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Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys.

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  1. [1] § Methods › Relate neural activity to DDM components ↔ Code/Figure4_splitFR.m, lines 1–115 · score 0.54 · epoch combination, DDM parameter, firing rates, split, fitted, neural

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The authors' code

MATLAB · 333 lines · 12 KB · no license · 1 match

  1. function Figure4_splitFR
  2. % Perform regression analysis
  3. % For each unit:
  4. % X set: firing rates for the split trials
  5. % Row 1-4: FR-H/L~rew0/1-based on Ep3, for the three epochs
  6. % Row 5-8: FR-H/L~rew0/1-based on Ep4, for the three epochs
  7. % Row 9-12: FR-H/L~rew0/1-based on Ep5, for the three epochs
  8. % Y set: DDM parameters
  9. % DDM para ~ FR for the three epochs
  10. % For the regression: DDM ~ b0 + b1 * rewcont + b2 * (FR-meanThisrewcont) + b3 * rewcont * (FR-meanThisrewcont)
  11. close all
  12. dataPath = '.';
  13. RAW_FILE = fullfile(dataPath, 'STN_spikeMatrix_ARgood20260217.mat');
  14. FR_FILE = sprintf('%s_epochNeural.csv', RAW_FILE(1:end-4));
  15. SPLITFRDDM_FILE = sprintf('%ssplitFRDDM.csv', FR_FILE(1:end-4));
  16. REG_FILE = sprintf('%s_reg.mat', SPLITFRDDM_FILE(1:end-4));
  17. FIT_FILE_POSTFIX = '_best20260217.csv';
  19. paraNames = {'a', 'B_alpha', 'B_d', 'k', 'me', 'z', 't1', 't0'};
  20. nPara = length(paraNames);
  21. epochs = {'FR3', 'FR4', 'FR5'};
  22. nepochs = length(epochs);
  24. % generate epochNeural file if necessary
  25. if ~isfile(FR_FILE)
  26. exportBehNeural4DDM(RAW_FILE);
  27. end
  29. % generate splitFR_DDM file if necessary (gather vaules of epoch-based FR and DDM fits)
  30. if ~isfile(SPLITFRDDM_FILE)
  31. T = genSPLITFR_DDM_FILE(FR_FILE, SPLITFRDDM_FILE, epochs, nepochs, paraNames, dataPath, FIT_FILE_POSTFIX);
  32. else
  33. T = readtable(SPLITFRDDM_FILE);
  34. end
  36. % compute the regression
  37. if ~isfile(REG_FILE)
  38. regSettings = {{'FR3'}, {'FR4'}, {'FR5'}}; % FR epoch combinations for robustness comparison
  39. regResults = compute_regression(REG_FILE, T, regSettings, paraNames);
  40. else
  41. load(REG_FILE);
  42. end
  44. % check results for the population
  46. pTh = 0.05;
  47. nSettings = length(regResults);
  48. nrows = 0; rowNames = {};
  49. for iSetting = 1:nSettings
  50. nepochsThis(iSetting) = length(regResults{iSetting}.regressors);
  51. nrows = nrows + nepochsThis(iSetting);
  52. s = strjoin(regResults{iSetting}.regressors, '+');
  53. rowNames = [rowNames, arrayfun(@(x) [s ':' x{1}], regResults{iSetting}.regressors, 'UniformOutput', false)];
  54. end
  55. [figFR, axFR] = genFigRectangleAxes(nrows, nPara, 1, 1);
  56. [figFRrew, axFRrew] = genFigRectangleAxes(nrows, nPara, 1, 1);
  57. irow = 1;
  58. for iSetting = 1:nSettings
  59. for iepoch = 1:nepochsThis(iSetting)
  60. para2plot = arrayfun(@(x) [x{1} '_' regResults{iSetting}.regressors{iepoch}], paraNames, 'UniformOutput', false);
  61. para2plot = [para2plot arrayfun(@(x) [x{1} '_' regResults{iSetting}.regressors{iepoch} 'rew'], paraNames, 'UniformOutput', false)];
