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The long-term impact of neonatal hypoxic-ischemic brain injury on neural dynamics in deep-layer retrosplenial cortex and hippocampus in mice.

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  1. [1] § STAR★Methods › Method details › Field-spike coherence and phase-locking ↔ HI_phase-amplitude coupling.m, lines 1 to 43 · score 0.66 · phase amplitude coupling, frequency band, modulation, 100 Hz · Report a mismatch

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The authors' code

MATLAB · 302 lines · 11 KB · CC-BY-4.0 · 1 match

  1. % Script for simulating EEG data for testing cross-frequency
  2. % phase-amplitude coupling methods
  4. % defining center frequencies, for which to calculate coupling
  5. freq4phs = 9; % in Hz
  6. freq4amp = 60; % in Hz
  7. % amount of simulations (e. g. 100 for coupling or 10000 for no coupling)
  8. nrpts = 100;
  10. % defining frequency band borders accoring to the center frequencies
  11. freq_ph_min = freq4phs-1; % in Hz
  12. freq_ph_max = freq4phs+1; % in Hz
  13. freq_amp_min = freq4amp-freq_ph_max; % in Hz
  14. freq_amp_max = freq4amp+freq_ph_max; % in Hz
  17. % define parameters used for modifying the simulated signal
  18. trial_length = [400,2500,5000]; % in msec
  19. sampling_rate = [500 1000]; % in Hz
  20. noise = [0.9,1,1.1]; % additional noise
  21. modulation_strength = [0.9,1,1.1];
  22. mw_max_dur = (1000/((freq_ph_min+freq_ph_max)/2))/4;
  23. modulation_width = [0.9*mw_max_dur,mw_max_dur,1.1*mw_max_dur]; % in ms
  24. phasic = {'mono','bi'};
  26. ntrial = 30; % amount of trials extracted from the simulated data
  27. niter = 1000; % amount of iterations for permutation testing
  31. % defining data length according to trial length and amount of trials
  32. datalength = zeros(size(trial_length));
  33. for n=1:length(trial_length)
  34. datalength(n) = (trial_length(n)/1000 * ntrial) + ntrial;
  35. end
  37. % initialising phase-amplitude plot
  38. nbin = 18;
  39. winsize = 2*pi/nbin;
  40. position = zeros(1,nbin); % in radians
  41. for j=1:nbin
  42. position(j) = -pi+(j-1)*winsize;
  43. end
  45. % initialise empty cell matrix for PAC values
  46. PAC_values = cell(nrpts,length(datalength),length(sampling_rate), ...
  47. length(noise),length(modulation_strength),length(modulation_width), ...
  48. length(phasic));
  50. % shuffling random number generator
  51. rng('shuffle'); % creates a different seed for the rand-nr-generator
  52. rng_settings = rng; % saves current settings of the generator
  53. rng_example = rand(1,5); % saves 5 examples of the generated random nrs
  55. eeglab;
  56. cd 'C:\spike analysis\maria\modulation index\noise_generation' % function for ...
  57. % noise generation downloaded from ...
  58. % https://www.mathworks.com/matlabcentral/fileexchange/42919-pink-red- ...
  59. % blue-and-violet-noise-generation-with-matlab
  60. runIdx = 1;
  61. for iteri=1:nrpts
  62. % simulating EEG structure for eegfilt
  63. EEG.srate = 1000; % initial sampling rate
  64. EEG.pnts = max(datalength)*EEG.srate;
  65. EEG.nbchan = 1;
  66. EEG.trials = 1;
  67. EEG.data = rednoise(EEG.pnts);
  68. EEG.times = 0:1/EEG.srate:max(datalength)-1/EEG.srate;
