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Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Classification analysis of trials of single features ↔ Figure_6_computation_synchrony.py, lines 145–223 · score 0.74 · random forest classifier, Classification accuracy, binary mask, trained, matrices, baseline
  2. [2] § Methods › Classification analysis of trials of single features ↔ Figure_6_computation_amplitude.py, lines 63–142 · score 0.64 · random forest classifier, Classification accuracy, trained, amplitudes, matrices, baseline
  3. [3] § Methods › Classification analysis of trials of single features ↔ Figure_6_computation_synchrony.py, lines 145–223 · score 0.60 · random forest classification, binary mask, matrices, baseline, synchronization
  4. [4] § Results › The memorized visual feature can be decoded from α-band synchronization patterns ↔ Figure_6_computation_amplitude.py, lines 63–142 · score 0.55 · random forest classifier, Classification accuracy, trained, baseline, Figure 6
  5. [5] § Results › α-band network synchronization predicts individual behavioral performance ↔ Figure_5_visualization.py, lines 127–167 · score 0.54 · Hit Rates, graph strength, iPLV, Pearson, HR, behavioral

Paper

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The authors' code

Python · 223 lines · 8.8 KB · no license · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Sat Sep 9 11:55:43 2023
  5. @author: hhaque
  6. """
  8. import numpy as np
  9. import os
  10. import pandas as pd
  11. from sklearn.ensemble import RandomForestClassifier
  12. from sklearn.model_selection import LeaveOneOut
  15. def get_complex(a, b):
  16. c = complex(a,b)
  17. return c
  18. vcomplex = np.vectorize(get_complex)
  20. def bin_to_npy(subject, condition, condition_set_name, frequency):
  21. fpath = '{}\\__trial_connectomes_csv\\{}\\{} {} Hz\\'.format(subject, condition_set_name, condition, frequency)
  22. nb_of_trials = int(len(os.listdir(fpath)) / 2)
  23. for trial in range(nb_of_trials):
  24. filename_re = '{} {} {} Hz trial_{} re.bin'.format(condition_set_name, condition, frequency, trial)
  25. filename_im = '{} {} {} Hz trial_{} im.bin'.format(condition_set_name, condition, frequency, trial)
  26. fpath_re = fpath + filename_re
  27. fpath_im = fpath + filename_im
  28. IM_real = np.fromfile(fpath_re, dtype='>f')
  29. IM_imag = np.fromfile(fpath_im, dtype='>f')
  30. trial_IM = vcomplex(IM_real, IM_imag)
  31. baseline_IM = trial_IM[0:79800]
  32. firstTW = trial_IM[79800:159600]
  33. secondTW = trial_IM[159600:]
  35. if trial == 0:
  36. all_IM = np.stack([baseline_IM, firstTW, secondTW])
  37. all_IM = np.expand_dims(all_IM, axis = 0)
  38. if trial != 0:
  39. IM = np.stack([baseline_IM, firstTW, secondTW])
  40. IM = np.expand_dims(IM, axis = 0)
  42. all_IM = np.vstack([all_IM, IM])
  44. return all_IM
  46. def get_iPLV_matrix(all_matrices, TW):
  47. IMs = all_matrices[:, TW, :] #extracting single TW from 3D (results in 2D)
  48. average = np.mean(IMs, axis = 0) #averages across all trials (results in 1D)
  49. iPLV_matrix = abs(average.imag) #taking iPLV of each element
  50. return iPLV_matrix
  52. def get_binary_mask(all_matrices, TW, tie = 1000):
  53. baseline_iPLV = get_iPLV_matrix(all_matrices, 0) #getting 1D matrix of average iPLV values for baseline
  54. retention_iPLV = get_iPLV_matrix(all_matrices, TW) #getting 1D matrix of average iPLV values for a retention window
  55. difference_iPLV = np.subtract(retention_iPLV, baseline_iPLV) #difference between the two
  57. max_indices = np.argsort(difference_iPLV)[::-1]
  59. binary_mask = np.zeros(np.shape(difference_iPLV))
  60. for i in range(tie):
  61. binary_mask[max_indices[i]] = 1
  62. return binary_mask
  64. def get_1D_sync_vector(input_matrix, binary_mask):
  65. input_matrix = input_matrix.imag
  66. input_matrix = np.where(input_matrix == 0, 1e-12, input_matrix)
  67. input_matrix = input_matrix * binary_mask #taking imag ONLY and then masking it
  68. output_matrix = input_matrix[input_matrix != 0] #removing elements with 0 and vector gets shorter
  69. return output_matrix
  71. def get_feature_matrix(npy_file, TW, binary_mask):
  72. nb_trials = np.shape(npy_file)[0]
  73. for i in range(nb_trials):
  74. trial_matrix = npy_file[i, TW, :]
  75. trial_vector = get_1D_sync_vector(trial_matrix, binary_mask)
  76. if i == 0:
  77. feature_matrix = trial_vector
  78. else:
  79. feature_matrix = np.vstack((feature_matrix, trial_vector))
  80. return feature_matrix
  84. '''Import trial IM and create feature array'''
  86. subject_list = ['S{:03d}'.format(i) for i in range(1, 21)]
