Behavioral engagement and stimulus regularities coordinate auditory-prefrontal network activity.

Code ↔ Paper

18 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 18 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Materials and methods › Data preprocessing ↔ Step2_Preprocess_Granger.m, lines 1–31 · score 0.87 · orthogonal source derivation, electrode impedance, temporal standard, volume, deviation, vertical
[2] § Materials and methods › Data preprocessing ↔ Step2_Preprocess_Rereference.m, lines 1–23 · score 0.87 · orthogonal source derivation, electrode impedance, temporal standard, volume, deviation, vertical
[3] § Results › Repetition-related dynamics during novelty detection in AC and PFC under behavioral conditions ↔ private/plotConfig.m, lines 1–16 · score 0.85 · 0.6–4 Hz, 14–30 Hz, 8–14 Hz, 30–70 Hz, 1.8–2.2 Hz, 4–8 Hz
[4] § Materials and methods › Local deviance-related time–frequency analyses ↔ private/plotConfig.m, lines 1–16 · score 0.79 · 0.6–4 Hz, 14–30 Hz, 8–14 Hz, 30–70 Hz, 4–8 Hz, frequency band
[5] § Materials and methods › Data preprocessing ↔ Step2_Preprocess_Granger.m, lines 1–31 · score 0.78 · phase locked, stimulus onsets, preprocessing, offsets, components, event
[6] § Materials and methods › Local deviance-related time–frequency analyses ↔ utils/plotPE_TFRBandStats.m, lines 1–52 · score 0.77 · deviant minus standard, CWT magnitude, 0–350 ms, FDR corrected, frequency band, 14 Hz
[7] § Materials and methods › Statistical analysis ↔ utils/plotPE_TFRBandStats.m, lines 1–52 · score 0.77 · deviant minus standard, CWT magnitude, 0–350 ms, FDR corrected, frequency band, 14 Hz
[8] § Materials and methods › Statistical analysis ↔ Step8_8_SF11.m, lines 1–21 · score 0.71 · prediction error GC, correlation matrices, Spearman correlations, right tailed, topographic, temporal
[9] § Results › Deviance-related responses during novelty detection in AC and PFC under behavioral conditions ↔ Step8_5_SF8.m, lines 1–30 · score 0.68 · deviant minus standard, 0–100 ms, tailed permutation, GC topographies, Rows, frequency band
[10] § Results › Active behavioral states amplify repetition-related and deviance-related dynamics in AC and PFC ↔ Step8_6_F6.m, lines 1048–1112 · score 0.62 · magnitude slope, suppression channels, negative slopes, 1.8–2.2 Hz, repetition enhancement, classified
[11] § Materials and methods › Repetition-related suppression and enhancement analyses ↔ Step8_6_F6.m, lines 1048–1112 · score 0.60 · suppression channels, magnitude slope, negative slope, repetition enhancement, classified, passive
[12] § Results › Active behavioral states amplify repetition-related and deviance-related dynamics in AC and PFC ↔ utils/summarizeSlopeReduction.m, the whole file · a weak match · score 0.59 · enhancement channels, Hz band magnitude, repetition enhancement, quantified, slope, active
[13] § Materials and methods › Statistical analysis ↔ Step8_2_SF3.m, lines 675–715 · score 0.58 · way ANOVA, repetition rate magnitude, identity, slopes, monkeys, channel
[14] § Materials and methods › Granger causality (GC) analyses ↔ Step8_8_SF11.m, lines 1–21 · score 0.57 · prediction error GC, correlation matrices, cross, Spearman, topographies, windows
[15] § Results › Repetition-related dynamics during novelty detection in AC and PFC under behavioral conditions ↔ Step8_2_SF3.m, lines 675–715 · score 0.55 · way ANOVA, 1.8–2.2 Hz, 1.8 Hz, fits, slope, magnitude
[16] § Materials and methods › Statistical analysis ↔ Step8_5_SF8.m, lines 1–30 · score 0.54 · deviant minus standard, tailed permutation, frequency band, FDR, topographic, map
[17] § Materials and methods › Repetition-related suppression and enhancement analyses ↔ Step8_2_F2.m, lines 960–1055 · score 0.53 · Hz magnitude slope, tailed bootstrap, suppression, enhancement, min
[18] § Results › Deviance-related responses during novelty detection in AC and PFC under behavioral conditions ↔ Step8_5_F5.m, lines 601–678 · score 0.51 · 4–14 Hz, theta GC, deviance related, alpha, ratio, deviant

