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An auditory "low road" for threat processing in humans sensitive to fast temporal cues.

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  1. [1] § STAR★Methods › Method details › Study 1: EEG and pupillometry › Electrophysiology ↔ EEG/ERPs/SUBEMOcond_analysis1.m, the whole file · a weak match · score 0.61 · EEGlab, Kaiser, SOBI, component, filtered, 0.1 Hz
  2. [2] § STAR★Methods › Method details › Study 1: EEG and pupillometry › Electrophysiology ↔ EEG/ERPs/SUBEMOcond_analysis2_1.m, the whole file · a weak match · score 0.60 · 200–1000 ms, baseline corrected, EEGLAB, filtered, 200 Hz, 200 ms
  3. [3] § STAR★Methods › Quantification and statistical analysis › Study 1: EEG and pupillometry › Electrophysiology ↔ EEG/ERPs/Cluster-based/clusterStatistics_Martina.m, the whole file · a weak match · score 0.57 · cluster statistic, FieldTrip, tailed, neighboring

Paper

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The authors' code

MATLAB · 134 lines · 5 KB · CC-BY-4.0 · 1 match

  1. function SUBEMOcond_analysis1
  2. % Applies ICA correction on the original data after having identified the
  3. % components that need to be removed.
  5. % Last update: 21-11-2023 by Martina (Trisia)
  7. eeglab;
  8. close;
  9. clear;
  11. % Save (defining whether you want to save files or not):
  12. Options_group = {'Yes', 'No'};
  13. Save = char(listdlg('PromptString','Do you want to save files? (Yes // No)',...
  14. 'SelectionMode','single',...
  15. 'ListString',Options_group,...
  16. 'InitialValue', 1, ...
  17. 'ListSize', [300 70]));
  18. Save = Options_group{Save};
  20. %% Paths and codes
  21. % Port
  22. if exist('F:','dir')
  23. DIR = 'F:';
  24. elseif exist('D:','dir')
  25. DIR = 'D:';
  26. end
  28. Analysis_folder = fullfile(DIR,'SUBEMOcond','ANALYSIS_results',filesep);
  29. anal_logfile = [Analysis_folder 'analysis_log.txt'];
  31. % Subjects and conditions
  32. SubArray = [1:32];
  33. nBlocks = 14; % subject 9 only 20 blocks - if statement added below
  35. %ICA components to remove for each subject
  36. % subjects x components
  37. components = {
  38. [3 10 13 15 24 26 29 30 31 34 43 47]; %subj 1
  39. [2 37 38 45 50 51 52 53]; %subj 2
  40. [2 10 16 18 20 21 23 24 25 35 36 37 39 40]; %subj 3
  41. [1 5 14 15 16 17 20 21 29 36]; %subj 4
  42. [2 21 22 23 27 28 32 33 35 36 37 41]; %subj 5
  43. [1 2 5 9 10 13 20 21 23 24 25 26 28 29 36 37]; %subj 6
  44. [2 3 6 12 14 15 24 32 36 39 40 45 46 48]; %subj 7
  45. [2 3 14 15 22 23 28 30 33 38 41 42 44 47 55]; %subj 8
  46. [1 21 22 23 30 42 43 47 48 50]; %subj 9
  47. [2 3 31 34 37 38 45]; %subj 10
  48. [3 4 8 10 12 16 17 23 24 26 29 31 36 37 39 42 45 46]; %subj 11
  49. [2 4 6 14 16 17 21 23 28 30 33 36 38 39 42 49]; %subj 12
  50. [1 4 6 8 9 10 12 15 18 20 22 24 25 27 28 29 32 33 35 39 42]; %subj 13
  51. [2 10 13 22 44]; %subj 14
  52. [1 3 6 7 11 12 13 14 16 20 22 23 26 34 41 42 45 55 58 59]; %subj 15
  53. [1 2 6 16 21 23 24 29 37 39 44 45]; %subj 16
  54. [2 3 13 14 15 16 18 28 34 35 39 40 43]; %subj 17
  55. [2 4 6 9 12 13 17 21 22 24 26 27 28 32 44]; %subj 18
  56. [3 7 14 20 21 26 35 39 49]; %subj 19
  57. [2 3 10 15 16 17 18 21 26 27 30 31 32 36 45 50]; %subj 20
  58. [6 7 20 34 36 37 45 47 49 53 57 58 59]; %subj 21
  59. [4 10 27 28 33 37 39 45 50 54 55 63]; %subj 22
  60. [1 6 8 19 26 28 34 49 52 53]; %subj 23
  61. [3 4 15 18 22 35 36 38 42]; %subj 24
  62. [2 4 6 9 10 15 16 21 23 24 25 26 27 29 30 31 33 34 35 36 40 46 48 53 54 56 62 63 64]; %subj 25
  63. [1 7 9 14 17 24 28 31 34 45 54 60]; %subj 26
  64. [2 15 16 26 30 32 54 56]; %subj 27
  65. [2 17 18 23 26]; %subj 28
  66. [2 12 13 17 19 31 32 34 36 38 39 41]; %subj 29
  67. [2 3 4 5 6 9 10 12 13 14 15 16 17 19 22 23 24 26 28 31 33 44 49 57]; %subj 30
  68. [1 6 12 14 18 19 30 32 36 38 40 46 48 50]; %subj 31
  69. [1 10 11 13 18 25 30 32 38 50 54 55]};% subject 32
