vector image
  268. exportgraphics(fig, fullfile(p.path, ...
  269. replace(p.plot.save_hist, ".eps", "_tick.eps")),...
  270. 'ContentType', 'vector',...
  271. 'BackgroundColor', 'none', ...
  272. 'Resolution', 300)
  274. set(gca, 'XTickLabel', [], 'YTickLabel', []);
  276. exportgraphics(fig, fullfile(p.path, p.plot.save_hist),...
  277. 'ContentType', 'vector',...
  278. 'BackgroundColor', 'none', ...
  279. 'Resolution', 300)
  281. close
  283. % Write results
  284. results.(['decoder_distance_rfe_' ...
  285. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  286. neurovascular_distance_ids = [ ...
  287. subject_idx_dist, ...
  288. channel_idx_dist];
  289. results.(['decoder_distance_rfe_' ...
  290. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  291. neurovascular_distance = [ ...
  292. dist_total, ...
  293. dist_total_rv];
  295. %% SVM-RFE
  296. % SVM—RFE stands for Support Vector Machine- Recursive Feature Elimination.
  297. % It is an embedded approach that recursively removes unimportant features
  298. % rather than using the weights for ranking criterion as in Relief F. It
  299. % helps to provide better performance by selecting best features subset.
  300. % It takes training instances and their class labels as an input to the
  301. % algorithm and uses ranking criterion based on the weight vector of SVM.
  302. % From the weight vector of SVM, the ranking of each feature is identified
  303. % and then features are selected by eliminating those features which have
  304. % lowest ranking. In bioinformatics, it is a powerful feature selection
  305. % algorithm to avoid overfitting in case of high number of features. But
  306. % this algorithm can only be used to linear kernel SVM, because in case of
  307. % non linear kernel it is quiet difficult to find the weight vector (Cho,
  308. % B. H., Yu, H., Kim, K. W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008).
  309. % Application of irregular and unbalanced data to predict diabetic
  310. % nephropathy using visualization and feature selection methods.
  311. % Artificial intelligence in medicine, 42(1), 37-53.)
  313. % RFE-SVM
  314. for iCriteria = 1:size(p.plot.rfe.ranking_criteria, 1)
  315. fprintf('Starting RFE with criteria: %s beta value...\n', ...
  316. p.plot.rfe.ranking_criteria(iCriteria))
  318. subject_idx_rfe = [];
  319. beta_rfe_total = [];
  320. ranking_rfe_total = [];
  321. for iSubjects = 1:size(subject_idx, 1)
  323. % Determine subject
  324. target_subject = subject_idx{iSubjects};
  325. target_channel_idx = cells_ind_channel_idx( ...
  326. cells_ind_subject_idx == target_subject, :);
  328. % Get data
  329. x_neural = cells_ind_traces( ...
  330. cells_ind_subject_idx == target_subject, :)';
  331. y_bold = mri_avg_traces( ...
  332. mri_avg_subject_idx == target_subject, :)';
  334. % Initialize
  335. included = true(size(x_neural, 2), 1);
  336. betas = nan(size(x_neural, 2), 1);
  337. ranking = nan(size(x_neural, 2), 1);
  339. for iFeatures = 1:size(x_neural, 2)
  340. rsvm = fitrsvm(x_neural(:, included), y_bold, ...
  341. 'Standardize', true);
  343. if size(unique(rsvm.Beta)) ~= size(rsvm.Beta)
  344. fprintf('Duplicate beta values...\n')
  345. end
  347. % Find value based on ranking criterion
  348. if strcmp(p.plot.rfe.ranking_criteria(iCriteria), ...
  349. 'lowest_weight_elimination')
  350. [value_ranking, idx_ranking] = min(rsvm.Beta);
  351. elseif strcmp(p.plot.rfe.ranking_criteria(iCriteria), ...
  352. 'highest_weight_elimination')
  353. [value_ranking, idx_ranking] = max(rsvm.Beta);
  354. elseif strcmp(p.plot.rfe.ranking_criteria(iCriteria), ...
  355. 'lowest_abs_weight_elimination')
  356. [value_ranking, idx_ranking] = min(abs(rsvm.Beta));
  357. end
  359. % Calculate the cell number based on the idx_criteria
  360. idx_criteria_ = target_channel_idx(idx_ranking);
  361. target_channel_idx(idx_ranking) = [];
  363. % Remove cell from included feature set
  364. included(idx_criteria_) = false;
  366. % Results
  367. betas(idx_criteria_) = value_ranking;
  368. ranking(idx_criteria_) = ((size(x_neural, 2) + 1) - iFeatures);
  370. end
  372. % Results
  373. subject_idx_rfe = [subject_idx_rfe; ...
  374. repmat(string(target_subject), size(betas))];
  375. beta_rfe_total = [beta_rfe_total; betas];
  376. ranking_rfe_total = [ranking_rfe_total; ranking];
  378. end
  380. % Write results
  381. if isfield(results.(['decoder_distance_rfe_' ...
  382. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]), ...
  383. 'ranking')
  385. results.(['decoder_distance_rfe_' ...
  386. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  387. ranking = [ ...
  388. results.(['decoder_distance_rfe_' ...
  389. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  390. ranking, ...
  391. beta_rfe_total, ...
  392. ranking_rfe_total];
  394. results.(['decoder_distance_rfe_' ...
  395. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  396. ranking_ids = [ ...
  397. results.(['decoder_distance_rfe_' ...
  398. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  399. ranking_ids, ...
  400. subject_idx_rfe];
  402. else
  404. results.(['decoder_distance_rfe_' ...
  405. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  406. ranking = [ ...
  407. beta_rfe_total, ...
  408. ranking_rfe_total];
  410. results.(['decoder_distance_rfe_' ...
  411. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  412. ranking_ids = subject_idx_rfe;
  414. end
  415. end
  417. % Write results
  418. results.(['decoder_distance_rfe_' ...
  419. p.plot.cells_ind.y_target '_to_' p.plot.mri.y_target]). ...
  420. ranking_criteria = p.plot.rfe.ranking_criteria;