  88. condition_set_name = '3x2_Feature_x_Obj'
  90. load = '2'
  91. conditions = ['Shape{}_HIT'.format(load), 'Color{}_HIT'.format(load), 'Spatial{}_HIT'.format(load)]
  93. frequencies = ['3.00', '3.28', '3.68', '4.02', '4.29', '4.52', '5.05',
  94. '5.63', '6.00', '6.56', '7.40', '8.05', '8.57', '9.03',
  95. '10.09', '11.25', '12.00', '13.06', '14.89', '16.00',
  96. '17.14', '18.00', '20.10', '22.50', '24.00', '25.95',
  97. '30.00', '34.29', '36.00', '40.00', '45.00', '51.43',
  98. '60.00', '72.00', '80.00', '90.00', '102.86', '120.00']
  101. for frequency in frequencies:
  102. frequency = frequency.replace('.', '-')
  103. print(frequency)
  104. all_matrices = []
  105. for subject in subject_list:
  106. for idx, condition in enumerate(conditions):
  107. npy_matrix = bin_to_npy(subject, condition, condition_set_name, frequency)
  109. if idx == 0:
  110. subject_matrices = npy_matrix
  111. if idx > 0:
  112. subject_matrices = np.vstack((subject_matrices, npy_matrix))
  114. all_matrices.append(subject_matrices)
  115. print('{} added to all_matrices for {}'.format(subject, frequency))
  117. for TW in [1, 2]:
  118. binary_mask = get_binary_mask(all_matrices, TW)
  120. for id_s, subject in enumerate(subject_list):
  121. subject_IMs_allTW = all_matrices[id_s]
  123. subject_IMs = subject_IMs_allTW[:, TW, :]
  124. subject_IMs_baseline = subject_IMs_allTW[:, 0, :]
  126. trial_nbs = int(subject_IMs.shape[0] / len(conditions))
  128. output_fpath = '\\Single_trial_matrices\\{}\\{}'.format(subject, condition_set_name)
  129. if not os.path.exists(output_fpath):
  130. os.makedirs(output_fpath)
  132. for id_c, condition in enumerate(conditions):
  133. if id_c == 0:
  134. condition_IM = subject_IMs[0:trial_nbs, :]
  135. condition_IM_baseline = subject_IMs_baseline[0:trial_nbs, :]
  136. if id_c == 1:
  137. condition_IM = subject_IMs[trial_nbs:(trial_nbs*2), :]
  138. condition_IM_baseline = subject_IMs_baseline[trial_nbs:(trial_nbs*2), :]
  139. if id_c == 2:
  140. condition_IM = subject_IMs[(trial_nbs*2):, :]
  141. condition_IM_baseline = subject_IMs_baseline[(trial_nbs*2):, :]
  143. feature_matrix = get_feature_matrix(condition_IM, binary_mask)
  145. feature_matrix_baseline = get_feature_matrix(condition_IM_baseline, binary_mask)
  146. feature_matrix_baseline = np.mean(feature_matrix_baseline, axis = 0)
  148. feature_matrix = feature_matrix - feature_matrix_baseline
  150. feature_matrix_output = '{}\\{}_TW{}_{}_feature_array.npy'.format(output_fpath, condition, TW, frequency)
  151. np.save(feature_matrix_output, feature_matrix)
  155. '''Perform random forest classification'''
  157. fpath = '\\Single_trial_matrices'
  159. conditions = ['Shape4_HIT', 'Color4_HIT', 'Spatial4_HIT']
  161. for subject in subject_list:
  162. print(subject)
  163. output_name = 'Synchrony_three_features_load4_{}'.format(subject)
  165. list_of_accuracies = []
  167. for TW in [1, 2]:
  168. classification_accuracy = []
  170. for frequency in frequencies:
  171. frequency = frequency.replace('.', '-')
  172. fpath = 'Single_trial_matrices\\{}\\{}'.format(subject, condition_set_name)
  174. for idx, condition in enumerate(conditions):
  175. npy_file = '{}\\{}_TW{}_{}_feature_array.npy'.format(fpath, condition, TW, frequency)
  176. npy_matrix = np.load(npy_file)
  178. y_condition = [idx] * np.shape(npy_matrix)[0]
  180. if idx == 0:
  181. X = npy_matrix
  182. y = y_condition
  183. if idx > 0:
  184. X = np.concatenate([X, npy_matrix])
  185. y.extend(y_condition)
  187. y = np.array(y)
  189. corr_predict = 0
  190. wrong_predict = 0
  192. loo = LeaveOneOut()
  193. for train_index, test_index in loo.split(X):
  194. X_train, X_test = X[train_index], X[test_index]
  195. y_train, y_test = y[train_index], y[test_index]
  197. clf = RandomForestClassifier(random_state=0)
  198. clf.fit(X_train, y_train)
  199. X_predicted = clf.predict(X_test)
  201. if np.array_equal(X_predicted, y_test) == True:
  202. corr_predict += 1
  203. else:
  204. wrong_predict += 1
  206. if (corr_predict + wrong_predict) == np.shape(X)[0]:
  207. print('{} is done'.format(frequency))
  208. else:
  209. print('Hmm, sth went wrong')
  211. classification_accuracy.append(corr_predict / np.shape(X)[0])
  213. list_of_accuracies.append(classification_accuracy)
  215. df = pd.DataFrame(list_of_accuracies)
  216. df = df.transpose()
  217. df.columns = ['Early', 'Late']
  218. df['Frequency'] = frequencies
  219. df = df[['Frequency', 'Early', 'Late']]
  221. output_fpath = 'Classification_output\\'
  222. output_fpath = output_fpath + output_name + '.csv'
  223. df.to_csv(output_fpath, index = False, sep = ',')