  71. % High Filter settings before ICA
  72. filterFreq_beforeICA = 0.1;
  73. filterWinBeta_beforeICA = 5.65326;
  74. filterOrder_beforeICA = 9056;
  76. % Low Filter settings after ICA
  77. filterFreq_afterICA = 20;
  78. filterWinBeta_afterICA = 5.65326;
  79. filterOrder_afterICA = 1812;
  81. Window = [num2str(filterFreq_beforeICA) '-' num2str(filterFreq_afterICA)];
  83. if ~exist([Analysis_folder 'After' filesep Window],'dir')
  84. mkdir([Analysis_folder 'After' filesep Window])
  85. end
  87. Analysis_folder_After = [Analysis_folder 'After' filesep Window];
  88. Analysis_folder_After_pre = [Analysis_folder 'After'];
  90. %% Main files loop
  92. for iSub = 1:length(SubArray)
  94. % load the file with ICA weights for this subject
  95. EEG_ICA = pop_loadset('filename',[num2str(SubArray(iSub), '%02d') '_allrej_sobi.set'], 'filepath', Analysis_folder_After_pre);
  97. for iBlock = 1:nBlocks
  98. %% Import files to EEGlab
  100. EEG = pop_loadset('filename',[num2str(SubArray(iSub), '%02d') '_' num2str(iBlock) '.set'], 'filepath', [Analysis_folder filesep 'Before']);
  102. %% High pass filter data before ICA
  104. EEG = pop_firws(EEG, 'fcutoff', filterFreq_beforeICA, 'ftype', 'highpass', 'wtype', 'kaiser', 'warg', filterWinBeta_beforeICA, 'forder', filterOrder_beforeICA);
  105. EEG = eeg_checkset(EEG);
  108. %% Apply ICA correction
  110. EEG.icawinv = EEG_ICA.icawinv;
  111. EEG.icasphere = EEG_ICA.icasphere;
  112. EEG.icaweights = EEG_ICA.icaweights;
  113. EEG.icachansind = EEG_ICA.icachansind;
  115. EEG = pop_subcomp(EEG,components{SubArray(iSub)});
  117. if strcmp(Save, 'Yes')
  118. % Save HPfiltered and corrected with ICA file before LP filter
  119. EEG = pop_saveset(EEG, 'filename', [num2str(SubArray(iSub), '%02d') '_' num2str(iBlock) '_Hf_withICA.set'], 'filepath', Analysis_folder_After);
  120. end
  122. %% Low pass filter after ICA correction
  123. EEG = pop_firws(EEG, 'fcutoff', filterFreq_afterICA, 'ftype', 'lowpass', 'wtype', 'kaiser', 'warg', filterWinBeta_afterICA, 'forder', filterOrder_afterICA);
  124. EEG = eeg_checkset(EEG);
  126. if strcmp(Save, 'Yes')
  127. % save filtered and corrected data with ICA weights
  128. EEG = pop_saveset(EEG, 'filename', [num2str(SubArray(iSub), '%02d') '_' num2str(iBlock) '_Lf_withICA.set'], 'filepath', Analysis_folder_After);
  129. end
  130. end
  131. end
  133. fclose(anal_logfile);
  134. end

SUBEMOcond_analysis1.m at commit c429a95, under CC-BY-4.0 · at the source

Overview

Authors: Martina T Cinca-Tomás1,2, Emmanouela Kosteletou-Kassotaki1,2, Jordi Costa-Faidella1,2,3, Carles Escera1,2,3, Judith Domínguez-Borràs1,2
ORCID iDs: Martina T Cinca-Tomás
  1. Brainlab-Cognitive Neuroscience Research Group, Department of Clinical Psychology and Psychobiology, University of Barcelona, 08035 Barcelona, Spain
  2. Institute of Neurosciences, University of Barcelona, 08035 Barcelona, Spain
  3. Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Esplugues de Llobregat, Spain
Institutions: Universitat de Barcelona (Spain); Institut de Recerca Sant Joan de Déu
Journal: iScience, volume 29, issue 9, article 117436
Dates: received 11 February 2026; accepted 19 August 2026; published online 4 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.117436 · PMID 42733837 · PMCID PMC13571650 · OpenAlex W7208761554
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: auditory threat, fast temporal cues, amygdala, subcortical “low road”, fear conditioning
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (101088954)
Citations: not cited yet (Europe PMC); 116 references in the paper
Research resources: R Version 4.2.1 RRID:SCR_000432, MATLAB 2019b RRID:SCR_001622, FreeSurfer RRID:SCR_001847, Psychophysics Toolbox 3 RRID:SCR_002881, FieldTrip RRID:SCR_004849, Statistical Parametric Mapping (SPM12) RRID:SCR_007037, EEGLAB v2022.1 RRID:SCR_007292, RRID:SCR_007378, MRtrix3 RRID:SCR_024123

