OSCR

Midbrain endocannabinoids actuate dopamine-based action selection.

Code ↔ Paper

7 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 7 matches
  1. [1] § STAR METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Random forest classifiers ↔ Lujan26-timeseries-decoder-CV.R, lines 1–87 · score 0.71 · sliding window, random forest, trees, fold, decoding, bin
  2. [2] § STAR METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Functional linear mixed models (FLMM) ↔ Tutorials/Python rpy2 installation/R and rpy2 installation guide.ipynb, lines 75–149 · score 0.64 · installation instructions, CRAN package, fastFMM
  3. [3] § STAR METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Functional linear mixed models (FLMM) ↔ Figures/Simulations/Simulation_Code/photometry_sim_fLME_fn_multi.R, lines 3–41 · score 0.63 · random slope, design matrices, random intercepts, variance, coefficients, covariates
  4. [4] § STAR METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Functional linear mixed models (FLMM) ↔ Figures/Simulations/Simulation_Code/fui.R, lines 571–615 · score 0.62 · random slope, design matrices, random intercepts, selection, variables, covariates
  5. [5] § STAR METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Random forest classifiers ↔ Lujan26-timeseries-decoder-CV.R, lines 240–316 · score 0.62 · decoding accuracy, FDR corrected, threshold, permutation, window, onset
  6. [6] § RESULTS › Momentary uncoupling of NAc dopamine release dynamics and active avoidance in DGLaTH cKO mice ↔ Lujan26-timeseries-decoder-CV.R, lines 1–87 · score 0.57 · sliding window, random forest, decode, accuracy, escape, avoidance
  7. [7] § STAR METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Functional linear mixed models (FLMM) ↔ Figures/Simulations/Simulation_Code/photometry_sim_fLME-Science-lengthenRewardPeriod.R, lines 766–804 · score 0.54 · regression coefficients, joint CI, pointwise, CIs, covariate