Paper

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

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The authors' code

MATLAB · 119 lines · 5.3 KB · MIT · 2 matches

%% Description
% This script does further preprocessing after trial
% exclusion, trial classification, and ICA.
% Four steps are included in this procedure:
% 1. Common average referencing (CAR)
% CAR re-references data by subtracting the common
% average of trial data from channels of the same
% array, to reduce signal differences between
% arrays due to variations in electrode impedance
% and other array-specific differences.
% 2. Orthogonal source derivation
% Data are re-referenced by subtracting the average
% of the signals from all four horizontally and
% vertically adjacent electrodes, to mitigate
% problems caused by common reference and volume
% conduction and improve spatial localization of
% signals.
% 3. Normalization
% Signals are normalized by the temporal standard
% deviation of the amplitude to ensure equal
% weighting of data from each electrode and trial.
% 4. Subtract by ERP
% Finally, to remove evoked potentials locked to
% trial events, such as stimulus onsets and offsets,
% and to isolate internally generated components
% that were not phase-locked ("induced oscillations"),
% the mean potential across trials (average event-
% related potential) was subtracted from individual
% trials.
ccc;
%% Path
MATPATHs = dir("..\MAT Data\pre\single\**\*Data_*.mat");
MATPATHs = arrayfun(@(x) fullfile(x.folder, x.name), MATPATHs, "UniformOutput", false);
SAVEPATHs = strrep(MATPATHs, '\pre\', '\pre (granger)\');
%% Find orthogonal neighbours
neighbours = mu_prepareNeighboursArray(1:64, [8, 8], "orthogonal");
%% Preprocess
for mIndex = 1:length(MATPATHs)
if exist(SAVEPATHs{mIndex}, "file")
disp([SAVEPATHs{mIndex}, ' already exist. Skip']);
continue;
end
% Load
res = matfile(MATPATHs{mIndex});
vars = fieldnames(res);
vars = vars(2:end);
load(MATPATHs{mIndex});
% PE
if contains(MATPATHs{mIndex}, 'PE Data_') || contains(MATPATHs{mIndex}, 'Push Data_')
% Common average referencing - CAR
trialsECOG = cellfun(@(x) x - mean(x, 1), trialsECOG, "UniformOutput", false);
% Orthogonal source derivation
trialsECOG = cellfun(@(x) cell2mat(mu.rowfun(@(y, z) y - mean(x(z.neighbch, :), 1), x, neighbours, "UniformOutput", false)), trialsECOG, "UniformOutput", false);
% Normalization - normalized by the temporal standard deviation of the amplitude
trialsECOG = cellfun(@(x) x ./ std(x, [], 2), trialsECOG, "UniformOutput", false);
% Subtract by ERP
chMean = arrayfun(@(x) calchMean(trialsECOG(dRatioAll == x)), dRatio, "UniformOutput", false);
for tIndex = 1:length(trialsECOG)
trialsECOG{tIndex} = trialsECOG{tIndex} - chMean{dRatio == dRatioAll(tIndex)};
end
end
% DM
if contains(MATPATHs{mIndex}, 'DM Data_')
for dIndex = 1:length(dRatio_selected)
% Common average referencing - CAR
trialsECOG_W{dIndex} = cellfun(@(x) x - mean(x, 1), trialsECOG_W{dIndex}, "UniformOutput", false);
trialsECOG_C{dIndex} = cellfun(@(x) x - mean(x, 1), trialsECOG_C{dIndex}, "UniformOutput", false);
% Orthogonal source derivation
trialsECOG_W{dIndex} = cellfun(@(x) cell2mat(mu.rowfun(@(y, z) y - mean(x(z.neighbch, :), 1), x, neighbours, "UniformOutput", false)), trialsECOG_W{dIndex}, "UniformOutput", false);
trialsECOG_C{dIndex} = cellfun(@(x) cell2mat(mu.rowfun(@(y, z) y - mean(x(z.neighbch, :), 1), x, neighbours, "UniformOutput", false)), trialsECOG_C{dIndex}, "UniformOutput", false);
% Normalization - normalized by the temporal standard deviation of the amplitude
trialsECOG_W{dIndex} = cellfun(@(x) x ./ std(x, [], 2), trialsECOG_W{dIndex}, "UniformOutput", false);
trialsECOG_C{dIndex} = cellfun(@(x) x ./ std(x, [], 2), trialsECOG_C{dIndex}, "UniformOutput", false);
% Subtract by ERP
chMean = calchMean(cat(1, trialsECOG_C{dIndex}, trialsECOG_W{dIndex}));
trialsECOG_W{dIndex} = cellfun(@(x) x - chMean, trialsECOG_W{dIndex}, "UniformOutput", false);
trialsECOG_C{dIndex} = cellfun(@(x) x - chMean, trialsECOG_C{dIndex}, "UniformOutput", false);
end
end
% Prediction
if contains(MATPATHs{mIndex}, 'Prediction Data_')
% Common average referencing - CAR
trialsECOG = cellfun(@(x) x - mean(x, 1), trialsECOG, "UniformOutput", false);
% Orthogonal source derivation
trialsECOG = cellfun(@(x) cell2mat(mu.rowfun(@(y, z) y - mean(x(z.neighbch, :), 1), x, neighbours, "UniformOutput", false)), trialsECOG, "UniformOutput", false);
% Normalization - normalized by the temporal standard deviation of the amplitude
trialsECOG = cellfun(@(x) x ./ std(x, [], 2), trialsECOG, "UniformOutput", false);
% Subtract by ERP
nSTD = unique([trialAll.stdNum])';
chMean = arrayfun(@(x) calchMean(trialsECOG([trialAll.stdNum] == x)), nSTD, "UniformOutput", false);
for tIndex = 1:length(trialsECOG)
trialsECOG{tIndex} = trialsECOG{tIndex} - chMean{nSTD == trialAll(tIndex).stdNum};
end
end
% Save
mkdir(fileparts(SAVEPATHs{mIndex}));
save(SAVEPATHs{mIndex}, vars{:});
end

