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Being praised and criticized in adolescents and adults: Age-related changes in neural responses to social evaluations.

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The 5 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Dynamic causal modeling (DCM) analysis ↔ DCM analysis/Secondlevel_DCM_PEB.m, the whole file · a weak match · score 0.78 · free energy, connectivity parameters, design matrix, PEB, BMA, Pp · Report a mismatch
  2. [2] § Methods › MRI data preprocessing ↔ DCM analysis/Preparing_DCM_Denoising_data.py, lines 23 to 39 · score 0.69 · global signal, framewise displacement, preprocessed, confounding, space · Report a mismatch
  3. [3] § Methods › General linear model (GLM) analysis for fMRI data ↔ GLM analysis/GLM_firstlevel.py, lines 53 to 106 · score 0.62 · design matrix, CSF, FWHM, smoothing, preprocessed, signals · Report a mismatch
  4. [4] § Methods › MRI data acquisition ↔ DCM analysis/Firstlevel_DCM.m, lines 1 to 21 · score 0.59 · MRI scanner, echo, repetition, TE, TR · Report a mismatch
  5. [5] § Methods › MRI data preprocessing ↔ GLM analysis/GLM_firstlevel.py, lines 53 to 106 · score 0.54 · framewise displacement, preprocessed, confounding, space, signal · Report a mismatch

