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SUR1-TRPM4 is expressed in human epilepsy and promotes neuron hyperactivity and seizures in rodents.

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The authors' code

MATLAB · 341 lines · 13 KB · MIT

  1. % Calcium Response Time Series Analysis
  2. % Created By: Ujwal Boddeti, BS
  3. % Created Date: 10/9/24
  4. % Last Editted Date: 10/29/24
  5. %
  6. % Aim: ***
  7. %
  9. %% Open data files
  10. clear all
  11. close all
  12. num_cells_wt = 19;
  13. num_cells_delta = 18;
  14. fs = 1000;
  15. temp = readtable("Ca_Imaging_Analysis.xlsx");
  16. times = table2array(temp(:,1));
  17. ca_wt_time_series = [table2array(temp(:,2)) table2array(temp(:,3)) table2array(temp(:,4)) table2array(temp(:,5)) table2array(temp(:,6)) table2array(temp(:,7)) table2array(temp(:,8)) table2array(temp(:,9)) table2array(temp(:,10)) table2array(temp(:,11)) table2array(temp(:,12)) table2array(temp(:,13)) table2array(temp(:,14)) table2array(temp(:,15)) table2array(temp(:,16)) table2array(temp(:,17)) table2array(temp(:,18)) table2array(temp(:,19)) table2array(temp(:,20))];
  18. ca_delta_time_series = [table2array(temp(:,22)) table2array(temp(:,23)) table2array(temp(:,24)) table2array(temp(:,25)) table2array(temp(:,26)) table2array(temp(:,27)) table2array(temp(:,28)) table2array(temp(:,29)) table2array(temp(:,30)) table2array(temp(:,31)) table2array(temp(:,32)) table2array(temp(:,33)) table2array(temp(:,34)) table2array(temp(:,35)) table2array(temp(:,36)) table2array(temp(:,37)) table2array(temp(:,38)) table2array(temp(:,39))];
  19. analysis_results = struct;
  21. %% Figure 1
  22. figure;
  23. t = tiledlayout(1,2,"TileSpacing","compact","Padding","compact");
  24. t1 = nexttile(1);
  25. plot(ca_wt_time_series);
  26. title("WT");
  27. xlabel("Time (sec)");
  28. ylabel("\DeltaF/F0","Interpreter","tex");
  30. t2 = nexttile(2);
  31. plot(ca_delta_time_series);
  32. title("Delta");
  33. xlabel("Time (sec)");
  34. ylabel("\DeltaF/F0","Interpreter","tex");
  36. linkaxes([t1,t2],'y');
  38. %% Figure 2
  39. figure;
  40. t = tiledlayout('flow',"TileSpacing","compact","Padding","compact");
  41. nexttile; hold on
  42. plot(mean(ca_wt_time_series,2),'Color','red','DisplayName',"WT");
  43. plot(mean(ca_delta_time_series,2),'Color','blue','DisplayName',"Delta");
  44. xlim([0 605]);
  45. title("Calcium Response");
  46. xlabel("Time (sec)");
  47. ylabel("\DeltaF/F0","Interpreter","tex");
  48. legend;
  50. %% Figure 3
  51. figure;
  52. t = tiledlayout(1,3,"TileSpacing","compact","Padding","compact");
  53. t1 = nexttile;
  54. hold on
  55. plot(diff(mean(ca_wt_time_series,2)),'Color','red','DisplayName',"WT");
  56. plot(diff(mean(ca_delta_time_series,2)),'Color','blue','DisplayName',"Delta");
  57. title("Calcium Response (WT and Delta)");
  58. xlabel("Time (sec)");
  59. ylabel("\DeltaF/F0","Interpreter","tex");
  60. legend;
  61. t2 = nexttile;
  62. plot(diff(mean(ca_wt_time_series,2)),'Color','red','DisplayName',"WT");
  63. title("Calcium Response (WT)");
  64. xlabel("Time (sec)");
  65. ylabel("\DeltaF/F0","Interpreter","tex");
  66. t3 = nexttile;
  67. plot(diff(mean(ca_delta_time_series,2)),'Color','blue','DisplayName',"Delta");
  68. xlim([0 605]);
  69. title("Calcium Response (Delta)");
  70. xlabel("Time (sec)");
  71. ylabel("\DeltaF/F0","Interpreter","tex");
  72. linkaxes([t1,t2,t3],'y');
  74. %% Figure 4
  75. axes = [];
  76. figure;
  77. t = tiledlayout('flow',"TileSpacing","compact","Padding","compact");
  78. diff_wt_time_series = [];
  79. for ii = 1:num_cells_wt
