OSCR

Amygdalar Pattern Similarity to Negative and Neutral Images, Ratings of the Images, and PET-Measured Amyloid and Tau Levels in Older Adults Without Dementia.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Method › Representational Similarity Analysis ↔ Matlab scripts for manuscript/s1_Recipe_fMRI_EMO_ININ.m, lines 9–33 · score 0.67 · Harvard Oxford, right amygdala, voxel, masks, transformation, beta
  2. [2] § Method › Representational Similarity Analysis ↔ Matlab scripts for manuscript/s2_ExtractIndividualDifferences_ININ.m, the whole file · a weak match · score 0.54 · Fisher, inverted, reshaped, dissimilarity, matrices, transformation
  3. [3] § Method › Participants ↔ R scripts for manuscript/s1_combineTables.R, lines 45–127 · score 0.52 · age range, SD, Females, behaviorally, PET, scan

Paper

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

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The authors' code

MATLAB · 90 lines · 6.8 KB · no license · 1 match

  1. % Recipe_fMRI
  2. % this 'recipe' performs region of interest analysis on fMRI data.
  3. % Cai Wingfield 5-2010, 6-2010, 7-2010, 8-2010
  4. %__________________________________________________________________________
  5. % Copyright (C) 2010 Medical Research Council
  7. %%%%%%%%%%%%%%%%%%%%
  8. %% Initialisation %%
  9. %%%%%%%%%%%%%%%%%%%%
  11. toolboxRoot = '/emowrap/processed_data/EMOWRAP_Imaging_Analysis_task/scripts/tsfMRI/rsatoolbox-develop'; addpath(genpath(toolboxRoot));
  12. returnHere = '/emowrap/processed_data/EMOWRAP_Imaging_Analysis_task/scripts/tsfMRI/rsatoolbox-develop';
  13. MaskDir = '/emowrap/processed_data/EMOWRAP_Imaging_Analysis_task/scripts/tsfMRI/rsatoolbox-develop/Harvard_Oxford/ROIs/[[maskName]].nii';
  14. BetaDir = '/emowrap/processed_data/EMOWRAP_Imaging_Analysis_task/scripts/rsa/[[subjectName]]/[[betaIdentifier]]';
  16. userOptions = defineUserOptions();
  18. mkdir('EMO_RSA_ININ');
  19. addpath('EMO_RSA_ININ');
  20. userOptions.rootPath = [pwd,filesep,'EMO_RSA_ININ'];
  21. userOptions.analysisName = 'EMO_RSA_ININ';
  23. userOptions.maskPath = MaskDir;
  24. userOptions.maskNames = ["Left_Amygdala_50","Right_Amygdala_50","Left_Accumbens_50","Right_Accumbens_50","Left_Accumbens_25","Right_Accumbens_25","Left_Caudate_50","Right_Caudate_50","Left_Putamen_50","Right_Putamen_50","Middle_Frontal_Gyrus_50","Occipital_Pole_50"];
  25. userOptions.betaPath = BetaDir;
  26. userOptions.subjectNames = { ...
  27. 'rsa001run1','rsa001run2','rsa001run3','rsa002run1','rsa002run2','rsa002run3','rsa003run1','rsa003run2','rsa003run3','rsa004run1','rsa004run2','rsa004run3','rsa005run1','rsa005run2','rsa005run3','rsa006run1','rsa006run2','rsa006run3','rsa007run1','rsa007run2','rsa007run3','rsa008run1','rsa008run2','rsa008run3','rsa009run1','rsa009run2','rsa009run3','rsa010run1','rsa010run2','rsa010run3','rsa011run1','rsa011run2','rsa011run3','rsa012run1','rsa012run2','rsa012run3','rsa013run1','rsa013run2','rsa013run3','rsa014run1','rsa014run2','rsa014run3','rsa016run1','rsa016run2','rsa016run3','rsa017run1','rsa017run2','rsa017run3','rsa018run1','rsa018run2','rsa018run3','rsa020run1','rsa020run2','rsa020run3','rsa021run2','rsa021run3','rsa022run1','rsa022run2','rsa022run3','rsa023run1','rsa023run2','rsa024run1','rsa024run2','rsa024run3','rsa025run1','rsa025run2','rsa025run3','rsa026run1','rsa026run2','rsa026run3','rsa027run1','rsa027run2','rsa027run3','rsa028run1','rsa028run2','rsa028run3','rsa029run1','rsa029run2','rsa029run3','rsa030run1','rsa030run2','rsa030run3','rsa031run1','rsa031run2','rsa031run3','rsa032run1','rsa032run2','rsa032run3','rsa033run1','rsa033run2','rsa033run3','rsa034run1','rsa034run2','rsa034run3','rsa035run1','rsa035run3','rsa036run1','rsa036run2','rsa036run3','rsa037run1','rsa037run2','rsa037run3','rsa038run1','rsa038run2','rsa038run3','rsa039run1','rsa039run2','rsa039run3','rsa040run1','rsa040run2','rsa040run3','rsa041run1','rsa041run2','rsa041run3','rsa042run1','rsa042run2','rsa042run3','rsa043run1','rsa043run2','rsa043run3','rsa044run1','rsa044run2','rsa044run3','rsa045run1','rsa045run2','rsa045run3','rsa046run1','rsa046run2','rsa046run3','rsa047run1','rsa047run2','rsa047run3','rsa048