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Resting-state neural oscillations predict individual differences in verbal learning and encoding strategy use.

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  1. [1] § STAR★Methods › Method details › Neuroimaging › Multi-feature machine learning regression analysis ↔ regress_ML_cvlt_7.py, lines 21–64 · score 0.54 · square error, fits, fold, SVR, model, Linear
  2. [2] § STAR★Methods › Method details › Neuroimaging › Multi-feature machine learning regression analysis ↔ regress_ML_cvlt_8.py, lines 21–64 · score 0.54 · square error, fits, fold, SVR, model, Linear

Paper

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The authors' code

Python · 154 lines · 5.2 KB · no license · 1 match

  1. from joblib import Parallel, delayed
  2. from sklearn.svm import SVR
  3. import matplotlib.pyplot as plt
  4. import os.path as op
  5. import scipy.io as sio
  6. import numpy as np
  7. from sklearn.model_selection import KFold
  8. from sklearn.feature_selection import SequentialFeatureSelector
  9. import statsmodels.api as sm
  10. from sklearn.metrics import mean_squared_error
  11. from sklearn.linear_model import LinearRegression
  12. from sklearn.preprocessing import StandardScaler
  13. from sklearn.ensemble import GradientBoostingRegressor
  14. import time
  16. start_time = time.time()
  17. def load_data(freq, root, matrice_name):
  18. X11= sio.loadmat(op.join(root, matrice_name))[lulu[FREQ.index(freq)]]
  19. return X11
  21. def compute_feature(freq, num_feat, X11, target):
  22. reg = SVR(kernel="linear")
  23. #reg = GradientBoostingRegressor()
  24. cv_split = KFold(5)
  26. y_pred = []
  27. target_new = []
  29. for train, test in cv_split.split(X11):
  31. scaler = StandardScaler()
  32. norm = scaler.fit(X11[train])
  33. X1 = norm.transform(X11[train])
  34. X2 = norm.transform(X11[test])
  36. reg = SVR(kernel="linear")
  37. #reg = GradientBoostingRegressor()
  38. cv = KFold(5)
  39. sfs = SequentialFeatureSelector(reg, n_features_to_select=num_feat, cv=cv)
  40. XX1 = sfs.fit_transform(X1, target[train])
  41. XX2 = sfs.transform(X2)
  42. reg = SVR(kernel="linear")
  43. #reg = GradientBoostingRegressor()
  44. reg.fit(XX1, target[train])
  46. y_pred_mini = reg.predict(XX2)
  47. y_pred.append(y_pred_mini)
  48. target_new.append(target[test])
  50. target_new = np.concatenate(target_new, axis=0)
  51. y_pred = np.concatenate(y_pred, axis=0)
  53. lm = LinearRegression().fit(target_new.reshape(-1, 1), y_pred.reshape(-1, 1))
  54. rsq = lm.score(target_new.reshape(-1, 1), y_pred.reshape(-1, 1))
  55. mse = mean_squared_error(target_new.reshape(-1, 1), y_pred.reshape(-1, 1))
  56. coefficient = lm.coef_[0]
  57. print(f"mean square error: {mse}")
  59. XX = sm.add_constant(target_new.reshape(-1, 1))
  60. model = sm.OLS(y_pred.reshape(-1, 1), XX)
  61. result = model.fit()
  62. p_value = result.f_pvalue
  64. return rsq, mse, coefficient, p_value
  66. ## params ##
  67. #matrice_cvlt_2 = 'mat_cvlt.mat'
  68. matrice_cvlt_2 = 'cvlt_2.mat'
  69. matrice_name = 'mat_cvlt_300_4.mat'
  70. FREQ = ['delta', 'theta', 'alpha', 'beta', 'gamma1', 'gamma2', 'gamma3','exp']
  71. lulu = ['mat_d', 'mat_t', 'mat_a', 'mat_b', 'mat_g1', 'mat_g2', 'mat_g3', 'mat_e']
  72. #FREQ = ['delta']
  73. #lulu = ['mat_d']
  74. #test = 'Absorption'
  75. #test ='correct_reponse_raw'
  76. #test ='correct_reponse_std'
  77. #test ='semantic_clustering'
  78. #test ='Serial_Clustering_Forward'
  79. #test ='Serial_Clustering_Bidirectional'
  80. #test ='Subjective_Clustering_Bidirectional'
  81. test ='learning_slope_2_5'
  83. ## path ##
  84. #root = op.join('/Users', 'victoroswald', 'Documents', 'code', 'Trance','result')
  85. root = op.join('/home', 'vicolab', 'projects', 'def-kjerbi', 'vicolab', 'cc', 'hamza')
  88. ## load target ##
  89. target = sio.loadmat(op.join(root, matrice_cvlt_2))['tata'][:,10]
  91. mat_rsq = []
  92. mat_coef = []
  93. mat_p_val = []
  94. mat_mse = []
  96. ## Parallel loop on feature and frequency
  97. results = Parallel(n_jobs=-1)(delayed(compute_feature)(freq, num_feat, load_data(freq, root, matrice_name), target) for freq in FREQ for num_feat in range(2, 300, 1))
  99. ## Unpack results
  100. for result in results:
  101. mat_rsq.append(result[0])
  102. mat_mse.append(result[1])
  103. mat_coef.append(result[2])
  104. mat_p_val.append(result[3])
  106. n=298
  107. ## Save the results in text files
  108. for i, freq in enumerate(FREQ):
  109. np.savetxt(root + '/rsq_' + test + '_' + FREQ[i] + '.txt', mat_rsq[i*n:(i+1)*n])
  110. np.savetxt(root + '/coef_' + test + '_' + FREQ[i]+ '.txt', mat_coef[i*n:(i+1)*n])
  111. np.savetxt(root + '/p_val_' + test + '_' + FREQ[i] + '.txt', mat_p_val[i*n:(i+1)*n])
  112. np.savetxt(root + '/MSE_' + test + '_' + FREQ[i] + '.txt', mat_mse[i*n:(i+1)*n])
  114. ## Save the results in a single file
  115. np.savetxt(root + '/results_' + test + '.txt', np.column_stack((mat_rsq, mat_mse, mat_coef, mat_p_val)),
  116. header="R-squared, Mean Squared Error, Coefficient, P-value", delimiter=",", fmt='%.6f')
  118. ## Plot the results for all frequencies on a single plot
  119. for i, freq in enumerate(FREQ):
  120. plt.plot(range(2, 300), mat_rsq[i*n:(i+1)*n], label=freq)
  121. plt.xlabel('Number of Features')
  122. plt.ylabel('R-squared')
  123. plt.legend()
  124. plt.savefig(root + '/rsq_' + test + '.png')
  125. plt.clf()
  127. for i, freq in enumerate(FREQ):
  128. plt.plot(range(2, 300), mat_coef[i*n:(i+1)*n], label=freq)
  129. plt.xlabel('Number of Features')
  130. plt.ylabel('Coefficient')
  131. plt.legend()
  132. plt.savefig(root + '/coef_' + test + '.png')
  133. plt.clf()
  135. for i, freq in enumerate(FREQ):
  136. plt.plot(range(2, 300), mat_p_val[i*n:(i+1)*n], label=freq)
  137. plt.axhline(y=0.05, color='black', linestyle='--')
  138. plt.xlabel('Number of Features')
  139. plt.ylabel('P-value')
  140. plt.legend()
  141. plt.savefig(root + '/p_val_' + test + '.png')
  142. plt.clf()
  144. for i, freq in enumerate(FREQ):
  145. plt.plot(range(2, 300), mat_mse[i*n:(i+1)*n], label=freq)
  146. plt.xlabel('Number of Features')
  147. plt.ylabel('Mean Squared Error')
  148. plt.legend()
  149. plt.savefig(root + '/MSE_' + test + '.png')
  150. plt.clf()
  152. end_time = time.time() # Enregistrer l'heure de fin
  153. elapsed_time = end_time - start_time # Calculer le temps écoulé
  154. print(f"Elapsed time : {elapsed_time:.2f} seconds")

