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] § 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] § 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] § 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] § 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] § 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

%% Thresholds are calulcated as 2SD above baseline
%% Extracting and Ploting sound evoked DF/F Data across time and individual neuron (MK)
% Requires NormCorrFissa extracted data (_tifFIleData.mat), cellOrder and Soundfile
% header in the workspace to work
% DF/F using mean F from 1 sec before each sound stimulai
% Adapted from CTA extractTuningCurves (cerebral cortex 2020 paper analyssis)
%% DF/F extaction using mean F from whole trace (all frames)
Data = tifFileData.stim;
nNeurons = size(Data(1).SCALEDfissaFroi, 1);% no. of nNeurons;
nTrials = length(Data); % no. of nTrials
Frames = Data.nFrames; % no of Tiff File frams
FrameRate = Data.frameRate; % tiff file Recording frame rate (Hz)
totalTime = Frames/FrameRate; %(sec)
timeVector = [0:1/FrameRate:totalTime - 1/FrameRate]; %sec
soundStartDelay = 6; %sec
header = tuningCurveSound;
soundDuration = header.totalDuration; %sec
%%
for t = 1:nTrials
MoCorRawFroi{t,1} = Data(t).moCorRawFroi; % ROI Flou Values extracted from NormCorr
RawFroi{t,1} = Data(t).rawFroi; % ROI Flou Values w/o fissa correction extracted from fissa
FissaFroi{t,1} = Data(t).fissaFroi; % fissa corrected ROI Flou Values
ScaledFissaFroi{t,1} = Data(t).SCALEDfissaFroi; % Scaled Fissa corrected ROI Flou Values eg. 80% fissa corrected
end
%% Converting Fluo Structure into cell:(Trial x Neuron x Frame)
for t = 1:nTrials
% greenFluo(t,:,:) = (FissaFroi{t,:}(:,1:1:end)); % all frames starting with frame 1 using FissaFroi
% greenFluo(t,:,:) = (RawFroi{t,:}(:,1:1:end)); % all frames starting with frame 1 using RawFroi
greenFluo(t,:,:) = (ScaledFissaFroi{t,:}(:,1:1:end)); % all frames starting with frame 1 using ScaledFissaFroi
end
for n = 1:size(greenFluo,1) % no of trials
for m = 1:size(greenFluo,2) % no of neurons
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
%highpass flatTens the trace and preserves the events
end
greenFluoFilteredF(n,:,:) = greenFluo(n,cellOrder(:,n),:); %make sure the same cells are lined up in rows across the different movies
end
%%
%figure out the sound array
levelOrder = header.speedOrder;
soundLevels = unique(levelOrder);
for n = 1:numel(soundLevels)
levelInds(n,:) = find(levelOrder == soundLevels(n));
end
soundStartInd = find(timeVector>soundStartDelay-1, 1, 'first'); %set the start of the analysis window 1 sec prior to the start of each sound
snipSizeInds = floor(header.interSoundInterval*FrameRate);
% soundStartInd = find(timeVector>soundStartDelay-.8, 1, 'first'); %There is frame offset, to normalize that, using 0.8 (MK)
% snipSizeInds = floor(header.interSoundInterval*FrameRate);
for n = 1:numel(levelOrder)
traceSnipsF{n} = squeeze(greenFluoFilteredF(:,:,[soundStartInd:1:soundStartInd+snipSizeInds-1]));
soundStartInd = soundStartInd+snipSizeInds; %double check that the 7 frames are doing the fenceposts correctly
end
%%
