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Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental 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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Ripple detection and analysis ↔ Code/batch_analysis.m, lines 36–68 · score 0.66 · continuous wavelet transform, blobs, cycles, maps, detection, 80 Hz
  2. [2] § Methods › Power analyses ↔ Code/Functions/cwt_power_analysis.m, the whole file · a weak match · score 0.64 · filter bank, bump, cwt, octave, magnitude, wavelet
  3. [3] § Methods › Entrainment analyses ↔ Code/Functions/circ_r.m, the whole file · a weak match · score 0.62 · circular statistics, vector length, CircStat, MATLAB, angle, bins
  4. [4] § Methods › Power analyses ↔ Code/batch_analysis.m, lines 36–68 · score 0.58 · continuous wavelet transform, octave, magnitude, cwt, threshold, Power

Paper

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

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The authors' code

MATLAB · 140 lines · 7.9 KB · MIT · 2 matches

  1. clc;
  2. clear;
  3. %%
  4. current_folder=pwd;
  5. addpath('.\Functions');
  6. data_root=fullfile([current_folder(1),'Data']);
  7. %%
  8. folders=dir(data_root);
  9. folders=folders([folders.isdir]);
  10. %%
  11. velocity_threshold= 10; % Setting velocity threshold for moving vs. still
  12. %%
  13. analyze_power=true;
  14. %%
  15. analyze_PAC=true;
  16. if analyze_PAC
  17. channel_phase=12; % Channel for phase
  18. channel_amplitude=12; % Channel for amplitude
  19. phase_freq_range=logspace(log10(1),log10(15),40); % Frequencies for the phases to be checked
  20. amp_freq_range=logspace(log10(20),log10(180),20); % Frequencies for the amplitude to be checked
  21. phase_freq_width=1; % Width of frequency bins for phase
  22. amp_freq_width=20; % Width of frequency bins for amplitude
  23. num_bins=18; % Number of frequency binds
  24. num_perm_PAC=500; % Number of permutations
  25. n_down=5; % Second downsampling ratio
  26. end
  27. %%
  28. analyze_entrainment=false;
  29. if analyze_entrainment
  30. franges = [30;59];
  31. num_phases=length(franges);
  32. num_perm_Ent = 500;
  33. unit_numer=[1;4];
  34. channel_number=[10;12];
  35. end
  36. %%
  37. analyze_SWR=true;
  38. if analyze_SWR
  39. parameters.channels= 1:64; % Channels to loop through
  40. parameters.fs= 3000; % Sampling frequency
  41. parameters.chunks= 1; % Breaking data into smaller chunks
  42. parameters.verbose= false; % Display some information
  43. parameters.save_map=true;
  44. % Continuous wavelet transform (CWT) parameters
  45. parameters.wavelet= 'morse'; % Type of wavelet
  46. parameters.WaveletParameters= [9,120]; % Parameters of wavelet.
  47. parameters.VoicesPerOctave= 48; % Voices per octave
  48. parameters.FrequencyLimits= [95,250]; % Frequncy range for CWT analysis
  49. parameters.gpu= true; % Using gpu for CWT
  50. parameters.save_map=true;
  51. % Parameters for detecting and characterizing blobs
  52. parameters.compare= 'magnitude'; % Use "amplidute" or "power" for comparison
  53. parameters.center= 'max'; % Use "mean" or "max" as the center
  54. parameters.unimodal= false; % Making sure detected blobs are unimodal
  55. % Ripple detection parameters
  56. parameters.range=[80,250;250,500]; % Cutoff threshold
  57. parameters.ecdf= [0.993,0.998]; % Frequency range to detect ripples
  58. parameters.n_cyles= 3; % Number of cycles a ripple must have at the "center" frequency
  59. parameters.frequency_range_th= 98/100; % Allowable difference between lowest and higher frequncy of a blob
  60. parameters.std= 2; % Threshold that a blob mean/max needs to be compared to all the data at its central frequency
  61. parameters.ext= 1e6; % Upper limit allowed for comparison
  62. parameters.smoothing_window=10;
  63. % Detecting HFOs
  64. parameters_SW.lower=8;
  65. parameters_SW.upper=40;
  66. parameters_SW.fs=3000;
  67. parameters_SW.order=2;
  68. end
  69. %% Looping through folders
  70. for i=1:size(folders,1)
  71. animal_name=folders(i).name;
  72. % Going through the folders for each animal
