OSCR

Towards precision EEG connectomics: Evaluating the benefits of dense sampling.

Code ↔ Paper

9 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 9 matches
  1. [1] § Methods › Connectivity measures and connectome generation ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/3 Make Intermediate Files/ageeffect_timesubset_nov2024_pt2.py, lines 20–68 · score 0.72 · 13–30 Hz, 8–13 Hz, 2.5–45 Hz, frequency bands, 2.5 Hz, FC
  2. [2] § Methods › Connectivity measures and connectome generation ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/4 Make Plots/fig6_fingerprintingbylength.py, lines 19–76 · score 0.71 · 13–30 Hz, 8–13 Hz, 2.5–45 Hz, frequency bands, 2.5 Hz
  3. [3] § Methods › Preprocessing ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/1 Preprocessing/03 ICA v9.py, lines 545–585 · score 0.66 · notch filtered, bandpass filtered, downsampled, MNE, Channels, epochs
  4. [4] § Methods › Preprocessing ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/2 Make Connectomes/source_localization_8_june2024.py, lines 186–246 · score 0.62 · pca_flip, FreeSurfer, Source localization, Source space, MNE, vertices
  5. [5] § Methods › Preprocessing ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/2 Make Connectomes/source_localization_8_task_june2024_agesessplit.py, lines 414–461 · score 0.62 · pca_flip, FreeSurfer, Source localization, Source space, MNE, vertices
  6. [6] § Methods › Preprocessing ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/1 Preprocessing/05 finalize v4.py, lines 779–839 · score 0.59 · mne icalabel, classified, eye, component, Channels, noise
  7. [7] § Methods › Simulated brain activity ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/2 Make Connectomes/source_localization_8_makesimconnectomes.py, lines 253–290 · score 0.55 · Source Simulator, source space, MNE, noise, epochs, preprocessing
  8. [8] § Methods › Connectivity measures and connectome generation ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/2 Make Connectomes/source_localization_8_makesimconnectomes.py, lines 319–378 · score 0.51 · envelope correlations, spectral, adaptive, multitaper, orthogonalization, simulated
  9. [9] § Methods › Connectivity measures and connectome generation ↔ EEG_Preprocessing_and_Analysis/MNE Programs Final March 2025/2 Make Connectomes/source_localization_8_june2024.py, lines 248–295 · score 0.51 · envelope correlations, spectral, adaptive, multitaper, MNE, orthogonalization

