OSCR

Cognitive control networks causally support implicit emotion regulation: evidence from dlPFC stimulation and directed functional connectivity.

Code ↔ Paper

16 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 16 matches
  1. [1] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › Brain connectivity: preprocessing and analysis ↔ analysis/scripts/tDCS_CONN_OSF.m, lines 72–107 · score 0.82 · artifact rejection, deflation, fastICA, nonlinearity, pow3, classified
  2. [2] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › Brain connectivity: preprocessing and analysis ↔ analysis/scripts/tDCS_conn_theta_OSF.R, lines 1–44 · score 0.78 · Theta band connectivity, dACC, left IPS, right IPS, cingulo opercular, dlPFC
  3. [3] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › Brain connectivity: preprocessing and analysis ↔ analysis/scripts/tDCS_CONN_OSF.m, lines 127–272 · score 0.77 · dACC, MNE, lambda, ROI, noise, localized
  4. [4] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › EMG: preprocessing and analysis ↔ analysis/scripts/tDCS_EMG_corrugator_OSF.m, lines 135–170 · score 0.75 · 450–2000 ms, post stimulus, 450 ms, EMG, baseline, 200 ms
  5. [5] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › ERP: preprocessing and analysis ↔ analysis/scripts/tDCS_CONN_OSF.m, lines 72–107 · score 0.74 · artifact rejection, channel screening, IQR, windowed, muscle, thresholding
  6. [6] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › ERP: preprocessing and analysis ↔ analysis/scripts/tDCS_ERP_OSF.m, lines 61–83 · score 0.73 · artifact rejection, range artifacts, IQR, windowed, muscle, thresholding
  7. [7] § Materials and methods › Statistical analysis › Linear mixed-effects models ↔ analysis/scripts/tDCS_subj_rat_MC_OSF.R, lines 1–39 · score 0.71 · baseline emotional reactivity, Manipulation checks, NREG NEU, NREG NEG, tDCS, arousal
  8. [8] § Results › Brain connectivity ↔ analysis/scripts/tDCS_conn_theta_OSF.R, lines 1–44 · score 0.69 · theta band connectivity, left IPS, right IPS, dlPFC, NREG NEG, network
  9. [9] § Materials and methods › Statistical analysis › Linear mixed-effects models ↔ analysis/scripts/tDCS_subj_rat_ER_OSF.R, lines 454–522 · score 0.63 · p.adjust, boot.ci, bootMer, emotion regulation, linear mixed, Sham session
  10. [10] § Materials and methods › Statistical analysis › Linear mixed-effects models ↔ analysis/scripts/tDCS_subj_rat_MC_OSF.R, lines 527–596 · score 0.62 · p.adjust, boot.ci, bootMer, linear mixed, Sham session, FDR
  11. [11] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › ERP: preprocessing and analysis ↔ analysis/scripts/tDCS_EMG_corrugator_OSF.m, lines 135–170 · score 0.59 · 450–2000 ms, 450 ms, post, sham
  12. [12] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › Brain connectivity: preprocessing and analysis ↔ analysis/scripts/tDCS_ERP_OSF.m, lines 61–83 · score 0.58 · artifact rejection, detection, IQR, muscle, threshold, 47 Hz
  13. [13] § Materials and methods › Statistical analysis › Linear mixed-effects models ↔ analysis/scripts/tDCS_EMG_corrugator_ER_OSF.R, lines 1–47 · score 0.57 · Manipulation checks, emotional reactivity, NREG NEG, amplitudes, tDCS, EMG
  14. [14] § Results › Pre-stimulus alpha power ↔ analysis/scripts/tDCS_alpha_power_OSF.R, lines 1–78 · score 0.54 · alpha power, left V1, NREG NEG, tDCS, dlPFC, regulation
  15. [15] § Materials and methods › Electrophysiological data recording, preprocessing, and analysis › Brain connectivity: preprocessing and analysis ↔ analysis/scripts/tDCS_CONN_OSF.m, lines 127–272 · score 0.52 · 15–30 Hz, 3–7 Hz, 15 Hz, theta, beta
  16. [16] § Materials and methods › Statistical analysis › Linear mixed-effects models ↔ analysis/scripts/tDCS_conn_beta_OSF.R, lines 1–53 · score 0.52 · beta band connectivity, NREG NEG, hypothesis, tDCS, disruption, dlPFC