run1','rsa048run2','rsa048run3','rsa049run1','rsa049run2','rsa049run3','rsa050run1','rsa050run2','rsa050run3','rsa051run1','rsa051run2','rsa051run3','rsa052run1','rsa052run2','rsa052run3','rsa055run1','rsa055run2','rsa055run3','rsa056run1','rsa056run2','rsa056run3','rsa057run1','rsa057run2','rsa058run1','rsa058run2','rsa058run3','rsa059run1','rsa059run2','rsa059run3','rsa060run1','rsa060run3','rsa061run1','rsa061run2','rsa061run3','rsa062run1','rsa062run2','rsa062run3','rsa063run1','rsa063run2','rsa063run3','rsa064run1','rsa064run2','rsa065run1','rsa065run2','rsa065run3','rsa067run1','rsa067run2','rsa067run3','rsa068run1','rsa068run2','rsa068run3','rsa069run1','rsa069run2','rsa069run3','rsa071run1','rsa071run2','rsa071run3','rsa072run1','rsa072run2','rsa072run3','rsa073run1','rsa073run2','rsa073run3','rsa074run1','rsa074run2','rsa074run3','rsa075run1','rsa075run2','rsa077run1','rsa077run2','rsa077run3','rsa078run1','rsa078run2','rsa078run3','rsa079run1','rsa079run2','rsa079run3','rsa080run1','rsa080run2','rsa080run3','rsa082run1','rsa082run2','rsa082run3','rsa084run1','rsa084run2','rsa084run3','rsa085run1','rsa085run2','rsa085run3','rsa086run1','rsa086run2','rsa086run3','rsa087run1','rsa087run2','rsa087run3','rsa088run1','rsa088run2','rsa088run3','rsa089run1','rsa089run2','rsa089run3','rsa090run1','rsa090run2','rsa090run3',...
  28. };% eg CBUXXXXX SHOULDN'T BE NUMBER ONLY, NUMBER CANNOT BE SAVED AS FIELD NAME, NO '-' EITHER
  29. userOptions.voxelSize = [2 2 2];
  30. userOptions.rankTransform = true;
  31. userOptions.saveFiguresFig = true;
  33. betaCorrespondence = betaCorrespondence;
  35. %%%%%%%%%%%%%%%%%%%%%%
  36. %% Data preparation %%
  37. %%%%%%%%%%%%%%%%%%%%%%
  39. fullBrainVols = fMRIDataPreparation(betaCorrespondence, userOptions);
  40. binaryMasks_nS = fMRIMaskPreparation(userOptions);
  41. responsePatterns = fMRIDataMasking(fullBrainVols, binaryMasks_nS, betaCorrespondence, userOptions);
  42. [newresponsePatterns, ComparisonNames] = IAPSSwapBetweenruns_responsePatterns(responsePatterns, userOptions);
  44. %%%%%%%%%%%%%%%%%%%%%
  45. %% RDM calculation %%
  46. %%%%%%%%%%%%%%%%%%%%%
  47. userOptions.subjectNames = ComparisonNames';
  49. RDMs = constructRDMs(newresponsePatterns, betaCorrespondence, userOptions);
  50. [subnames, avgpermRDMs] = average_RDMs_with_permutations(RDMs,ComparisonNames,userOptions);
  51. userOptions.subjectNames = subnames;
  52. avgRDMs = averageRDMs_subjectSession(avgpermRDMs, 'subject');
  54. %avgRDMs = averageRDMs_subjectSession(RDMs, 'session');
  55. %RDMs = averageRDMs_subjectSession(RDMs, 'session', 'subject');
  56. %Models = rsa.constructModelRDMs(modelRDMs(), userOptions);
  58. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  59. %% First-order visualisation %%
  60. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  62. figureRDMs(avgRDMs, userOptions, struct('fileName', 'RoIRDMs', 'figureNumber', 1));
  63. %figureRDMs(Models, userOptions, struct('fileName', 'ModelRDMs', 'figureNumber', 2));
  65. %MDSConditions(avgRDMs, userOptions);
  66. %dendrogramConditions(avgRDMs, userOptions);
  68. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  69. %% relationship amongst multiple RDMs %%
  70. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  72. %pairwiseCorrelateRDMs({RDMs, Models}, userOptions);
  73. %MDSRDMs({RDMs, Models}, userOptions);
  74. %%%%%%%%%%%%%%%%%%%%%%%%%%%
  75. %% statistical inference %%
  76. %%%%%%%%%%%%%%%%%%%%%%%%%%%
  77. %roiIndex = 1;% index of the ROI for which the group average RDM will serve
  78. % as the reference RDM.
  79. %for i=1:numel(Models)
  80. % models{i}=Models(i);
  81. %end
  82. userOptions.RDMcorrelationType='Kendall_taua';
  83. userOptions.RDMrelatednessTest = 'subjectRFXsignedRank';
  84. userOptions.RDMrelatednessThreshold = 0.05;
  85. userOptions.figureIndex = [10 11];
  86. userOptions.RDMrelatednessMultipleTesting = 'FDR';
  87. userOptions.candRDMdifferencesTest = 'subjectRFXsignedRank';
  88. userOptions.candRDMdifferencesThreshold = 0.05;
  89. userOptions.candRDMdifferencesMultipleTesting = 'none';
  90. %stats_p_r=rsa.compareRefRDM2candRDMs(RDMs(roiIndex), models, userOptions);