regress_ML_cvlt_7.py at commit 8500f5b, no license · at the source

Overview

Authors: Victor Oswald1,2, Mathieu Landry1,2, Hamza Abdelhedi1,3, Sarah Lippé2,4, Philippe Robaey4,5,6,7, Karim Jerbi1,2,3,8
ORCID iDs: Victor Oswald
  1. Computational and Cognitive Neuroscience Laboratory, University of Montreal, Montreal, QC, Canada
  2. Department of Psychology, University of Montreal, Montreal, QC, Canada
  3. Mila – Quebec AI Institute, Montreal, QC, Canada
  4. CHU Sainte-Justine Research Center, Montreal, QC, Canada
  5. Department de Psychiatry, University of Montreal, Montreal, QC, Canada
  6. Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada
  7. Department de Psychiatry, University of Ottawa, Ottawa, ON, Canada
  8. UNIQUE Center (Unifying Neuroscience and Artificial Intelligence – Québec), Montreal, QC, Canada
Institutions: Université de Montréal (Canada); Mila - Quebec Artificial Intelligence Institute (Canada); CHU Sainte-Justine Research Center (Canada); University of Ottawa (Canada); Children's Hospital of Eastern Ontario (Canada)
Journal: iScience, volume 29, issue 9, article 117377
Dates: received 26 September 2025; accepted 13 August 2026; published online 31 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.117377 · PMID 42729535 · PMCID PMC13562390 · OpenAlex W7204782202
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), cognitive (subfield)
Methods: Preprocessing, Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, Statistics, Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: resting-state, MEG, encoding strategies, verbal memory
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Canada Research Chairs (RGPIN-2015-04854); Université de Montréal; Natural Sciences and Engineering Research Council of Canada (RGPIN-2015, RGPIN-2015-04854); Fonds de recherche du Québec – Nature et technologies (RGPIN-2015-04854, RQT00121)
Citations: not cited yet (Europe PMC); 78 references in the paper
Research resources: MATLAB; Python RRID:SCR_001622, Brainstorm (MEG analysis) RRID:SCR_001761, scikit-learn (SVR regression) RRID:SCR_002577, MATLAB; Python RRID:SCR_008394