Paper

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The authors' code

MATLAB · 277 lines · 8.3 KB · no license · 4 matches

  1. %%% ATLANTIS PROCESSING PIPELINE
  3. clear all
  4. % % %
  5. % begId = 61; %specify begining index
  6. % endId = 103; %specify end index
  8. %%% LOADING
  10. % Specify main data folder where the files of interest are located
  11. rc.expFolder = '/mnt/iSCSI2/Exp/implicit_tDCS/';
  12. % rc.analysisName = 'PCAprepost4_RDLLDL_128'; %specify the name of your analysis (will save everything in a subfolder)
  13. rc.analysisName = 'fix3'; %fixation cross bef images
  14. rc.dataFormat = 'bdf'; % bdf ctf itab
  15. rc.dataRawFolder = '0_bdf';
  16. rc.mriRawFolder = ''; %'0_mri';
  17. % rc.mriExt = 'nii.gz';
  18. % rc.mriCoordsys = 'ras';
  20. rc.useParallel = 0; % max number of parallel woorkers. use zero for disabling parallel processing
  22. %%% SCRIPT RUN CONTROL
  24. % 0 - raw file read and preproc
  25. % 1 - segmentation
  26. % 2 - artifact rejection
  27. % 3 - ICA1 decomposition
  28. % 4 - ICA1 artifact rejection
  29. % 5 - ICA2 decomposition
  30. % 6 - IC classification
  31. % 7 - realignment/headmodel/sourcemodel
  32. % 8 - leadfield/localization
  33. % 9 - reconstruct ROI signals
  34. % 10 - connectivity estimation
  35. % 11 - connectivity statistics
  37. rc.load = 0; % start from stage
  38. rc.stop = 0; % stop at stage
  40. %%% PREPROCESSING %%%
  42. chan.sensMatFile = 'elec_1020.mat';
  43. chan.elcFile = 'biosemi64.elc';
  45. chan.ref = [71,72];
  46. chan.meeg = [1:64]; %here specify electric channels taht should be taken into account during ICA
  47. chan.elec = [65:69]; % 65 - VEOG1, HEOG2, EX5 (corrugator)
  48. chan.eog = [65:68];
  50. %list of relevant triggers is specified below in the DESIGN section. In the case recoding is needed, please specify it just below
  51. % recodeFn = 'Recoding_TMS_ica.; %provide trigger recoding function or comment to ignore
  53. filter.HPfreq = 2; %sets up high pass frequency
  54. filter.HPtype = 'firws'; %sets up hp filter type
  55. filter.HPord = 3380; %sets up hp filter order (3.3*fs/trans_width)
  56. filter.LPfreq = 47; %sets up lp frequency
  57. filter.LPtype = 'firws'; %sets up lp filter type
  58. filter.LPord = 1126; %sets up lp filter order
  59. filter.plotResp = 'no'; %specify where you want to see filer response ('yes') or 'no'
  60. filter.resampleTo = 256;
  62. %%% SEGMENTATION
  64. seg.studyType = 'ER'; % either 'ER' for event related; 'RS' for resting state
  65. seg.prestim = 1; % pre-stimulus period for epoching
  66. seg.poststim = 2; % post-stimulus period for epoching
  67. seg.baseline = 0; % baseline correction (from base_line to zero)
  68. seg.demean = 'Y'; % recommended for ica. ovverrides baseline correction
  69. %rsSegments = % length of RS for dummy segmenting (required for AR) [RS only]
  70. %list of triggers is specified below in the design section
  72. %%% ARTIFACT REJECTION
  74. % preliminary channel screening
  75. ar.chIQR = 5; % chan iqr threshold for rejection
  76. ar.chRmLimit = 8; % sets max number of channels that can be removed from the DS. if exceeded, whole DS is removed
  77. % trial variance
  78. ar.varIQR = 3;
  79. % range
  80. ar.rangeThr = 300;