Paper

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

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The authors' code

Python · 317 lines · 17 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. This creates connectomes for each file
  6. """
  10. import mne
  11. import pandas as pd
  12. import os
  13. from mne.minimum_norm import make_inverse_operator, apply_inverse_epochs
  14. from mne_connectivity import spectral_connectivity_epochs
  15. from mne_connectivity import phase_slope_index
  16. from mne_connectivity import envelope_correlation
  17. import time
  18. import numpy as np
  19. import mne_connectivity
  22. #where is structural data saved. There should be one structural file per participant
  23. subjects_dir = '/Users/ivy/Desktop/Graff_EEG_stuff/ZPrecise2_MRI_fixed_rename_FS'
  25. #folder with all the participant folders and EEG outputs
  26. dir_start = '/Users/ivy/Desktop/Graff_EEG_stuff/precise_KIDS_final/prepro1/'
  28. #file with manual adjustments for epochs to include/exclude
  29. manadj = '/Users/ivy/Desktop/Graff_EEG_stuff/precise_KIDS_final/manualadj_done.csv'
  32. #write over files if they already exist? If yes, replacer = True. Or ignore them?
  33. replacer = False
  36. #which parcellation(s) to use. This probably only works with aparc(?) but at one point intended to check other parcellations
  37. parclist = ['aparc']
  40. #which frequency bands and FC measures to generate connectomes for
  41. fbands = [[8.0,13.0],[13.0,30.0],[2.5,45.0]]
  42. conmethods = ['wpli','imcoh','coh','plv','pli','ciplv','psi','ecpwo','ecso']
  46. #which file(s) do you want to look at?
  47. families = list(range(2,27))
  48. participant_ages = ['P','C']
  49. sessions = [1,2,3,4,'4x']
  50. tasks = ['DORA','YT','RX']
  53. #source localization method
  54. method = "eLORETA"
  55. snr = 3.0
  58. #what subsets of data to use? 150 = use first 150 epochs, for example. 'full' uses the full scan
  59. #subsections = [150]
  60. subsections = [150,'full']
  64. #filter data, for envelope correlations
  65. def bp_gen(label_ts,fmin,fmax):
  66. """Make a generator that band-passes on the fly."""
  67. for ts in label_ts:
  68. yield mne.filter.filter_data(ts, sfreq, fmin, fmax)
  72. adjdf = pd.read_csv(manadj)
  75. for family in families:
  76. strnum = str(family)
  77. if len(strnum) == 1:
  78. strnum = '0' + strnum
  79. strfam = 'sub-19730' + strnum
  81. for page in participant_ages:
  82. strper = strfam + str(page)
  83. personfolder = dir_start + strper + '/'
  85. prefixed = [filename for filename in os.listdir(subjects_dir) if filename.startswith(strper)]
  87. personfolder2 = subjects_dir + '/' + prefixed[0]
  88. subject = prefixed[0]
  91. if len(prefixed) != 1:
  92. print('Uh oh, problem with structural data, either 0 or multiple structural files for participant')
  94. else:
  96. if not os.path.exists(personfolder):
  97. print("This folder doesn't exist: " + personfolder)
  98. else:
  100. for parc in parclist:
  102. for session in sessions:
  103. input_data_folder = personfolder + 'ses-' + str(session) + '/eeg/'
  104. connectome_folder = personfolder + 'ses-' + str(session) + '/connectomes_0624/' + parc + '/'
  106. if not os.path.exists(connectome_folder):
  107. os.makedirs(connectome_folder)
  109. for task in tasks:
  110. raw_data_file_sub = strper + '_ses-' + str(session) + '_task-' + task
  112. if os.path.exists(input_data_folder):
  113. prefixed = [filename for filename in os.listdir(input_data_folder) if filename.startswith(raw_data_file_sub)]
  114. else:
  115. prefixed = []
  117. if len(prefixed) == 0:
  118. print("There are no files starting with: " + raw_data_file_sub)
  120. else:
  121. fullfile = [filename for filename in prefixed if filename.endswith('remontage_epo.fif')]
  122. fwdfile = [filename for filename in prefixed if filename.endswith('fwd.fif')]
  124. epochdatafolder = [filename for filename in prefixed if filename.endswith('eeg')]
  126. if len(fullfile) > 1 or len(fwdfile) > 1:
  127. print("There are multiple files that exist of the same task:")
  128. for val in fullfile:
  129. print(val)
  130. for val in fwdfile:
  131. print(val)
  132. print("For now, ignoring all of them")
  133. elif len(fullfile) == 0 or len(fwdfile) == 0:
  134. print("There are missing files starting with: " + raw_data_file_sub)
  135. else:
  136. remontage_path = input_data_folder + fullfile[0]
  137. fwd_path = input_data_folder + fwdfile[0]
  139. filetosaveprefix = fullfile[0].split('eeg_')[0]
  142. outputfiles = [filename for filename in os.listdir(connectome_folder) if filename.startswith(filetosaveprefix)]
  145. if len(outputfiles) > 0 and replacer == False:
  146. print("Output files already exist starting with: " + filetosaveprefix)
  147. else:
  148. print("Makin' connectomes starting with: " + filetosaveprefix)
  150. adjdfname = filetosaveprefix + 'eeg.raw'
  151. adjdata = adjdf[adjdf['File'] == adjdfname]
  153. if len(adjdata) != 1:
  154. print("Info on this scan in adjepochdf is wrong")
  156. else:
  157. omitdata = adjdata.iloc[0]['Omit']
  158. dropstart = int(adjdata.iloc[0]['Drop_start'])
  159. dropend = int(adjdata.iloc[0]['Drop_end'])
  161. if omitdata != 'no':
  162. print(filetosaveprefix + ' is being omitted due to the adjepochdf')
  164. else:
  166. starttime = time.time()
  168. epochsadj = mne.read_epochs(remontage_path,preload=True)
  169. if dropstart > 0:
  170. print("Dropping epochs at start: " + str(dropstart))
  171. epochsadj = epochsadj[dropstart:].copy()
  173. if dropend > 0:
  174. print("Dropping epochs at end: " + str(dropend))
  175. epochsadj = epochsadj[:-1*dropend].copy()
  178. fwd = mne.read_forward_solution(fwd_path)
  180. sfreq = epochsadj.info['sfreq'] # the sampling frequency
  182. noise_cov = mne.make_ad_hoc_cov(epochsadj.info)
  186. #inverse operator
  187. inverse_operator = make_inverse_operator(info=epochsadj.info, forward=fwd, noise_cov=noise_cov, depth=3.5)
  190. #compute inverse solution
  192. lambda2 = 1. / snr ** 2
  193. #stcs = apply_inverse_epochs(epochsadj, inverse_operator, lambda2, method,pick_ori="normal", nave=len(epochsadj))
  194. stcs = apply_inverse_epochs(epochsadj, inverse_operator, lambda2, method)
  196. print("")
  197. print("Parcellating")
  198. print("")
  201. #read labels from the freesurfer outputs
  202. labels_parc = mne.read_labels_from_annot(subject, parc=parc,subjects_dir=subjects_dir)
  203. label_names = [label.name for label in labels_parc]
  205. #generate time course data
  206. #this is of length 200ish (one for each epoch), then each of those is length 68 (for each region), then each of those is 500 (for each timepoint)
  207. try:
  208. label_ts = mne.extract_label_time_course(stcs=stcs, labels=labels_parc, src=inverse_operator['src'], mode='pca_flip')
  209. except:
  210. label_ts = mne.extract_label_time_course(stcs=stcs, labels=labels_parc, src=inverse_operator['src'], mode='pca_flip',allow_empty=True)
  212. log = []
  213. savelog = connectome_folder + filetosaveprefix + parc + '_errorlog.txt'
  214. warningmessage = 'Source space does not contain any vertices for 1+ label. Allowing empty'
  215. log.append(warningmessage)
  216. print("")
  217. with open(savelog, 'w') as f:
  218. for item in log:
  219. f.write("%s\n" % item)
  220. print(item)
  221. f.close()
  223. for subsection in subsections:
  225. if subsection != 'full':
  226. sublabel_ts = label_ts[:subsection]
  227. else:
  228. sublabel_ts = label_ts.copy()
  231. for fband in fbands:
  233. fmin = fband[0]
  234. fmax = fband[1]
  236. for conmethod in conmethods:
  238. filetosave = connectome_folder + filetosaveprefix + parc + '_' + conmethod + '_' + str(fmin) + 'Hz-' + str(fmax) + 'Hz_' + str(subsection) + 'e.csv'
  239. print("Working on " + filetosave)
  241. corr = []
  243. if conmethod == 'psi':
  245. psi = phase_slope_index(sublabel_ts,sfreq=sfreq,fmin=fmin, fmax=fmax,mt_adaptive=True)
  246. corr = psi.get_data(output='dense')[:, :, 0]
  248. elif conmethod == 'ecpwo':
  250. corr_obj = envelope_correlation(bp_gen(sublabel_ts,fmin,fmax), orthogonalize="pairwise")
  251. corr = corr_obj.combine()
  252. corr = corr.get_data(output="dense")[:, :, 0]
  254. elif conmethod == 'ecso':
  257. label_ts_orth = mne_connectivity.envelope.symmetric_orth(sublabel_ts)
  259. corr_obj = envelope_correlation(bp_gen(label_ts_orth,fmin,fmax), orthogonalize=False)
  260. corr2 = corr_obj.combine()
  261. corr2 = corr2.get_data(output="dense")[:, :, 0]
  262. corr2.flat[:: corr2.shape[0] + 1] = 0 # zero out the diagonal
  263. corr = np.abs(corr2)
  265. else:
  267. con = spectral_connectivity_epochs(sublabel_ts, method=conmethod, mode='multitaper', sfreq=sfreq, fmin=fmin,fmax=fmax, faverage=True, mt_adaptive=True)
  268. corr = con.get_data(output='dense')[:, :, 0]
  271. conmatdf = pd.DataFrame(data=corr)
  272. conmatdf.index = label_names
  273. conmatdf.columns = label_names
  274. conmatdf.to_csv(filetosave)
  279. endtime = time.time()
  280. timepassed = endtime-starttime
  281. timepassed_min = str(round(timepassed/60,2))
  283. print("")
  284. print("")
  285. print("Done for " + filetosaveprefix)
  286. print("Done for " + filetosaveprefix)
  287. print("Time passed for this file = " + timepassed_min + ' min')
  288. print("")
  289. print("")
  290. print("")
  291. print("")
  292. print("")
  293. print("")
  294. print("")
  295. print("")