  62. p2plot = arrayfun(@(x) [x{1} '_p'], para2plot, 'UniformOutput', false);
  63. for ipara = 1:nPara*2
  64. if ipara<=nPara
  65. axThis = axFR(irow, ipara);
  66. else
  67. axThis = axFRrew(irow, ipara-nPara);
  68. end
  69. if irow == 1
  70. if ipara>nPara
  71. title(axThis,replace( paraNames{ipara-nPara}, '_', '_-'));
  72. else
  73. title(axThis,replace( paraNames{ipara}, '_', '_-'));
  74. end
  75. end
  76. plot_eachPara(axThis, regResults{iSetting}.T.(para2plot{ipara}), regResults{iSetting}.T.(p2plot{ipara})<pTh, para2plot{ipara}, pTh, pTh/(2 * nPara *3));
  77. switch ipara
  78. case 1
  79. ylabel(axThis, rowNames{irow});
  80. case nPara+1
  81. ylabel(axThis, [rowNames{irow} ' x Rew']);
  82. otherwise
  83. set(axThis, 'YTickLabel', '');
  84. end
  85. end
  86. irow = irow + 1;
  87. end
  88. end
  89. % sgtitle(figFR, 'DDM:FR');
  90. % sgtitle(figFRrew, 'DDM:FR x Rew');
  93. % standardize plots
  94. ymax = 0; xmax = 0;
  95. for i=1:nrows
  96. for j=1:nPara
  97. x = get(axFR(i,j), 'XLim');
  98. xmax = max([xmax, x(2)]);
  99. y = get(axFR(i,j), 'YLim');
  100. ymax = max([ymax, y(2)]);
  101. x = get(axFRrew(i,j), 'XLim');
  102. xmax = max([xmax, x(2)]);
  103. y = get(axFRrew(i,j), 'YLim');
  104. ymax = max([ymax, y(2)]);
  105. end
  106. end
  107. ymax = 30;
  108. for i=1:nrows
  109. for j=1:nPara
  110. xlim(axFR(i,j), [-1,1]*xmax);
  111. ylim(axFR(i,j), [0,1]*ymax);
  112. xlim(axFRrew(i,j), [-1,1]*xmax);
  113. ylim(axFRrew(i,j), [0,1]*ymax);
  114. end
  115. end
  120. function exportBehNeural4DDM(dataFile)
  121. % export behavioral and neural data on ARdotsRT for fitting with DDM
  122. % generate a csv file with
  123. % rt response coh rewindex correct session FR fittedPara
  124. % rt in seconds; response 1 for contralateral, 2 for ipsi; coh: %coh
  126. FR_FILE = sprintf('%s_epochNeural.csv', dataFile(1:end-4));
  127. outFile = extract_epoch_FR(dataFile);
  128. load(outFile);
  129. nUnits = length(FR);
  130. Tall = table();
  131. nepochs = size(FR(1).val,2);
  132. epochNames = arrayfun(@(x) ['FR' num2str(x)], 1:nepochs, 'UniformOutput', false);
  133. for i=1:nUnits
  134. Ti = FR(i).trials(:, {'RT', 'choice', 'rewcont', 'signedCoh', 'correct'});
  135. Ti.session = ones(height(FR(i).trials),1) * i;
  136. fname = [FR(i).file '_unit' num2str(FR(i).unitID)];
  137. Ti.file = repmat({fname}, height(FR(i).trials), 1);
  138. Tneural = array2table(FR(i).val, 'VariableNames', epochNames);
  139. Ti = [Ti, Tneural];
  140. Tall = [Tall; Ti];
  141. end
  142. Tall = renamevars(Tall, {'RT', 'choice', 'rewcont', 'signedCoh'}, {'rt', 'response', 'rewindex', 'coh'});
  143. Tall.coh = Tall.coh * 100;
  144. Tall.rt = Tall.rt * 0.001;
  145. writetable(Tall, FR_FILE);
  147. function T = genSPLITFR_DDM_FILE(dataFile, outFile, epochs, nepochs, paraNames, dataPath, FIT_FILE_POSTFIX)
  148. % format X set:
  149. T = readtable(dataFile);
  150. [G, fileID] = findgroups(T.file);
  151. fileInd = splitapply(@(x) {x}, (1:length(T.file))', G);
  152. nUnits = length(fileInd);
  153. FR = table();
  154. for iunit = 1:nUnits
  155. FRthis = get_FR_matrix(T(fileInd{iunit}, :), epochs, nepochs);