Paper

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The authors' code

R · 334 lines · 10 KB · CC-BY-4.0 · 3 matches

  1. # Time-resolved decoder comparison using sliding-window random forests.
  2. #
  3. # Expected input:
  4. # - Two CSV files (e.g. "WTdata.csv" and "DGLcKOdata.csv") with one trial per row.
  5. # - All columns except the last are time-resolved features.
  6. # - The last column contains the class label for each trial (e.g., "escape", "avoidance")
  7. #
  8. # Output:
  9. # - Console summary of significant decoding differences.
  10. # - A plot of decoding accuracy over time.
  11. # - Optional CSV export with adjusted p-values per time window.
  13. # ---------------------------
  14. # 1. Analysis configuration
  15. # ---------------------------
  17. analysis_config <- list(
  18. input_files = c(
  19. # Replace these with your own file paths. Relative paths work if the files
  20. # are in the working directory; absolute paths can also be used.
  21. group1 = "path/to/group1_timeseries_data.csv",
  22. group2 = "path/to/group2_timeseries_data.csv"
  23. ),
  24. group_labels = c(
  25. group1 = "Group 1",
  26. group2 = "Group 2"
  27. ),
  28. group_colors = c(
  29. group1 = "blue",
  30. group2 = "red"
  31. ),
  32. time_range_sec = c(-4, 6),
  33. sliding_window_bins = 4,
  34. sliding_step_bins = 1,
  35. n_trees = 100,
  36. n_folds = 10,
  37. q_threshold = 0.05,
  38. n_permutations = 200,
  39. random_seed = 123,
  40. positive_label = "escape",
  41. export_results = TRUE,
  42. export_file = "decoder_adjusted_p_values_by_time_bin.csv",
  43. save_plot = FALSE,
  44. plot_file = "decoder_accuracy_over_time.png"
  45. )
  47. # Set this to TRUE if you want the script to install missing packages automatically.
  48. install_missing_packages <- FALSE
  50. required_packages <- c("randomForest", "caret", "ggplot2")
  51. group_keys <- names(analysis_config$input_files)
  53. if (length(group_keys) != 2) {
  54. stop("This script currently expects exactly two groups in analysis_config$input_files.")
  55. }
  57. if (!all(group_keys %in% names(analysis_config$group_labels))) {
  58. stop("Each input file must have a matching entry in analysis_config$group_labels.")
  59. }
  61. if (!all(group_keys %in% names(analysis_config$group_colors))) {
  62. stop("Each input file must have a matching entry in analysis_config$group_colors.")
  63. }
  65. for (pkg in required_packages) {
  66. if (!requireNamespace(pkg, quietly = TRUE)) {
  67. if (!install_missing_packages) {
  68. stop(
  69. sprintf(
  70. "Package '%s' is required but not installed. Install it or set install_missing_packages <- TRUE.",
  71. pkg
  72. )
  73. )
  74. }
  75. install.packages(pkg, dependencies = TRUE)
  76. }
  77. }
  79. suppressPackageStartupMessages({
  80. library(randomForest)
  81. library(caret)
  82. library(ggplot2)
  83. })
  85. # ---------------------------
  86. # 2. Helper functions
  87. # ---------------------------
  89. load_timeseries_dataset <- function(file_path, positive_label = "escape") {
  90. raw_data <- read.csv(file_path, header = FALSE, stringsAsFactors = FALSE)
  91. data_matrix <- as.matrix(raw_data)
  93. if (ncol(data_matrix) < 2) {
  94. stop(sprintf("File '%s' must contain at least one feature column and one label column.", file_path))
  95. }
  97. feature_matrix <- data.matrix(raw_data[, -ncol(raw_data), drop = FALSE])
  98. labels <- data_matrix[, ncol(data_matrix)]
  100. if (is.character(labels)) {
  101. labels <- ifelse(labels == positive_label, 1, 0)
  102. }
  104. labels <- as.numeric(labels)
  106. if (anyNA(feature_matrix) || anyNA(labels)) {
  107. stop(sprintf("File '%s' contains values that could not be converted to numeric.", file_path))
  108. }
  110. cbind(feature_matrix, label = labels)
  111. }
  113. build_time_vector <- function(data_matrix, time_range_sec) {
  114. n_timepoints <- ncol(data_matrix) - 1
  116. if (length(time_range_sec) != 2 || time_range_sec[1] >= time_range_sec[2]) {
  117. stop("time_range_sec must contain two increasing values: c(start_time, end_time).")
  118. }
  120. seq(time_range_sec[1], time_range_sec[2], length.out = n_timepoints)
  121. }
  123. format_significant_windows <- function(results_df) {
  124. significant_windows <- results_df[results_df$Is_Significant, , drop = FALSE]
  126. if (nrow(significant_windows) == 0) {
  127. return("None")
  128. }
  130. apply(significant_windows, 1, function(row) {
  131. sprintf("[%.2f, %.2f]", as.numeric(row["Time_Bin_Start"]), as.numeric(row["Time_Bin_End"]))
  132. })
  133. }
  135. # Compare decoder accuracy across groups for each sliding time window.
  136. compare_model_accuracies_permtest <- function(data1,
  137. data2,
  138. time_vector,
  139. window_size = 4,
  140. step = 1,
  141. n_trees = 100,
  142. n_folds = 10,
  143. q_thresh = 0.05,
  144. n_perm = 200,
  145. random_seed = 123) {
  146. if (ncol(data1) != ncol(data2)) {
  147. stop("Both datasets must contain the same number of feature columns plus one label column.")
  148. }
  150. n_timepoints <- ncol(data1) - 1
  152. if (length(time_vector) != n_timepoints) {
  153. stop("time_vector length must match the number of feature columns in each dataset.")
  154. }
  156. if (window_size > n_timepoints) {
  157. stop("window_size cannot be larger than the number of timepoints.")
  158. }
  160. n_trials1 <- nrow(data1)
  161. n_trials2 <- nrow(data2)
  162. labels1 <- as.factor(data1[, ncol(data1)])
  163. labels2 <- as.factor(data2[, ncol(data2)])
  165. time_indices <- seq(1, n_timepoints - window_size + 1, by = step)
  166. accs1_all <- vector("list", length(time_indices))
  167. accs2_all <- vector("list", length(time_indices))