%rearrange the traces from low to high dB SPL (35 db to 80 db)
for n = 1:numel(soundLevels)
[~,idLevel] = find(levelOrder == soundLevels(n)); %find all of the same level
traceSnipSubsetF = traceSnipsF(idLevel);
sortedTraceSnipsF{n,1} = traceSnipSubsetF{:}; %you have to do in a nested for loop to preserve the tuning curve structure arrayed by freq and level
end
%%
for n = 1:numel(soundLevels)
for j = 1:nNeurons
cellGroupedTracesF{j}{n,1} = squeeze(sortedTraceSnipsF{n,1}(:,j,:)) ;
end
end
%% plots F traces
for k = 1:numel(cellGroupedTracesF)
h1 = figure('name', ['Cell number ', num2str(k), 'Fluorescence', 'k'], 'units', 'normalized', 'position', [ .2 .2 .6 .4]);
timeSnip = [0:1/FrameRate:header.interSoundInterval-1/FrameRate];
for m = 1:numel(soundLevels)
subplot(1, numel(soundLevels), m)
plot(timeSnip, cellGroupedTracesF{k}{m,1}', 'color' , [ .8 .8 .8])
hold on
plot(timeSnip, nanmean(cellGroupedTracesF{k}{m,1})', 'k', 'linewidth',2 )
xlabel('time (sec)')
end
ylimGroup(:,n) = get(gca,'ylim'); %get the ylims foer each subplot to lim them all the same at the end
h = get(h1, 'Children');
set(h, 'ylim', [min(ylimGroup(1,:)) max(ylimGroup(2,:))]) %set all the ylims the same throughout the figure
end
close all; % comment this to see Fluoroscence traces
%% Calculating DF/F traces. BaselineF is the mean of Fluo signals 1 sec before the each sound stimuli
for k = 1:numel(cellGroupedTracesF)
for m = 1:numel(soundLevels)
baselineInds = 1:1:find(timeSnip<1, 1, 'last'); % Baseline window 1 sec before the sound
responseInds = find(timeSnip>1, 1, 'first'):1:find(timeSnip<2.4, 1, 'last'); % response window from 1 sec to 2.4 sec
thisTraceF = (cellGroupedTracesF{k}{m,1});
thisTraceDFOverF{k}{m,1} = bsxfun(@rdivide, bsxfun(@minus, thisTraceF, mean(thisTraceF(:,baselineInds),2)), mean(thisTraceF(:,baselineInds),2));
end
end
%% plots DF/F traces
for k = 1:numel(thisTraceDFOverF)
h1 = figure('name', ['Cell number ', num2str(k), 'DF/F', 'k'], 'units', 'normalized', 'position', [ .2 .2 .6 .4]);
timeSnip = [0:1/FrameRate:header.interSoundInterval-1/FrameRate];
for m = 1:numel(soundLevels)
subplot(1, numel(soundLevels), m)
plot(timeSnip, thisTraceDFOverF{k}{m,1}', 'color' , [ .8 .8 .8])
hold on
plot(timeSnip, nanmean(thisTraceDFOverF{k}{m,1})', 'k', 'linewidth',2 )
xlabel('time (sec)')
end
ylimGroup(:,n) = get(gca,'ylim'); %get the ylims foer each subplot to lim them all the same at the end
h = get(h1, 'Children');
set(h, 'ylim', [min(ylimGroup(1,:)) max(ylimGroup(2,:))]) %set all the ylims the same throughout the figure
end
% close all; % comment this to see DF/F traces
%% Calculating Response threshold and response amplitude
thresholdSD = 2.0; %number of standard deviations above baseline to count as a resposne
for k = 1:numel(thisTraceDFOverF)
for m = 1:numel(soundLevels)
baselineInds = 1:1:find(timeSnip<1, 1, 'last'); % Baseline window 1 sec before the sound
responseInds = find(timeSnip>1, 1, 'first'):1:find(timeSnip<2.4, 1, 'last'); % response window from 1 sec to 2.4 sec
thisTraceDFoFmean = nanmean(thisTraceDFOverF{k}{m,1});
thresholdDFoF{k}(1,m) = mean(thisTraceDFoFmean(baselineInds)) + std(thisTraceDFoFmean(baselineInds))* thresholdSD;