  73. if startsWith(animal_name,'Animal1')
  74. disp(animal_name)
  75. data_path=fullfile(data_root,animal_name);
  76. day_folders=dir(data_path);
  77. day_folders=day_folders([day_folders.isdir]);
  78. for j=1:size(day_folders,1)
  79. day_name=day_folders(j).name;
  80. % Going through each day of recording for each animal
  81. if startsWith(day_name,'Day1')
  82. disp(day_name)
  83. data_path=fullfile(data_root,animal_name,day_name);
  84. condition_folders=dir(data_path);
  85. condition_folders=condition_folders([condition_folders.isdir]);
  86. for k=1:size(condition_folders,1)
  87. condition_name=condition_folders(k).name;
  88. % Going through each condition (Familiar/Novel) for
  89. % each animal
  90. if ~startsWith(condition_name,'.')
  91. disp(condition_name);
  92. data_path=fullfile(data_root,animal_name,day_name,condition_name);
  93. data=load(fullfile(data_root,animal_name,day_name,condition_name,'Data3k.mat')); % Loading the downsampled recording for each animal-day-condition
  94. fs=data.fs;
  95. data=data.Data3k;
  96. velocity=load(fullfile(data_root,animal_name,day_name,condition_name,'InstVelocity_3k.mat')).velocity3k; % Loading the instantaneous velocity for each animal-day-condition
  97. if analyze_power
  98. disp('Power analysis')
  99. results=cwt_power_analysis(data,fs,velocity,velocity_threshold);
  100. path_to_save=fullfile(data_root,animal_name,day_name,condition_name,'cwt_power_analysis_test');
  101. save(path_to_save,'results','velocity_threshold','-v7.3');
  102. end
  103. if analyze_PAC
  104. disp('PAC analysis')
  105. data_phase=data(channel_phase,:);
  106. data_amplitude=data(channel_amplitude,:);
  107. [MI_more,MI_sh_more,p_more,p_sh_more,MVL_more,MVL_sh_more,MA_more,MA_sh_more,MI_less,MI_sh_less,p_less,p_sh_less,MVL_less,MVL_sh_less,MA_less,MA_sh_less] = PAC_analysis( ...
  108. data_phase,data_amplitude,fs,phase_freq_range,phase_freq_width,amp_freq_range,amp_freq_width,num_bins,num_perm_PAC,velocity,velocity_threshold,n_down);
  109. path_to_save=fullfile(data_root,animal_name,day_name,condition_name,'PAC_analysis_test');
  110. save(path_to_save,'MI_more','MI_sh_more','p_more','p_sh_more','MVL_more','MVL_sh_more','MA_more','MA_sh_more','MI_less','MI_sh_less','p_less','p_sh_less','MVL_less','MVL_sh_less','MA_less','MA_sh_less','velocity_threshold','-v7.3');
  111. end
  112. if analyze_entrainment
  113. disp('Entrainment analysis')
  114. Units=load(fullfile(data_root,animal_name,day_name,condition_name,'Units.mat')).spike_times;
  115. for i_unit=unit_numer
  116. current_unit=Units(i_unit);
  117. signal=data(channel_number(i_unit),:);
  118. [MVL_more,MVL_less,mAng_more,mAng_less,entBinned_more,entBinned_less,MI_more,MI_less,MVL_more_sh,MVL_less_sh,mAng_more_sh,mAng_less_sh,entBinned_more_sh,entBinned_less_sh,MI_more_sh,MI_less_sh]=entrainment_analysis( ...
  119. signal,current_unit,fs,franges,velocity,velocity_threshold);
  120. path_to_save=fullfile(data_root,animal_name,day_name,condition_name,sprintf('Ent_Unit_%0.0f_Channel_%0.0f',i_unit,channel_number(i_unit)));
  121. save(path_to_save,'MVL_more','MVL_less','mAng_more','mAng_less','entBinned_more','entBinned_less','MI_more','MI_less','MVL_more_sh','MVL_less_sh','mAng_more_sh','mAng_less_sh','entBinned_more_sh','entBinned_less_sh','MI_more_sh','MI_less_sh','franges','velocity_threshold','-v7.3');
  122. end
  123. end
  124. if analyze_SWR
  125. disp('SWR analysis')
  126. [results,results_map]= HFO_detector_sm(data,parameters); % Calling HFO detector function
  127. path_to_save=fullfile(data_root,animal_name,day_name,condition_name,'HFOs_events_test');
  128. save(path_to_save,'results','-v7.3');
  129. path_to_save=fullfile(data_root,animal_name,day_name,condition_name,'HFOs_maps_test');
  130. save(path_to_save,'results_map','-v7.3');
  131. SW=sharp_wave_detector(samples,parameters_SW);
  132. path_to_save=fullfile(data_root,animal_name,day_name,condition_name,'Sharp_waves_test');
  133. save(path_to_save,'SW','-v7.3');
  134. end
  135. end
  136. end
  137. end
  138. end
  139. end
  140. end

