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Synaptic zinc plasticity shapes adaptive and maladaptive cortical plasticity following cochlear injury.

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] § MATERIALS AND METHODS › In vivo wide-field imaging and analysis ↔ GUIs/meanfluoROIvtGUI.m, lines 1043–1127 · score 0.70 · low pass Butterworth, consecutive frames, pixel, filter, baseline, fluorescence
  2. [2] § MATERIALS AND METHODS › In vivo wide-field imaging and analysis ↔ GUIs/meanfluoROIvtGUI.m, lines 1043–1127 · score 0.66 · low pass Butterworth, consecutive frames, temporally, filter, stimulus, field
  3. [3] § MATERIALS AND METHODS › 2PCI analysis ↔ Extract_BB_Responses_2SD_MK.m, lines 225–271 · score 0.65 · 30–80 dB, responsive neurons, response threshold, locations, SPL, 40 kHz
  4. [4] § RESULTS › Recovery of A1 sound-evoked neural response amplitude and perceptual threshold after NIHL ↔ Extract_BB_Responses_2SD_MK.m, lines 274–298 · score 0.62 · Response amplitudes, sound evoked responses, pre NE, response thresholds, SPL, 80 dB
  5. [5] § RESULTS › Recovery of A1 sound-evoked neural response amplitude and perceptual threshold after NIHL ↔ Extract_BB_Responses_2SD_MK.m, lines 274–298 · score 0.58 · 20–80 dB, sound evoked responses, pre NE, SPL, amplitude, threshold