Figure_6_computation_synchrony.py at commit 0ebb16f, no license · at the source

Overview

Authors: Hamed Haque1,2, Sheng H Wang1,3, Felix Siebenhühner1, Edwin M Robertson4, J Matias Palva1,3, Satu Palva1,4
ORCID iDs: Hamed Haque, Sheng H Wang, Felix Siebenhühner, Edwin M Robertson, J Matias Palva, Satu Palva
  1. Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
  2. BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Central Hospital, Helsinki, Finland
  3. Department of Neuroscience and Biomedical Engineering (NBE), Aalto University, Espoo, Finland
  4. Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, University of Glasgow, Glasgow, United Kingdom
Institutions: University of Helsinki (Finland); Helsinki University Hospital (Finland); HiLIFE – Elämäntieteiden Instituutti; Aalto University (Finland); University of Glasgow (United Kingdom)
Journal: n/a, volume 3, article IMAG.a.1034
Dates: received 15 November 2024; accepted 30 October 2025; published online 23 December 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1162/imag.a.1034 · PMCID PMC12723408 · OpenAlex W4416707876
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency, Graphs, fMRI & imaging, Physiology & signal measures
Keywords: MEG, oscillation, working memory, machine learning, synchronization
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Academy of Finland (SA 253130, SA 325404); Sigrid-Juselius-Foundation
Citations: not cited yet (Europe PMC); 104 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

palvalab/vwm_synchronization

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 0ebb16fda87a10118f2fea3dd6cadb707dc77064, 21 December 2023
Languages: Python (15)
Size: 17 files, 15 scripts
Software Heritage: archived
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (13 files), NumPy (12 files), Matplotlib (9 files), seaborn (8 files), SciPy (7 files), scikit-learn (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
15 files
At the source: github.com/palvalab/vwm_synchronization

doi:10.5061/dryad.np5hqc00n

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5061/dryad.np5hqc00n

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

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Read it in the paper: doi.org/10.1162/imag.a.1034.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 2 funders, 104 references.

Cite

This paper

Haque, H., Wang, S. H., Siebenhühner, F., Robertson, E. M., Palva, J. M., & Palva, S. (2025). Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks. Imaging Neuroscience, 3, IMAG.a.1034. https://doi.org/10.1162/imag.a.1034

BibTeX

@article{haque2025top,
author = {Haque, Hamed and Wang, Sheng H and Siebenhühner, Felix and Robertson, Edwin M and Palva, J Matias and Palva, Satu},
title = {{Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks}},
journal = {Imaging Neuroscience},
year = {2025},
volume = {3},
pages = {IMAG.a.1034},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1034},
url = {https://doi.org/10.1162/imag.a.1034},
pmcid = {PMC12723408}
}

RIS

TY - JOUR
AU - Haque, Hamed
AU - Wang, Sheng H
AU - Siebenhühner, Felix
AU - Robertson, Edwin M
AU - Palva, J Matias
AU - Palva, Satu
TI - Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks
T2 - Imaging Neuroscience
J2 - Imaging Neurosci (Camb)
PY - 2025
DA - 2025
VL - 3
SP - IMAG.a.1034
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1034
UR - https://doi.org/10.1162/imag.a.1034
LA - en
ER -

CSL-JSON

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"container-title": "Imaging Neuroscience",
"author": [
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