  80. t1 = nexttile;
  81. hold on
  82. Raw = plot(ca_wt_time_series(:,ii),'Color','red','HandleVisibility', 'off');
  83. AOC = area(ca_wt_time_series(:,ii), 'facecolor', [254 220 220]/256, 'HandleVisibility', 'off');
  84. uistack(Raw,'top');
  85. legend('boxoff')
  86. title("Cell "+ii);
  87. xlabel("Time (sec)");
  88. ylabel("\DeltaF/F0","Interpreter","tex");
  89. legend;
  90. axes = [axes t1];
  92. diff_wt_time_series = [diff_wt_time_series diff(ca_wt_time_series(:,ii))];
  93. end
  94. title(t,"Wildtype Ca Response");
  96. %% Figure 5
  97. figure;
  98. t = tiledlayout('flow',"TileSpacing","compact","Padding","compact");
  99. diff_delta_time_series = [];
  100. for ii = 1:num_cells_delta
  101. t2 = nexttile;
  102. hold on
  103. Raw = plot(ca_delta_time_series(:,ii),'Color','blue','HandleVisibility', 'off');
  104. AOC = area(ca_delta_time_series(:,ii), 'facecolor', [222 242 254]/256, 'HandleVisibility', 'off');
  105. uistack(Raw,'top');
  106. legend('boxoff')
  107. title("Cell "+ii);
  108. xlabel("Time (sec)");
  109. ylabel("\DeltaF/F0","Interpreter","tex");
  110. legend;
  111. axes = [axes t2];
  113. diff_delta_time_series = [diff_delta_time_series diff(ca_delta_time_series(:,ii))];
  114. end
  115. title(t,"Delta Ca Response");
  116. linkaxes(axes,'y');
  118. % Analysis for baseline slope
  119. wt_slopes_baseline = [];
  120. for ii = 1:num_cells_wt
  121. diff_signal = diff(ca_wt_time_series(:,ii));
  122. amplitude_threshold = mean(diff_signal)+6*std(diff_signal);
  123. [~,locs] = findpeaks(diff_signal,'MinPeakHeight',amplitude_threshold);
  124. if isempty(locs)
  125. x1 = 0;
  126. x2 = 90;
  127. y1 = ca_wt_time_series(1,ii);
  128. y2 = ca_wt_time_series(x2,ii);
  129. else
  130. x1 = 0;
  131. x2 = times(locs(1));
  132. y1 = ca_wt_time_series(1,ii);
  133. y2 = ca_wt_time_series(locs(1),ii);
  134. end
  135. dydt = (y2-y1)/(x2-x1);
  136. if x2>90;x2_area=90; end
  137. wt_slopes_baseline = [wt_slopes_baseline; x2 y2 dydt trapz(ca_wt_time_series(1:x2_area,ii)) trapz(ca_wt_time_series(x2_area:end,ii)) trapz(ca_wt_time_series(:,ii)) max(ca_wt_time_series(:,ii))];
  138. end
  140. delta_slopes_baseline = [];
  141. for ii = 1:num_cells_delta
  142. diff_signal = diff(ca_delta_time_series(:,ii));
  143. amplitude_threshold = mean(diff_signal)+6*std(diff_signal);
  144. [pks,locs] = findpeaks(diff_signal,'MinPeakHeight',amplitude_threshold);
  145. if isempty(locs)
  146. x1 = 0;
  147. x2 = 90;
  148. y1 = ca_delta_time_series(1,ii);
  149. y2 = ca_delta_time_series(x2,ii);
  150. else
  151. x1 = 0;
  152. x2 = times(locs(1));
  153. y1 = ca_delta_time_series(1,ii);
  154. y2 = ca_delta_time_series(locs(1),ii);
  155. end
  156. dydt = (y2-y1)/(x2-x1);
  157. if x2>90;x2_area=90; end
  158. delta_slopes_baseline = [delta_slopes_baseline; x2 y2 dydt trapz(ca_delta_time_series(1:x2_area,ii)) trapz(ca_delta_time_series(x2_area:end,ii)) trapz(ca_delta_time_series(:,ii)) max(ca_delta_time_series(:,ii))];
  159. end
  161. analysis_results.wt.slopes_baseline = wt_slopes_baseline(:,3);
  162. analysis_results.delta.slopes_baseline = delta_slopes_baseline(:,3);
  164. % Analysis for instantaneous spiking rate
  165. %% Figure 6
  166. figure;
  167. t = tiledlayout('flow',"TileSpacing","compact","Padding","compact");
  168. wt_slopes_low_mg = [];
  169. wt_low_mg_amplitudes = [];
  170. axes = [];
  171. for ii = 1:num_cells_wt
  172. t1 = nexttile;
  173. hold on
  174. signal = smoothdata(smoothdata(ca_wt_time_series(:,ii),'gaussian'),'sgolay');
  175. findpeaks(signal)