  156. FRthis.file = repmat({fileID{iunit}}, height(FRthis), 1);
  157. FR = [FR; FRthis];
  158. end
  160. % format Y set:
  161. paraRew0 = arrayfun(@(x) [x{1} '_rew0'], paraNames, 'UniformOutput', false);
  162. paraRew1 = arrayfun(@(x) [x{1} '_rew1'], paraNames, 'UniformOutput', false);
  164. fits = table();
  165. for iepoch = 1:nepochs
  166. Ttemp = readtable(fullfile(dataPath, [epochs{iepoch} FIT_FILE_POSTFIX]));
  167. nrows = height(Ttemp);
  168. epoch_basedOn = epochs{iepoch};
  169. for irow = 1:nrows
  170. fitsThis = reformatDDMfits(Ttemp(irow,:), paraNames, paraRew0, paraRew1, epoch_basedOn);
  171. fits = [fits; fitsThis];
  172. end
  173. end
  175. T = innerjoin(FR, fits);
  176. writetable(T, outFile);
  178. function FR = get_FR_matrix(T, epochs, nepochs)
  179. trialTypes = {'0H', '1H', '0L', '1L'};
  180. nTrialTypes = length(trialTypes);
  182. ind_rew1 = T.rewindex == 1;
  183. ind_rew0 = T.rewindex == 0;
  184. FR = table();
  185. for iepoch = 1:nepochs
  186. val = T.(epochs{iepoch});
  187. mFR0 = nanmedian(val(~ind_rew1));
  188. mFR1 = nanmedian(val(ind_rew1));
  189. ind_H0 = val>mFR0;
  190. ind_H1 = val>mFR1;
  191. ind.i0H = ind_rew0 & ind_H0;
  192. ind.i0L = ind_rew0 & ~ind_H0;
  193. ind.i1H = ind_rew1 & ind_H1;
  194. ind.i1L = ind_rew1 & ~ind_H1;
  195. for iType=1:nTrialTypes
  196. for j = 1:nepochs
  197. meanFR(iType, j) = nanmean(T.(epochs{j})(ind.(['i' trialTypes{iType}])));
  198. end
  199. end
  200. TthisEpoch = array2table(meanFR, 'VariableNames', arrayfun(@(x) ['mean' x{1}], epochs, 'UniformOutput', false));
  201. TthisEpoch.basedOn = arrayfun(@(x) [epochs{iepoch} '_' x{1}], trialTypes, 'UniformOutput', false)';
  202. FR = [FR; TthisEpoch];
  203. end
  205. function fits = reformatDDMfits(row, paraNames, paraRew0, paraRew1, epoch_basedOn)
  206. s = split(row.fname, '_');
  207. FRtype = s{end}(1);
  208. fits = table;
  209. for i=0:1
  210. fitsThis = row(:, eval( ['paraRew' num2str(i)] ) );
  211. fitsThis.Properties.VariableNames = paraNames;
  212. fitsThis.basedOn = { [epoch_basedOn '_' num2str(i) FRtype] };
  213. fitsThis.file = {strjoin(s(1:end-1), '_')};
  214. fits = [fits; fitsThis];
  215. end
  217. function regResults = compute_regression(REG_FILE, T, regSettings, paraNames)
  218. nSettings = length(regSettings);
  219. [G, fileID] = findgroups(T.file);
  220. fileInd = splitapply(@(x) {x}, (1:height(T))', G);
  221. [file, unitID] = arrayfun(@(x) split_fname(x), fileID, 'UniformOutput', false);
  222. nUnits = length(fileInd);
  223. unitIDvec = [unitID{:}]';
  224. nPara = length(paraNames);
  226. for iSetting = 1:nSettings
  228. thisSetting = regSettings{iSetting};
  229. nEpochs = length(thisSetting);
  230. xCols = cellfun(@(x) ['mean' x], thisSetting, 'UniformOutput', false);
  232. regResults{iSetting}.regressors = thisSetting;
  234. for iUnit = 1:nUnits
  236. Tthis = T(fileInd{iUnit}, :);
  237. isRew1 = contains(Tthis.basedOn, '1');
  239. % Reward context (+1 / -1)
  240. rewCont = ones(height(Tthis), 1);
  241. rewCont(~isRew1) = -1;
  243. % Z-score FR values separately within each reward condition