  168. p_vals <- numeric(length(time_indices))
  170. set.seed(random_seed)
  171. folds1 <- createFolds(labels1, k = n_folds, list = TRUE)
  172. folds2 <- createFolds(labels2, k = n_folds, list = TRUE)
  174. for (window_idx in seq_along(time_indices)) {
  175. start_idx <- time_indices[window_idx]
  176. end_idx <- start_idx + window_size - 1
  177. accs1 <- numeric(n_folds)
  178. accs2 <- numeric(n_folds)
  180. for (fold_idx in seq_len(n_folds)) {
  181. train_idx1 <- setdiff(seq_len(n_trials1), folds1[[fold_idx]])
  182. test_idx1 <- folds1[[fold_idx]]
  183. train_idx2 <- setdiff(seq_len(n_trials2), folds2[[fold_idx]])
  184. test_idx2 <- folds2[[fold_idx]]
  186. X_train1 <- data1[train_idx1, start_idx:end_idx, drop = FALSE]
  187. y_train1 <- labels1[train_idx1]
  188. X_test1 <- data1[test_idx1, start_idx:end_idx, drop = FALSE]
  189. y_test1 <- labels1[test_idx1]
  191. X_train2 <- data2[train_idx2, start_idx:end_idx, drop = FALSE]
  192. y_train2 <- labels2[train_idx2]
  193. X_test2 <- data2[test_idx2, start_idx:end_idx, drop = FALSE]
  194. y_test2 <- labels2[test_idx2]
  196. model1 <- randomForest(x = X_train1, y = y_train1, ntree = n_trees)
  197. model2 <- randomForest(x = X_train2, y = y_train2, ntree = n_trees)
  199. pred1 <- predict(model1, X_test1)
  200. pred2 <- predict(model2, X_test2)
  202. accs1[fold_idx] <- mean(pred1 == y_test1)
  203. accs2[fold_idx] <- mean(pred2 == y_test2)
  204. }
  206. accs1_all[[window_idx]] <- accs1
  207. accs2_all[[window_idx]] <- accs2
  209. observed_diff <- mean(accs1) - mean(accs2)
  210. combined <- c(accs1, accs2)
  211. group1_size <- length(accs1)
  213. perm_diffs <- replicate(n_perm, {
  214. permuted <- sample(combined)
  215. mean(permuted[seq_len(group1_size)]) - mean(permuted[(group1_size + 1):length(combined)])
  216. })
  218. p_vals[window_idx] <- mean(abs(perm_diffs) >= abs(observed_diff))
  219. }
  221. adj_p <- p.adjust(p_vals, method = "BH")
  222. significant <- adj_p < q_thresh
  223. first_sig_idx <- which(significant)[1]
  224. window_start_times <- time_vector[time_indices]
  225. window_end_times <- time_vector[time_indices + window_size - 1]
  226. onset_time <- if (!is.na(first_sig_idx)) window_start_times[first_sig_idx] else NA_real_
  228. data.frame(
  229. Time_Bin_Start = window_start_times,
  230. Time_Bin_End = window_end_times,
  231. Accuracy_Group1 = vapply(accs1_all, mean, numeric(1)),
  232. Accuracy_Group2 = vapply(accs2_all, mean, numeric(1)),
  233. P_Value = p_vals,
  234. Adj_P_Value = adj_p,
  235. Is_Significant = significant,
  236. Onset_Time = onset_time
  237. )
  238. }
  240. # ---------------------------
  241. # 3. Load data and run analysis
  242. # ---------------------------
  244. data1 <- load_timeseries_dataset(
  245. analysis_config$input_files[group_keys[1]],
  246. positive_label = analysis_config$positive_label
  247. )
  248. data2 <- load_timeseries_dataset(
  249. analysis_config$input_files[group_keys[2]],
  250. positive_label = analysis_config$positive_label
  251. )
  253. time_vector <- build_time_vector(data1, analysis_config$time_range_sec)
  255. results_df <- compare_model_accuracies_permtest(
  256. data1 = data1,
  257. data2 = data2,
  258. time_vector = time_vector,
  259. window_size = analysis_config$sliding_window_bins,
  260. step = analysis_config$sliding_step_bins,
  261. n_trees = analysis_config$n_trees,
  262. n_folds = analysis_config$n_folds,
  263. q_thresh = analysis_config$q_threshold,
  264. n_perm = analysis_config$n_permutations,
  265. random_seed = analysis_config$random_seed
  266. )
  268. onset_time <- unique(results_df$Onset_Time)[1]
  270. if (is.na(onset_time)) {
  271. cat("No significant group difference detected after FDR correction.\n")
  272. } else {
  273. cat(sprintf("First significant time bin start: %.2f s\n", onset_time))
  274. }
  276. significant_windows <- format_significant_windows(results_df)
  277. cat("Significant time windows:", paste(significant_windows, collapse = ", "), "\n")
  279. # ---------------------------
  280. # 4. Plot decoding accuracy
  281. # ---------------------------
  283. plot_df <- data.frame(
  284. Time = results_df$Time_Bin_Start,
  285. Group_1 = results_df$Accuracy_Group1,
  286. Group_2 = results_df$Accuracy_Group2,
  287. Significant = results_df$Is_Significant
  288. )
  290. accuracy_plot <- ggplot(plot_df, aes(x = Time)) +
  291. geom_line(aes(y = Group_1, color = analysis_config$group_labels[group_keys[1]]), linewidth = 0.9) +
  292. geom_line(aes(y = Group_2, color = analysis_config$group_labels[group_keys[2]]), linewidth = 0.9) +
  293. geom_point(
  294. data = subset(plot_df, Significant),
  295. aes(y = pmax(Group_1, Group_2) + 0.02),
  296. color = "black",
  297. shape = 8,
  298. size = 2
  299. ) +
  300. geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray50") +
  301. coord_cartesian(xlim = analysis_config$time_range_sec) +
  302. scale_color_manual(
  303. values = setNames(
  304. analysis_config$group_colors[group_keys],
  305. analysis_config$group_labels[group_keys]
  306. )
  307. ) +
  308. labs(
  309. x = "Time (s)",
  310. y = "Decoding accuracy",
  311. color = "Group",
  312. title = "Sliding-window decoding accuracy comparison"
  313. ) +
  314. theme_minimal()
  316. print(accuracy_plot)
  318. # ---------------------------
  319. # 5. Export results
  320. # ---------------------------
  322. if (isTRUE(analysis_config$export_results)) {
  323. write.csv(results_df, file = analysis_config$export_file, row.names = FALSE)
  324. }
  326. if (isTRUE(analysis_config$save_plot)) {
  327. ggsave(
  328. filename = analysis_config$plot_file,
  329. plot = accuracy_plot,
  330. width = 8,
  331. height = 5,
  332. dpi = 300
  333. )
  334. }

