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Neuronal dynamics, timing, and flow of sensory and choice-related information in auditory-prefrontal circuitry.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Flow of information ↔ InformationTransfer_Choice.m, lines 189–263 · score 0.73 · intrinsic temporal dependencies, autocorrelated, autoregressive, validate, lags, zero
  2. [2] § Methods › Flow of information ↔ InformationTransfer_Sensory.m, lines 191–265 · score 0.72 · intrinsic temporal dependencies, autocorrelated, autoregressive, validate, lags, zero

Paper

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The authors' code

MATLAB · 284 lines · 9.8 KB · no license · 1 match

  1. clearvars
  2. cd('C:\Users\giarroccof2\OneDrive - National Institutes of Health\Franco\Corrie Data\Task')
  5. load M1_PFC_Data_50ms_Tg.mat
  6. load M1_A1_Data_50ms_Tg.mat
  7. load M1_BehavioralData.mat
  8. load M1_BehavioralCodes.mat
  10. Times=-2100:50:1700;
  11. Times_fig=-2000:50:1700;
  12. Choice_Window=42:75;
  15. for s=1: size (M1_A1_Data_50ms_Tg,2)
  16. clearvars -except M1_PFC_Data_50ms_Tg M1_A1_Data_50ms_Tg Choice_Window ...
  17. Times Times_fig s R_s_Predictions M1_BehavioralData M1_BehavioralCodes
  19. size (M1_A1_Data_50ms_Tg,2) - s
  21. An_AC_Sess = M1_A1_Data_50ms_Tg{s};
  22. An_PFC_Sess = M1_PFC_Data_50ms_Tg{s};
  24. An_Behav_Sess = M1_BehavioralData{s};
  26. All_Trials = 1:size(An_AC_Sess,1);
  28. catchtr = M1_BehavioralCodes{s}.iscatch;
  29. Catch_Trials = find (catchtr==1);
  31. acc = M1_BehavioralCodes{s}.acc;
  32. acc_Nan = find(isnan(acc));
  33. RemovedTrials=[];
  34. RemovedTrials = union(Catch_Trials,acc_Nan);
  35. All_Trials(RemovedTrials) = [];
  37. Used_Trials = All_Trials;
  39. pos_acc=find(acc==1);
  40. acc_1= intersect(Used_Trials,pos_acc);
  42. neg_acc=find(acc==0);
  43. acc_0= intersect(Used_Trials,neg_acc);
  45. RTs_1= M1_BehavioralCodes{s}.t1rt ;
  47. RTs_1_Corr =RTs_1(Used_Trials);
  49. p33 = prctile(RTs_1_Corr(:,1), 33);
  50. p66 = prctile(RTs_1_Corr(:,1), 66);
  52. Trials_RT = nan(size(RTs_1_Corr,1),3);
  55. % Categorize the trials based on RT
  56. for i = 1:size(RTs_1_Corr,1)
  58. if RTs_1_Corr(i,1) <= p33
  59. Trials_RT(i,1) = RTs_1_Corr(i,1); % Collect trial numbers for short RTs
  60. elseif RTs_1_Corr(i,1) > p33 && RTs_1_Corr(i,1) <= p66
  61. Trials_RT(i,2) = RTs_1_Corr(i,1); % Collect trial numbers for short RTs
  62. else
  63. Trials_RT(i,3) = RTs_1_Corr(i,1); % Collect trial numbers for short RTs
  64. end
  65. end
  67. acc_RTs1=Used_Trials(find(isnan(Trials_RT(:,1))==0));
  68. acc_RTs2=Used_Trials(find(isnan(Trials_RT(:,2))==0));
  69. acc_RTs3=Used_Trials(find(isnan(Trials_RT(:,3))==0));
  71. acc_RTs={acc_RTs1,acc_RTs2,acc_RTs3};
  73. CueLocation = An_Behav_Sess.A(:,7);
  74. CueLocation(CueLocation==2) = -1;
  75. CueSide_Used_trials = CueLocation(Used_Trials);
  76. CueSide_Correct_trials = CueLocation(acc_1);
  78. TargetSide = M1_BehavioralCodes{s}.tside;
  79. TargetSide(TargetSide==2) = -1;
  80. TargetSide_Used_trials = TargetSide(Used_Trials);
  81. TargetSide_Correct_trials = TargetSide(acc_1);
  83. Match_Trials = zeros(size(TargetSide_Used_trials));
  84. Match = CueSide_Used_trials==TargetSide_Used_trials;
  85. idx_match = find(Match==1);
  86. idx_nonmatch = find(Match==0);
  87. Match_Trials (idx_match)= 1;
  88. Match_Trials (idx_nonmatch)= -1;
  90. Match_Corr_Trials = zeros(size(TargetSide_Correct_trials));
  91. Match_Corr = CueSide_Correct_trials==TargetSide_Correct_trials;
