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Emotion regulation success involves systematic gradient-based reconfigurations of large-scale activation patterns in the human brain.

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  1. [1] § Materials and methods › Neurosynth functional decoding ↔ Code/Neurosynth_Decoding/Neurosynth_Functional_Decoding.ipynb, lines 35–50 · score 0.60 · Neurosynth database, functional decoding, NiMARE
  2. [2] § Materials and methods › Gradient analysis › Out-of-sample predictions. ↔ Code/LMM/Cross-sample_Validation.R, lines 1–52 · score 0.60 · cross sample, joint sample, validation, lmer, trained, correlating

Paper

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The authors' code

Jupyter notebook · 211 lines · 6.8 KB · no license · 1 match

  1. # %% [markdown]
  2. # ## ER gradient Neurosynth Meta-Analytic Functional Decoding
  4. # %%
  5. import os
  6. import pickle
  7. import pandas as pd
  8. import numpy as np
  9. from wordcloud import WordCloud
  10. import matplotlib.pyplot as plt
  11. from matplotlib import font_manager as fm
  12. from pprint import pprint
  13. from nimare.extract import download_abstracts, fetch_neurosynth
  14. from nimare.io import convert_neurosynth_to_dataset
  15. from nimare import decode, meta
  17. out_dir = os.path.abspath("")
  18. os.makedirs(out_dir, exist_ok=True)
  20. # %% [markdown]
  21. # ### Downlaod Neurosynth database
  23. # %%
  24. # Fetch Neurosynth with *just* the LDA50 features
  25. files = fetch_neurosynth(
  26. data_dir=out_dir,
  27. version="7",
  28. overwrite=True,
  29. source="abstract",
  30. vocab="LDA50", # LDA 100, 200 , or term
  31. )
  32. neurosynth_db = files[0]
  33. pprint(neurosynth_db)
  35. # %% [markdown]
  36. # ### Convert Neurosynth database to NiMARE dataset file
  38. # %%
  39. # Get the Dataset object
  40. neurosynth_dset = convert_neurosynth_to_dataset(
  41. coordinates_file=neurosynth_db["coordinates"],
  42. metadata_file=neurosynth_db["metadata"],
  43. annotations_files=neurosynth_db["features"],
  44. )
  46. neurosynth_dset.save(os.path.join(out_dir, 'neurosynth',"neurosynth_dataset_LDA50.pkl.gz"))
  47. print(neurosynth_dset)
  49. neurosynth_dset = download_abstracts(neurosynth_dset, "[email hidden]")
  50. neurosynth_dset.save(os.path.join(out_dir, 'neurosynth' ,"neurosynth_dataset_LDA50_with_abstracts.pkl.gz"))
  53. # %% [markdown]
  54. # ### Feature selection
  56. # %%
  57. id_cols = ["id", "study_id", "contrast_id"]
  58. frequency_threshold = 0.001
  59. cols = neurosynth_dset.annotations.columns
  60. cols = [c for c in cols if c not in id_cols]
  61. df = neurosynth_dset.annotations.copy()[cols]
  62. n_studies = df.shape[0]
  63. feature_counts = (df >= frequency_threshold).sum(axis=0)
  64. target_features = feature_counts.between(n_studies * 0.1, n_studies * 0.9)
  65. target_features = target_features[target_features]
  66. target_features = target_features.index.values
  67. print(f"{len(target_features)} features selected.", flush=True)
  70. # %% [markdown]
  71. # ### Decoding continuous map
  73. # %%
  74. corr_decoder = decode.continuous.CorrelationDecoder(
  75. frequency_threshold=0.001,
  76. meta_estimator = meta.cbma.mkda.MKDAChi2(),
  77. features=target_features)
  78. corr_decoder.fit(neurosynth_dset)
  80. # save the correlation decoder
  81. with open(os.path.join(out_dir, 'neurosynth',"corr_decoder.pkl"), "wb") as file:
  82. pickle.dump(corr_decoder, file)
  84. # %%
  85. map = {'er_beta': "../Data/Unthresholded_Tmaps/RS_Covariate_unthresholded_Tmap.nii.gz",
  86. 'grad1' : "../Gradients_Map/volume.all.0.nii.gz",
  87. 'grad2' : "../Gradients_Map/volume.all.1.nii.gz",
  88. 'grad3' : "../Gradients_Map/volume.all.2.nii.gz",
  89. 'grad4' : "../Gradients_Map/volume.all.3.nii.gz",
  90. 'grad5' : "../Gradients_Map/volume.all.4.nii.gz"}
  92. with open(os.path.join(out_dir, 'neurosynth',"corr_decoder.pkl"), "rb") as file:
  93. corr_decoder = pickle.load(file)
  95. corr_df = {}
  96. for i in map.keys():
  97. corr_df[i] = corr_decoder.transform(map[i])
  99. lda50_keys = pd.read_csv(os.path.join(out_dir, 'neurosynth', 'LDA50_keys.csv')).loc[:,['idx','key']]