Paper

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The authors' code

Python · 131 lines · 5.4 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. by Qinyuan Chen at Ghent University
  5. """
  6. import os
  7. from os.path import join
  8. import pandas as pd
  9. import numpy as np
  10. from nilearn.glm.first_level import FirstLevelModel
  11. import glob
  14. # Define list of subject IDs
  16. def get_subject_ids(datadir):
  17. subject_paths = glob.glob(os.path.join(datadir, 'sub-*'))
  18. subject_ids = [os.path.basename(p).replace('sub-', '') for p in subject_paths if os.path.isdir(p)]
  19. return sorted(subject_ids)
  21. # Define contrasts
  23. def make_contrasts(design_matrix):
  24. #first generate canonical contrast
  25. contrast_matrix = np.eye(design_matrix.shape[-1])
  26. contrasts = {
  27. column: contrast_matrix[i]
  28. for i , column in enumerate(design_matrix.columns)
  29. }
  31. contrasts["positive"] = (contrasts["positive"])
  33. contrasts["negative"] = (contrasts["negative"])
  35. contrasts["neutral"] = (contrasts["neutral"])
  37. contrasts["positive - neutral"] = (contrasts["positive"] - contrasts["neutral"])
  39. contrasts["negative - neutral"] = (contrasts["negative"] - contrasts["neutral"])
  41. contrasts["positive - negative"] = (contrasts["positive"] - contrasts["negative"])
  43. contrasts["neutral - positive"] = (contrasts["neutral"] - contrasts["positive"])
  45. contrasts["neutral - negative"] = (contrasts["neutral"] - contrasts["negative"])
  47. contrasts["negative - positive"] = (contrasts["negative"] - contrasts["positive"])
  49. return contrasts
  51. # First level GLM analysis
  53. def first_level_glm(datadir, subject_id):
  55. print(f"Processing {datadir} subject {subject_id}...")
  57. fmri_glm = FirstLevelModel(t_r=2.5,
  58. noise_model='ar1',
  59. hrf_model='spm',
  60. drift_model='cosine',
  61. high_pass=1./128,
  62. smoothing_fwhm=6.0,
  63. signal_scaling=False,
  64. minimize_memory=False)
  66. # Paths to file
  67. fmri_img = f"{datadir}/sub-{subject_id}/func/sub-{subject_id}_task-kritiek_run-1_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz"
  68. events_file = f"{outdir}/criticism_events.tsv"
  69. confounds_file = f"{datadir}/sub-{subject_id}/func/sub-{subject_id}_task-kritiek_run-1_desc-confounds_timeseries.tsv"
  71. # Load file
  72. events = pd.read_table(events_file)
  73. confounds = pd.read_table(confounds_file)
  74. confounds_glm = confounds.fillna(0)[['csf', 'white_matter', 'framewise_displacement', 'trans_x', 'trans_y', 'trans_z', 'rot_x', 'rot_y', 'rot_z']]
  76. # Fit model
  77. fmri_glm = fmri_glm.fit(fmri_img, events, confounds_glm)
  79. # Design matrix
  80. design_matrix = fmri_glm.design_matrices_[0]
  82. # Define contrasts
  83. contrasts = make_contrasts(design_matrix)
  85. # Compute and save z-map for each contrasts
  86. # compute the contrast as z map
  87. zmap_pos = fmri_glm.compute_contrast(contrasts["positive"], output_type='z_score')
  88. zmap_neg = fmri_glm.compute_contrast(contrasts["negative"], output_type='z_score')
  89. zmap_neu = fmri_glm.compute_contrast(contrasts["neutral"], output_type='z_score')
  90. zmap_pos_neu = fmri_glm.compute_contrast(contrasts["positive - neutral"], output_type='z_score')
  91. zmap_neg_neu = fmri_glm.compute_contrast(contrasts["negative - neutral"], output_type='z_score')
  92. zmap_pos_neg = fmri_glm.compute_contrast(contrasts["positive - negative"], output_type='z_score')
  93. zmap_neu_pos = fmri_glm.compute_contrast(contrasts["neutral - positive"], output_type='z_score')
  94. zmap_neu_neg = fmri_glm.compute_contrast(contrasts["neutral - negative"], output_type='z_score')
  95. zmap_neg_pos = fmri_glm.compute_contrast(contrasts["negative - positive"], output_type='z_score')
  97. # save the z map
  98. zmap_pos.to_filename(join(f'{outdir}/first_level/positive/sub-{subject}_task-kritiek_run-01_desc_pos_zmap.nii.gz'))
  99. zmap_neg.to_filename(join(f'{outdir}/first_level/negative/sub-{subject}_task-kritiek_run-01_desc_neg_zmap.nii.gz' ))
  100. zmap_neu.to_filename(join(f'{outdir}/first_level/neutral/sub-{subject}_task-kritiek_run-01_desc_neu_zmap.nii.gz'))
  101. zmap_pos_neu.to_filename(join(f'{outdir}/first_level/con001/sub-{subject}_task-kritiek_run-01_desc_pos-neu_zmap.nii.gz'))
  102. zmap_neg_neu.to_filename(join(f'{outdir}/first_level/con002/sub-{subject}_task-kritiek_run-01_desc_neg-neu_zmap.nii.gz'))
  103. zmap_pos_neg.to_filename(join(f'{outdir}/first_level/con003/sub-{subject}_task-kritiek_run-01_desc_pos-neg_zmap.nii.gz'))
  104. zmap_neu_pos.to_filename(join(f'{outdir}/first_level/con004/sub-{subject}_task-kritiek_run-01_desc_neu_pos_zmap.nii.gz'))
  105. zmap_neu_neg.to_filename(join(f'{outdir}/first_level/con005/sub-{subject}_task-kritiek_run-01_desc_neu_neg_zmap.nii.gz'))
  106. zmap_neg_pos.to_filename(join(f'{outdir}/first_level/con006/sub-{subject}_task-kritiek_run-01_desc_neg_pos_zmap.nii.gz'))
  109. # Output directory
  110. outdir = '/home/ghep/qiny/Age/nilearn_analysis'
  113. # Run first level glm for each participant in adolescent group
  114. datadir = '/home/ghep/qiny/Age/nifti/adolescents'
  115. subject_list = get_subject_ids(datadir)
  118. for subject_id in subject_list:
  119. subject = f'1{subject_id}'
  120. first_level_glm(datadir, subject_id)
  123. # Run first level glm for each participant in adult group
  124. datadir = '/home/ghep/qiny/Age/nifti/adults'
  125. subject_list = get_subject_ids(datadir)
  127. for subject_id in subject_list:
  128. subject = f'2{subject_id}'
  129. first_level_glm(datadir, subject_id)