  81. % parameters for MUSCLE
  82. ar.muscHP = 35; % low freq window for muscle detection
  83. ar.muscLP = 47; % high freq window for muscle rejection
  84. ar.muscThr = 24; % muscle z-value (converted threshold) // this value should be adjusted for given m_lp and m_hp filter specification respectively
  86. % ICA
  88. ica.ICA1_ic2screen = 20; %max no of ICs to screen by ica.. set to 0 for all (default)
  90. ica.ica.method = 'fastica'; % 'gpuica' (Infomax) or 'binica' (Infomax) or 'runica' (more in sc_ft_componentanalysis help) %TBD: I = Infomax (CPU), G = Infomax (GPU), F = Fastica.standard, D = Fastica.with deflation
  91. ica.approach = 'defl'; %defl: deflation of dimensionality
  92. ica.ica.epsilon = 0.000001;
  93. ica.ica_nonlinearity = 'pow3';
  94. ica.num_ica_iter = 3;
  95. ica.max_ica_steps = 100;
  97. ica.num_ica_iter = 15;
  98. ica.max_ica_steps = 200;
  99. ica.ica_nonlinearity = 'pow3';
  100. ica.ICA1_classif_mode = 'full';
  101. ica.EOGchan.include = 0;
  102. ica.visualizeLim = 0; %hom many IC to plot (0 - no limit)
  103. % ica.AR
  104. ar.ica_topoZThr = 6; % sensor rejection that deviate significantly based on topography
  105. ar.ica_singleicZthe = 25; %trial rejection based on z-scores of individual IC
  106. ar.ica_multiicZThr = 15; %trial rejection based on z-scores of multiple IC simultaneously
  107. ar.ica_multiicZCnt = 6; %trial rejection based on z-scores of multiple IC simultaneously - no of IC that exceed ar.ica_multiicZThr
  110. %%%%% DESIGN %%%%%
  112. % indexes of characters of the file name (used for coding indiv id, sesions and groups) ZERO (0) if not applicable
  113. design.idPosStart = 13;
  114. design.idPosEnd = 14;
  115. % design.grpPosStart = 0;
  116. % design.grpPosEnd = 0;
  117. design.sesPosStart = 1;
  118. design.sesPosEnd = 2;
  120. design.COND(1).name = {'NEUNS-NEU', 'IMP-NEG', 'NEUNS-NEG'};
  121. design.COND(1).trig = { {91 92}, {95 96}, {97 98} };
  123. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  124. %%% Below you specify Data Configuration,
  125. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  127. %%%%% LOCALIZATION %%%%%%
  128. loc.norm = 'cov'; % lambda | cov (default)
  129. loc.lambda = 3;
  130. loc.noiseLev = 2;
  131. loc.method = 'mne'; % mne loreta sloreta
  132. loc.recon = 'pca';
  134. loc.ROI(1).name = 'LVis';
  135. loc.ROI(1).pos = [-11 -81 7];
  137. loc.ROI(2).name = 'RVis';
  138. loc.ROI(2).pos = [11 -78 9];
  140. loc.ROI(3).name = 'LIPS'; % mostly for conn analysis
  141. loc.ROI(3).pos = [-38 -48 44];
  143. loc.ROI(4).name = 'RIPS';
  144. loc.ROI(4).pos = [32 -52 50];
  146. loc.ROI(5).name = 'dACC';
  147. loc.ROI(5).pos = [-1 -10 46];
  149. loc.ROI(6).name = 'LaIfO';
  150. loc.ROI(6).pos = [-35 14 5]; %mostly error monit but also sustained, part of the sustained task network with opercular (Dosenbach, Neuron 2006)
  152. loc.ROI(7).name = 'RaIfO';
  153. loc.ROI(7).pos = [36 16 4];
  155. loc.ROI(8).name = 'LDLPFC'; % task prepar and exec (no error control) MacDonald III, Cohen, Science 2000
  156. loc.ROI(8).pos = [-43 18 29];
  158. loc.ROI(9).name = 'RDLPFC'; % mirror of LDL
  159. loc.ROI(9).pos = [43 18 29];
  161. loc.ROI(10).name = 'LPrec';
  162. loc.ROI(10).pos = [-2 -57 42]; % mostly for oscil analysis