  70. rawEEG = EEG;
  72. for dli=1:length(datalength)
  73. for sri=1:length(sampling_rate)
  74. for noi=1:length(noise)
  75. for msi=1:length(modulation_strength)
  76. for mwi=1:length(modulation_width)
  77. for phi=1:length(phasic)
  79. EEG = rawEEG;
  81. % change original data to defined length
  82. EEG.data = EEG.data(1:datalength(dli)*EEG.srate);
  83. EEG.pnts = length(EEG.data);
  84. EEG.times = 0:1/EEG.srate:datalength(dli)-1/EEG.srate;
  85. EEG.xmax = (datalength(dli)-1/EEG.srate);
  87. % resample original data
  88. EEG = pop_resample(EEG, sampling_rate(sri));
  90. % initialise random time points for permuted phase time series in data
  91. % points
  92. numpoints_trial = (datalength(dli) - ntrial) / ntrial * sampling_rate(sri);
  94. % bandpass filtering phase_signal
  95. sig_ph = pop_eegfiltnew(EEG, freq_ph_min, freq_ph_max, [], 0, [], 0);
  96. sig_ph = sig_ph.data; % lower frequency signal for phase
  98. % bandpass filtering amplitude_signal
  99. sig_amp = pop_eegfiltnew(EEG,freq_amp_min, freq_amp_max, [], 0, [], 0);
  100. sig_amp = sig_amp.data; % higher frequency signal for power
  102. % modulate amplitude signal, if there is modulation, i.e. I~=0;
  103. if modulation_strength(msi)~=0
  104. % creation of a Hanning window with "uneven" length, in order to be
  105. % able to place it evenly around the extreme values
  106. if rem(ceil((modulation_width(mwi)/1000*EEG.srate)),2)
  107. myHanning = modulation_strength(msi) .* ...
  108. hann(ceil(modulation_width(mwi)/1000*EEG.srate))';
  109. else % the data length of the Hanning window is even;
  110. % therefore one data point is subtracted
  111. myHanning = modulation_strength(msi) .* ...
  112. hann(ceil((modulation_width(mwi)/1000*EEG.srate)-1))';
  113. end
  114. modulation_width_final = length(myHanning) * 1000/EEG.srate;
  116. % find peak indexes
  117. IdxMaxima = findpeaks(sig_ph);
  119. if strcmp(phasic(phi),'mono') % modulation only at peaks
  120. % Looping through all peaks and multiply amplitude with hanning
  121. % window
  122. for exi=1:length(IdxMaxima.loc)
  123. StartIdx = IdxMaxima.loc(exi)-floor(length(myHanning)/2);
  124. EndIdx = IdxMaxima.loc(exi)+floor(length(myHanning)/2);
  125. if StartIdx < 1
  126. y = 1-StartIdx;
  127. shortenedHann = myHanning(y+1:end);
  128. StartIdx = 1;
  129. sig_amp(1,StartIdx:EndIdx) = sig_amp(1,StartIdx:EndIdx).*(1+shortenedHann);
  130. elseif EndIdx > length(sig_amp)
  131. y = EndIdx - length(sig_amp);
  132. shortenedHann = myHanning(1:end-y);
  133. EndIdx = length(sig_amp);
  134. sig_amp(1,StartIdx:EndIdx) = sig_amp(1,StartIdx:EndIdx).*(1+shortenedHann);
  135. else
  136. sig_amp(1,StartIdx:EndIdx) = sig_amp(1,StartIdx:EndIdx).*(1+myHanning);
  137. end
  138. end
  139. elseif strcmp(phasic(phi),'bi') % modulation at peaks and throughs
  140. % find peaks indexes: already done
  141. % find through indexes
  142. DataInv = 1.01*max(sig_ph) - sig_ph;
  143. IdxMinima = findpeaks(DataInv);
  144. IdxExtrms(1:length(IdxMaxima.loc)) = IdxMaxima.loc;
  145. IdxExtrms(length ...
  146. (IdxMaxima.loc)+1:length(IdxMaxima.loc)+length(IdxMinima.loc)) = ...
  147. IdxMinima.loc;
  148. % Looping through all peaks and throughs and multiply amplitude with