Step2_Preprocess_Granger.m at commit baedc13, under MIT · at the source

Overview

Authors: Haoxuan Xu^1,2,3,4, Peirun Song^1,3, Hangting Ye^1,3, Ana Belén Lao-Rodríguez^5,6,7, Qichen Zhang¹, Yuying Zhai^1,3, Xuehui Bao⁴, Ishrat Mehmood⁴, Hisashi Tanigawa⁴, Zhiyi Tu², Lingling Zhang², Xuan Zhao², David Pérez-González^5,6,8, Manuel S Malmierca^5,6,7, Xiongjie Yu^1,2,3,4

ORCID iDs: Manuel S Malmierca, Xiongjie Yu

Department of Anesthesia, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
Department of Anesthesiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
Cognitive and Auditory Neuroscience Laboratory (CANELAB), Institute of Neuroscience of Castilla y León (INCYL), University of Salamanca, Salamanca, Spain
Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
Department of Cell Biology and Pathology, Faculty of Medicine, University of Salamanca, Salamanca, Spain
Department of Basic Psychology, Psychobiology and Methodology of Behavioral Sciences, Faculty of Psychology, University of Salamanca, Salamanca, Spain

Institutions: Tongji University (China); Shanghai Tenth People's Hospital (China); Women's Hospital, School of Medicine, Zhejiang University (China); Zhejiang University (China); Universidad de Salamanca (Spain); Instituto de Investigación Biomédica de Salamanca (Spain); Instituto de Neurociencias de Castilla y León

