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GABA<sub>A</sub> binding correlates with high-frequency EEG: a possible proxy for depolarization in traumatic brain injury.

Code ↔ Paper

4 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 4 matches
  1. [1] § Materials and methods › EEG data acquisition and analysis ↔ generate_Fig1.py, lines 49–96 · score 0.95 · 12–22 Hz, 22–30 Hz, 30–50 Hz, 8–12 Hz, low gamma, high gamma
  2. [2] § Materials and methods › EEG data acquisition and analysis ↔ generate_Fig2.py, lines 25–40 · score 0.94 · 12–22 Hz, 22–30 Hz, 30–50 Hz, 8–12 Hz, low gamma, high gamma
  3. [3] § Materials and methods › Statistical analysis ↔ ANCOVA.py, lines 31–45 · score 0.69 · low gamma, high gamma, low beta, high beta, ANCOVA, theta
  4. [4] § Materials and methods › Statistical analysis ↔ generate_Fig1.py, lines 49–96 · score 0.69 · low gamma, high gamma, low beta, high beta, band power, theta

Paper

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The authors' code

Python · 173 lines · 6.2 KB · no license · 2 matches

  1. from statsmodels.stats.weightstats import ttest_ind as sm_ttest_ind
  2. from statsmodels.stats.multitest import multipletests
  3. import matplotlib.pyplot as plt
  4. import pandas as pd
  5. import numpy as np
  8. ORANGE_COLOR = "orange"
  9. GREEN_COLOR = "green"
  10. def split_violin(ax, data, pos=0, width=0.6):
  12. center = pos
  13. data_left = data[0].dropna()
  14. data_right = data[1].dropna()
  16. # Plot violins with median and extrema shown
  17. right_median = np.median(data_right)
  18. left_median = np.median(data_left)
  20. parts_left = ax.violinplot(data_left, positions=[pos], widths=width,
  21. showmeans=False, showextrema=False, showmedians=False)
  22. parts_right = ax.violinplot(data_right, positions=[pos], widths=width,
  23. showmeans=False, showextrema=False, showmedians=False)
  25. # Split the violins by clipping the density shape
  26. # For left (TBI): keep only left side (x <= center)
  27. if True:
  28. for pc in parts_left['bodies']:
  29. verts = pc.get_paths()[0].vertices
  30. verts[:, 0] = np.minimum(verts[:, 0], center)
  31. pc.set_facecolor(GREEN_COLOR)
  32. pc.set_edgecolor("black")
  33. pc.set_alpha(0.7)
  34. # For right (HC): keep only right side (x >= center)
  35. for pc in parts_right['bodies']:
  36. verts = pc.get_paths()[0].vertices
  37. verts[:, 0] = np.maximum(verts[:, 0], center)
  38. pc.set_facecolor(ORANGE_COLOR)
  39. pc.set_edgecolor("black")
  40. pc.set_alpha(0.9)
  42. ax.plot([pos,pos+0.15],[right_median, right_median], 'k')
  43. ax.plot([pos-0.15,pos],[left_median, left_median], 'k')
  47. df = pd.read_excel("eeg_data.xlsx", sheet_name=['TBI','HC'])
  49. # 2) Prepare for BH correction: collect p-values
  50. pvals_list = []
  52. for ses_inx , ses in enumerate(['V1']):
  53. for inx_reg, chans_regions in enumerate( ['global']): #,'frontal','temporal','parietal','occipital'] ):
  55. fig, ax = plt.subplots(1, 7, figsize=(7, 3) )
  56. freq_ranges = ['1-4Hz','4-8Hz','8-12Hz','12-22Hz','22-30Hz','30-50Hz','50-80Hz']
  57. ax[0].set_ylabel('Relative band power', fontweight='bold')
  58. ax[3].set_xlabel('Conditions', fontweight='bold')
  59. for inx, band in enumerate(['delta','theta','alpha','low_beta','high_beta','low_gamma','high_gamma']):
  60. split_violin(ax[inx], [ df['HC'][chans_regions+'_'+band+' EO '+ses],df['TBI'][chans_regions+'_'+band+' EO '+ses]], pos=0 )
  61. split_violin(ax[inx], [ df['HC'][chans_regions+'_'+band+' EC '+ses],df['TBI'][chans_regions+'_'+band+' EC '+ses]], pos=1 )
  62. ax[inx].set_xticks([0,1], ['EO','EC'])
  63. ax[inx].set_ylim([0,1])
  64. plot_band = band.replace('_'," ").title()
  65. ax[inx].set_title(f'{plot_band}', fontsize=9)
  67. if inx != 0:
  68. ax[inx].set_yticks([])
  69. else:
  70. ax[inx].set_yticks([0,0.2,0.4,0.6,0.8,1],[0,0.2,0.4,0.6,0.8,1],fontsize=8)
  72. ################
  73. # Calculate significance for EO condition
  74. data_HC_EO = df['HC'][chans_regions+'_'+band+' EO '+ses].dropna()
  75. data_TBI_EO = df['TBI'][chans_regions+'_'+band+' EO '+ses].dropna()
  76. age_HC = df['HC']['Age at V1']
  77. age_TBI = df['TBI']['Age at V1']
  79. t_eo, p_val_EO, df_welch_EO = sm_ttest_ind(
  80. data_HC_EO,
  81. data_TBI_EO,
  82. usevar='unequal' # Welch’s test
  83. )
  86. pvals_list.append({'Band': band, 'Condition': 'EO', 'p-value': p_val_EO})
  88. print(f"{band} EO {ses}. Welch’s t-test: t({int(df_welch_EO)}) = {t_eo:.2f}, p = {p_val_EO:.3f}")
  89. if p_val_EO < 0.001:
  90. sig_marker_EO = '***'
  91. elif p_val_EO < 0.01:
  92. sig_marker_EO = '**'
  93. elif p_val_EO < 0.05:
  94. sig_marker_EO = '*'
  95. else:
  96. sig_marker_EO = 'n.s.'
  98. # Draw significance bar for EO above pos=0
  99. # Here the bar spans a little to the left and right of the center
  100. if sig_marker_EO != 'n.s.':
  101. x1 = 0 - 0.15
  102. x2 = 0 + 0.15
  103. y_bar = .875 # vertical position for the bar
  104. bar_height = 0.03
  105. ax[inx].plot([x1, x1, x2, x2], [y_bar, y_bar+bar_height, y_bar+bar_height, y_bar],
  106. lw=1.5, c='k')
  107. ax[inx].text((x1+x2)/2, y_bar+bar_height+0.01, sig_marker_EO, ha='center', va='bottom', color='k')
  109. # Calculate significance for EC condition
  110. data_HC_EC = df['HC'][chans_regions+'_'+band+' EC '+ses].dropna()
  111. data_TBI_EC = df['TBI'][chans_regions+'_'+band+' EC '+ses].dropna()
  113. t_ec, p_val_EC, df_welch_EC = sm_ttest_ind(
  114. data_HC_EC,
  115. data_TBI_EC,
  116. usevar='unequal' # Welch's test
  117. )
  119. pvals_list.append({'Band': band, 'Condition': 'EC', 'p-value': p_val_EC})
  120. print(f"{band} EC {ses}. Welch’s t-test: t({(df_welch_EC)}) = {t_ec:.2f}, p = {p_val_EC:.3f}")
  122. if p_val_EC < 0.001:
  123. sig_marker_EC = '***'
  124. elif p_val_EC < 0.01:
  125. sig_marker_EC = '**'
  126. elif p_val_EC < 0.05:
  127. sig_marker_EC = '*'
  128. else:
  129. sig_marker_EC = 'n.s.'
  131. # Draw significance bar for EC above pos=1
  132. if sig_marker_EC != 'n.s.':
  133. x1 = 1 - 0.15
  134. x2 = 1 + 0.15
  135. ax[inx].plot([x1, x1, x2, x2], [y_bar, y_bar+bar_height, y_bar+bar_height, y_bar],
  136. lw=1.5, c='k')
  137. ax[inx].text((x1+x2)/2, y_bar+bar_height+0.01, sig_marker_EC, ha='center', va='bottom', color='k')
  139. # Make the borders of each subplot gray and thin
  140. for spine in ax[inx].spines.values():
  141. spine.set_edgecolor('gray')
  142. spine.set_linewidth(0.25)
  144. n_hc = len(data_HC_EC)
  145. n_tbi = len(data_TBI_EC)
  147. plt.suptitle(f'Relative band power during eyes opened (EO) and eyes closed (EC) \nresting EEG in controls (n={n_hc}) vs subjects with TBI (n={n_tbi})', fontsize=10, fontweight='bold')
  149. plt.tight_layout()
  150. plt.subplots_adjust(wspace=0, hspace=0) # Set spacing between subplots close to 0
  152. plt.plot(0,0,color=GREEN_COLOR,label=f'Controls')
  153. plt.plot(0,0,color=ORANGE_COLOR,label=f'TBI' )
  154. plt.legend(fontsize=8)
  156. fig.patch.set_edgecolor('black')
  157. fig.patch.set_linewidth(1)
  159. print("Saving figure: figures/fig1.png")
  160. plt.savefig("figures/fig1.png", format="png", dpi=500)
  161. print("Saving figure: figures/fig1.pdf")
  162. plt.savefig("figures/fig1.pdf", format="pdf")
  164. plt.show()
  166. pvals_df = pd.DataFrame(pvals_list)
  167. rej_bh, pvals_bh, _, _ = multipletests(pvals_df['p-value'], alpha=0.05, method='fdr_bh')
  168. pvals_df['p_adj_bh'] = pvals_bh
  169. pvals_df['signif_bh'] = rej_bh
  171. # 5) Print the BH‐corrected results
  172. print("\nBH‐corrected p‑values for Welch t‑tests (alpha=0.05):")
  173. print(pvals_df.to_string(index=False))