  149. % Hanning window
  150. for exi=1:length(IdxExtrms)
  151. StartIdx = IdxExtrms(exi)-floor(length(myHanning)/2);
  152. EndIdx = IdxExtrms(exi)+floor(length(myHanning)/2);
  153. if StartIdx < 1
  154. y = 1-StartIdx;
  155. shortenedHann = myHanning(y+1:end);
  156. StartIdx = 1;
  157. sig_amp(1,StartIdx:EndIdx) = sig_amp(1,StartIdx:EndIdx).*(1+shortenedHann);
  158. elseif EndIdx > length(sig_amp)
  159. y = EndIdx - length(sig_amp);
  160. shortenedHann = myHanning(1:end-y);
  161. EndIdx = length(sig_amp);
  162. sig_amp(1,StartIdx:EndIdx) = sig_amp(1,StartIdx:EndIdx).*(1+shortenedHann);
  163. else
  164. sig_amp(1,StartIdx:EndIdx) = sig_amp(1,StartIdx:EndIdx).*(1+myHanning);
  165. end
  166. end
  167. end
  168. else
  169. % if there is no modulation, the above procedure
  170. % does not need to be carried out
  171. modulation_width_final = 0;
  172. end
  174. % generate additional noise
  175. EEG.data = rednoise(EEG.pnts);
  176. noise_ph = pop_eegfiltnew(EEG, freq_ph_min, freq_ph_max,[],0,[],0);
  177. noise_ph = noise_ph.data;
  178. noise_amp = pop_eegfiltnew(EEG,freq_amp_min,freq_amp_max,[],0,[],0);
  179. noise_amp = noise_amp.data;
  181. % final synthetic signal
  182. sig_ph = sig_ph + noise(noi) .* noise_ph;
  183. sig_amp = sig_amp + noise(noi) .* noise_amp;
  185. % extract amplitude and phase from simulated continiuous data
  186. phase_ts_cont = angle(hilbert(sig_ph));
  187. amplt_ts_cont = abs(hilbert(sig_amp));
  188. ph_ap_ts_cont = angle(hilbert(abs(hilbert(sig_amp))));
  190. % epoch data (as often real data does conatin data inconsistencies)
  191. triallength = (datalength(dli) - ntrial) / ntrial; % in sec
  192. marker_dist = 1+triallength;
  193. event_latency = ...
  194. EEG.srate/2:marker_dist*EEG.srate:marker_dist*ntrial*EEG.srate;
  196. start = 1;
  197. phase_ts = zeros(1,triallength*EEG.srate*ntrial);
  198. amplt_ts = zeros(1,triallength*EEG.srate*ntrial);
  199. ph_ap_ts = zeros(1,triallength*EEG.srate*ntrial);
  200. for eli=1:ntrial
  201. phase_ts(1,start:eli*triallength*EEG.srate) = ...
  202. phase_ts_cont(event_latency(eli):event_latency(eli)+ ...
  203. (triallength*EEG.srate-1));
  204. amplt_ts(1,start:eli*triallength*EEG.srate) = ...
  205. amplt_ts_cont(event_latency(eli):event_latency(eli)+ ...
  206. (triallength*EEG.srate-1));
  207. ph_ap_ts(1,start:eli*triallength*EEG.srate) = ...
  208. ph_ap_ts_cont(event_latency(eli):event_latency(eli)+ ...
  209. (triallength*EEG.srate-1));
  210. start = start + triallength*EEG.srate;
  211. end
  213. % calculate mean amplitude in phase bins for MI
  214. MeanAmp = zeros(1,nbin);
  215. for j=1:nbin
  216. MeanAmp(j) = mean(amplt_ts(find ...
  217. (phase_ts >= position(j) & phase_ts < position(j)+winsize)));
  218. end
  220. % calculate observed MI (complex-valued composite signal)
  221. NormAmp = MeanAmp/sum(MeanAmp);
  222. ShannonEntropy = -sum(NormAmp.*log(NormAmp));
  223. KL_Dist = log(nbin)-ShannonEntropy;
  224. obs_MI = KL_Dist / log(nbin);
  225. % calculate observed MVL (complex-valued composite signal)
  226. obs_MVL = mean(amplt_ts(:).*exp(1i*phase_ts(:)));
  227. % calculate observed PLV (complex-valued composite signal)