Journal: PLoS biology, volume 24, issue 8, article e3003966

Dates: received 9 March 2026; accepted 7 August 2026; published online 26 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pbio.3003966 · PMID 42647585 · PMCID PMC13537682 · OpenAlex W7204260198

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), non-human primate (organism)

Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Machine learning, Single-unit activity, calcium imaging

MeSH: Auditory Cortex*, Auditory Perception*, Behavior, Animal*, Prefrontal Cortex*, Acoustic Stimulation, Animals, Electrocorticography, Evoked Potentials, Auditory, Macaca mulatta, Male, Nerve Net (* major topic)

Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Consejería de Educación, Junta de Castilla y León (strategic research programs of excellence from the Regional Government of Castile and León, co-funded by the ERDF Operational Programme (ref. CLU-2023-1-01), SA218P23); National Natural Science Foundation of China (32171044, 32100827, 32571216); Major Projects of Natural Sciences of University in Jiangsu Province of China (2022ZD0204800); National Major Science and Technology Projects of China (2022ZD0204600); Agencia Estatal de Investigación (PID2023-148541OB-I00, funded by MICIU/AEI (https://doi.org/10.13039/501100011033) and FEDER EU)

Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

The ability to detect deviations from expected sensory input is fundamental for adaptive behavior. We recorded electrocorticographic activity from the auditory (AC) and prefrontal (PFC) cortices of behaving macaques during an auditory oddball task to probe the cortical dynamics of predictive processing. Repetition of standard stimuli evoked suppression and facilitation in AC and strong low-frequency (2 Hz) enhancement in PFC, accompanied by bidirectional delta-band GC-defined interactions indicative of a shared predictive state. Deviant stimuli triggered earlier AC responses followed by PFC activation and increased GC-defined directed interactions across theta, alpha, and conventional gamma bands. Behavioral engagement amplified both repetition-related and deviance-related ECoG responses, strengthening cortical network coordination. Together, these findings reveal behaviorally gated auditory–prefrontal dynamics that are consistent with hierarchical predictive-processing accounts, while also allowing for contributions from repetition-, novelty-, and salience-related mechanisms.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

Zenodo 21768371

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

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 (6 files), fdr_bh (Benjamini-Hochberg FDR) (3 files), Image Processing Toolbox (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