  244. FRthis = table2array(Tthis(:, xCols));
  245. FRthis( isRew1, :) = zscore(FRthis( isRew1, :));
  246. FRthis(~isRew1, :) = zscore(FRthis(~isRew1, :));
  248. % Z-score behavioural parameters
  249. fitsThis = zscore(table2array(Tthis(:, paraNames)));
  251. % Design matrix: [intercept implicitly added by regstats]
  252. % Columns: rewCont | FR epochs | FR x rew interactions
  253. X = [rewCont, FRthis, FRthis .* rewCont];
  255. % Fit one model per parameter
  256. for iPara = 1:nPara
  257. regResults{iSetting}.raw(iUnit, iPara) = regstats(fitsThis(:, iPara), X);
  258. end
  259. end
  261. % --- Extract betas / p-values into arrays -------------------------
  263. iFR = 2 + (1:nEpochs); % FR main-effect indices
  264. iFRrew = iFR(end) + (1:nEpochs); % FR x reward interaction indices
  266. b_FR = zeros(nUnits, nPara, nEpochs);
  267. p_FR = zeros(nUnits, nPara, nEpochs);
  268. b_FRrew = zeros(nUnits, nPara, nEpochs);
  269. p_FRrew = zeros(nUnits, nPara, nEpochs);
  271. for iUnit = 1:nUnits
  272. for iPara = 1:nPara
  273. ts = regResults{iSetting}.raw(iUnit, iPara).tstat;
  274. b_FR (iUnit, iPara, :) = ts.beta(iFR);
  275. p_FR (iUnit, iPara, :) = ts.pval(iFR);
  276. b_FRrew(iUnit, iPara, :) = ts.beta(iFRrew);
  277. p_FRrew(iUnit, iPara, :) = ts.pval(iFRrew);
  278. end
  279. end
  281. % --- Pack results into a table ------------------------------------
  282. Tfile = table();
  283. Tfile.file = file;
  284. Tfile.unitID = unitIDvec;
  286. for iEpoch = 1:nEpochs
  287. epoch = thisSetting{iEpoch};
  289. % Column-name helpers
  290. mkNames = @(suffix) cellfun(@(p) [p '_' epoch suffix], ...
  291. paraNames, 'UniformOutput', false);
  293. bFRNames = mkNames('');
  294. pFRNames = mkNames('_p');
  295. bFRrewNames = mkNames('rew');
  296. pFRrewNames = mkNames('rew_p');
  298. Tfile = [Tfile, ...
  299. array2table(squeeze(b_FR (:, :, iEpoch)), 'VariableNames', bFRNames), ...
  300. array2table(squeeze(p_FR (:, :, iEpoch)), 'VariableNames', pFRNames), ...
  301. array2table(squeeze(b_FRrew(:, :, iEpoch)), 'VariableNames', bFRrewNames), ...
  302. array2table(squeeze(p_FRrew(:, :, iEpoch)), 'VariableNames', pFRrewNames)];
  303. end
  305. regResults{iSetting}.T = Tfile;
  306. end
  308. save(REG_FILE, 'regResults', 'fileID', 'T', 'regSettings');
  310. function plot_eachPara(ax, R, iSig, name, pTh1, pTh2)
  311. plot_stacked_hist(R, ax, iSig, [], [], [], 1);
  312. ymax = 30;
  313. plot(ax, [0,0], [0, ymax], 'k--');
  314. p = signtest(R);
  315. if p<pTh1 && p>pTh2
  316. plot(ax, median(R), 29, 'rv', 'MarkerFaceColor', 'none');
  317. elseif p<pTh2
  318. plot(ax, median(R), 29, 'rv', 'MarkerFaceColor', 'r');
  319. end
  320. n = length(R); nSig = sum(iSig);
  321. p = chi_square_testLD([nSig, n-nSig], [0.05, 0.95]*n); % proportion above chance
  322. if p<pTh1 && p>pTh2
  323. plot(ax, 0.5, 29, 'bs', 'MarkerFaceColor', 'none');
  324. elseif p<pTh2
  325. plot(ax, 0.5, 29, 'bs', 'MarkerFaceColor', 'b');
  326. end
  327. set(ax, 'XTick', [-0.5, 0, 0.5]);
  329. function [file, unitID] = split_fname(x)
  330. s = split(x, '_');
  331. file = s{1};
  332. unitID = str2num(s{2}(5:end));

