OSCR

Developmental disinhibition gates language lateralization in childhood.

Code ↔ Paper

26 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 26 matches
  1. [1] § Methods › Empirical data pre-processing and analysis › Diffusion MRI ↔ Code/ModelInputs/3_WeightsfromDwMRI.sh, lines 89–128 · score 0.95 · Gibbs ringing, Bias field, multi shell, MRtrix, CSD, CSF
  2. [2] § Methods › Model fitting and parameter estimation using MEG data ↔ examples/eg__ismail2025.py, lines 113–159 · score 0.87 · Talairach Daemon database, right Heschl, ROI mask, verb generation, gyri, auditory
  3. [3] § Methods › Model fitting and parameter estimation using MEG data ↔ examples/eg__ismail2026.py, lines 101–147 · score 0.87 · Talairach Daemon database, right Heschl, ROI mask, verb generation, gyri, auditory
  4. [4] § Methods › Computational brain network models ↔ whobpyt/models/jansen_rit/jansen_rit.py, lines 42–104 · score 0.79 · Jansen Rit, pyramidal cells, neural mass model, excitatory interneurons, inhibitory interneurons, JR
  5. [5] § Methods › Task-related beta ERD/ERS quantification ↔ Code/Analysis/noun_noise_beta_power_bars.py, lines 34–57 · score 0.78 · Power spectral density, 700–1200 ms, beta power, 700 ms, noun, window
  6. [6] § Methods › Computational brain network models ↔ whobpyt/depr/momi2025/jansen_rit.py, lines 289–352 · score 0.77 · pyramidal cells, neural mass model, excitatory interneurons, Jansen Rit, inhibitory interneurons, JR
  7. [7] § Methods › Empirical data pre-processing and analysis › MEG ↔ Code/ModelInputs/1_EarlyEvokedMEG.m, lines 55–115 · score 0.76 · jump artifacts, FieldTrip, demeaned, ICA, filter, Component
  8. [8] § Methods › Model fitting and parameter estimation using MEG data ↔ whobpyt/models/jansen_rit/jansen_rit.py, lines 326–403 · score 0.71 · forward Euler integration, Jansen Rit, neural populations, PyTorch, batches, JR
  9. [9] § Methods › Model fitting and parameter estimation using MEG data ↔ whobpyt/depr/momi2025/jansen_rit.py, lines 546–626 · score 0.71 · forward Euler integration, Jansen Rit, neural populations, PyTorch, batches, JR
  10. [10] § Methods › Model beta ERD/S prediction ↔ Code/Analysis/noun_noise_beta_power_bars.py, lines 34–57 · score 0.68 · 700–1200 ms, Beta power, Welch, 700 ms, noun, window
  11. [11] § Results › Local inhibition gates effects of interhemispheric inhibition on lateralization ↔ Code/Analysis/noun_noise_beta_power_bars.py, lines 162–170 · score 0.60 · noun noise beta, adolescent simulations, beta power, simulated beta, lateralization
  12. [12] § Methods › Computational brain network models ↔ whobpyt/models/jansen_rit/jansen_rit.py, lines 42–104 · score 0.60 · pyramidal cell, excitatory interneuron, inhibitory interneuron, modeled