  100. def match_corr_df(corr_df, lda_keys):
  101. ## Sort the df based on r score
  102. corr_df_sorted = corr_df.reindex(corr_df["r"].abs().sort_values(ascending=False).index)
  103. corr_df_sorted['feature'] = corr_df_sorted.index
  104. corr_df_sorted = corr_df_sorted.reset_index(drop=True)
  105. for index, row in corr_df_sorted.iterrows():
  106. idx = row['feature'].split('_')[4]
  107. corr_df_sorted.loc[index,'idx'] = int(idx)
  109. ## merge the correlation df with the keywords
  110. dff = pd.merge(corr_df_sorted, lda_keys, on='idx')
  111. ## Delete the rows with the method-related keywords
  112. dff = dff.dropna().reset_index(drop=True)
  114. return dff
  116. dff_dict = {}
  117. for i in corr_df.keys():
  118. dff_dict[i] = match_corr_df(corr_df[i], lda50_keys)
  119. dff_dict[i].to_csv(os.path.join(out_dir, f'{i}_decoding_frequency.csv'), index=False)
  121. # %%
  122. key_list = pd.read_csv(os.path.join(out_dir, 'neurosynth', 'LDA50_keys_4table.csv'))['key'].to_list()
  123. dff_list = []
  124. for i in dff_dict.keys():
  125. df = dff_dict[i].loc[:,['r','key']]
  126. df = df.set_index('key')
  127. df = df.rename(columns={"r": f"{i}_r"})
  128. dff = df.reindex(key_list)
  129. dff_list.append(dff)
  131. df_all = pd.concat(dff_list, axis=1)
  132. df_all.to_csv(os.path.join(out_dir, 'functional_decoding_summary.csv'), index=True)
  134. # %% [markdown]
  135. # ## Radar plot
  137. # %%
  138. df1 = dff_dict['grad1'].loc[:,['key','r']]
  139. df1.columns = ['key', 'grad1_r']
  141. df2 = dff_dict['er_beta'].loc[:,['key','r']]
  142. df2.columns = ['key', 'er_r']
  143. dff = pd.merge(df1, df2, on='key')
  145. key_radar = dff['key'].to_list()
  146. dff_list = []
  147. for i in dff_dict.keys():
  148. df = dff_dict[i].loc[:,['r','key']]
  149. df = df.set_index('key')
  150. df = df.rename(columns={"r": f"{i}_r"})
  151. dff = df.reindex(key_radar)
  152. dff_list.append(dff)
  154. df_radar = pd.concat(dff_list, axis=1)
  156. # %%
  157. def decoding_radar_plot(df_decoding, map1, map2, map1_legend, map2_legend, label=True):
  158. categories=df_decoding.index.to_list()
  159. N = 25
  161. # We are going to plot the first line of the data frame.
  162. # But we need to repeat the first value to close the circular graph:
  163. value1=df_decoding[map1].to_list()
  164. value1 += value1[:1]
  165. value1
  167. value2=df_decoding[map2].to_list()
  168. value2 += value2[:1]
  169. value2
  171. # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
  172. angles = [n / float(N) * 2 * np.pi for n in range(N)]
  173. angles += angles[:1]
  175. # Initialise the spider plot
  176. plt.figure(figsize=(8, 8))
  177. fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
  179. # Plot two map
  180. ax.fill(angles, value1, color='#7F7F7F', alpha=0.5, label=map1_legend)
  181. ax.plot(angles, value1, color='#4A4A4A', alpha=0.6, linewidth=1)
  183. ax.fill(angles, value2, color='#9ACD32', alpha=0.6, label=map2_legend)
  184. ax.plot(angles, value2, color='#6B8E23', alpha=0.6,linewidth=1)
  186. # Draw one axe per variable + add labels
  187. if label:
  188. plt.xticks(angles[:-1], categories, color='black', size=8,fontdict={'family':'Arial', 'size':10} )
  189. else:
  190. plt.xticks(angles[:-1], [], color='black', size=8,fontdict={'family':'Arial', 'size':10} )
  191. # Draw ylabels
  192. ax.set_rlabel_position(0)
  193. plt.yticks([-0.5,0,0.5], ['','0',''], color="grey", size=7)
  194. plt.ylim(-0.5,0.5)
  196. legend_font = fm.FontProperties(family='Arial', size=15)
  197. ax.legend(loc='center left', bbox_to_anchor=(1, 1.05), prop=legend_font)
  199. # Show the graph
  200. #plt.show()
  202. return fig
  205. # %%
  206. decoding_radar_plot(df_radar, 'grad1_r', 'er_beta_r', 'Gradient 1 Map', 'Regulatory success (RS)\ncovariate map', True)
  208. # %%
  209. for i in [1,2,3,4,5]:
  210. fig = decoding_radar_plot(df_radar, f'grad{str(i)}_r', 'er_beta_r', f'Gradient {str(i)} Map', 'RS covariate map', False)
  211. fig.savefig(f'D:/0_ER_Gradient/Manuscript/plots/raw/func_decoding_rada_grad{str(i)}.png',bbox_inches='tight',dpi=900,pad_inches=0.1,transparent=False)