s1_Recipe_fMRI_EMO_ININ.m, no license · at the source

Overview

Authors: Mingtong Liu1, Lauren K Gresham1, Nikki A Puccetti2, Tobey J Betthauser3,4, Sterling C Johnson1,3,4, Stacey M Schaefer1
ORCID iDs: Mingtong Liu, Lauren K Gresham, Nikki A Puccetti, Tobey J Betthauser, Sterling C Johnson, Stacey M Schaefer
  1. Institute on Aging, University of Wisconsin-Madison, Madison, WI 53706 USA
  2. The Ohio State University, Columbus, OH 43210 USA
  3. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, MC 2420, 600 Highland Avenue, Madison, WI 53792-2420 USA
  4. Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705-2281 USA
Institutions: University of Wisconsin–Madison (United States); The Ohio State University (United States)
Journal: Affective science, volume 7, issue 3, pages 483-497
Dates: received 7 January 2026; accepted 20 July 2026; published online 11 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s42761-026-00394-5 · PMID 42756234 · PMCID PMC13582699 · OpenAlex W7202205480
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), PET / SPECT (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, fMRI & imaging, Machine learning, Smoothing, state filtering, decompositions
Keywords: Emotional reactivity, Amygdala, Alzheimer’s disease, Tau, fMRI, Representational similarity analysis
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: NIA NIH HHS (R01 AG021155); NIMH NIH HHS (R21 MH113227); NICHD NIH HHS (P50 HD105353)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

This study examines associations between brain levels of amyloid-β and tau with representational pattern similarity in amygdalar reactivity to negative and neutral images in older adults without dementia. 81 participants viewed affective images during functional magnetic resonance imaging (fMRI). Participants rated the images on valence and arousal outside the scanner. Amyloid-β and tau were measured with 11C-Pittsburgh compound B and 18F-MK-6240 positron emission tomography (PET) imaging. Representational pattern similarity analyses compared amygdalar responses to negative and neutral stimuli, while preserving the voxel-wise pattern of fMRI activation. Greater differentiation in the pattern of responding across the amygdala was indicated by lower similarity between negative and neutral stimuli. Greater tau levels in the entorhinal cortex and amygdalae were associated with less pattern similarity in left amygdalar reactivity to negative and neutral images in participants whose tau level was below standard positivity thresholds. Less pattern similarity in right amygdalar reactivity to negative and neutral images was also associated with the participants’ valence and arousal ratings of the stimuli. No associations were found with global amyloid levels and only the association between entorhinal tau and amygdalar similarity remained significant after Bonferroni correction. Greater amygdalar pattern separation in response to negative and neutral stimuli with higher levels of entorhinal tau suggest greater amygdalar sensitivity to negative compared to neutral information as tau accumulates in the brain, suggesting a potential underlying mechanism for the disrupted emotional processes often observed in preclinical Alzheimer’s Disease and other Related Dementias.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s42761-026-00394-5.