  13. [13] § Methods › Computational brain network models ↔ whobpyt/depr/momi2025/jansen_rit.py, lines 289–352 · score 0.59 · pyramidal cell, excitatory interneuron, inhibitory interneuron, modeled
  14. [14] § Methods › Computational brain network models ↔ whobpyt/depr/momi2025/jansen_rit.py, lines 546–626 · score 0.59 · conduction velocity, pyramidal population, voltage, delays, external, excitatory
  15. [15] § Methods › Task-related beta ERD/ERS quantification ↔ examples/eg__ismail2025.py, lines 202–227 · score 0.59 · 700–1200 ms, beta power, noise trials, 700 ms, MEG, simulation
  16. [16] § Methods › Empirical data pre-processing and analysis › MEG ↔ Code/ModelInputs/3_WeightsfromDwMRI.sh, lines 130–187 · score 0.58 · Shen atlas, individual space, transformations, linear, matrices, brain
  17. [17] § Methods › Statistical analyses ↔ Code/Analysis/noun_noise_beta_power_bars.py, lines 98–126 · score 0.57 · noun noise beta, right frontal, power
  18. [18] § Methods › Model beta ERD/S prediction ↔ examples/eg__ismail2025.py, lines 202–227 · score 0.57 · 700–1200 ms, Beta power, 700 ms, noise, simulations, model
  19. [19] § Methods › Computational brain network models ↔ whobpyt/models/jansen_rit/jansen_rit.py, lines 447–495 · score 0.56 · firing rate, neural population, pulse, wave, sigmoid, network
  20. [20] § Methods › Computational brain network models ↔ whobpyt/depr/momi2025/jansen_rit.py, lines 669–755 · score 0.56 · firing rate, neural population, pulse, wave, sigmoid, network
  21. [21] § Methods › Empirical data pre-processing and analysis › MEG ↔ Code/ModelInputs/2_distancemat.m, lines 111–163 · score 0.56 · Shen atlas, individual space, centroids, matrices, models
  22. [22] § Results › Inhibitory circuit engagement varies with age, task condition, and lateralization ↔ Code/Analysis/local_kde_plots.py, lines 5–65 · score 0.54 · kernel density, Young children, Local inhibition, age, adolescents
  23. [23] § Results › Local inhibition gates effects of interhemispheric inhibition on lateralization ↔ Code/Analysis/local_kde_plots.py, lines 5–65 · score 0.54 · kernel density, young children, local inhibition, adolescents
  24. [24] § Methods › Computational brain network models ↔ Code/ModelInputs/4_SourceLocalization_Leadfield.m, lines 1–9 · score 0.52 · FieldTrip, leadfield matrix, Computational, MEG, activity, model
  25. [25] § Methods › Computational brain network models ↔ Code/JR_Model_Fitting.py, lines 595–645 · score 0.52 · JR model, firing rate, sigmoid, v0
  26. [26] § Results › Early auditory neural responses predict lateralized expressive language activity across age ↔ Code/Analysis/noun_noise_beta_power_bars.py, lines 172–180 · score 0.50 · noun noise beta, young children, beta power, lateralization, empirical, simulated