Neurosynth_Functional_Decoding.ipynb, no license · at the source

Overview

Authors: Ruien Wang1, Remi Janet1,2, Carmen Morawetz3, Anita Tusche1,4
ORCID iDs: Ruien Wang
  1. Department of Psychology, Queen’s University, Kingston, Canada
  2. Institut des Sciences Cognitives Marc Jeannerod, UMR-5229 CNRS, Bron, France
  3. Department of Psychology, University of Innsbruck, Innsbruck, Austria
  4. Center for Neuroscience Studies, Queen’s University, Kingston, Canada
Institutions: Queen's University (Canada); Centre National de la Recherche Scientifique (France); Institut des Sciences Cognitives Marc Jeannerod (France); Universität Innsbruck (Austria)
Journal: PLoS biology, volume 24, issue 4, article e3003666
Dates: received 2 August 2025; accepted 6 February 2026; published online 2 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003666 · PMID 41926350 · PMCID PMC13046165 · OpenAlex W7148532673
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Brain*, Emotional Regulation*, Emotions*, Adult, Brain Mapping, Connectome, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Journal subjects: Biology and Life Sciences, Psychology, Emotions, Social Sciences, Neuroscience, Brain Mapping, Functional Magnetic Resonance Imaging, Medicine and Health Sciences, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Neuroimaging, Cognitive Science, Cognitive Psychology, Attention, Computer and Information Sciences, Neural Networks, Mathematical and Statistical Techniques, Statistical Methods, Metaanalysis, Physical Sciences, Mathematics, Statistics, Perception, Sensory Perception
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Natural Sciences and Engineering Research Council of Canada; Social Sciences and Humanities Research Council of Canada
Citations: cited by 1 paper (Europe PMC); 104 references in the paper
Notices: A comment on this paper has been published (41931465, from Europe PMC)