generate_Fig1.py at commit 15dcfdc, no license · at the source

Overview

Authors: Sudhin A Shah1,2, Ludvik Alkhoury1, Isabelle Martin1, Ana Radanovic1, Giacomo Scanavini1, Seyed Hani Hojjati1, Gloria C Chiang1, Tracy A Butler1, Yeona Kang3, Keith W Jamison1,4, Amy Kuceyeski1,4, Nicholas D Schiff2
ORCID iDs: Ana Radanovic, Giacomo Scanavini, Seyed Hani Hojjati, Tracy A Butler, Amy Kuceyeski
  1. Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
  2. Department of BMRI & Neurology, Weill Cornell Medicine, New York, NY 10065, USA
  3. Department of Mathematics, Howard University, Washington, DC 20059, USA
  4. Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
Institutions: Weill Cornell Medicine (United States); Howard University (United States); Cornell University (United States)
Journal: Brain communications, volume 8, issue 3, article fcag145
Dates: received 1 May 2025; accepted 23 April 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag145 · PMID 42099303 · PMCID PMC13148767 · OpenAlex W7156927585
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), PET / SPECT (modality), human (organism), traumatic brain injury (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, fMRI & imaging, Physiology & signal measures
Keywords: neuroimaging, cognitive recovery, biomarkers of impairment, neocortical circuits
Topic: Traumatic Brain Injury Research (Epidemiology, Medicine), according to OpenAlex
Funding: NCATS NIH HHS (UL1 TR002384); NIA NIH HHS (R01 AG077576); NINDS NIH HHS (R01 NS102646)
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Traumatic brain injury (TBI) frequently results in long-term cognitive and functional deficits, yet routine diagnostic tools often fail to capture the diffuse network dysfunction and underlying neurochemical changes that contribute to poor recovery. Resting-state EEG is sensitive to injury-related abnormalities in neural oscillations, while [11C]flumazenil PET quantifies gamma-aminobutyric acid (GABAA) receptor availability—a key determinant of inhibitory tone and cortical synchronization—but these modalities are rarely integrated. Linking EEG spectral features to molecular measures of GABAA function may provide translational biomarkers that bridge non-invasive neurophysiological findings with underlying neurochemical status. The primary aim of this study was to determine whether resting-state EEG spectral features, particularly in the high-frequency beta and gamma bands, track longitudinal changes in GABAA receptor availability measured with [11C]flumazenil PET in individuals recovering from TBI. A secondary aim was to characterize the persistence of low-frequency abnormalities (increased delta, reduced alpha) over the first year of recovery and explore their potential relevance as non-invasive markers of network dysfunction. We analysed EEG data from 68 subjects with TBI and 75 non-brain-injured controls; longitudinal follow-up EEG data were available for 37 TBI participants and 20 non-brain-injured controls. We found that the TBI subjects exhibited significantly higher delta power and lower alpha power; this remained at the chronic visit. Among the longitudinally studied subjects, a subset of seven TBI participants and six non-brain-injured controls were studied with [11C]flumazenil PET data. We found strong positive correlations between longitudinal changes in [11C]flumazenil PET measured GABAA receptor availability and concurrent changes in EEG high beta (r2 = 0.79, P < 0.01) as well as low and high gamma power (r2 = 0.71, P < 0.05; r2 = 0.77, P < 0.01) in TBI subjects for the ‘eyes-open’ condition. These exploratory findings provide preliminary evidence that GABAA receptor availability, measured via [11C] flumazenil PET, is associated with high-frequency EEG power in TBI. This PET–EEG coupling may reflect underlying changes in excitatory–inhibitory network balance consistent with restoration of fronto-striatal arousal and neuronal membrane ‘tone’ under the mesocircuit model, although the small sample size of the cohort with multimodal measurements warrants cautious interpretation and further replication. Nonetheless, the observations in this cohort are consistent with a key role of increasing inhibitory activity across fronto-striatal neurons and networks in recovery from TBI.