  228. obs_PLV = mean(exp(1i*(phase_ts(:) - ph_ap_ts(:))));
  229. % calculate observed GLM
  230. [obs_GLM,obs_GLM_CI,NrCtrlPts] = glmfun(phase_ts,amplt_ts,10,'AIC');
  231. % The glmfun.m provided by Kramer & Eden (2013), ...
  232. % was modified, such that it takes the phase time series and amplitude
  233. % time series instead of the band pass filtered signal.
  235. % split continious data into trials
  236. phase_ts = reshape(phase_ts,triallength*EEG.srate,ntrial);
  237. amplt_ts = reshape(amplt_ts,triallength*EEG.srate,ntrial);
  238. ph_ap_ts = reshape(ph_ap_ts,triallength*EEG.srate,ntrial);
  239. EEG.trials = ntrial;
  241. % calculate surrogate values
  242. permuted_phase = zeros(length(phase_ts),ntrial);
  243. permuted_MI = zeros(1,niter);
  244. permuted_MVL = zeros(1,niter);
  245. permuted_PLV = zeros(1,niter);
  246. permuted_GLM = zeros(1,niter);
  248. for s=1:niter
  249. skip = randsample ...
  250. (round(numpoints_trial*.8),ntrial) + round(numpoints_trial*.1);
  251. for ti=1:ntrial
  252. permuted_phase(:,ti) = phase_ts([skip(ti):end 1:skip(ti)-1],ti);
  253. end
  255. MeanAmpShuffeld = zeros(1,nbin);
  256. for j=1:nbin
  257. MeanAmpShuffeld(j) = mean(amplt_ts(find ...
  258. (permuted_phase>=position(j)&permuted_phase<position(j)+winsize)));
  259. end
  260. permuted_MI(s) = (log(nbin)- ...
  261. (-sum((MeanAmpShuffeld/sum(MeanAmpShuffeld)) .* ...
  262. log((MeanAmpShuffeld/sum(MeanAmpShuffeld)))))) / log(nbin);
  263. permuted_MVL(s) = abs(mean( amplt_ts(:).*exp(1i*permuted_phase(:)) ));
  264. permuted_PLV(s) = abs(mean(exp(1i*(permuted_phase(:) - ph_ap_ts(:)))));
  265. [permuted_GLM(s),~,~] =glmfun(permuted_phase(:),amplt_ts(:),NrCtrlPts);
  266. end
  268. % calculate z-standardized PAC values
  269. MIz = ( obs_MI - mean(permuted_MI)) / std(permuted_MI);
  270. MVLz = (abs(obs_MVL) - mean(permuted_MVL)) / std(permuted_MVL);
  271. PLVz = (abs(obs_PLV) - mean(permuted_PLV)) / std(permuted_PLV);
  272. GLMz = ( obs_GLM - mean(permuted_GLM)) / std(permuted_GLM);
  273. MVLphase = angle(obs_MVL);
  274. PLVphase = angle(obs_PLV);
  276. % save PAC values
  277. parameter_name = ['freq' num2str(freq4phs) 'to' num2str(freq4amp) '_' ...
  278. num2str(datalength(dli)) 'DL_' ...
  279. num2str(EEG.srate) 'SR_' ...
  280. num2str(noise(noi)*100) 'NO_' ...
  281. num2str(modulation_strength(msi)*10) 'MS_' ...
  282. num2str(modulation_width_final) 'MW_' char(phasic(phi))];
  283. toSave = {parameter_name,obs_MI,MIz,obs_MVL,MVLz,MVLphase, ...
  284. obs_PLV,PLVz,PLVphase,obs_GLM,obs_GLM_CI,NrCtrlPts,GLMz};
  285. PAC_values{iteri,dli,sri,noi,msi,mwi,phi} = toSave;
  287. % clear variables because of varying data length
  288. EEG.data=[]; EEG.srate=[]; EEG.times=[]; EEG.pnts=[]; EEG.nbchan=[];
  289. EEG.trials=[];
  290. clear sig_amp noise_amp amplt_ts amplt_ts_cont sig_ph noise_ph
  291. clear phase_ts phase_ts_cont permuted_phase ph_ap_ts ph_ap_ts_cont
  292. clear MeanAmp MeanAmpShuffeld NormAmp event_latency IdxMaxima myHanning
  293. clear bedname toSave
  294. end
  295. end
  296. end
  297. end
  298. end
  299. end
  300. save('tmp_results','PAC_values','rng_settings')
  301. end
  302. save('results','PAC_values','rng_settings','rng_example')