aboveThresh= find(thisTraceDFoFmean(responseInds)>thresholdDFoF{k}(1,m), 1, 'first');
if isempty(aboveThresh)
responseYesNo{k}(1,m) = 0;
else
responseYesNo{k}(1,m) = 1;
end
% responseAmplitude{k}(1,m) = sum(thisTraceDFoFmean(responseInds))-sum(thisTraceDFoFmean(baselineInds)); %Interegral of DF/F during response window
responseAmplitude{k}(1,m) = max(thisTraceDFoFmean(responseInds)); % Peak DF/F during response window
end
end
% % Replacing non-signicant responses with Zeros
% for k = 1:numel(thisTraceDFOverF)
% responseAmplitude{k}(:) = responseAmplitude{k}(:).*responseYesNo{k}(:);
% end
%% Calculates response threshold
% % response threshold is defined as the minimum sound level of the two
% % succesively increasing sound levels which ilicits significant responses
%
% %names of the location of two succesive significant response ( 30 - 80 db)
for s = 1:size(soundLevels,2)-1 % no of sounds till 75 dB SPL since there is NO succesive sound after 80 dB
for n = 1:size(responseYesNo,2) % of neurons
SuccesiveRespTen{1,n}(1,s) = responseYesNo{1,n}(1,s) + responseYesNo{1,n}(1,s+1);
end
end
names = [30,40,45,50,55,60,65,70,75,80]; % alloting 35dB responses as 30 dB responses
for n = 1:size(responseYesNo,2)
% Non-responsive neurons and neurons with significant response at 80 dB, either are given a threshold of 80 dB
if SuccesiveRespTen{1,n}(1,:) < 2
threshold(n) = 80;
else
threshold(n) = names(min(find(SuccesiveRespTen{1,n}(1,:) == 2)));
%threshold(n) = names(min(find(SuccesiveRespTen{1,n}(1,:) == 1))); %Minimum sound level with significant response
end
end
% % response threshold is defined as the minimum sound level with significant responses
%
% names = [30,40,45,50,55,60,65,70,75,80]; % alloting 35dB responses as 30 dB responses
%
% for n = 1:size(responseYesNo,2)
% % Non-responsive neurons and neurons with significant response at 80 dB, either are given a threshold of 80 dB
% if responseYesNo{1,n}(1,:) < 1
%
% threshold(n) = 80;
%
% else
%
% threshold(n) = names(min(find(responseYesNo{1,n}(1,:) == 1))); %Minimum sound level with significant response
% end
% end
soundLevels = names; % alloting 35dB responses as 30 dB responses
%% ploting thresholds and Response amplitues DF/F across sound levels
figure('name',['FR02_BB_2SD_Pre_NE_Summary'], 'DefaultAxesFontSize',8)
for k = 1:numel(cellGroupedTracesF)
subplot(1, 2, 1)
plot(soundLevels, responseAmplitude{k}(:), '-');
hold on
end
hold on
plot(soundLevels, nanmean(cat(1, responseAmplitude{1,:})), '-ok', 'linewidth',2)
title('Sound-evoked Responses')
xlabel('dB SPL')
ylabel('DF/F(%)')
subplot(1, 2, 2)
bar(0, nanmean(threshold,2), 'k');
hold on
plot(0, threshold(:), '-o');
title('Response Threshold')
ylabel('dB SPL')
xlim([-.5 .5])
ylim([20 80])
savefig([dataPath '\' 'FR02_BB_2SD_Pre_NE_Summary.fig'])
%%
FR02_BB_2SD_Pre_NE_Responses.nNeurons = nNeurons;
FR02_BB_2SD_Pre_NE_Responses.soundLevels = soundLevels;
FR02_BB_2SD_Pre_NE_Responses.responseYesNo = responseYesNo;
FR02_BB_2SD_Pre_NE_Responses.threshold = threshold;
FR02_BB_2SD_Pre_NE_Responses.responseAmplitude = responseAmplitude;
save([dataPath '\' 'FR02_BB_2SD_Pre_NE_Responses.mat'], 'FR02_BB_2SD_Pre_NE_Responses')
%%