72 files

Main.m, MATLAB, 99 lines
Step1_Preprocess_Export_Active.m, MATLAB, 268 lines
Step1_Preprocess_Export_ActiveExt.m, MATLAB, 182 lines
Step1_Preprocess_Export_Passive.m, MATLAB, 118 lines
Step2_Preprocess_ExcludeBadChannels.m, MATLAB, 22 lines
Step2_Preprocess_Granger.m, MATLAB, 119 lines
Step2_Preprocess_Rereference.m, MATLAB, 74 lines
Step3_GenCWT_Baseline.m, MATLAB, 51 lines
Step3_GenCWT_BaselineExt.m, MATLAB, 50 lines
Step3_GenCWT_PE.m, MATLAB, 51 lines
Step3_GenCWT_Prediction.m, MATLAB, 60 lines
Step3_GenCWT_PredictionExt.m, MATLAB, 59 lines
Step4_GenGranger_Baseline.m, MATLAB, 84 lines
Step4_GenGranger_BaselineExt.m, MATLAB, 84 lines
Step4_GenGranger_PE.m, MATLAB, 95 lines
Step4_GenGranger_Prediction.m, MATLAB, 80 lines
Step4_GenGranger_PredictionExt.m, MATLAB, 80 lines
Step5_AssignFreqBands.m, MATLAB, 56 lines
Step5_MinusBaseGranger_PE.m, MATLAB, 68 lines
Step5_MinusBaseGranger_Prediction.m, MATLAB, 77 lines
Step5_MinusBaseGranger_PredictionExt.m, MATLAB, 76 lines
Step6_AverageGranger_PE.m, MATLAB, 40 lines
Step6_AverageGranger_Prediction.m, MATLAB, 40 lines
Step7_DiffGrangerPTAvg_PE.m, MATLAB, 543 lines
Step7_DiffGrangerPT_PE.m, MATLAB, 282 lines
Step7_DiffGrangerStat_PE.m, MATLAB, 128 lines
Step8_1_F1A.m, MATLAB, 47 lines
Step8_1_F1C.m, MATLAB, 142 lines
Step8_1_SF2.m, MATLAB, 262 lines
Step8_2_F2.m, MATLAB, 1,055 lines
Step8_2_SF3.m, MATLAB, 784 lines
Step8_3_F3_SF4.m, MATLAB, 516 lines
Step8_3_SF5.m, MATLAB, 556 lines
Step8_4_F4.m, MATLAB, 515 lines
Step8_4_SF6.m, MATLAB, 251 lines
Step8_4_SF7.m, MATLAB, 281 lines
Step8_5_F5.m, MATLAB, 678 lines
Step8_5_SF8.m, MATLAB, 500 lines
Step8_5_SF9.m, MATLAB, 180 lines
Step8_6_F6.m, MATLAB, 1,193 lines
Step8_7_SF10.m, MATLAB, 501 lines
Step8_8_SF11.m, MATLAB, 289 lines
Step9_1_RF1.m, MATLAB, 93 lines
private/loadCwtMultiChannelCrop_.m, MATLAB, 35 lines
private/loadCwtOneChannelCrop_.m, MATLAB, 20 lines
private/plotConfig.m, MATLAB, 48 lines
private/section_coherence_subplotImpl.m, MATLAB, 95 lines
private/section_granger_categorization.m, MATLAB, 41 lines
private/section_granger_subplot.m, MATLAB, 24 lines
private/section_granger_subplotImpl.m, MATLAB, 72 lines
private/section_granger_subplotImpl_population.m, MATLAB, 77 lines
private/section_granger_subplot_population.m, MATLAB, 24 lines
private/section_granger_tuning_population.m, MATLAB, 32 lines
utils/batch_Plot_Granger.m, MATLAB, 30 lines
utils/calSlope.m, MATLAB, 22 lines
utils/computeDevRatio.m, MATLAB, 8 lines
utils/hideAxesExceptBox.m, MATLAB, 25 lines
utils/layout/JPG2PNG_No_BG.m, MATLAB, 8 lines
utils/layout/setLayout.m, MATLAB, 39 lines
utils/makeChannelCmb_.m, MATLAB, 19 lines
utils/makeUnionIndex_.m, MATLAB, 27 lines
utils/matIndex.m, MATLAB, 25 lines
utils/mu_combine_granger_pt_days.m, MATLAB, 110 lines
utils/mu_combine_granger_pt_ratios.m, MATLAB, 263 lines
utils/plotLayout.m, MATLAB, 90 lines
utils/plotPE_TFRBandStats.m, MATLAB, 263 lines
utils/prunePTResult_.m, MATLAB, 30 lines
utils/rms.m, MATLAB, 45 lines
utils/sortCwtPathByChannel_.m, MATLAB, 24 lines
utils/summarizeSlopeReduction.m, MATLAB, 114 lines
LICENSE, License, 21 lines
README.md, Text, 295 lines

At the source: doi.org/10.5281/zenodo.21768371

TOMORI233/MonkeyNovelty

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: baedc138695e4909c007508a777e419ae5ef629d, 3 August 2026

Languages: MATLAB (70)

Size: 81 files, 70 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (6 files), fdr_bh (Benjamini-Hochberg FDR) (3 files), Image Processing Toolbox (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