Lujan26-timeseries-decoder-CV.R, under CC-BY-4.0 · at the source

Overview

Authors: MÁ Luján1,2, R Young-Morrison2, V Kashtelyan3, G Loewinger4, Tanner D Klingenberg5, AF Hoffman5, I Gildish2, DP Covey2,6, EE McDonnell2, F Morgado2, K Peters2, AY Kim2, F Pereira4, CR Lupica5, JF Cheer2,7,8,9
ORCID iDs: AF Hoffman, JF Cheer
  1. Department of Anatomy and Cell Biology, University of Illinois College of Medicine, Chicago, IL, USA
  2. Department of Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
  3. Neuronal Networks Section, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
  4. Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  5. Electrophysiology Research Section, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
  6. Department of Neuroscience, Lovelace Biomedical Research Institute, Albuquerque, NM, USA
  7. Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
  8. Senior author
  9. Lead contact
Institutions: University of Maryland, Baltimore (United States); National Institutes of Health (United States); National Institute on Drug Abuse (United States); National Institute of Mental Health (United States)
Journal: Cell reports, volume 45, issue 5, article 117298
Dates: published online 21 April 2026; in print 26 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.celrep.2026.117298 · PMID 42024504 · PMCID PMC13285665 · OpenAlex W7155034964
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, Connectivity
Keywords: Dopamine, Midbrain, Motivation, Conditioning, Cannabinoids, Escape, Pallidum, Avoidance, Cp: Neuroscience
MeSH: Dopamine*, Endocannabinoids*, Mesencephalon*, Animals, Arachidonic Acids, Dopaminergic Neurons, Glycerides, Male, Mice, Mice, Inbred C57BL, Receptor, Cannabinoid, CB1, Reward, Ventral Tegmental Area (* major topic)
Topic: Cannabis and Cannabinoid Research (Pharmacology, Medicine), according to OpenAlex
Funding: National Institute on Drug Abuse; NIDA NIH HHS (R01 DA022340)
Citations: not cited yet (Europe PMC); 104 references in the paper
Research resources: Chicken anti-GAD67/GAD1 RRID:AB_1310248, Donkey anti-rabbit Alexa Fluor 647 RRID:AB_2340626, Rabbit anti-substance P RRID:AB_572266, Mouse anti-TH RRID:AB_572268, AAV9-EF1α-fDIO-cre RRID:Addgene_121675, AAV9-hsyn-GrabDA4.4 RRID:Addgene_140553, AAVrg-EF1α-mCherry-Flpo RRID:Addgene_55634, Mouse: DAT-cre RRID:IMSR_JAX:006660, Mouse: CB1f/f RRID:IMSR_JAX:036107