  176. [~,locs] = findpeaks(signal);
  177. x1 = wt_slopes_baseline(ii,1);
  178. y1 = wt_slopes_baseline(ii,2);
  180. if isempty(locs)
  181. [max_1, index] = max(signal);
  182. x2 = index;
  183. y2 = max_1;
  184. else
  185. x2 = locs(1);
  186. y2 = signal(x2);
  187. if y2 < 0
  188. x2 = locs(2);
  189. y2 = signal(x2);
  190. end
  191. end
  192. dydt = (y2-y1)/(x2-x1);
  193. wt_low_mg_amplitudes = [wt_low_mg_amplitudes; y2];
  194. wt_slopes_low_mg = [wt_slopes_low_mg; dydt];
  196. plot(signal,'Color','blue','DisplayName','Raw Trace');
  197. title("Cell "+ii);
  198. xlabel("Time (sec)");
  199. ylabel("\DeltaF/F0","Interpreter","tex");
  200. axes = [axes t1];
  201. end
  202. title(t,"Instantaneous WT Ca Response");
  203. linkaxes(axes,'y');
  205. %% Figure 7
  206. figure;
  207. t = tiledlayout('flow',"TileSpacing","compact","Padding","compact");
  208. delta_slopes_low_mg = [];
  209. delta_low_mg_amplitudes = [];
  210. axes = [];
  211. for ii = 1:num_cells_delta
  212. t1 = nexttile;
  213. hold on
  214. signal = smoothdata(smoothdata(ca_delta_time_series(:,ii),'gaussian'),'sgolay');
  215. findpeaks(signal)
  216. [~,locs] = findpeaks(signal);
  217. x1 = delta_slopes_baseline(ii,1);
  218. y1 = delta_slopes_baseline(ii,2);
  220. if isempty(locs)
  221. [max_1, index] = max(signal);
  222. x2 = index;
  223. y2 = max_1;
  224. else
  225. x2 = locs(1);
  226. y2 = signal(x2);
  227. end
  228. dydt = (y2-y1)/(x2-x1);
  229. delta_low_mg_amplitudes = [delta_low_mg_amplitudes; y2];
  230. delta_slopes_low_mg = [delta_slopes_low_mg; dydt];
  232. plot(signal,'Color','blue','DisplayName','Raw Trace');
  233. title("Cell "+ii);
  234. xlabel("Time (sec)");
  235. ylabel("\DeltaF/F0","Interpreter","tex");
  236. axes = [axes t1];
  237. end
  238. title(t,"Instantaneous Delta Ca Response");
  239. linkaxes(axes,'y');
  241. analysis_results.wt.amplitudes_low_mg = wt_low_mg_amplitudes;
  242. analysis_results.delta.amplitudes_delta = delta_low_mg_amplitudes;
  244. analysis_results.wt.slopes_low_mg = wt_slopes_low_mg;
  245. analysis_results.delta.slopes_delta = delta_slopes_low_mg;
  247. %% Spike frequency analysis
  248. spike_detection_threshold = 2;
  249. refractory_period = 2;
  251. ca_wt_spike_frequencies = [];
  252. ca_wt_spike_amplitude = [];
  253. for ii = 1:num_cells_wt
  254. low_mg_time_series_segment = diff(ca_wt_time_series(round(wt_slopes_baseline(ii,1)):end,ii));
  255. spike_threshold = mean(low_mg_time_series_segment) + spike_detection_threshold*std(low_mg_time_series_segment);
  256. [pks,locs] = findpeaks(low_mg_time_series_segment,'MinPeakHeight',0.01,'MinPeakDistance',refractory_period);
  257. third_length = round(length(low_mg_time_series_segment)/3);
  258. first_bin_spike_frequency = length(locs(locs<third_length))/third_length;
  259. second_bin_spike_frequency = length(locs(locs>=third_length & locs<=third_length*2))/third_length;
  260. third_bin_spike_frequency = length(locs(locs>third_length*2))/third_length;
  261. ca_wt_spike_frequencies = [ca_wt_spike_frequencies; first_bin_spike_frequency second_bin_spike_frequency third_bin_spike_frequency];
  263. first_bin_spike_amplitude = mean(pks(locs<third_length),'all','omitnan');
  264. second_bin_spike_amplitude = mean(pks(locs>=third_length & locs<=third_length*2),'all','omitnan');
  265. third_bin_spike_amplitude = mean(pks(locs>third_length*2),'all','omitnan');
  266. ca_wt_spike_amplitude = [ca_wt_spike_amplitude; first_bin_spike_amplitude second_bin_spike_amplitude third_bin_spike_amplitude];
  267. end