72 files

Main.m, MATLAB, 99 lines
Step1_Preprocess_Export_Active.m, MATLAB, 268 lines
Step1_Preprocess_Export_ActiveExt.m, MATLAB, 182 lines
Step1_Preprocess_Export_Passive.m, MATLAB, 118 lines
Step2_Preprocess_ExcludeBadChannels.m, MATLAB, 22 lines
Step2_Preprocess_Granger.m, MATLAB, 119 lines, 2 matches
Step2_Preprocess_Rereference.m, MATLAB, 74 lines, 1 match
Step3_GenCWT_Baseline.m, MATLAB, 51 lines
Step3_GenCWT_BaselineExt.m, MATLAB, 50 lines
Step3_GenCWT_PE.m, MATLAB, 51 lines
Step3_GenCWT_Prediction.m, MATLAB, 60 lines
Step3_GenCWT_PredictionExt.m, MATLAB, 59 lines
Step4_GenGranger_Baseline.m, MATLAB, 84 lines
Step4_GenGranger_BaselineExt.m, MATLAB, 84 lines
Step4_GenGranger_PE.m, MATLAB, 95 lines
Step4_GenGranger_Prediction.m, MATLAB, 80 lines
Step4_GenGranger_PredictionExt.m, MATLAB, 80 lines
Step5_AssignFreqBands.m, MATLAB, 56 lines
Step5_MinusBaseGranger_PE.m, MATLAB, 68 lines
Step5_MinusBaseGranger_Prediction.m, MATLAB, 77 lines
Step5_MinusBaseGranger_PredictionExt.m, MATLAB, 76 lines
Step6_AverageGranger_PE.m, MATLAB, 40 lines
Step6_AverageGranger_Prediction.m, MATLAB, 40 lines
Step7_DiffGrangerPTAvg_PE.m, MATLAB, 543 lines
Step7_DiffGrangerPT_PE.m, MATLAB, 282 lines
Step7_DiffGrangerStat_PE.m, MATLAB, 128 lines
Step8_1_F1A.m, MATLAB, 47 lines
Step8_1_F1C.m, MATLAB, 142 lines
Step8_1_SF2.m, MATLAB, 262 lines
Step8_2_F2.m, MATLAB, 1,055 lines, 1 match
Step8_2_SF3.m, MATLAB, 784 lines, 2 matches
Step8_3_F3_SF4.m, MATLAB, 516 lines
Step8_3_SF5.m, MATLAB, 556 lines
Step8_4_F4.m, MATLAB, 515 lines
Step8_4_SF6.m, MATLAB, 251 lines
Step8_4_SF7.m, MATLAB, 281 lines
Step8_5_F5.m, MATLAB, 678 lines, 1 match
Step8_5_SF8.m, MATLAB, 500 lines, 2 matches
Step8_5_SF9.m, MATLAB, 180 lines
Step8_6_F6.m, MATLAB, 1,193 lines, 2 matches
Step8_7_SF10.m, MATLAB, 501 lines
Step8_8_SF11.m, MATLAB, 289 lines, 2 matches
Step9_1_RF1.m, MATLAB, 93 lines
private/loadCwtMultiChannelCrop_.m, MATLAB, 35 lines
private/loadCwtOneChannelCrop_.m, MATLAB, 20 lines
private/plotConfig.m, MATLAB, 48 lines, 2 matches
private/section_coherence_subplotImpl.m, MATLAB, 95 lines
private/section_granger_categorization.m, MATLAB, 41 lines
private/section_granger_subplot.m, MATLAB, 24 lines
private/section_granger_subplotImpl.m, MATLAB, 72 lines
private/section_granger_subplotImpl_population.m, MATLAB, 77 lines
private/section_granger_subplot_population.m, MATLAB, 24 lines
private/section_granger_tuning_population.m, MATLAB, 32 lines
utils/batch_Plot_Granger.m, MATLAB, 30 lines
utils/calSlope.m, MATLAB, 22 lines
utils/computeDevRatio.m, MATLAB, 8 lines
utils/hideAxesExceptBox.m, MATLAB, 25 lines
utils/layout/JPG2PNG_No_BG.m, MATLAB, 8 lines
utils/layout/setLayout.m, MATLAB, 39 lines
utils/makeChannelCmb_.m, MATLAB, 19 lines
utils/makeUnionIndex_.m, MATLAB, 27 lines
utils/matIndex.m, MATLAB, 25 lines
utils/mu_combine_granger_pt_days.m, MATLAB, 110 lines
utils/mu_combine_granger_pt_ratios.m, MATLAB, 263 lines
utils/plotLayout.m, MATLAB, 90 lines
utils/plotPE_TFRBandStats.m, MATLAB, 263 lines, 2 matches
utils/prunePTResult_.m, MATLAB, 30 lines
utils/rms.m, MATLAB, 45 lines
utils/sortCwtPathByChannel_.m, MATLAB, 24 lines
utils/summarizeSlopeReduction.m, MATLAB, 114 lines, 1 match
LICENSE, License, 21 lines
README.md, Text, 295 lines