decoder_distance_rfe.m at commit f6aba95, no license · at the source

Overview

Authors: Rik L.E.M. Ubaghs1, Roman Boehringer1, Markus Marks2, Helke K. Hesse3, Mehmet Fatih Yanik1, Valerio Zerbi4,5, Benjamin F. Grewe1,6
ORCID iDs: Rik L.E.M. Ubaghs, Markus Marks, Mehmet Fatih Yanik, Benjamin F. Grewe
  1. Institute of Neuroinformatics, ETH Zurich and University of Zurich,Zurich, Switzerland
  2. Division of Engineering and Applied Science, California Institute of Technology,Pasadena, USA
  3. Hesse - Optical Consulting, Gießen, Germany
  4. Department of Psychiatry, Faculty of Medicine, University of Geneva,Genève, Switzerland
  5. Department of Basic Neurosciences, Faculty of Medicine, University of Geneva,Genève, Switzerland
  6. ETH AI Center, ETH Zurich,Zurich, Switzerland
Institutions: University of Zurich (Switzerland); California Institute of Technology (United States); University of Geneva (Switzerland); ETH Zurich (Switzerland)
Journal: Nature methods, volume 23, issue 8, pages 1637-1646
Dates: received 10 November 2023; accepted 4 June 2026; published online 15 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41592-026-03154-2 · PMID 42458100 · PMCID PMC13441971 · OpenAlex W4388744342
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2023.11.14.566368
Status: code verified
Categories: fMRI (modality), optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuro-vascular interactions, Ca2+ imaging, Fluorescence imaging, Functional magnetic resonance imaging
MeSH: Brain*, Calcium*, Magnetic Resonance Imaging*, Neurons*, Single-Cell Analysis*, Animals, Brain Mapping, Mice, Oxygen (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Swiss National Science Foundation (173721, 198739, 315230, 189251, 226025, 211760); NIMH NIH HHS (R01 MH123612)
Citations: cited by 1 paper (Europe PMC); 80 references in the paper

Abstract

Functional magnetic resonance imaging (fMRI) based on the blood-oxygen-level-dependent (BOLD) signal is widely used to study brain activity non-invasively. However, the relationship between the local neural population activity and vascular activity, as measured by BOLD fMRI, remains incompletely understood. Here we show that simultaneous measurement of cellular calcium (Ca2+) activity and whole-brain BOLD fMRI in awake mice can reveal spatially specific relationships between neurons and the surrounding vasculature. We introduce an MRI-compatible single-photon microscope that enables recordings from genetically defined neurons at cellular resolution during fMRI. Using this approach, we find that neurons located close to blood vessels often show a negative relationship with the local BOLD signal, whereas neurons farther away display a more variable, often positive relationship. We further demonstrate that local neural activity can be linked to BOLD responses across distributed, connected brain regions. Together, these results provide insight into how vascular organization shapes fMRI signals and establish a powerful tool for bridging cellular and whole-brain measurements of brain function.