source_localization_8_june2024.py at commit 234648d, no license · at the source

Overview

Authors: Kirk Graff1,2,3, Shefali Rai1,2,3, Shelly Yin4, Kate J. Godfrey1,2,3, Daria Merrikh2,3,5, Ryann Tansey3,6,7, Tamara Vanderwal8,9, Andrea B. Protzner3,7,10, Signe Bray1,2,3,11
ORCID iDs: Kirk Graff, Shefali Rai, Shelly Yin, Kate J. Godfrey, Ryann Tansey, Tamara Vanderwal, Andrea B. Protzner, Signe Bray
  1. Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada
  2. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
  3. Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
  4. Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
  5. Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
  6. Department of Psychiatry, University of Calgary, Calgary, AB, Canada
  7. Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
  8. Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
  9. BC Children’s Hospital Research Institute, Vancouver, BC, Canada
  10. Department of Psychology, University of Calgary, Calgary, AB, Canada
  11. Department of Radiology, University of Calgary, Calgary, AB, Canada
Institutions: University of Calgary (Canada); Alberta Children's Hospital Research Institute; Hotchkiss Brain Institute (Canada); Mathison Centre for Mental Health Research and Education (Canada); University of British Columbia (Canada); BC Children's Hospital Research Institute (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1245
Dates: received 2 January 2024; accepted 20 April 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1245 · PMID 42253605 · PMCID PMC13237998 · OpenAlex W7160212111
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: EEG, functional connectivity, functional connectome, reliability, validity, volume conduction
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Government of Alberta; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

EEG connectomics research offers the potential to better understand human neurodevelopment, brain disorders, and brain–behavior associations. Several functional connectivity (FC) measures are widely used but few studies have directly compared metrics of reliability across measures and how data quantity influences these properties. Here, we collected a densely sampled dataset from 25 parent–child pairs, with 80 minutes of passive viewing EEG data collected per participant over 4 sessions, and calculated connectomes using 9 popular phase- and envelope-based measures (coherence, COH; phase locking value, PLV; corrected imaginary phase locking value, CIPLV; imaginary coherence, IMCOH; phase-lag index, PLI; weighted phase-lag index, WPLI; envelope correlation with pairwise orthogonalization, ECPWO; and envelope correlation with symmetric orthogonalization, ECSO), including one effective connectivity measure (phase slope index; PSI). We used connectome individualization, derived from fingerprinting-style analyses, as a multivariate reliability metric, as used in fMRI-FC studies. We used simulations with individual head geometry but no “true” connectivity to assess whether identifiability was influenced by volume conduction. We found that COH and PLV were vulnerable to volume conduction influence on identifiability; CIPLV, ECSO, and ECPWO were semi-vulnerable; and IMCOH, WPLI, PLI, and PSI were minimally vulnerable. Next, we considered individualization and reliability of age-group effects with increasing time of data collection. IMCOH had the overall best performance among minimally vulnerable measures, considering individualization and reliability of group effects. We further found that reliability of IMCOH along with other volume conduction-corrected EEG-FC measures continued to improve with up to 25–30 minutes of data. Together, our findings can support study design decisions in EEG connectomics research.