Paper

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The authors' code

MATLAB · 309 lines · 11 KB · CC-BY-4.0 · 3 matches

  2. %% Thresholds are calulcated as 2SD above baseline
  3. %% Extracting and Ploting sound evoked DF/F Data across time and individual neuron (MK)
  4. % Requires NormCorrFissa extracted data (_tifFIleData.mat), cellOrder and Soundfile
  5. % header in the workspace to work
  6. % DF/F using mean F from 1 sec before each sound stimulai
  7. % Adapted from CTA extractTuningCurves (cerebral cortex 2020 paper analyssis)
  8. %% DF/F extaction using mean F from whole trace (all frames)
  10. Data = tifFileData.stim;
  11. nNeurons = size(Data(1).SCALEDfissaFroi, 1);% no. of nNeurons;
  12. nTrials = length(Data); % no. of nTrials
  13. Frames = Data.nFrames; % no of Tiff File frams
  14. FrameRate = Data.frameRate; % tiff file Recording frame rate (Hz)
  15. totalTime = Frames/FrameRate; %(sec)
  16. timeVector = [0:1/FrameRate:totalTime - 1/FrameRate]; %sec
  17. soundStartDelay = 6; %sec
  18. header = tuningCurveSound;
  19. soundDuration = header.totalDuration; %sec
  21. %%
  23. for t = 1:nTrials
  24. MoCorRawFroi{t,1} = Data(t).moCorRawFroi; % ROI Flou Values extracted from NormCorr
  25. RawFroi{t,1} = Data(t).rawFroi; % ROI Flou Values w/o fissa correction extracted from fissa
  26. FissaFroi{t,1} = Data(t).fissaFroi; % fissa corrected ROI Flou Values
  27. ScaledFissaFroi{t,1} = Data(t).SCALEDfissaFroi; % Scaled Fissa corrected ROI Flou Values eg. 80% fissa corrected
  28. end
  30. %% Converting Fluo Structure into cell:(Trial x Neuron x Frame)
  32. for t = 1:nTrials
  33. % greenFluo(t,:,:) = (FissaFroi{t,:}(:,1:1:end)); % all frames starting with frame 1 using FissaFroi
  34. % greenFluo(t,:,:) = (RawFroi{t,:}(:,1:1:end)); % all frames starting with frame 1 using RawFroi
  35. greenFluo(t,:,:) = (ScaledFissaFroi{t,:}(:,1:1:end)); % all frames starting with frame 1 using ScaledFissaFroi
  36. end
  40. for n = 1:size(greenFluo,1) % no of trials
  42. for m = 1:size(greenFluo,2) % no of neurons
  45. greenFluoFilteredF(n,m,:) = highpass(squeeze(greenFluo(n,m,:)),0.03, FrameRate); %high pass filter with 0.03 Hz corner, only works in matlab r2018a and later
  46. %highpass flatTens the trace and preserves the events
  48. end
  50. greenFluoFilteredF(n,:,:) = greenFluo(n,cellOrder(:,n),:); %make sure the same cells are lined up in rows across the different movies
  52. end
  54. %%
  55. %figure out the sound array
  57. levelOrder = header.speedOrder;
  58. soundLevels = unique(levelOrder);
  60. for n = 1:numel(soundLevels)
  62. levelInds(n,:) = find(levelOrder == soundLevels(n));
  64. end
  67. soundStartInd = find(timeVector>soundStartDelay-1, 1, 'first'); %set the start of the analysis window 1 sec prior to the start of each sound
  68. snipSizeInds = floor(header.interSoundInterval*FrameRate);
  70. % soundStartInd = find(timeVector>soundStartDelay-.8, 1, 'first'); %There is frame offset, to normalize that, using 0.8 (MK)
  71. % snipSizeInds = floor(header.interSoundInterval*FrameRate);
  75. for n = 1:numel(levelOrder)
  77. traceSnipsF{n} = squeeze(greenFluoFilteredF(:,:,[soundStartInd:1:soundStartInd+snipSizeInds-1]));
  79. soundStartInd = soundStartInd+snipSizeInds; %double check that the 7 frames are doing the fenceposts correctly
  80. end
  83. %%
  84. %rearrange the traces from low to high dB SPL (35 db to 80 db)
  85. for n = 1:numel(soundLevels)
  88. [~,idLevel] = find(levelOrder == soundLevels(n)); %find all of the same level