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

gloewing/photometry_flmm

License: CC0-1.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 6139fc14acd3f0f8d60608f9d8850fe19f240e61, 30 July 2025
Languages: R (44), Jupyter (6), Shell (1)
Size: 180 files, 51 scripts
Software Heritage: not archived
Found in: the text, “Footnotes”
Holds: README, license file, environment (pyproject.toml, uv.lock), 11 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (32 files), data.table (19 files), lme4 (17 files), mgcv (15 files), ggplot2 (14 files), patchwork (8 files), nlme (3 files), rpy2 (3 files), Matplotlib (2 files), pandas (2 files), ComplexHeatmap (1 file), emmeans (1 file), lmerTest (1 file), Plotly (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
53 files
At the source: github.com/gloewing/photometry_FLMM/tree/main

Zenodo 19154695

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: the text, “Footnotes”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: caret (1 file), ggplot2 (1 file), randomForest (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
1 file
At the source:

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;
  • 52 scripts, each with its path and the digest of its content;
  • 7 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.

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, 15 authors, 9 keywords, 13 MeSH terms, 2 funders, 101 references, 9 RRIDs.

Cite

This paper

Luján, M., Young-Morrison, R., Kashtelyan, V., Loewinger, G., Klingenberg, T. D., Hoffman, A., Gildish, I., Covey, D., McDonnell, E., Morgado, F., Peters, K., Kim, A., Pereira, F., Lupica, C., & Cheer, J. (2026). Midbrain endocannabinoids actuate dopamine-based action selection. Cell reports, 45(5), 117298. https://doi.org/10.1016/j.celrep.2026.117298

BibTeX

@article{lujan2026midbrain,
author = {Luján, MÁ and Young-Morrison, R and Kashtelyan, V and Loewinger, G and Klingenberg, Tanner D and Hoffman, AF and Gildish, I and Covey, DP and McDonnell, EE and Morgado, F and Peters, K and Kim, AY and Pereira, F and Lupica, CR and Cheer, JF},
title = {{Midbrain endocannabinoids actuate dopamine-based action selection}},
journal = {Cell reports},
year = {2026},
month = apr,
volume = {45},
number = {5},
pages = {117298},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117298},
url = {https://doi.org/10.1016/j.celrep.2026.117298},
pmid = {42024504},
pmcid = {PMC13285665}
}

RIS

TY - JOUR
AU - Luján, MÁ
AU - Young-Morrison, R
AU - Kashtelyan, V
AU - Loewinger, G
AU - Klingenberg, Tanner D
AU - Hoffman, AF
AU - Gildish, I
AU - Covey, DP
AU - McDonnell, EE
AU - Morgado, F
AU - Peters, K
AU - Kim, AY
AU - Pereira, F
AU - Lupica, CR
AU - Cheer, JF
TI - Midbrain endocannabinoids actuate dopamine-based action selection
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/04/21
VL - 45
IS - 5
SP - 117298
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117298
UR - https://doi.org/10.1016/j.celrep.2026.117298
LA - en
ER -

CSL-JSON

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}
}

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