Paper

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The authors' code

Python · 181 lines · 7.8 KB · no license · 5 matches

  1. import numpy as np
  2. import pandas as pd
  3. import matplotlib.pyplot as plt
  4. import seaborn as sns
  5. import scipy.signal
  6. from scipy import stats
  8. # Sampling parameters
  9. fs = 1000 # Sampling frequency (Hz)
  10. nperseg = 512 # Segment length (500 ms)
  11. noverlap = 256 # 50% overlap
  13. # Index of frequency range for beta power corresponding to (13-30 Hz)
  14. start_freq = 7
  15. end_freq = 16
  17. # Define frontal ROIs of shen atlas based on mask (subtract 1 for Python indexing)
  18. frontal_rois = np.array([2, 7, 10, 17, 18, 24, 25, 26, 28, 30, 31, 33,
  19. 37, 38, 42, 50, 56, 59, 61, 62, 65, 66, 68, 71, 77,
  20. 78, 83, 91, 92, 94, 96, 98, 99, 100, 101, 102, 103,
  21. 108, 110, 113, 117, 125, 126, 129, 132, 133, 135, 137,
  22. 140, 142, 150, 158, 161, 172, 178, 180, 182, 183]) - 1
  24. # Separate left and right hemisphere indices
  25. right_frontal_idx = frontal_rois[frontal_rois < 93]
  26. left_frontal_idx = frontal_rois[frontal_rois > 93]
  28. # Initialize storage lists
  29. adol_emp_verb_psd, adol_emp_noise_psd = [], []
  30. yc_emp_verb_psd, yc_emp_noise_psd = [], []
  31. adol_sim_verb_psd, adol_sim_noise_psd = [], []
  32. yc_sim_verb_psd, yc_sim_noise_psd = [], []
  34. # Compute power spectral density (PSD) using Welch's method for 700-1200 ms window (time series are -500 to 1500 ms)
  35. for i in range(len(Adol_subs)):
  36. emp_verb = scipy.signal.welch(Adol_emp_verb[i][:, :, 1200:1700], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  37. emp_noise = scipy.signal.welch(Adol_emp_noise[i][:, :, 1200:1700], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  39. sim_verb = scipy.signal.welch(Adol_sim_verb[i][:, 800:1300], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  40. sim_noise = scipy.signal.welch(Adol_sim_noise[i][:, 800:1300], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  42. adol_emp_verb_psd.append(emp_verb)
  43. adol_emp_noise_psd.append(emp_noise)
  44. adol_sim_verb_psd.append(sim_verb)
  45. adol_sim_noise_psd.append(sim_noise)
  47. for i in range(len(YC_subs)):
  48. emp_verb = scipy.signal.welch(YC_emp_verb[i][:, :, 1200:1700], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  49. emp_noise = scipy.signal.welch(YC_emp_noise[i][:, :, 1200:1700], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  51. sim_verb = scipy.signal.welch(YC_sim_verb[i][:, 800:1300], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  52. sim_noise = scipy.signal.welch(YC_sim_noise[i][:, 800:1300], fs=fs, noverlap=noverlap, nperseg=nperseg, detrend='linear')
  54. yc_emp_verb_psd.append(emp_verb)
  55. yc_emp_noise_psd.append(emp_noise)
  56. yc_sim_verb_psd.append(sim_verb)
  57. yc_sim_noise_psd.append(sim_noise)
  59. # Compute beta power averages
  60. def compute_beta_power_difference(emp_verb_psd, emp_noise_psd, sim_verb_psd, sim_noise_psd):
  61. emp_verb_beta, emp_noise_beta = [], []
  62. sim_verb_beta, sim_noise_beta = [], []
  64. for i in range(len(emp_verb_psd)):
  65. emp_verb_beta.append(np.mean(emp_verb_psd[i][1][:, :, start_freq:end_freq], axis=(2))[frontal_rois])
  66. emp_noise_beta.append(np.mean(emp_noise_psd[i][1][:, :, start_freq:end_freq], axis=(2))[frontal_rois])
  68. sim_verb_beta.append(np.mean(sim_verb_psd[i][1][:, start_freq:end_freq], axis=1)[frontal_rois])
  69. sim_noise_beta.append(np.mean(sim_noise_psd[i][1][:, start_freq:end_freq], axis=1)[frontal_rois])
  71. emp_beta_diff = np.array(emp_verb_beta) - np.array(emp_noise_beta)
  72. sim_beta_diff = np.array(sim_verb_beta) - np.array(sim_noise_beta)
  74. return emp_beta_diff, sim_beta_diff
  76. # Compute beta power differences for both groups
  77. adol_emp_beta_diff, adol_sim_beta_diff = compute_beta_power_difference(adol_emp_verb_psd, adol_emp_noise_psd, adol_sim_verb_psd, adol_sim_noise_psd)
  78. yc_emp_beta_diff, yc_sim_beta_diff = compute_beta_power_difference(yc_emp_verb_psd, yc_emp_noise_psd, yc_sim_verb_psd, yc_sim_noise_psd)
  80. # Compute mean beta power for left and right frontal regions