  90. traceSnipSubsetF = traceSnipsF(idLevel);
  92. sortedTraceSnipsF{n,1} = traceSnipSubsetF{:}; %you have to do in a nested for loop to preserve the tuning curve structure arrayed by freq and level
  94. end
  97. %%
  98. for n = 1:numel(soundLevels)
  100. for j = 1:nNeurons
  102. cellGroupedTracesF{j}{n,1} = squeeze(sortedTraceSnipsF{n,1}(:,j,:)) ;
  104. end
  105. end
  108. %% plots F traces
  109. for k = 1:numel(cellGroupedTracesF)
  111. h1 = figure('name', ['Cell number ', num2str(k), 'Fluorescence', 'k'], 'units', 'normalized', 'position', [ .2 .2 .6 .4]);
  112. timeSnip = [0:1/FrameRate:header.interSoundInterval-1/FrameRate];
  115. for m = 1:numel(soundLevels)
  118. subplot(1, numel(soundLevels), m)
  120. plot(timeSnip, cellGroupedTracesF{k}{m,1}', 'color' , [ .8 .8 .8])
  121. hold on
  122. plot(timeSnip, nanmean(cellGroupedTracesF{k}{m,1})', 'k', 'linewidth',2 )
  125. xlabel('time (sec)')
  127. end
  129. ylimGroup(:,n) = get(gca,'ylim'); %get the ylims foer each subplot to lim them all the same at the end
  132. h = get(h1, 'Children');
  133. set(h, 'ylim', [min(ylimGroup(1,:)) max(ylimGroup(2,:))]) %set all the ylims the same throughout the figure
  135. end
  137. close all; % comment this to see Fluoroscence traces
  139. %% Calculating DF/F traces. BaselineF is the mean of Fluo signals 1 sec before the each sound stimuli
  142. for k = 1:numel(cellGroupedTracesF)
  144. for m = 1:numel(soundLevels)
  146. baselineInds = 1:1:find(timeSnip<1, 1, 'last'); % Baseline window 1 sec before the sound
  147. responseInds = find(timeSnip>1, 1, 'first'):1:find(timeSnip<2.4, 1, 'last'); % response window from 1 sec to 2.4 sec
  149. thisTraceF = (cellGroupedTracesF{k}{m,1});
  151. thisTraceDFOverF{k}{m,1} = bsxfun(@rdivide, bsxfun(@minus, thisTraceF, mean(thisTraceF(:,baselineInds),2)), mean(thisTraceF(:,baselineInds),2));
  154. end
  155. end
  157. %% plots DF/F traces
  158. for k = 1:numel(thisTraceDFOverF)
  160. h1 = figure('name', ['Cell number ', num2str(k), 'DF/F', 'k'], 'units', 'normalized', 'position', [ .2 .2 .6 .4]);
  161. timeSnip = [0:1/FrameRate:header.interSoundInterval-1/FrameRate];
  164. for m = 1:numel(soundLevels)
  167. subplot(1, numel(soundLevels), m)
  169. plot(timeSnip, thisTraceDFOverF{k}{m,1}', 'color' , [ .8 .8 .8])
  170. hold on
  171. plot(timeSnip, nanmean(thisTraceDFOverF{k}{m,1})', 'k', 'linewidth',2 )
  174. xlabel('time (sec)')
  176. end
  178. ylimGroup(:,n) = get(gca,'ylim'); %get the ylims foer each subplot to lim them all the same at the end
  181. h = get(h1, 'Children');
  182. set(h, 'ylim', [min(ylimGroup(1,:)) max(ylimGroup(2,:))]) %set all the ylims the same throughout the figure
  184. end
  186. % close all; % comment this to see DF/F traces
  188. %% Calculating Response threshold and response amplitude
  189. thresholdSD = 2.0; %number of standard deviations above baseline to count as a resposne
  191. for k = 1:numel(thisTraceDFOverF)
  193. for m = 1:numel(soundLevels)
  195. baselineInds = 1:1:find(timeSnip<1, 1, 'last'); % Baseline window 1 sec before the sound
  196. responseInds = find(timeSnip>1, 1, 'first'):1:find(timeSnip<2.4, 1, 'last'); % response window from 1 sec to 2.4 sec
  198. thisTraceDFoFmean = nanmean(thisTraceDFOverF{k}{m,1});
  201. thresholdDFoF{k}(1,m) = mean(thisTraceDFoFmean(baselineInds)) + std(thisTraceDFoFmean(baselineInds))* thresholdSD;
  203. aboveThresh= find(thisTraceDFoFmean(responseInds)>thresholdDFoF{k}(1,m), 1, 'first');
  205. if isempty(aboveThresh)
  206. responseYesNo{k}(1,m) = 0;