GLM_firstlevel.py, no license · at the source

Overview

Authors: Qinyuan Chen1, Sam Luc Bart Bonduelle1,2, Guo-Rong Wu1,3, Rudi De Raedt4, Chris Baeken1,5,6,7
ORCID iDs: Qinyuan Chen, Sam Luc Bart Bonduelle, Rudi De Raedt, Chris Baeken
  1. Ghent Experimental Psychiatry (GHEP) lab, Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent University, Ghent, Belgium
  2. Department of Child and Adolescent Psychiatry, Vrije Universiteit Brussel (VUB), University Hospital Brussels (UZ Brussel), Brussels, Belgium
  3. Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
  4. Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
  5. Department of Psychiatry, University Hospital Brussel (UZ Brussel), Brussels, Belgium
  6. Neuroprotection and Neuromodulation Research Group (NEUR), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
  7. Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
Institutions: Ghent University (Belgium); Vrije Universiteit Brussel (Belgium); Universitair Ziekenhuis Brussel (Belgium); Southwest University (China); Eindhoven University of Technology (Netherlands)
Journal: Developmental cognitive neuroscience, volume 80, article 101737
Dates: received 2 July 2025; accepted 4 May 2026; published online 7 May 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.dcn.2026.101737 · PMID 42143546 · PMCID PMC13200081 · OpenAlex W7160540478
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging, Preprocessing, Machine learning
Keywords: Neurodevelopment, Adolescence, Social evaluation, Criticism sensitivity, FMRI, Dynamic causal modelling
MeSH: Brain*, Social Cognition*, Social Perception*, Adolescent, Adult, Age Factors, Brain Mapping, Emotions, Female, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Research Foundation Flanders (G.0F46.17N); Rode Neuzen; National Natural Science Foundation of China (62271415, 61876156); Ghent University (BOF 16/GOA/017); Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders) (T000720N); EOS (EOS 40007528. - G0I2422N); Queen Elisabeth Medical Foundation for Neurosciences; Agentschap voor Innovatie door Wetenschap en Technologie; BRAINN HORIZON-WIDERA-2021-ACCESS-03 – Twinning project (101079001); BeNeFIT (10390082210018); SOA; FRA (2024 0023)
Citations: not cited yet (Europe PMC); 119 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.

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OSF fr8mn

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (4), Python (2)
Size: 22 files, 6 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (4 files), Nilearn (2 files), pandas (2 files), NumPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
6 files
At the source: osf.io/fr8mn/

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1016/j.dcn.2026.101737.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 14 MeSH terms, 12 funders, 113 references.

Cite

This paper

Chen, Q., Bonduelle, S. L. B., Wu, G.-R., De Raedt, R., & Baeken, C. (2026). Being praised and criticized in adolescents and adults: Age-related changes in neural responses to social evaluations. Developmental cognitive neuroscience, 80, 101737. https://doi.org/10.1016/j.dcn.2026.101737

BibTeX

@article{chen2026being,
author = {Chen, Qinyuan and Bonduelle, Sam Luc Bart and Wu, Guo-Rong and De Raedt, Rudi and Baeken, Chris},
title = {{Being praised and criticized in adolescents and adults: Age-related changes in neural responses to social evaluations}},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = may,
volume = {80},
pages = {101737},
publisher = {Elsevier},
issn = {1878-9293},
doi = {10.1016/j.dcn.2026.101737},
url = {https://doi.org/10.1016/j.dcn.2026.101737},
pmid = {42143546},
pmcid = {PMC13200081}
}

RIS

TY - JOUR
AU - Chen, Qinyuan
AU - Bonduelle, Sam Luc Bart
AU - Wu, Guo-Rong
AU - De Raedt, Rudi
AU - Baeken, Chris
TI - Being praised and criticized in adolescents and adults: Age-related changes in neural responses to social evaluations
T2 - Developmental cognitive neuroscience
J2 - Dev Cogn Neurosci
PY - 2026
DA - 2026/05/07
VL - 80
SP - 101737
SN - 1878-9293
PB - Elsevier
DO - 10.1016/j.dcn.2026.101737
UR - https://doi.org/10.1016/j.dcn.2026.101737
LA - en
ER -

CSL-JSON

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