Abstract

Emotion regulation is essential for well-being and mental health, yet individuals vary widely in their emotion regulation success. Why? Traditional neuroimaging studies of emotion regulation often focus on localized neural activity or isolated networks, overlooking how large-scale brain organization relates to the integration of distributed systems and sub-processes supporting regulatory success. Here, we applied a novel system-level framework based on spatial gradients of macroscale brain organization to study variance in emotion regulation success. Using two large functional magnetic resonance imaging (fMRI) datasets (n = 358, n = 263), we projected global activation patterns from a laboratory emotion regulation task onto principal gradients derived from independent resting-state fMRI data from the Human Connectome Project. These gradients capture low-dimensional patterns of neural variation, providing a topographical framework within which complex mental phenomena, such as emotion regulation, emerge. In both datasets, individual differences in regulation success were associated with systematic reconfiguration along Gradient 1—a principal axis differentiating unimodal and heteromodal brain areas. This gradient-based neural reconfiguration also associates with lower negative affect in daily life, as measured via smartphone-based experience sampling in a subset of participants (n = 55). Meta-analytic decoding via Neurosynth revealed that Gradient 1 and regulation success align with multiple psychological processes, including social cognition, memory, attention, and negative emotion, suggesting this gradient reflects diverse, integrative demands during effective emotion regulation. These findings introduce a gradient-based perspective on emotion regulation success that is biologically grounded in well-established large-scale brain organization and ecologically valid through its links with real-world emotional experience. Such gradient-based dynamics may serve as predictive biomarkers of regulatory success and inform targeted interventions in clinical populations.

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 2 matches between paragraphs and lines of code.

OSF yk85c

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: Jupyter (2), R (2), Python (1)
Size: 221 files, 5 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), pandas (3 files), easystats (2 files), ggplot2 (2 files), lme4 (2 files), lmerTest (2 files), scikit-learn (2 files), SciPy (2 files), seaborn (2 files), tidyverse (2 files), data.table (1 file), emmeans (1 file), NiBabel (1 file), Nilearn (1 file), NiMARE (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
5 files
At the source: osf.io/yk85c/

The paper's code and data availability statement is in the Data section.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

Datasets cited

Data Availability

Relevant data and analysis code supporting the key findings of this study have been made available on the Open Science Framework (OSF, https://osf.io/yk85c/).

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 11 MeSH terms, 2 funders, 96 references, 1 integrity notice.

Cite

This paper

Wang, R., Janet, R., Morawetz, C., & Tusche, A. (2026). Emotion regulation success involves systematic gradient-based reconfigurations of large-scale activation patterns in the human brain. PLoS biology, 24(4), e3003666. https://doi.org/10.1371/journal.pbio.3003666

BibTeX

@article{wang2026emotion,
author = {Wang, Ruien and Janet, Remi and Morawetz, Carmen and Tusche, Anita},
title = {{Emotion regulation success involves systematic gradient-based reconfigurations of large-scale activation patterns in the human brain}},
journal = {PLoS biology},
year = {2026},
month = apr,
volume = {24},
number = {4},
pages = {e3003666},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003666},
url = {https://doi.org/10.1371/journal.pbio.3003666},
pmid = {41926350},
pmcid = {PMC13046165}
}

RIS

TY - JOUR
AU - Wang, Ruien
AU - Janet, Remi
AU - Morawetz, Carmen
AU - Tusche, Anita
TI - Emotion regulation success involves systematic gradient-based reconfigurations of large-scale activation patterns in the human brain
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/04/02
VL - 24
IS - 4
SP - e3003666
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003666
UR - https://doi.org/10.1371/journal.pbio.3003666
LA - en
ER -

CSL-JSON

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