  92. idx_Match_Corr = find(Match_Corr==1);
  93. idx_nonMatch_Corr = find(Match_Corr==0);
  94. Match_Corr_Trials (idx_Match_Corr)= 1;
  95. Match_Corr_Trials (idx_nonMatch_Corr)= -1;
  97. AllDelayTimes = An_Behav_Sess.A(Used_Trials,8);
  99. Delay_1= find(AllDelayTimes==-1000);
  100. Delay_2= find(AllDelayTimes==-1300);
  101. Delay_3= find(AllDelayTimes==-1800);
  103. AllDelay_CorrTimes = An_Behav_Sess.A(acc_1,8);
  105. Delay_Corr_1= find(AllDelay_CorrTimes==-1000);
  106. Delay_Corr_2= find(AllDelay_CorrTimes==-1300);
  107. Delay_Corr_3= find(AllDelay_CorrTimes==-1800);
  109. RTs_1_Used_Trials =RTs_1(Used_Trials);
  111. RT_not_nan=find(isnan(RTs_1_Used_Trials)==0);
  112. Used_Trials_with_RTs=Used_Trials(RT_not_nan);
  113. RT_values_UsedTrials = RTs_1_Used_Trials(RT_not_nan);
  115. median_Used_Trials = median( RTs_1_Used_Trials, "omitnan");
  116. Slow_RTidx=find (RTs_1>median_Used_Trials);
  117. Fast_RTidx=find (RTs_1<median_Used_Trials);
  119. SlowRT_Used_Trials= intersect(Slow_RTidx,Used_Trials);
  120. FastRT_Used_Trials= intersect(Fast_RTidx,Used_Trials);
  121. Slow_RT=RTs_1 (SlowRT_Used_Trials);
  122. MnM_Label_SlowRT=Match_Trials(find(ismember(Used_Trials,SlowRT_Used_Trials)));
  123. MnM_Label_FastRT=Match_Trials(find(ismember(Used_Trials,FastRT_Used_Trials)));
  125. trials_For_Regression=Used_Trials;
  127. BetaCue_AC=[]; BetaTg_AC=[]; NormCue_AC=[]; NormTg_AC=[];
  128. %% First regression used to define 1D-dimension
  129. % A1 regression
  130. for AC_neu= 1 : size (An_AC_Sess,3) % # neurons
  131. AC_single_Neu=[]; AC_single_Neu = An_AC_Sess(:,:,AC_neu);
  133. for bin=1:size(AC_single_Neu,2)
  134. B = fitlm([Match_Trials],AC_single_Neu(trials_For_Regression,bin));
  136. BetaCue_AC(AC_neu,bin)=B.Coefficients{2,1};
  137. end
  138. end
  140. BetaCue_PFC=[]; BetaTg_PFC=[]; NormCue_PFC=[]; NormTg_PFC=[];
  141. % PFC regression
  142. for PFC_neu= 1 : size (An_PFC_Sess,3) % # neurons
  143. PFC_single_Neu=[]; PFC_single_Neu = An_PFC_Sess(:,:,PFC_neu);
  145. for bin=1:size(PFC_single_Neu,2)
  146. B = fitlm([Match_Trials],PFC_single_Neu(trials_For_Regression,bin));
  148. BetaCue_PFC(PFC_neu,bin)=B.Coefficients{2,1};
  149. end
  150. end
  153. %% Define 1D-dimension
  154. NormCue_AC=vecnorm(BetaCue_AC);
  155. Max_Norm_Cue(s,1)=max(NormCue_AC(Choice_Window));
  156. bin_betaCue_AC=find(NormCue_AC==max(NormCue_AC(Choice_Window)));
  157. BetaCue_Vector_AC=BetaCue_AC(:,bin_betaCue_AC(1));
  159. NormCue_PFC=vecnorm(BetaCue_PFC);
  160. bin_betaCue_PFC=find(NormCue_PFC==max(NormCue_PFC(Choice_Window)));
  161. BetaCue_Vector_PFC=BetaCue_PFC(:,bin_betaCue_PFC(1)); %?
  162. Max_Norm_Cue(s,2)=max(NormCue_PFC(Choice_Window));
  164. betas(s,1)=Times(bin_betaCue_AC);
  165. betas(s,2)=Times(bin_betaCue_PFC);
  167. AC_PFC_Data= {permute(An_AC_Sess, [3 2 1]), permute(An_PFC_Sess, [3 2 1])} ;
  168. IT_Micro_Tg_MnM_Values_All_Trials_2= [Max_Norm_Cue betas ];
  169. BetaCue_Vectors= {BetaCue_Vector_AC,BetaCue_Vector_PFC };
  170. % NormTg_AC=vecnorm(BetaTg_AC);
  171. % bin_betaTg_AC=find(NormTg_AC==max(NormTg_AC(Choice_Window)));
  172. % BetaTg_Vector_AC=BetaTg_AC(:,bin_betaTg_AC(1));
  175. %% Targeted dimensionality reduction
  176. n_areas=2;
  178. Proj_B_Cue=nan( size(trials_For_Regression,2), size(AC_PFC_Data{1,1},2), size(AC_PFC_Data,2)); % Trials x bin x Areas
  179. for n_Areas= 1:n_areas
  180. PermData=[]; PermData=AC_PFC_Data{1,n_Areas};
  181. for Trial_Beta=1:size(trials_For_Regression,2)