  268. ca_wt_spike_frequencies = ca_wt_spike_frequencies';
  269. ca_wt_spike_amplitude = ca_wt_spike_amplitude';
  271. ca_delta_spike_frequencies = [];
  272. ca_delta_spike_amplitude = [];
  273. for ii = 1:num_cells_delta
  274. low_mg_time_series_segment = diff(ca_delta_time_series(round(delta_slopes_baseline(ii,1)):end,ii));
  275. spike_threshold = mean(low_mg_time_series_segment) + spike_detection_threshold*std(low_mg_time_series_segment);
  276. [pks,locs] = findpeaks(low_mg_time_series_segment,'MinPeakHeight',0.01,'MinPeakDistance',refractory_period);
  277. third_length = round(length(low_mg_time_series_segment)/3);
  278. first_bin_spike_frequency = length(locs(locs<third_length))/third_length;
  279. second_bin_spike_frequency = length(locs(locs>=third_length & locs<=third_length*2))/third_length;
  280. third_bin_spike_frequency = length(locs(locs>third_length*2))/third_length;
  281. ca_delta_spike_frequencies = [ca_delta_spike_frequencies; first_bin_spike_frequency second_bin_spike_frequency third_bin_spike_frequency];
  283. first_bin_spike_amplitude = mean(pks(locs<third_length),'all','omitnan');
  284. second_bin_spike_amplitude = mean(pks(locs>=third_length & locs<=third_length*2),'all','omitnan');
  285. third_bin_spike_amplitude = mean(pks(locs>third_length*2),'all','omitnan');
  286. ca_delta_spike_amplitude = [ca_delta_spike_amplitude; first_bin_spike_amplitude second_bin_spike_amplitude third_bin_spike_amplitude];
  288. end
  289. ca_delta_spike_frequencies = ca_delta_spike_frequencies';
  290. ca_delta_spike_amplitude = ca_delta_spike_amplitude';
  292. analysis_results.wt.spike_freq = ca_wt_spike_frequencies;
  293. analysis_results.delta.spike_freq = ca_delta_spike_frequencies;
  295. analysis_results.wt.spike_amplitude = ca_wt_spike_amplitude;
  296. analysis_results.delta.spike_amplitude = ca_delta_spike_amplitude;
  298. % Figure 8
  299. figure;
  300. t1 = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
  301. ax1 = nexttile;
  302. plot(analysis_results.wt.spike_freq,'Marker','.','MarkerSize',40);
  303. title("WT Spike Frequencies");
  304. ax2 = nexttile;
  305. plot(analysis_results.delta.spike_freq,'Marker','.','MarkerSize',40);
  306. title("Delta Spike Frequencies");
  307. ax3 = nexttile;
  308. plot(mean(analysis_results.wt.spike_freq,2),'Marker','.','MarkerSize',40);
  309. title("Average WT Spike Frequencies");
  310. ax4 = nexttile;
  311. plot(mean(analysis_results.delta.spike_freq,2),'Marker','.','MarkerSize',40);
  312. title("Average Delta Spike Frequencies");
  314. linkaxes([ax1 ax2 ax3 ax4],'y');
  316. % Figure 9
  317. figure;
  318. t1 = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
  319. ax1 = nexttile;
  320. plot(analysis_results.wt.spike_amplitude,'Marker','.','MarkerSize',40);
  321. title("WT Spike Amplitude");
  322. ax2 = nexttile;
  323. plot(analysis_results.delta.spike_amplitude,'Marker','.','MarkerSize',40);
  324. title("Delta Spike Ampltiude");
  325. ax3 = nexttile;
  326. plot(mean(analysis_results.wt.spike_amplitude,2,"omitnan"),'Marker','.','MarkerSize',40);
  327. title("Average WT Spike Amplitude");
  328. ax4 = nexttile;
  329. plot(mean(analysis_results.delta.spike_amplitude,2,"omitnan"),'Marker','.','MarkerSize',40);
  330. title("Average Delta Spike Amplitude");
  332. linkaxes([ax1 ax2 ax3 ax4],'y');
  334. %%
  335. figure;
  336. t1 = tiledlayout(1,2,'Padding','compact','TileSpacing','compact');
  337. ax1 = nexttile;
  338. plot(mean(diff_wt_time_series,2));
  339. ax2 = nexttile;
  340. plot(mean(diff_delta_time_series,2));
  341. linkaxes([ax1 ax2],'y');