Abstract

Individuals adopt different encoding strategies to facilitate learning, yet few studies have examined the neurophysiological basis of these strategies across individuals. The present work addresses this gap by extending our previous findings on the direct relationship between cortical spectral power, measured via resting-state magnetoencephalography, and standard cognitive performance, to test whether resting-state neural features predict individual differences in encoding strategy preferences. Our results highlight the complex interactions between endogenous brain oscillations, learning, and verbal encoding strategies assessed by the California Verbal Learning Test-Second Edition (CVLT-2). First, resting-state theta oscillations were significantly associated with verbal learning and subjective clustering strategies. Second, semantic clustering was facilitated by oscillatory patterns in the left sensory-motor regions. Finally, serial and semantic clustering strategies showed opposite regression patterns, indicating a competitive interaction. Together, these findings provide insights into resting-state neural markers associated with diverse encoding strategies in verbal learning.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Zenodo 21395000

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), scikit-learn (3 files), SciPy (3 files), statsmodels (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
6 files
At the source: doi.org/10.5281/zenodo.21395000

LIKACT/CVLT_MEG_ML

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8500f5b209f2f696773739267fda2473d4236868, 16 July 2026
Languages: Shell (3), Python (3)
Size: 6 files, 6 scripts
Software Heritage: not archived
Found in: the text, “Multi-feature machine learning regression analys”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), scikit-learn (3 files), SciPy (3 files), statsmodels (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/LIKACT/CVLT_MEG_ML

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 2 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 and code availability

Data: All data reported in this paper have been deposited at Zenodo and are publicly available as of the date of publication. The DOI is listed in the key resources table (https://doi.org/10.5281/zenodo.21499549). No restrictions apply to the accessibility of these data, which are openly available under a Creative Commons Attribution 4.0 (CC BY 4.0) license; no material transfer agreement or additional authorization is required.

Code: All original code used in this study (the multi-feature machine learning regression and mediation analyses) has been deposited at Zenodo and is publicly available as of the date of publication. The DOI is listed in the key resources table (https://doi.org/10.5281/zenodo.21395000).

Additional information: 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-NC), 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 2, 28 September 2026

  • Authors: added Victor Oswald (0000-0002-4684-3965); removed Victor Oswald
  • Funding: added Canada Research Chairs: RGPIN-2015-04854; Université de Montréal; Natural Sciences and Engineering Research Council of Canada: RGPIN-2015, RGPIN-2015-04854; Fonds de recherche du Québec – Nature et technologies: RGPIN-2015-04854, RQT00121

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 75 references, 4 RRIDs.

Cite

This paper

Oswald, V., Landry, M., Abdelhedi, H., Lippé, S., Robaey, P., & Jerbi, K. (2026). Resting-state neural oscillations predict individual differences in verbal learning and encoding strategy use. iScience, 29(9), 117377. https://doi.org/10.1016/j.isci.2026.117377

BibTeX

@article{oswald2026resting,
author = {Oswald, Victor and Landry, Mathieu and Abdelhedi, Hamza and Lippé, Sarah and Robaey, Philippe and Jerbi, Karim},
title = {{Resting-state neural oscillations predict individual differences in verbal learning and encoding strategy use}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {117377},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117377},
url = {https://doi.org/10.1016/j.isci.2026.117377},
pmid = {42729535},
pmcid = {PMC13562390}
}

RIS

TY - JOUR
AU - Oswald, Victor
AU - Landry, Mathieu
AU - Abdelhedi, Hamza
AU - Lippé, Sarah
AU - Robaey, Philippe
AU - Jerbi, Karim
TI - Resting-state neural oscillations predict individual differences in verbal learning and encoding strategy use
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/31
VL - 29
IS - 9
SP - 117377
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117377
UR - https://doi.org/10.1016/j.isci.2026.117377
LA - en
ER -

CSL-JSON

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