  182. for tb=1:size (PermData,2)
  183. Proj_B_Cue(Trial_Beta,tb,n_Areas)=dot(PermData(:,tb,trials_For_Regression(Trial_Beta)),BetaCue_Vectors{1,n_Areas});
  184. end
  185. end
  186. end
  189. %% To remove same area intrinsic temporal dependencies within each area
  190. Residual_SameArea=[];
  191. for n_Areas= 1:n_areas
  192. Area_Feature= Proj_B_Cue(:,:,n_Areas);
  194. for b= 1:size(Proj_B_Cue,2)-1
  195. Self_Prediction=fitlm(Area_Feature(:,b),Area_Feature(:,b+1));
  196. Residual_SameArea(:,b,n_Areas)=Self_Prediction.Residuals{:,2} ;
  197. end
  198. end
  200. % %%%%%
  201. % %%%%% to validate the previous autocorrelation model...
  202. % %%%%%
  203. % %%%%%
  205. % residuals = Residual_SameArea(:,:,1);
  206. %
  207. % max_lag = 5; %%%%% Define the maximum lag to test
  208. % num_trials = size(residuals, 1); %%%%% Number of trials
  209. % num_lags = 2 * max_lag + 1; %%%%%% Lags from -max_lag to max_lag
  210. % xcorr_vals = zeros(num_trials, num_lags); %%%%%% Store cross-correlations
  211. %
  212. % for trial = 1:num_trials
  213. % xcorr_vals(trial, :) = xcorr(residuals(trial, :), max_lag, 'coeff'); %%%%%%% Compute xcorr for each trial
  214. % end
  215. %
  216. % %%%%%%%% Average across trials to obtain session-level cross-correlation
  217. % mean_xcorr = mean(xcorr_vals, 1);
  218. %
  219. % %%%%%%%% Plot results
  220. % lags = -max_lag:max_lag;
  221. %
  222. %
  223. % %%%%%%%%% this is to plot the autocorrelation to ensure that the previous
  224. % %%%%%%%%% autoregression whitened the time series
  225. % figure;
  226. % stem(lags,mean_xcorr)
  227. % xlabel('Lag');
  228. % ylabel('Autocorrelation coefficient');
  229. % hold off;
  230. %
  231. % %%%%%%% residuals: matrix of size (nTrials x nTimeBins)
  232. % %%%%%%% Each row is one trial's residual time series
  233. %
  234. % maxLag = 5; %%%%%%%% Maximum lag to evaluate
  235. % numTrials = size(residuals, 1);
  236. %
  237. % %%%%%%%% Preallocate to store the xcorr results for each trial
  238. % ccVals = zeros(numTrials, 2*maxLag + 1);
  239. %
  240. % for tr = 1:numTrials
  241. % %%%%%%%% Compute xcorr for one trial, up to maxLag, normalized by 'coeff'
  242. % [cc, lags] = xcorr(residuals(tr, :), maxLag, 'coeff');
  243. % ccVals(tr, :) = cc; %%%%%%%% Store the cross-correlation values
  244. % end
  245. %
  246. % %%%%%%% Average across trials
  247. % meanCC = mean(ccVals, 1);
  248. % semCC = std(ccVals, [], 1) / sqrt(numTrials); %%%%%%%% Optional SEM
  249. %
  250. % %%%%%%%%%% Plot the mean cross-correlation
  251. % figure;
  252. % errorbar(lags, meanCC, semCC, '-o', 'LineWidth', 2, 'MarkerFaceColor','black');
  253. % xlabel('Lag');
  254. % ylabel('Autocorrelation coefficient');
  255. % grid on;
  256. %
  257. % alpha = 0.05;
  258. % for i = 1:length(lags)
  259. % if lags(i) ~= 0
  260. % [~, p] = ttest(ccVals(:, i), 0, 'Alpha', alpha);
  261. % fprintf('Lag %d -> p = %.4f\n', lags(i), p);
  262. % end
  263. % end
  265. %%%%%%%%%
  266. %%%%%%%%% end of autocorrelation validation
  267. %%%%%%%%%
  269. %% Perform Cross-area prediction
  270. for n_Areas= 1:n_areas
  272. if n_Areas==1
  273. TargetAreas=1; SourceAreas=2;
  274. elseif n_Areas==2
  275. TargetAreas=2; SourceAreas=1;
  276. end
  278. for b= 1:size(Residual_SameArea,2)-1
  279. IT_Prediction=fitlm(Residual_SameArea(:,b,SourceAreas),Residual_SameArea(:,b+1,TargetAreas));
  280. R_s_Predictions(s,b,n_Areas)=IT_Prediction.Rsquared.Adjusted;
  281. end
  282. end
  284. end