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 4 matches between paragraphs and lines of code.

S-Shah-Lab/GABAABindingCorrelates

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 15dcfdc1ad18bfd74e5eb3e8373149b66b47d729, 26 January 2026
Languages: Python (5)
Size: 22 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (4 files), NumPy (4 files), pandas (4 files), statsmodels (3 files), Pingouin (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/S-Shah-Lab/GABAABindingCorrelates

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;
  • 5 scripts, each with its path and the digest of its content;
  • 4 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 availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request. The code used for our analysis can be found at this link: https://github.com/S-Shah-Lab/GABAABindingCorrelates.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 4 keywords, 3 funders, 54 references.

Cite

This paper

Shah, S. A., Alkhoury, L., Martin, I., Radanovic, A., Scanavini, G., Hojjati, S. H., Chiang, G. C., Butler, T. A., Kang, Y., Jamison, K. W., Kuceyeski, A., & Schiff, N. D. (2026). GABA&lt;sub&gt;A&lt;/sub&gt; binding correlates with high-frequency EEG: a possible proxy for depolarization in traumatic brain injury. Brain communications, 8(3), fcag145. https://doi.org/10.1093/braincomms/fcag145

BibTeX

@article{shah2026gaba,
author = {Shah, Sudhin A and Alkhoury, Ludvik and Martin, Isabelle and Radanovic, Ana and Scanavini, Giacomo and Hojjati, Seyed Hani and Chiang, Gloria C and Butler, Tracy A and Kang, Yeona and Jamison, Keith W and Kuceyeski, Amy and Schiff, Nicholas D},
title = {{GABA\&lt;sub\&gt;A\&lt;/sub\&gt; binding correlates with high-frequency EEG: a possible proxy for depolarization in traumatic brain injury}},
journal = {Brain communications},
year = {2026},
month = apr,
volume = {8},
number = {3},
pages = {fcag145},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag145},
url = {https://doi.org/10.1093/braincomms/fcag145},
pmid = {42099303},
pmcid = {PMC13148767}
}

RIS

TY - JOUR
AU - Shah, Sudhin A
AU - Alkhoury, Ludvik
AU - Martin, Isabelle
AU - Radanovic, Ana
AU - Scanavini, Giacomo
AU - Hojjati, Seyed Hani
AU - Chiang, Gloria C
AU - Butler, Tracy A
AU - Kang, Yeona
AU - Jamison, Keith W
AU - Kuceyeski, Amy
AU - Schiff, Nicholas D
TI - GABA&lt;sub&gt;A&lt;/sub&gt; binding correlates with high-frequency EEG: a possible proxy for depolarization in traumatic brain injury
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/04/27
VL - 8
IS - 3
SP - fcag145
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag145
UR - https://doi.org/10.1093/braincomms/fcag145
LA - en
ER -

CSL-JSON

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"id": "10.1093/braincomms/fcag145",
"type": "article-journal",
"title": "GABA&lt;sub&gt;A&lt;/sub&gt; binding correlates with high-frequency EEG: a possible proxy for depolarization in traumatic brain injury",
"container-title": "Brain communications",
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{
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"given": "Seyed Hani"
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{
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},
{
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"PMCID": "PMC13148767",
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"date-parts": [
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