At the source: github.com/TOMORI233/MonkeyNovelty

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
140 scripts, each with its path and the digest of its content;
18 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

zenodo:21767511, at Zenodo; found in “Data Availability”

Data Availability

The numerical source data underlying the main and supplementary figures, including the individual trial- and channel-level observations and associated labels used to generate the figures, are publicly available in Zenodo at https://doi.org/10.5281/zenodo.21767511. The analysis and figure-generation code used in this study has been permanently archived in Zenodo at https://doi.org/10.5281/zenodo.21768371. The actively maintained version of the code is publicly available at https://github.com/TOMORI233/MonkeyNovelty. The archived code is released under the MIT License and includes a README file describing the software requirements, repository structure, analysis workflow, and instructions for reproducing the analyses and figures.

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, issue, pages, dates, 15 authors, 11 MeSH terms, 5 funders, 99 references.

Cite

This paper

Xu, H., Song, P., Ye, H., Lao-Rodríguez, A. B., Zhang, Q., Zhai, Y., Bao, X., Mehmood, I., Tanigawa, H., Tu, Z., Zhang, L., Zhao, X., Pérez-González, D., Malmierca, M. S., & Yu, X. (2026). Behavioral engagement and stimulus regularities coordinate auditory-prefrontal network activity. PLoS biology, 24(8), e3003966. https://doi.org/10.1371/journal.pbio.3003966

BibTeX

@article{xu2026behavioral, author = {Xu, Haoxuan and Song, Peirun and Ye, Hangting and Lao-Rodríguez, Ana Belén and Zhang, Qichen and Zhai, Yuying and Bao, Xuehui and Mehmood, Ishrat and Tanigawa, Hisashi and Tu, Zhiyi and Zhang, Lingling and Zhao, Xuan and Pérez-González, David and Malmierca, Manuel S and Yu, Xiongjie}, title = {{Behavioral engagement and stimulus regularities coordinate auditory-prefrontal network activity}}, journal = {PLoS biology}, year = {2026}, month = aug, volume = {24}, number = {8}, pages = {e3003966}, publisher = {PLOS}, issn = {1544-9173}, doi = {10.1371/journal.pbio.3003966}, url = {https://doi.org/10.1371/journal.pbio.3003966}, pmid = {42647585}, pmcid = {PMC13537682} }

RIS

TY - JOUR AU - Xu, Haoxuan AU - Song, Peirun AU - Ye, Hangting AU - Lao-Rodríguez, Ana Belén AU - Zhang, Qichen AU - Zhai, Yuying AU - Bao, Xuehui AU - Mehmood, Ishrat AU - Tanigawa, Hisashi AU - Tu, Zhiyi AU - Zhang, Lingling AU - Zhao, Xuan AU - Pérez-González, David AU - Malmierca, Manuel S AU - Yu, Xiongjie TI - Behavioral engagement and stimulus regularities coordinate auditory-prefrontal network activity T2 - PLoS biology J2 - PLoS Biol PY - 2026 DA - 2026/08/26 VL - 24 IS - 8 SP - e3003966 SN - 1544-9173 PB - PLOS DO - 10.1371/journal.pbio.3003966 UR - https://doi.org/10.1371/journal.pbio.3003966 LA - en ER -

CSL-JSON

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