Abstract

Rapid threat processing is a fundamental and evolutionarily conserved function of the brain. In vision, influential models of emotion propose the existence of a fast subcortical pathway for threat processing, connecting the visual thalamus with the amygdala, driven by coarse visual inputs. In audition, whether such a “shortcut” operates in humans has remained unknown. Here, using psychophysiology and neuroimaging, we provide convergent human evidence for an auditory pathway that is sensitive to fast temporal acoustic cues, previously linked to salient alarm signals and coarse auditory processing. Threatening sounds with fast temporal cues elicited rapid neural and autonomic responses at early post-stimulus latencies. Notably, right amygdala responses to these cues covaried with individual differences in strength of a direct auditory thalamo-amygdala pathway. Together, these findings identify a previously uncharacterized “low road” for auditory threat processing in humans, and establish fast temporal acoustic structure as a key feature supporting rapid affective responses.

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 3 matches between paragraphs and lines of code.

MartinaTrisia/SUBEMOcond

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: c429a9577443cc2e2180a523c2f34793623242ba, 17 December 2025
Languages: MATLAB (11)
Size: 14 files, 11 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (9 files), FieldTrip (3 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
12 files
At the source: github.com/MartinaTrisia/SUBEMOcond

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;
  • 11 scripts, each with its path and the digest of its content;
  • 3 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

Data and code availability

Raw data have been deposited at OpenNeuro and are publicly available.

Study 1: https://doi.org/10.18112/openneuro.ds007114.v1.0.1; Study 2: https://doi.org/10.18112/openneuro.ds007690.v1.0.0.

Custom MATLAB scripts used for EEG preprocessing, ERP analyses, and cluster-based permutation testing have been deposited at GitHub and are publicly available (https://github.com/MartinaTrisia/SUBEMOcond). Pupillometry analyses were conducted using custom MATLAB scripts implementing the preprocessing procedures described in the STAR Methods. MRI and diffusion MRI analyses were conducted using the standard workflows implemented in SPM12 and MRtrix3, respectively, as detailed in the STAR Methods. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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 3, 28 September 2026

  • Authors: added Martina T Cinca-Tomás (0009-0003-0805-9445); removed Martina T Cinca-Tomás

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 116 references, 9 RRIDs.

Cite

This paper

Cinca-Tomás, M. T., Kosteletou-Kassotaki, E., Costa-Faidella, J., Escera, C., & Domínguez-Borràs, J. (2026). An auditory "low road" for threat processing in humans sensitive to fast temporal cues. iScience, 29(9), 117436. https://doi.org/10.1016/j.isci.2026.117436

BibTeX

@article{cincatomas2026auditory,
author = {Cinca-Tomás, Martina T and Kosteletou-Kassotaki, Emmanouela and Costa-Faidella, Jordi and Escera, Carles and Domínguez-Borràs, Judith},
title = {{An auditory "low road" for threat processing in humans sensitive to fast temporal cues}},
journal = {iScience},
year = {2026},
month = sep,
volume = {29},
number = {9},
pages = {117436},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117436},
url = {https://doi.org/10.1016/j.isci.2026.117436},
pmid = {42733837},
pmcid = {PMC13571650}
}

RIS

TY - JOUR
AU - Cinca-Tomás, Martina T
AU - Kosteletou-Kassotaki, Emmanouela
AU - Costa-Faidella, Jordi
AU - Escera, Carles
AU - Domínguez-Borràs, Judith
TI - An auditory "low road" for threat processing in humans sensitive to fast temporal cues
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/09/04
VL - 29
IS - 9
SP - 117436
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117436
UR - https://doi.org/10.1016/j.isci.2026.117436
LA - en
ER -

CSL-JSON

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"title": "An auditory \"low road\" for threat processing in humans sensitive to fast temporal cues",
"container-title": "iScience",
"author": [
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"family": "Cinca-Tomás",
"given": "Martina T"
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{
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"volume": "29",
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"page": "117436",
"DOI": "10.1016/j.isci.2026.117436",
"PMID": "42733837",
"PMCID": "PMC13571650",
"ISSN": "2589-0042",
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"language": "en",
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