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 11 matches between paragraphs and lines of code.

rlemubaghs/camri

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f6aba9583f088fab970628c9be9ba2291b00ae06, 5 August 2025
Languages: MATLAB (127), C (18), C/C++ (13), Python (2)
Size: 1,207 files, 160 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (27 files), Statistics and Machine Learning Toolbox (17 files), Signal Processing Toolbox (11 files), NumPy (2 files), SciPy (2 files), Violinplot-Matlab (2 files), CaImAn (1 file), FSL (1 file), h5py (1 file), Parallel Computing Toolbox (1 file), Matplotlib (1 file), NiBabel (1 file), Nilearn (1 file), Nipype (1 file), OpenCV (1 file), pandas (1 file), PyBIDS (1 file), scikit-learn (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
160 files
At the source: github.com/rlemubaghs/camri

Code availability

The MATLAB and Python code scripts detailing all aspects of the performed analysis are made publicly available on github (https://github.com/rlemubaghs/camri).

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 160 scripts, each with its path and the digest of its content;
  • 11 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

Source data is publicly available via figshare (10.6084/m9.figshare.31389115.v1). Due to substantial size and complexity, all other data (raw imaging data and pre-processed activity data) that support the findings of this study will be made available upon request.

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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 9 MeSH terms, 2 funders, 80 references.

Cite

This paper

Ubaghs, R. L., Boehringer, R., Marks, M., Hesse, H. K., Yanik, M. F., Zerbi, V., & Grewe, B. F. (2026). Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI. Nature methods, 23(8), 1637-1646. https://doi.org/10.1038/s41592-026-03154-2

BibTeX

@article{ubaghs2026simultaneous,
author = {Ubaghs, Rik L.E.M. and Boehringer, Roman and Marks, Markus and Hesse, Helke K. and Yanik, Mehmet Fatih and Zerbi, Valerio and Grewe, Benjamin F.},
title = {{Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI}},
journal = {Nature methods},
year = {2026},
month = jul,
volume = {23},
number = {8},
pages = {1637--1646},
publisher = {Nature Portfolio},
issn = {1548-7091},
doi = {10.1038/s41592-026-03154-2},
url = {https://doi.org/10.1038/s41592-026-03154-2},
pmid = {42458100},
pmcid = {PMC13441971}
}

RIS

TY - JOUR
AU - Ubaghs, Rik L.E.M.
AU - Boehringer, Roman
AU - Marks, Markus
AU - Hesse, Helke K.
AU - Yanik, Mehmet Fatih
AU - Zerbi, Valerio
AU - Grewe, Benjamin F.
TI - Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI
T2 - Nature methods
J2 - Nat Methods
PY - 2026
DA - 2026/07/15
VL - 23
IS - 8
SP - 1637
EP - 1646
SN - 1548-7091
PB - Nature Portfolio
DO - 10.1038/s41592-026-03154-2
UR - https://doi.org/10.1038/s41592-026-03154-2
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41592-026-03154-2",
"type": "article-journal",
"title": "Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI",
"container-title": "Nature methods",
"author": [
{
"family": "Ubaghs",
"given": "Rik L.E.M."
},
{
"family": "Boehringer",
"given": "Roman"
},
{
"family": "Marks",
"given": "Markus"
},
{
"family": "Hesse",
"given": "Helke K."
},
{
"family": "Yanik",
"given": "Mehmet Fatih"
},
{
"family": "Zerbi",
"given": "Valerio"
},
{
"family": "Grewe",
"given": "Benjamin F."
}
],
"container-title-short": "Nat Methods",
"volume": "23",
"issue": "8",
"page": "1637-1646",
"DOI": "10.1038/s41592-026-03154-2",
"PMID": "42458100",
"PMCID": "PMC13441971",
"ISSN": "1548-7091",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41592-026-03154-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
15
]
]
}
}

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