HI_phase-amplitude coupling.m, under CC-BY-4.0 · at the source

Overview

Authors: Lida Du1, Meng Yang1, Hendrik W Steenland1,2, Zhengwei Luo1, Andrea Ovcjak1, Ruiyan Hu1, Shuzo Sugita1, Kaori Takehara-Nishiuchi3, Luka Milosevic4, Zhong-Ping Feng1
ORCID iDs: Zhong-Ping Feng
  1. Department of Physiology, University of Toronto, Toronto, ON, Canada
  2. Neurotek Incorporated, York, ON, Canada
  3. Department of Psychology, University of Toronto, Toronto, ON, Canada
  4. Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
Institutions: University of Toronto (Canada)
Journal: iScience, volume 29, issue 4, article 115003
Dates: received 23 April 2025; accepted 9 February 2026; published online 28 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.115003 · PMID 42006293 · PMCID PMC13091419 · OpenAlex W7141541054
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), other condition (population), stroke (population), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Evoked potentials, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: neuroscience, behavioral neuroscience, sensory neuroscience
Topic: Neonatal and fetal brain pathology (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: Canadian Institutes of Health Research (PJT-191824, CIHR-PJT-153155)
Citations: cited by 1 paper (Europe PMC); 68 references in the paper

Abstract

Perinatal hypoxic-ischemia (HI) often results in hypoxic-ischemic encephalopathy (HIE), a major cause of neonatal mortality and long-term neurodevelopmental deficits. While motor and cognitive impairments are common, the underlying neuronal mechanisms remain poorly understood. Using a mouse model of neonatal HI, we examined long-term effects on neuronal activity in the retrosplenial cortex (RSC) and hippocampus through simultaneous electrophysiological recordings in freely moving adults. HI mice showed a significant reduction in neurons recorded per tetrode, and remaining pyramidal neurons in the ipsilateral hemisphere exhibited abnormal bursting, synchrony, and pathological spike-field coupling to theta-alpha oscillations. These neural abnormalities coincided with impaired motor learning performance across training sessions. Our findings reveal persistent disruptions in firing patterns and RSC-hippocampus communication, providing insight into long-term motor learning deficits at the single-neuron level, following neonatal HI brain injury.

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 1 match between paragraphs and lines of code.

Zenodo 18451609

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (8)
Size: 9 files, 8 scripts
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Open Ephys analysis tools (3 files), Signal Processing Toolbox (2 files), EEGLAB (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
8 files
At the source: doi.org/10.5281/zenodo.18451609

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;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match 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 and code availability

All MATLAB analysis scripts and data supporting the findings of this study are available at https://doi.org/10.5281/zenodo.18451609.

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, 10 authors, 3 keywords, 1 funder, 67 references.

Cite

This paper

Du, L., Yang, M., Steenland, H. W., Luo, Z., Ovcjak, A., Hu, R., Sugita, S., Takehara-Nishiuchi, K., Milosevic, L., & Feng, Z.-P. (2026). The long-term impact of neonatal hypoxic-ischemic brain injury on neural dynamics in deep-layer retrosplenial cortex and hippocampus in mice. iScience, 29(4), 115003. https://doi.org/10.1016/j.isci.2026.115003

BibTeX

@article{du2026long,
author = {Du, Lida and Yang, Meng and Steenland, Hendrik W and Luo, Zhengwei and Ovcjak, Andrea and Hu, Ruiyan and Sugita, Shuzo and Takehara-Nishiuchi, Kaori and Milosevic, Luka and Feng, Zhong-Ping},
title = {{The long-term impact of neonatal hypoxic-ischemic brain injury on neural dynamics in deep-layer retrosplenial cortex and hippocampus in mice}},
journal = {iScience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {115003},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115003},
url = {https://doi.org/10.1016/j.isci.2026.115003},
pmid = {42006293},
pmcid = {PMC13091419}
}

RIS

TY - JOUR
AU - Du, Lida
AU - Yang, Meng
AU - Steenland, Hendrik W
AU - Luo, Zhengwei
AU - Ovcjak, Andrea
AU - Hu, Ruiyan
AU - Sugita, Shuzo
AU - Takehara-Nishiuchi, Kaori
AU - Milosevic, Luka
AU - Feng, Zhong-Ping
TI - The long-term impact of neonatal hypoxic-ischemic brain injury on neural dynamics in deep-layer retrosplenial cortex and hippocampus in mice
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/03/28
VL - 29
IS - 4
SP - 115003
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115003
UR - https://doi.org/10.1016/j.isci.2026.115003
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.115003",
"type": "article-journal",
"title": "The long-term impact of neonatal hypoxic-ischemic brain injury on neural dynamics in deep-layer retrosplenial cortex and hippocampus in mice",
"container-title": "iScience",
"author": [
{
"family": "Du",
"given": "Lida"
},
{
"family": "Yang",
"given": "Meng"
},
{
"family": "Steenland",
"given": "Hendrik W"
},
{
"family": "Luo",
"given": "Zhengwei"
},
{
"family": "Ovcjak",
"given": "Andrea"
},
{
"family": "Hu",
"given": "Ruiyan"
},
{
"family": "Sugita",
"given": "Shuzo"
},
{
"family": "Takehara-Nishiuchi",
"given": "Kaori"
},
{
"family": "Milosevic",
"given": "Luka"
},
{
"family": "Feng",
"given": "Zhong-Ping"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "4",
"page": "115003",
"DOI": "10.1016/j.isci.2026.115003",
"PMID": "42006293",
"PMCID": "PMC13091419",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.115003",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
28
]
]
}
}

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Spatiotemporal patterns differentiate hippocampal sharp-wave ripples from interictal epileptiform discharges in mice and humans.
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