cellCaAnalysis.m, under MIT · at the source

Overview

Authors: Mitchell B Moyer1, Svetlana Ivanova1, Kaspar Keledjian1, Matthew Kreinbrink1, Jenna Langbein1, Penghua Yang1, Darrian McAfee1, Ujwal Boddeti1, Ziam Khan1, Jemima Olu-Owotade1, Timothy Zhang2, David R Benavides2, Joshua Diamond3, Kareem Zaghloul3, Muznabanu Bachani1, Volodymyr Gerzanich1, J Marc Simard1,4,5, Alexander Ksendzovsky1
ORCID iDs: Mitchell B Moyer, Darrian McAfee, Ziam Khan, David R Benavides, Kareem Zaghloul
  1. Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
  2. Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
  3. Surgical Neurology Branch, National Institute of Neurologic Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
  4. Department of Pathology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
  5. Department of Physiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Institutions: University of Maryland, Baltimore (United States); National Institutes of Health (United States); National Institute of Neurological Disorders and Stroke (United States)
Journal: Brain : a journal of neurology, volume 149, issue 6, pages 2124-2138
Dates: received 13 May 2025; accepted 7 October 2025; published online 15 November 2025; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/brain/awaf435 · PMID 41239869 · PMCID PMC13232044 · OpenAlex W4416248647
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), mouse (organism), rat (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: SUR1-TRPM4, epilepsy, seizure threshold, ion channels, channelopathy, hyperexcitation
MeSH: Epilepsy*, Neurons*, Seizures*, TRPM Cation Channels*, Adult, Animals, Female, Humans, Male, Mice, Mice, Inbred C57BL, Middle Aged, Pentylenetetrazole, Rats (* major topic)
Topic: Ion Channels and Receptors (Sensory Systems, Neuroscience), according to OpenAlex
Funding: National Institutes of Health; NIH HHS
Citations: cited by 2 papers (Europe PMC); 81 references in the paper

Abstract

One-third of epilepsy patients do not achieve sufficient seizure freedom with current standard anti-seizure medications. Better understanding of the pathological mechanisms contributing to epileptogenesis is thus necessary to improve current therapies. SUR1-TRPM4 is a depolarizing ion channel minimally expressed in a healthy brain that is upregulated de novo in neurons and glia after epileptogenic CNS injuries such as traumatic brain injury and stroke. However, its role in epilepsy is not well understood.