  207. else
  208. responseYesNo{k}(1,m) = 1;
  209. end
  211. % responseAmplitude{k}(1,m) = sum(thisTraceDFoFmean(responseInds))-sum(thisTraceDFoFmean(baselineInds)); %Interegral of DF/F during response window
  212. responseAmplitude{k}(1,m) = max(thisTraceDFoFmean(responseInds)); % Peak DF/F during response window
  215. end
  216. end
  218. % % Replacing non-signicant responses with Zeros
  219. % for k = 1:numel(thisTraceDFOverF)
  220. % responseAmplitude{k}(:) = responseAmplitude{k}(:).*responseYesNo{k}(:);
  221. % end
  225. %% Calculates response threshold
  226. % % response threshold is defined as the minimum sound level of the two
  227. % % succesively increasing sound levels which ilicits significant responses
  228. %
  229. % %names of the location of two succesive significant response ( 30 - 80 db)
  231. for s = 1:size(soundLevels,2)-1 % no of sounds till 75 dB SPL since there is NO succesive sound after 80 dB
  232. for n = 1:size(responseYesNo,2) % of neurons
  233. SuccesiveRespTen{1,n}(1,s) = responseYesNo{1,n}(1,s) + responseYesNo{1,n}(1,s+1);
  234. end
  235. end
  237. names = [30,40,45,50,55,60,65,70,75,80]; % alloting 35dB responses as 30 dB responses
  239. for n = 1:size(responseYesNo,2)
  240. % Non-responsive neurons and neurons with significant response at 80 dB, either are given a threshold of 80 dB
  241. if SuccesiveRespTen{1,n}(1,:) < 2
  243. threshold(n) = 80;
  245. else
  248. threshold(n) = names(min(find(SuccesiveRespTen{1,n}(1,:) == 2)));
  249. %threshold(n) = names(min(find(SuccesiveRespTen{1,n}(1,:) == 1))); %Minimum sound level with significant response
  250. end
  251. end
  254. % % response threshold is defined as the minimum sound level with significant responses
  255. %
  256. % names = [30,40,45,50,55,60,65,70,75,80]; % alloting 35dB responses as 30 dB responses
  257. %
  258. % for n = 1:size(responseYesNo,2)
  259. % % Non-responsive neurons and neurons with significant response at 80 dB, either are given a threshold of 80 dB
  260. % if responseYesNo{1,n}(1,:) < 1
  261. %
  262. % threshold(n) = 80;
  263. %
  264. % else
  265. %
  266. % threshold(n) = names(min(find(responseYesNo{1,n}(1,:) == 1))); %Minimum sound level with significant response
  267. % end
  268. % end
  271. soundLevels = names; % alloting 35dB responses as 30 dB responses
  274. %% ploting thresholds and Response amplitues DF/F across sound levels
  276. figure('name',['FR02_BB_2SD_Pre_NE_Summary'], 'DefaultAxesFontSize',8)
  277. for k = 1:numel(cellGroupedTracesF)
  278. subplot(1, 2, 1)
  279. plot(soundLevels, responseAmplitude{k}(:), '-');
  280. hold on
  281. end
  282. hold on
  283. plot(soundLevels, nanmean(cat(1, responseAmplitude{1,:})), '-ok', 'linewidth',2)
  284. title('Sound-evoked Responses')
  285. xlabel('dB SPL')
  286. ylabel('DF/F(%)')
  288. subplot(1, 2, 2)
  289. bar(0, nanmean(threshold,2), 'k');
  290. hold on
  291. plot(0, threshold(:), '-o');
  292. title('Response Threshold')
  293. ylabel('dB SPL')
  294. xlim([-.5 .5])
  295. ylim([20 80])
  298. savefig([dataPath '\' 'FR02_BB_2SD_Pre_NE_Summary.fig'])
  299. %%
  300. FR02_BB_2SD_Pre_NE_Responses.nNeurons = nNeurons;
  301. FR02_BB_2SD_Pre_NE_Responses.soundLevels = soundLevels;
  302. FR02_BB_2SD_Pre_NE_Responses.responseYesNo = responseYesNo;
  303. FR02_BB_2SD_Pre_NE_Responses.threshold = threshold;
  304. FR02_BB_2SD_Pre_NE_Responses.responseAmplitude = responseAmplitude;
  306. save([dataPath '\' 'FR02_BB_2SD_Pre_NE_Responses.mat'], 'FR02_BB_2SD_Pre_NE_Responses')
  307. %%