  81. def compute_mean_beta_per_hemisphere(emp_beta_diff, sim_beta_diff):
  82. right_emp_avg = np.mean(emp_beta_diff[:, right_frontal_idx], axis=1)
  83. left_emp_avg = np.mean(emp_beta_diff[:, left_frontal_idx], axis=1)
  85. right_sim_avg = np.mean(sim_beta_diff[:, right_frontal_idx], axis=1)
  86. left_sim_avg = np.mean(sim_beta_diff[:, left_frontal_idx], axis=1)
  88. return pd.DataFrame({'Empirical_Left': left_emp_avg, 'Empirical_Right': right_emp_avg,
  89. 'Simulated_Left': left_sim_avg, 'Simulated_Right': right_sim_avg})
  91. adol_beta_df = compute_mean_beta_per_hemisphere(adol_emp_beta_diff, adol_sim_beta_diff)
  92. yc_beta_df = compute_mean_beta_per_hemisphere(yc_emp_beta_diff, yc_sim_beta_diff)
  94. # Normalize with sign preservation
  95. adol_beta_df = adol_beta_df.apply(lambda x: x / np.abs(x).max())
  96. yc_beta_df = yc_beta_df.apply(lambda x: x / np.abs(x).max())
  98. # Function to plot results
  99. def plot_beta_barplot(beta_df, title, filename):
  100. mean_values = beta_df.mean()
  101. sem_values = beta_df.sem()
  103. labels = ['Empirical', 'Simulated']
  104. x = np.arange(len(labels))
  105. width = 0.35
  107. fig, ax = plt.subplots(figsize=(3, 3), dpi=300)
  109. ax.bar(x - width / 2, [mean_values['Empirical_Left'], mean_values['Simulated_Left']], width,
  110. yerr=[sem_values['Empirical_Left'], sem_values['Simulated_Left']], label='Left Frontal',
  111. capsize=5, color='#6a9ef9', edgecolor='#6a9ef9')
  113. ax.bar(x + width / 2, [mean_values['Empirical_Right'], mean_values['Simulated_Right']], width,
  114. yerr=[sem_values['Empirical_Right'], sem_values['Simulated_Right']], label='Right Frontal',
  115. capsize=5, color='#e97773', edgecolor='#e97773')
  117. ax.set_ylabel('Noun-Noise Beta Power', fontsize=14)
  118. ax.set_xticks(x)
  119. ax.set_xticklabels(labels)
  120. plt.axhline(0, color='black', linewidth=1)
  121. plt.xticks(fontsize=12)
  122. plt.yticks(fontsize=12)
  123. ax.set_ylim(-0.6, 0.6)
  125. fig.savefig(filename, dpi=300, bbox_inches='tight')
  126. plt.show()
  128. # Plot results
  129. plot_beta_barplot(adol_beta_df, "Adolescents Beta Power", "adol_beta_plot.png")
  130. plot_beta_barplot(yc_beta_df, "Young Children Beta Power", "yc_beta_plot.png")
  132. import numpy as np
  134. # Function to compute Laterality Index (LI)
  135. def compute_laterality_index(beta_power_diff):
  136. """
  137. Computes the Laterality Index (LI) based on beta power differences
  138. between left and right frontal regions.
  140. LI = (# Positive in Right + # Negative in Left) - (# Negative in Right + # Positive in Left)
  141. --------------------------------------------------------------------------------------
  142. Total Number of Regions (58)
  144. Args:
  145. beta_power_diff (numpy array): Array of beta power differences (size 58)
  147. Returns:
  148. float: Computed Laterality Index (LI)
  149. """
  150. num_pos_right = np.sum(beta_power_diff[:29] > 0)
  151. num_neg_right = np.sum(beta_power_diff[:29] <= 0)
  153. num_pos_left = np.sum(beta_power_diff[29:] > 0)
  154. num_neg_left = np.sum(beta_power_diff[29:] <= 0)
  156. # Compute LI
  157. num = (num_pos_right + num_neg_left) - (num_neg_right + num_pos_left)
  158. den = len(beta_power_diff) # 58 regions in total
  160. return num / den if den != 0 else float('nan')
  162. # Compute LI for Adolescents (Empirical vs. Simulated)
  163. adol_emp_beta_diff = np.mean(bytrial_avg_adols_fr_beta_diff, axis=0)
  164. adol_sim_beta_diff = np.mean(adols_sim_beta_diff, axis=0)
  166. adol_emp_LI = compute_laterality_index(adol_emp_beta_diff)
  167. adol_sim_LI = compute_laterality_index(adol_sim_beta_diff)
  169. print(f"Adolescent Empirical LI: {adol_emp_LI:.4f}")
  170. print(f"Adolescent Simulated LI: {adol_sim_LI:.4f}")
  172. # Compute LI for Young Children (Empirical vs. Simulated)
  173. yc_emp_beta_diff = np.mean(bytrial_avg_yc_fr_beta_diff, axis=0)
  174. yc_sim_beta_diff = np.mean(yc_sim_beta_diff, axis=0)
  176. yc_emp_LI = compute_laterality_index(yc_emp_beta_diff)
  177. yc_sim_LI = compute_laterality_index(yc_sim_beta_diff)
  179. print(f"Young Children Empirical LI: {yc_emp_LI:.4f}")
  180. print(f"Young Children Simulated LI: {yc_sim_LI:.4f}")