Figure4_splitFR.m, no license · at the source

Overview

Authors: Kathryn Branam1, Joshua I Gold1, Long Ding1
ORCID iDs: Kathryn Branam, Joshua I Gold, Long Ding
  1. Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
Institutions: University of Pennsylvania (United States)
Journal: eLife, volume 15, article RP109622
Dates: published online 1 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109622 · PMID 42383848 · PMCID PMC13322701 · OpenAlex W7127539481
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Rhesus macaque
MeSH: Decision Making*, Neurons*, Reward*, Subthalamic Nucleus*, Animals, Macaca mulatta, Male (* major topic)
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: NEI NIH HHS (P30 EY001583, R01-EY022411, R21EY029091)
Citations: not cited yet (Europe PMC); 50 references in the paper
Research resources: MATLAB RRID:SCR_001622, Python RRID:SCR_008394

Abstract

The subthalamic nucleus (STN) is a part of the indirect and hyperdirect pathways in the basal ganglia (BG) and has been implicated in movement control, impulsivity, and decision-making. We recently demonstrated that, for perceptual decisions, the STN includes at least three subpopulations of neurons with different decision-related activity patterns (Branam et al., 2024). Here, we show that, for decisions that require both perceptual and reward-based processing, many STN neurons are sensitive to both sensory evidence and reward expectations. Within a drift-diffusion framework, three STN subpopulations show different relationships to model components reflecting the formation of the decision variable, dynamics of the decision bound, and non-decision-related processes. Many STN neurons also represent quantities related to decision evaluation, including choice accuracy and reward expectation. These results help to further delineate the multiple roles that STN plays in forming and evaluating complex decisions that combine multiple sources of information.

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 1 match between paragraphs and lines of code.

OSF jdk9v

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (37)
Size: 48 files, 37 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (16 files), fdr_bh (Benjamini-Hochberg FDR) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
37 files
At the source: osf.io/jdk9v/

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;
  • 37 scripts, each with its path and the digest of its content;
  • 1 match 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 electrophysiological data and the code for the analyses presented in the paper are deposited at OSF (https://osf.io/jdk9v/).

The following previously published dataset was used:

Ding L. 2026. STN asymmetric reward decision making. Open Science Framework. jdk9v

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 1 keyword, 7 MeSH terms, 1 funder, 50 references, 2 RRIDs.

Cite

This paper

Branam, K., Gold, J. I., & Ding, L. (2026). Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys. eLife, 15, RP109622. https://doi.org/10.7554/elife.109622

BibTeX

@article{branam2026distinct,
author = {Branam, Kathryn and Gold, Joshua I and Ding, Long},
title = {{Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys}},
journal = {eLife},
year = {2026},
month = jul,
volume = {15},
pages = {RP109622},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109622},
url = {https://doi.org/10.7554/elife.109622},
pmid = {42383848},
pmcid = {PMC13322701}
}

RIS

TY - JOUR
AU - Branam, Kathryn
AU - Gold, Joshua I
AU - Ding, Long
TI - Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/07/01
VL - 15
SP - RP109622
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109622
UR - https://doi.org/10.7554/elife.109622
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Distinct involvements of the subthalamic nucleus subpopulations in reward-biased decision-making in monkeys",
"container-title": "eLife",
"author": [
{
"family": "Branam",
"given": "Kathryn"
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"family": "Gold",
"given": "Joshua I"
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{
"family": "Ding",
"given": "Long"
}
],
"container-title-short": "Elife",
"volume": "15",
"page": "RP109622",
"DOI": "10.7554/elife.109622",
"PMID": "42383848",
"PMCID": "PMC13322701",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.109622",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
]
]
}
}

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

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Heritability of movie-evoked brain activity and connectivity.
Journal: eLife
In common: fdr_bh (Benjamini-Hochberg FDR), Statistics and Machine Learning Toolbox, 1 reference
[10] doi:10.1038/s41467-026-74274-8 [code]
Regional sex differences in human cortical anatomy vary in their morphometric bases and overlap with sex chromosomal and gonadal influences.
Journal: Nature communications
In common: fdr_bh (Benjamini-Hochberg FDR), Statistics and Machine Learning Toolbox, 1 reference

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