Extract_BB_Responses_2SD_MK.m, under CC-BY-4.0 · at the source

Overview

Authors: Manoj Kumar1, Cassandra Linnertz1, Brandon Bizup1, Wenyuan Huang2, Huiwang Ai2, Thanos Tzounopoulos1
ORCID iDs: Manoj Kumar, Wenyuan Huang, Thanos Tzounopoulos
  1. Pittsburgh Hearing Research Center, Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15261
  2. Center for Membrane and Cell Physiology, Department of Molecular Physiology and Biological Physics, University of Virginia School of Medicine, Charlottesville, Virginia 22908, USA
Institutions: University of Pittsburgh (United States); University of Virginia (United States)
Journal: Science advances, volume 12, issue 33, article eaee9298
Dates: received 19 December 2025; accepted 9 July 2026; published online 12 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aee9298 · PMID 42585331 · PMCID PMC13464469 · OpenAlex W7202250103
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Adaptation, Physiological*, Auditory Cortex*, Cochlea*, Neuronal Plasticity*, Synapses*, Zinc*, Animals, Interneurons, Mice, Tinnitus (* major topic)
Journal subjects: Neuroscience, Cellular Neuroscience
Topic: Hearing, Cochlea, Tinnitus, Genetics (Sensory Systems, Neuroscience), according to OpenAlex
Funding: NIH (R01-DC019618, R01-DC020923, R01-EB033172); Health Foundation ERG (855358 and 1307587)
Citations: cited by 1 paper (Europe PMC); 80 references in the paper

Abstract

Cochlear damage triggers compensatory primary auditory cortex (A1) plasticity that amplifies responses to residual sensory inputs, thereby contributing to the restoration of both cortical responsiveness to sound and perceptual sound detection threshold. However, this adaptation can become maladaptive, producing neuronal hyperactivity that contributes to tinnitus and hyperacusis. The neuromodulatory mechanisms governing these adaptive and maladaptive changes remain unknown. Here, we demonstrate that noise-induced cochlear injury triggers bidirectional synaptic zinc signaling plasticity that potentiates activity in excitatory principal neurons and parvalbumin-expressing interneurons, while suppressing activity in somatostatin-expressing interneurons. These cell-type-specific effects of synaptic zinc plasticity contribute to restoring A1 responsiveness to sound and perceptual detection thresholds, while being necessary for neural hyperactivity. Together, our findings establish synaptic zinc as a pivotal neuromodulator that shapes both adaptive and maladaptive cortical plasticity and identify a promising therapeutic target for improving perceptual recovery after cochlear damage and mitigating tinnitus and hyperacusis.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Zenodo 8018820

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (21 files), Statistics and Machine Learning Toolbox (19 files), Image Processing Toolbox (9 files), Parallel Computing Toolbox (7 files), SciPy (6 files), tifffile (4 files), scikit-image (3 files), Pillow (2 files), imageio (1 file), Signal Processing Toolbox (1 file), Matplotlib (1 file), scikit-learn (1 file), Suite2p (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
111 files
At the source: zenodo.org/record/8018820

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

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Data

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Data, code, and materials availability

This study did not generate new materials. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Custom written MATLAB and Python codes used are available from https://zenodo.org/record/8018820.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 10 MeSH terms, 2 funders, 78 references.

Cite

This paper

Kumar, M., Linnertz, C., Bizup, B., Huang, W., Ai, H., & Tzounopoulos, T. (2026). Synaptic zinc plasticity shapes adaptive and maladaptive cortical plasticity following cochlear injury. Science advances, 12(33), eaee9298. https://doi.org/10.1126/sciadv.aee9298

BibTeX

@article{kumar2026synaptic,
author = {Kumar, Manoj and Linnertz, Cassandra and Bizup, Brandon and Huang, Wenyuan and Ai, Huiwang and Tzounopoulos, Thanos},
title = {{Synaptic zinc plasticity shapes adaptive and maladaptive cortical plasticity following cochlear injury}},
journal = {Science advances},
year = {2026},
month = aug,
volume = {12},
number = {33},
pages = {eaee9298},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aee9298},
url = {https://doi.org/10.1126/sciadv.aee9298},
pmid = {42585331},
pmcid = {PMC13464469}
}

RIS

TY - JOUR
AU - Kumar, Manoj
AU - Linnertz, Cassandra
AU - Bizup, Brandon
AU - Huang, Wenyuan
AU - Ai, Huiwang
AU - Tzounopoulos, Thanos
TI - Synaptic zinc plasticity shapes adaptive and maladaptive cortical plasticity following cochlear injury
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/08/12
VL - 12
IS - 33
SP - eaee9298
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aee9298
UR - https://doi.org/10.1126/sciadv.aee9298
LA - en
ER -

CSL-JSON

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