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

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: National Institutes of Health (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.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

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

Extract_BB_Responses_2SD_MK.m — MATLAB, 309 lines, 3 matches
FISSAscript_MK.py — Python, 75 lines
GUIs/meanfluoROIvtGUI.m — MATLAB, 1,733 lines, 2 matches
GUIs/minimalTifROIgui.m — MATLAB, 543 lines
GUIs/normCorreTifROIgui.m — MATLAB, 516 lines
GUIs/roiGUI.m — MATLAB, 1,191 lines
NoRMCorre/@MotionCorrection/MotionCorrection.m — MATLAB, 209 lines
NoRMCorre/NoRMCorreSetParms.m — MATLAB, 235 lines
NoRMCorre/apply_shifts.m — MATLAB, 306 lines
NoRMCorre/bigread2.m — MATLAB, 173 lines
NoRMCorre/cell2mat_ov.m — MATLAB, 27 lines
NoRMCorre/cell2mat_ov_sum.m — MATLAB, 42 lines
NoRMCorre/concatenate_files.m — MATLAB, 57 lines
NoRMCorre/construct_grid.m — MATLAB, 33 lines
NoRMCorre/construct_grid_even.m — MATLAB, 28 lines
NoRMCorre/construct_weights.m — MATLAB, 8 lines
NoRMCorre/correct_bidirectional_offset.m — MATLAB, 77 lines
NoRMCorre/demo.m — MATLAB, 82 lines
NoRMCorre/demo_1p.m — MATLAB, 172 lines
NoRMCorre/demo_1p_low_RAM.m — MATLAB, 62 lines
NoRMCorre/demo_mc_class.m — MATLAB, 86 lines
NoRMCorre/dftregistration_min_max.m — MATLAB, 283 lines
NoRMCorre/dftregistration_min_max_3d.m — MATLAB, 315 lines
NoRMCorre/downsample_data.m — MATLAB, 62 lines
NoRMCorre/h5_2_bin.m — MATLAB, 26 lines
NoRMCorre/han.m — MATLAB, 22 lines
NoRMCorre/loadtiff.m — MATLAB, 154 lines
NoRMCorre/loadtiff_old.m — MATLAB, 150 lines
NoRMCorre/mat2cell_ov.m — MATLAB, 29 lines
NoRMCorre/motion_metrics.m — MATLAB, 113 lines
NoRMCorre/normcorre.m — MATLAB, 536 lines
NoRMCorre/normcorre_batch.m — MATLAB, 486 lines
NoRMCorre/normcorre_batch_even.m — MATLAB, 330 lines
NoRMCorre/read_file.m — MATLAB, 89 lines
NoRMCorre/read_raw_file.m — MATLAB, 27 lines
NoRMCorre/refreshdisp.m — MATLAB, 12 lines
NoRMCorre/register_frame.m — MATLAB, 65 lines
NoRMCorre/remove_boundaries.m — MATLAB, 40 lines
NoRMCorre/saveash5.m — MATLAB, 36 lines
NoRMCorre/saveastiff.m — MATLAB, 304 lines
NoRMCorre/savefast.m — MATLAB, 70 lines
NoRMCorre/shift_reconstruct.m — MATLAB, 57 lines
NoRMCorre/split_frame.m — MATLAB, 14 lines
NoRMCorreFISSApipeline_MK.m — MATLAB, 349 lines
fissa/docs/conf.py — Python, 265 lines
fissa/examples/Basic usage.ipynb — Jupyter, 340 lines
fissa/examples/SIMA example.ipynb — Jupyter, 157 lines
fissa/examples/Suite2p example.ipynb — Jupyter, 161 lines
fissa/examples/basic_usage.py — Python, 28 lines
fissa/examples/basic_usage_windows.py — Python, 32 lines
fissa/examples/cNMF example.ipynb — Jupyter, 148 lines
fissa/examples/datahandler_custom.py — Python, 118 lines
fissa/fissa/ROI.py — Python, 147 lines
fissa/fissa/__init__.py — Python, 5 lines
fissa/fissa/__meta__.py — Python, 7 lines
fissa/fissa/core.py — Python, 627 lines
fissa/fissa/datahandler.py — Python, 130 lines
fissa/fissa/datahandler_framebyframe.py — Python, 153 lines
fissa/fissa/deltaf.py — Python, 95 lines
fissa/fissa/neuropil.py — Python, 237 lines
fissa/fissa/readimagejrois.py — Python, 436 lines
fissa/fissa/roitools.py — Python, 436 lines
fissa/fissa/tests/__init__.py — Python, 1 line
fissa/fissa/tests/base_test.py — Python, 74 lines
fissa/fissa/tests/generate_downsampled_resources.py — Python, 390 lines
fissa/fissa/tests/test_ROI.py — Python, 45 lines
fissa/fissa/tests/test_core.py — Python, 363 lines
fissa/fissa/tests/test_datahandler.py — Python, 94 lines
fissa/fissa/tests/test_datahandler_framebyframe.py — Python, 90 lines