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.

OSF qswk6

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2), MATLAB (2)
Size: 4 files, 4 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), broom (1 file), ggplot2 (1 file), ggpubr (1 file), lavaan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source: osf.io/qswk6/files/osfstorage

Code availability

The code used for analyses and plots are publicly available on the Open Science Framework at the following link: https://osf.io/qswk6/files/osfstorage.

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

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;
  • 4 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

No dataset and no data link were found in the paper.

Data Availability

Data are available upon request through the Wisconsin Registry for Alzheimer’s Prevention at the following link: https://wrap.wisc.edu/data-requests-2/.

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, 6 authors, 6 keywords, 3 funders, 58 references.

Cite

This paper

Liu, M., Gresham, L. K., Puccetti, N. A., Betthauser, T. J., Johnson, S. C., & Schaefer, S. M. (2026). Amygdalar Pattern Similarity to Negative and Neutral Images, Ratings of the Images, and PET-Measured Amyloid and Tau Levels in Older Adults Without Dementia. Affective science, 7(3), 483-497. https://doi.org/10.1007/s42761-026-00394-5

BibTeX

@article{liu2026amygdalar,
author = {Liu, Mingtong and Gresham, Lauren K and Puccetti, Nikki A and Betthauser, Tobey J and Johnson, Sterling C and Schaefer, Stacey M},
title = {{Amygdalar Pattern Similarity to Negative and Neutral Images, Ratings of the Images, and PET-Measured Amyloid and Tau Levels in Older Adults Without Dementia}},
journal = {Affective science},
year = {2026},
month = aug,
volume = {7},
number = {3},
pages = {483--497},
publisher = {Springer},
issn = {2662-2041},
doi = {10.1007/s42761-026-00394-5},
url = {https://doi.org/10.1007/s42761-026-00394-5},
pmid = {42756234},
pmcid = {PMC13582699}
}

RIS

TY - JOUR
AU - Liu, Mingtong
AU - Gresham, Lauren K
AU - Puccetti, Nikki A
AU - Betthauser, Tobey J
AU - Johnson, Sterling C
AU - Schaefer, Stacey M
TI - Amygdalar Pattern Similarity to Negative and Neutral Images, Ratings of the Images, and PET-Measured Amyloid and Tau Levels in Older Adults Without Dementia
T2 - Affective science
J2 - Affect Sci
PY - 2026
DA - 2026/08/11
VL - 7
IS - 3
SP - 483
EP - 497
SN - 2662-2041
PB - Springer
DO - 10.1007/s42761-026-00394-5
UR - https://doi.org/10.1007/s42761-026-00394-5
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s42761-026-00394-5",
"type": "article-journal",
"title": "Amygdalar Pattern Similarity to Negative and Neutral Images, Ratings of the Images, and PET-Measured Amyloid and Tau Levels in Older Adults Without Dementia",
"container-title": "Affective science",
"author": [
{
"family": "Liu",
"given": "Mingtong"
},
{
"family": "Gresham",
"given": "Lauren K"
},
{
"family": "Puccetti",
"given": "Nikki A"
},
{
"family": "Betthauser",
"given": "Tobey J"
},
{
"family": "Johnson",
"given": "Sterling C"
},
{
"family": "Schaefer",
"given": "Stacey M"
}
],
"container-title-short": "Affect Sci",
"volume": "7",
"issue": "3",
"page": "483-497",
"DOI": "10.1007/s42761-026-00394-5",
"PMID": "42756234",
"PMCID": "PMC13582699",
"ISSN": "2662-2041",
"publisher": "Springer",
"URL": "https://doi.org/10.1007/s42761-026-00394-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
11
]
]
}
}

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