  164. loc.ROI(11).name = 'RPrec';
  165. loc.ROI(11).pos = [3 -64 35];
  167. DC=1;
  168. design.DC(DC).name = {'NEUNS-NEU-allses'};
  169. design.DC(DC).COND = {'NEUNS-NEU'};
  170. design.DC(DC).SES = {''};
  172. DC=2;
  173. design.DC(DC).name = {'IMP-NEG-allses'};
  174. design.DC(DC).COND = {'IMP-NEG'};
  175. design.DC(DC).SES = {''};
  177. DC=3;
  178. design.DC(DC).name = {'NEUNS-NEG-allses'};
  179. design.DC(DC).COND = {'NEUNS-NEG'};
  180. design.DC(DC).SES = {''};
  182. DC=4;
  183. design.DC(DC).name = {'NEUNS-NEU-SH'};
  184. design.DC(DC).COND = {'NEUNS-NEU'};
  185. design.DC(DC).SES = {'SH'};
  187. DC=5;
  188. design.DC(DC).name = {'IMP-NEG-SH'};
  189. design.DC(DC).COND = {'IMP-NEG'};
  190. design.DC(DC).SES = {'SH'};
  192. DC=6;
  193. design.DC(DC).name = {'NEUNS-NEG-SH'};
  194. design.DC(DC).COND = {'NEUNS-NEG'};
  195. design.DC(DC).SES = {'SH'};
  197. DC=7;
  198. design.DC(DC).name = {'NEUNS-NEU-LA'};
  199. design.DC(DC).COND = {'NEUNS-NEU'};
  200. design.DC(DC).SES = {'LA'};
  202. DC=8;
  203. design.DC(DC).name = {'IMP-NEG-LA'};
  204. design.DC(DC).COND = {'IMP-NEG'};
  205. design.DC(DC).SES = {'LA'};
  207. DC=9;
  208. design.DC(DC).name = {'NEUNS-NEG-LA'};
  209. design.DC(DC).COND = {'NEUNS-NEG'};
  210. design.DC(DC).SES = {'LA'};
  212. DC=10;
  213. design.DC(DC).name = {'NEUNS-NEU-RA'};
  214. design.DC(DC).COND = {'NEUNS-NEU'};
  215. design.DC(DC).SES = {'RA'};
  217. DC=11;
  218. design.DC(DC).name = {'IMP-NEG-RA'};
  219. design.DC(DC).COND = {'IMP-NEG'};
  220. design.DC(DC).SES = {'RA'};
  222. DC=12;
  223. design.DC(DC).name = {'NEUNS-NEG-RA'};
  224. design.DC(DC).COND = {'NEUNS-NEG'};
  225. design.DC(DC).SES = {'RA'};
  228. rc.load = 11; % start from stage
  229. rc.stop = 11; % stop at stage
  231. % CONN - step 10
  233. con.conName = 'po7fullepoch'; % CONN subirectory
  234. con.mvarOrd = 7; % model order
  235. con.chanSel = '1-'; %'1-'; % chans to include (wout Prec wout RDL)
  236. con.useRange = false; % cut range from trials
  237. con.timeRangeStart = -1;
  238. con.timeRangeEnd = 0;
  239. con.winlen = []; % [] to disable
  240. con.winshf = 0; % 0 to disable
  241. con.winnum = 1; % 3 windows 3% sDTF no of wins
  242. con.decimate = 2; %decrease sampling freq before conn calculations
  244. % FREQ - step 11
  245. con.contrast2test = {[6,5], [9,8], [12,11]}; % RALA impneg=8,11; RALA neunsneg= 9,12, sham: imp-neg + neuns_neg=6,5
  246. con.subTitle = {'theta' , 'beta'};
  247. con.freqRange = {[3 7], [15 30]};
  248. con.pThresh = {0.05};
  249. con.oneTailed = true; % divides p-values from 2-tailed test by 2
  250. con.testType = 'within'; %must be within or between
  251. con.iqr = {3}; %remove extremes before stat (comment to disable)
  253. % WHAT TO SHOW
  254. con.pDTF = 0;
  255. con.pNDTF = 0;
  256. con.pSpect = 0;
  257. con.pCohs = 0;
  258. con.pdDTF = 0;
  259. con.pPDC = 0;
  260. con.pffDTF = 0;
  262. % WHAT TO SAVE
  263. con.sDTF = 0;
  264. con.sNDTF = 1;
  265. con.sSpect = 0;
  266. con.sCohs = 0;
  267. con.sdDTF = 0;
  268. con.sPDC = 0;
  269. con.sffDTF = 0;
  270. con.sAR = 1;
  271. con.savefigs = 0;
  272. con.showsign =0;
  273. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  275. tic
  276. sc_initAnalysis
  277. toc