batch_analysis.m at commit bf43e2c, under MIT · at the source

Overview

Authors: Christopher D Adam1, Ehsan Mirzakhalili1, Kimberly G Gagnon1, Carlo Cottone1, John D Arena1, Alexandra Ulyanova1,2, Victoria E Johnson1, John A Wolf1,2
ORCID iDs: Christopher D Adam, Ehsan Mirzakhalili, John D Arena, John A Wolf
  1. Center for Brain Injury and Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, United States
  2. Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, United States
Institutions: University of Pennsylvania (United States); Veterans Health Administration (United States); Philadelphia VA Medical Center (United States)
Journal: eLife, volume 13, article RP100642
Dates: published online 12 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.100642 · PMID 42284087 · PMCID PMC13263039 · OpenAlex W4402780104
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism), traumatic brain injury (population), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Rat
MeSH: Action Potentials*, Brain Injuries, Traumatic*, CA1 Region, Hippocampal*, Gamma Rhythm*, Hippocampus*, Theta Rhythm*, Animals, Disease Models, Animal, Interneurons, Male, Pyramidal Cells, Rats (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: United States Department of Veterans Affairs (RX002705); RRD VA (I01 RX002705); NINDS NIH HHS (NS043126, NS101108, R01 NS101108)
Citations: cited by 2 papers (Europe PMC); 163 references in the paper
Research resources: Young healthy adult male Long Evans rats RRID:RGD_2308852, RRID:SCR_000441, RRID:SCR_014480, RRID:SCR_016651

Abstract

Traumatic brain injury (TBI) often results in persistent learning and memory deficits, likely due to disrupted hippocampal circuitry underlying these processes. Precise temporal control of hippocampal neuronal activity is thought to be important for memory encoding and retrieval and is supported by oscillations that dynamically organize single-unit firing. Using high-density laminar electrophysiology, we found a loss of oscillatory power across CA1 lamina, with a profound, layer-specific reduction in theta-gamma phase-amplitude coupling in injured rats. Interneurons from injured animals were less strongly entrained to theta and gamma oscillations, but both interneurons and pyramidal cells from injured animals became more strongly entrained to theta during periods of high theta power. During quiet immobility, sharp-wave ripple amplitudes were lower in injured animals compared to shams. These results reveal physiological deficits across brain states that may contribute to TBI-associated learning and memory impairments and elucidate potential targets for future neuromodulation therapies.

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.

WolfLabPenn/Adam-eLife_2026

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bf43e2cb1054456c0f11a4c9da96bde2b7838e57, 6 June 2026
Languages: MATLAB (14)
Size: 16 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (3 files), CircStat (2 files), Wavelet Toolbox (2 files), Curve Fitting Toolbox (1 file), Image Processing Toolbox (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
16 files
At the source: github.com/WolfLabPenn/Adam-eLife_2026

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;
  • 14 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

Datasets cited

Data availability

All data has been deposited on Dryad and is publicly available (https://doi.org/10.5061/dryad.jsxksn0qz). Ephys analysis code is available on GitHub (https://github.com/WolfLabPenn/Adam-eLife_2026, copy archived at Adam, 2026).

The following dataset was generated:

Adam C, Mirzakhalili E, Gagnon KG, Cottone C, Arena JD, Ulyanova A, Johnson VE, Wolf JA. 2026. Data from: Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental traumatic brain injury. Dryad Digital Repository.

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, 8 authors, 1 keyword, 12 MeSH terms, 3 funders, 161 references, 4 RRIDs.

Cite

This paper

Adam, C. D., Mirzakhalili, E., Gagnon, K. G., Cottone, C., Arena, J. D., Ulyanova, A., Johnson, V. E., & Wolf, J. A. (2026). Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental traumatic brain injury. eLife, 13, RP100642. https://doi.org/10.7554/elife.100642

BibTeX

@article{adam2026disrupted,
author = {Adam, Christopher D and Mirzakhalili, Ehsan and Gagnon, Kimberly G and Cottone, Carlo and Arena, John D and Ulyanova, Alexandra and Johnson, Victoria E and Wolf, John A},
title = {{Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental traumatic brain injury}},
journal = {eLife},
year = {2026},
month = jun,
volume = {13},
pages = {RP100642},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.100642},
url = {https://doi.org/10.7554/elife.100642},
pmid = {42284087},
pmcid = {PMC13263039}
}

RIS

TY - JOUR
AU - Adam, Christopher D
AU - Mirzakhalili, Ehsan
AU - Gagnon, Kimberly G
AU - Cottone, Carlo
AU - Arena, John D
AU - Ulyanova, Alexandra
AU - Johnson, Victoria E
AU - Wolf, John A
TI - Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental traumatic brain injury
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/06/12
VL - 13
SP - RP100642
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.100642
UR - https://doi.org/10.7554/elife.100642
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Disrupted hippocampal theta-gamma coupling and spike-field coherence following experimental traumatic brain injury",
"container-title": "eLife",
"author": [
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"family": "Adam",
"given": "Christopher D"
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{
"family": "Mirzakhalili",
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{
"family": "Gagnon",
"given": "Kimberly G"
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{
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{
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"given": "John A"
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],
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"volume": "13",
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"DOI": "10.7554/elife.100642",
"PMID": "42284087",
"PMCID": "PMC13263039",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.100642",
"language": "en",
"issued": {
"date-parts": [
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2026,
6,
12
]
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}
}

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

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