InformationTransfer_Choice.m at commit 4600c23, no license · at the source

Overview

Authors: Franco Giarrocco1, Bruno B. Averbeck1
ORCID iDs: Franco Giarrocco, Bruno B. Averbeck
  1. Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health,Bethesda, MD USA
Institutions: National Institute of Mental Health (United States)
Journal: Nature communications, volume 17, issue 1, article 8443
Dates: received 24 April 2025; accepted 26 June 2026; published online 8 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75349-2 · PMID 42420269 · PMCID PMC13478149 · OpenAlex W7167690234
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Single-unit activity, calcium imaging, Connectivity
Keywords: Cognitive neuroscience, Cortex
MeSH: Auditory Cortex*, Auditory Perception*, Choice Behavior*, Dorsolateral Prefrontal Cortex*, Neurons*, Prefrontal Cortex*, Acoustic Stimulation, Animals, Decision Making, Macaca mulatta, Male (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (ZIA MH002928)
Citations: not cited yet (Europe PMC); 77 references in the paper

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.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

GiarroccoFranco/Auditory_Decision_Making

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4600c233c3a974f279255a7b30d180ef0c366e36, 4 May 2025
Languages: MATLAB (18)
Size: 19 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (13 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/GiarroccoFranco/Auditory_Decision_Making

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Read it in the paper: doi.org/10.1038/s41467-026-75349-2.

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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75349-2.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 2 keywords, 11 MeSH terms, 1 funder, 75 references.

Cite

This paper

Giarrocco, F., & Averbeck, B. B. (2026). Neuronal dynamics, timing, and flow of sensory and choice-related information in auditory-prefrontal circuitry. Nature communications, 17(1), 8443. https://doi.org/10.1038/s41467-026-75349-2

BibTeX

@article{giarrocco2026neuronal,
author = {Giarrocco, Franco and Averbeck, Bruno B.},
title = {{Neuronal dynamics, timing, and flow of sensory and choice-related information in auditory-prefrontal circuitry}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8443},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75349-2},
url = {https://doi.org/10.1038/s41467-026-75349-2},
pmid = {42420269},
pmcid = {PMC13478149}
}

RIS

TY - JOUR
AU - Giarrocco, Franco
AU - Averbeck, Bruno B.
TI - Neuronal dynamics, timing, and flow of sensory and choice-related information in auditory-prefrontal circuitry
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/08
VL - 17
IS - 1
SP - 8443
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75349-2
UR - https://doi.org/10.1038/s41467-026-75349-2
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-75349-2",
"type": "article-journal",
"title": "Neuronal dynamics, timing, and flow of sensory and choice-related information in auditory-prefrontal circuitry",
"container-title": "Nature communications",
"author": [
{
"family": "Giarrocco",
"given": "Franco"
},
{
"family": "Averbeck",
"given": "Bruno B."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8443",
"DOI": "10.1038/s41467-026-75349-2",
"PMID": "42420269",
"PMCID": "PMC13478149",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75349-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
8
]
]
}
}

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