tDCS_CONN_OSF.m, no license · at the source

Overview

Authors: Agnieszka K. Adamczyk1,2, Miroslaw Wyczesany2
ORCID iDs: Agnieszka K. Adamczyk
  1. Behavioural Science Institute, Radboud University,Thomas van Aquinostraat 4, Postbus 9104, 6525 GD Nijmegen, The Netherlands
  2. Institute of Psychology, Jagiellonian University,ul. Ingardena 6, 30-060 Krakow, Poland
Institutions: Radboud University Nijmegen (Netherlands); Jagiellonian University (Poland)
Journal: Scientific reports, volume 16, issue 1, article 25123
Dates: received 31 October 2025; accepted 12 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-53424-4 · PMID 42236532 · PMCID PMC13469618 · OpenAlex W7163392938
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: Automatic emotion regulation, Noninvasive brain stimulation, Theta-band connectivity, Neural oscillations, Late positive potential (LPP), Neuroscience, Psychology
MeSH: Cognition*, Dorsolateral Prefrontal Cortex*, Emotional Regulation*, Emotions*, Nerve Net*, Adult, Brain Mapping, Electroencephalography, Female, Humans, Transcranial Direct Current Stimulation, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Narodowym Centrum Nauki (2019/35/B/HS6/03687)
Citations: not cited yet (Europe PMC); 93 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 16 matches between paragraphs and lines of code.

OSF vz43g

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (9), MATLAB (4)
Size: 132 files, 13 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (9 files), BayesFactor (8 files), car (8 files), cowplot (8 files), ggplot2 (8 files), lme4 (6 files), lmerTest (6 files), brms (2 files), FieldTrip (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
13 files
At the source: osf.io/vz43g/

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;
  • 13 scripts, each with its path and the digest of its content;
  • 16 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.

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/s41598-026-53424-4.

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, 2 authors, 7 keywords, 12 MeSH terms, 1 funder, 83 references.

Cite

This paper

Adamczyk, A. K., & Wyczesany, M. (2026). Cognitive control networks causally support implicit emotion regulation: evidence from dlPFC stimulation and directed functional connectivity. Scientific reports, 16(1), 25123. https://doi.org/10.1038/s41598-026-53424-4

BibTeX

@article{adamczyk2026cognitive,
author = {Adamczyk, Agnieszka K. and Wyczesany, Miroslaw},
title = {{Cognitive control networks causally support implicit emotion regulation: evidence from dlPFC stimulation and directed functional connectivity}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {25123},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-53424-4},
url = {https://doi.org/10.1038/s41598-026-53424-4},
pmid = {42236532},
pmcid = {PMC13469618}
}

RIS

TY - JOUR
AU - Adamczyk, Agnieszka K.
AU - Wyczesany, Miroslaw
TI - Cognitive control networks causally support implicit emotion regulation: evidence from dlPFC stimulation and directed functional connectivity
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/03
VL - 16
IS - 1
SP - 25123
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-53424-4
UR - https://doi.org/10.1038/s41598-026-53424-4
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41598-026-53424-4",
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"title": "Cognitive control networks causally support implicit emotion regulation: evidence from dlPFC stimulation and directed functional connectivity",
"container-title": "Scientific reports",
"author": [
{
"family": "Adamczyk",
"given": "Agnieszka K."
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"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "25123",
"DOI": "10.1038/s41598-026-53424-4",
"PMID": "42236532",
"PMCID": "PMC13469618",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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3
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]
}
}

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

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In common: BayesFactor, brms, lmerTest, 4 other tools, cognitive
[10] doi:10.1038/s41467-026-70287-5 [code]
Cortical-limbic circuit dynamics of approach-avoidance conflict in humans.
Journal: Nature communications
In common: brms, lmerTest, lme4, 2 other tools, EEG, 3 references

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