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 9 matches between paragraphs and lines of code.

brayneuroimaginglab/bnl_open

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 234648debc953b86a182613619f64c3b23c74355, 23 March 2025
Languages: Python (93), MATLAB (48), R (18)
Size: 203 files, 159 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (73 files), NumPy (62 files), MNE-Python (33 files), SciPy (32 files), Matplotlib (29 files), cifti-matlab (26 files), Nipype (22 files), FSL (20 files), cowplot (17 files), ggplot2 (17 files), ggpubr (17 files), patchwork (17 files), tidyverse (17 files), ANTs (14 files), statsmodels (13 files), NiBabel (12 files), AFNI (8 files), MNE-Connectivity (7 files), FreeSurfer (6 files), ICLabel (4 files), autoreject (2 files), Statistics and Machine Learning Toolbox (2 files), Pingouin (2 files), reshape2 (2 files), rstatix (2 files), seaborn (2 files), FieldTrip (1 file), GIfTI library for MATLAB (1 file), lme4 (1 file), lmerTest (1 file), Nilearn (1 file), psych (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
160 files
At the source: github.com/BrayNeuroimagingLab/BNL_open/tree/main/EEG_Preprocessing_and_Analysis

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 159 scripts, each with its path and the digest of its content;
  • 9 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

Python scripts and specific details of the videos watched are available at: https://github.com/BrayNeuroimagingLab/BNL_open/tree/main/EEG_Preprocessing_and_Analysis. Connectomes and raw EEG or MRI data are available upon request.

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, pages, dates, 9 authors, 6 keywords, 3 funders, 57 references.

Cite

This paper

Graff, K., Rai, S., Yin, S., Godfrey, K. J., Merrikh, D., Tansey, R., Vanderwal, T., Protzner, A. B., & Bray, S. (2026). Towards precision EEG connectomics: Evaluating the benefits of dense sampling. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1245. https://doi.org/10.1162/imag.a.1245

BibTeX

@article{graff2026towards,
author = {Graff, Kirk and Rai, Shefali and Yin, Shelly and Godfrey, Kate J. and Merrikh, Daria and Tansey, Ryann and Vanderwal, Tamara and Protzner, Andrea B. and Bray, Signe},
title = {{Towards precision EEG connectomics: Evaluating the benefits of dense sampling}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1245},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1245},
url = {https://doi.org/10.1162/imag.a.1245},
pmid = {42253605},
pmcid = {PMC13237998}
}

RIS

TY - JOUR
AU - Graff, Kirk
AU - Rai, Shefali
AU - Yin, Shelly
AU - Godfrey, Kate J.
AU - Merrikh, Daria
AU - Tansey, Ryann
AU - Vanderwal, Tamara
AU - Protzner, Andrea B.
AU - Bray, Signe
TI - Towards precision EEG connectomics: Evaluating the benefits of dense sampling
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/04
VL - 4
SP - IMAG.a.1245
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1245
UR - https://doi.org/10.1162/imag.a.1245
LA - en
ER -

CSL-JSON

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"title": "Towards precision EEG connectomics: Evaluating the benefits of dense sampling",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Graff",
"given": "Kirk"
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"family": "Rai",
"given": "Shefali"
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{
"family": "Yin",
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{
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{
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"given": "Daria"
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{
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{
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{
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"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1245",
"DOI": "10.1162/imag.a.1245",
"PMID": "42253605",
"PMCID": "PMC13237998",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1245",
"language": "en",
"issued": {
"date-parts": [
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2026,
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4
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]
}
}

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[9] doi:10.1038/s41593-026-02228-w [code]
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Journal: Nature neuroscience
In common: Pingouin, rstatix, ANTs, 14 other tools, 1 reference
[10] doi:10.1002/hbm.70605 [code]
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Journal: Human brain mapping
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