fissa/fissa/tests/test_deltaf.py — Python, 105 lines
fissa/fissa/tests/test_neuropil.py — Python, 69 lines
fissa/fissa/tests/test_readimagejrois.py — Python, 200 lines
fissa/fissa/tests/test_roitools.py — Python, 320 lines
fissa/setup.py — Python, 89 lines
helperFcns/BFmap/BFuDBcalc.m — MATLAB, 11 lines
helperFcns/BFmap/TRFmap.m — MATLAB, 217 lines
helperFcns/BFmap/compileTRF.m — MATLAB, 180 lines
helperFcns/BFmap/extractMapPulseParams.m — MATLAB, 19 lines
helperFcns/BFmap/getMapAnalysisParams.m — MATLAB, 72 lines
helperFcns/BFmap/monotonicityCalc.m — MATLAB, 63 lines
helperFcns/BFmap/plotTRFmap.m — MATLAB, 205 lines
helperFcns/BFmap/repFreqMask2linTRFidx.m — MATLAB, 17 lines
helperFcns/BFmap/tuningQualityToTtrfAnml.m — MATLAB, 46 lines
helperFcns/cellFRA2dPrime.m — MATLAB, 42 lines
helperFcns/dFF/baseIDXfromPTonset.m — MATLAB, 23 lines
helperFcns/dFF/dFcalc.m — MATLAB, 23 lines
helperFcns/dFF/dFoFcalc.m — MATLAB, 39 lines
helperFcns/dFF/pkFcalc.m — MATLAB, 47 lines
helperFcns/dataOrg/FISSAoutput2tifFileList.m — MATLAB, 22 lines
helperFcns/dataOrg/anmlROIbyStimTable.m — MATLAB, 118 lines
helperFcns/dataOrg/compileAnimalDataTable.m — MATLAB, 249 lines
helperFcns/dataOrg/extractStimParams.m — MATLAB, 54 lines
helperFcns/dataOrg/getAnalysisParams.m — MATLAB, 81 lines
helperFcns/dataOrg/getContrastDRCvars.m — MATLAB, 17 lines
helperFcns/dataOrg/moCorRawF2tifList.m — MATLAB, 58 lines
helperFcns/dataOrg/oneAnimalPkPTresp.m — MATLAB, 186 lines
helperFcns/dataOrg/stimParams2TifTable.m — MATLAB, 11 lines
helperFcns/dataOrg/updateTifFileList.m — MATLAB, 37 lines
helperFcns/general/scaleZeroToOne.m — MATLAB, 6 lines
helperFcns/plotting/xlabelLowHighPkRespRatio.m — MATLAB, 24 lines
helperFcns/plotting/ylabelLowHighPkRespRatio.m — MATLAB, 45 lines
helperFcns/roi/TifROImask2rawFroi.m — MATLAB, 26 lines
helperFcns/roi/intersectROIfiles.m — MATLAB, 27 lines
helperFcns/roi/mask2polyCoord.m — MATLAB, 37 lines
helperFcns/roi/orderEllipsePtOnCurve.m — MATLAB, 20 lines
helperFcns/tif/justLoadTif.m — MATLAB, 18 lines
helperFcns/tif/readSCIMtif.m — MATLAB, 154 lines
helperFcns/tif/redGreenMerge.m — MATLAB, 5 lines
helperFcns/tif/splitTifChans.m — MATLAB, 60 lines
helperFcns/tif/writeMoCorTifs.m — MATLAB, 46 lines
LICENSE — License, 21 lines

At the source: zenodo.org/record/8018820

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;
110 scripts, each with its path and the digest of its content;
5 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, 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.

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, 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

{ "id": "10.1126/sciadv.aee9298", "type": "article-journal", "title": "Synaptic zinc plasticity shapes adaptive and maladaptive cortical plasticity following cochlear injury", "container-title": "Science advances", "author": [ { "family": "Kumar", "given": "Manoj" }, { "family": "Linnertz", "given": "Cassandra" }, { "family": "Bizup", "given": "Brandon" }, { "family": "Huang", "given": "Wenyuan" }, { "family": "Ai", "given": "Huiwang" }, { "family": "Tzounopoulos", "given": "Thanos" } ], "container-title-short": "Sci Adv", "volume": "12", "issue": "33", "page": "eaee9298", "DOI": "10.1126/sciadv.aee9298", "PMID": "42585331", "PMCID": "PMC13464469", "ISSN": "2375-2548", "publisher": "American Association for the Advancement of Science", "URL": "https://doi.org/10.1126/sciadv.aee9298", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 12 ] ] } }

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