noun_noise_beta_power_bars.py at commit b906277, no license · at the source

Overview

Authors: Minarose M Ismail1,2, Davide Momi3, Zheng Wang3, Sorenza P Bastiaens3,4, M Parsa Oveisi3,4, Hansel M Greiner5, Donald J Mabbott2,6, John D Griffiths3,4,7, Darren S Kadis1,2
ORCID iDs: Minarose M Ismail, Davide Momi, Zheng Wang, Sorenza P Bastiaens, M Parsa Oveisi, Donald J Mabbott, Darren S Kadis
  1. Department of Physiology, University of Toronto, Toronto, ON Canada
  2. Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON Canada
  3. Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON Canada
  4. Institute of Medical Sciences, University of Toronto, Toronto, ON Canada
  5. Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH USA
  6. Department of Psychology, University of Toronto, Toronto, ON Canada
  7. Department of Psychiatry, University of Toronto, Toronto, ON Canada
Institutions: University of Toronto (Canada); Hospital for Sick Children (Canada); Centre for Addiction and Mental Health (Canada); University of Cincinnati (United States)
Journal: Nature communications, volume 17, issue 1, article 5819
Dates: received 16 July 2025; accepted 31 March 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71918-7 · PMID 42049727 · PMCID PMC13328637 · OpenAlex W7156930890
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Network models, Neurophysiology, Language
MeSH: Brain*, Functional Laterality*, Language*, Language Development*, Adolescent, Auditory Perception, Child, Child, Preschool, Female, Humans, Male, Models, Neurological, Pyramidal Cells (* major topic)
Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (R21NS106631)
Citations: not cited yet (Europe PMC); 107 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 26 matches between paragraphs and lines of code.

Minarose/WBMLangBetaERDS

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b906277548dc2a45b2f13681761012b089d77c83, 15 July 2026
Languages: Python (9), MATLAB (3), Shell (1)
Size: 350 files, 13 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (7 files), pandas (7 files), Matplotlib (6 files), SciPy (4 files), seaborn (4 files), PyTorch (3 files), FieldTrip (2 files), scikit-learn (2 files), ANTs (1 file), dcm2niix (1 file), FreeSurfer (1 file), FSL (1 file), MNE-Python (1 file), MRtrix3 (1 file), SPM (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
14 files
At the source: github.com/Minarose/WBMLangBetaERDS

griffithslab/whobpyt

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 0fd8b9c446eb64fd3d623b2e0ab4c169520b43cf, 26 July 2026
Languages: Python (57)
Size: 131 files, 57 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, setup.py, .devcontainer/python-cpu-devcontainer/devcontainer.json, .devcontainer/python-docs-devcontainer/devcontainer.json, .devcontainer/python-gpu-devcontainer/devcontainer.json), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: PyTorch (31 files), NumPy (26 files), Matplotlib (10 files), scikit-learn (9 files), pandas (8 files), SciPy (7 files), seaborn (7 files), MNE-Python (6 files), NiBabel (1 file), Nilearn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
59 files
At the source: github.com/griffithslab/whobpyt

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Read it in the paper: doi.org/10.1038/s41467-026-71918-7.

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Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 13 MeSH terms, 1 funder, 102 references.

Cite

This paper

Ismail, M. M., Momi, D., Wang, Z., Bastiaens, S. P., Oveisi, M. P., Greiner, H. M., Mabbott, D. J., Griffiths, J. D., & Kadis, D. S. (2026). Developmental disinhibition gates language lateralization in childhood. Nature communications, 17(1), 5819. https://doi.org/10.1038/s41467-026-71918-7

BibTeX

@article{ismail2026developmental,
author = {Ismail, Minarose M and Momi, Davide and Wang, Zheng and Bastiaens, Sorenza P and Oveisi, M Parsa and Greiner, Hansel M and Mabbott, Donald J and Griffiths, John D and Kadis, Darren S},
title = {{Developmental disinhibition gates language lateralization in childhood}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5819},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71918-7},
url = {https://doi.org/10.1038/s41467-026-71918-7},
pmid = {42049727},
pmcid = {PMC13328637}
}

RIS

TY - JOUR
AU - Ismail, Minarose M
AU - Momi, Davide
AU - Wang, Zheng
AU - Bastiaens, Sorenza P
AU - Oveisi, M Parsa
AU - Greiner, Hansel M
AU - Mabbott, Donald J
AU - Griffiths, John D
AU - Kadis, Darren S
TI - Developmental disinhibition gates language lateralization in childhood
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/28
VL - 17
IS - 1
SP - 5819
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71918-7
UR - https://doi.org/10.1038/s41467-026-71918-7
LA - en
ER -

CSL-JSON

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"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71918-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
28
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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