Here, we demonstrate using immunofluorescent microscopy that SUR1-TRPM4 expression is elevated in neurons within an electrographically sorted human epileptic brain compared with a non-epileptic brain obtained after resection from six drug-resistant temporal lobe epilepsy patients. Additionally, we utilized immunofluorescence and co-immunoprecipitation to observe that SUR1-TRPM4 is upregulated within the hippocampus and temporal cortex in mice after pentylenetetrazol (PTZ) kindling, a chronic model of rodent epilepsy. Pharmacologic inhibition of SUR1-TRPM4 using either the US Food and Drug Administration (FDA)-approved drug glyburide or 9-phenanthrol, as well as either constitutive or neuron-specific knockout of this channel, attenuated chronic seizure development in this model. Exogenous overexpression of SUR1-TRPM4 by plasmid transfection in neurons in vitro increased neuronal hyperexcitability in response to low Mg2+ stimulation, while pharmacologic inhibition of endogenous TRPM4 attenuated neuronal population hyperexcitation.

Collectively, our results reveal that elevated SUR1-TRPM4 expression found in human and rodent epileptic neurons promotes chronic seizures by increasing neuronal excitation. These findings directly support clinical investigation of SUR1-TRPM4 inhibitors as potential anti-seizure therapies in epilepsy patients and suggest further investigations into the contribution of SUR1-TRPM4 to seizures induced by specific epileptogenic insults, such as traumatic brain injury (TBI), are warranted.

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

Repositories

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Zenodo 14908460

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: doi.org/10.5281/zenodo.14908460

brain-research-laboratory/sur1-trpm4

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e145f4874996b107bff1e1a4d37632d444c7a809, 29 January 2025
Languages: MATLAB (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/brain-research-laboratory/sur1-trpm4/tree/v1.0.0

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

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Data

No dataset and no data link were found in the paper.

Data availability

All data are available in the main text or the Supplementary material. All reported data will be shared by the corresponding author upon request. All original code has been deposited at Github and is publicly available at https://doi.org/10.5281/zenodo.14908460 as of the date of publication.

Reproduced under the paper's license (CC BY), 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, 18 authors, 6 keywords, 14 MeSH terms, 2 funders, 80 references.

Cite

This paper

Moyer, M. B., Ivanova, S., Keledjian, K., Kreinbrink, M., Langbein, J., Yang, P., McAfee, D., Boddeti, U., Khan, Z., Olu-Owotade, J., Zhang, T., Benavides, D. R., Diamond, J., Zaghloul, K., Bachani, M., Gerzanich, V., Simard, J. M., & Ksendzovsky, A. (2026). SUR1-TRPM4 is expressed in human epilepsy and promotes neuron hyperactivity and seizures in rodents. Brain : a journal of neurology, 149(6), 2124-2138. https://doi.org/10.1093/brain/awaf435

BibTeX

@article{moyer2026sur1,
author = {Moyer, Mitchell B and Ivanova, Svetlana and Keledjian, Kaspar and Kreinbrink, Matthew and Langbein, Jenna and Yang, Penghua and McAfee, Darrian and Boddeti, Ujwal and Khan, Ziam and Olu-Owotade, Jemima and Zhang, Timothy and Benavides, David R and Diamond, Joshua and Zaghloul, Kareem and Bachani, Muznabanu and Gerzanich, Volodymyr and Simard, J Marc and Ksendzovsky, Alexander},
title = {{SUR1-TRPM4 is expressed in human epilepsy and promotes neuron hyperactivity and seizures in rodents}},
journal = {Brain : a journal of neurology},
year = {2026},
month = jun,
volume = {149},
number = {6},
pages = {2124--2138},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awaf435},
url = {https://doi.org/10.1093/brain/awaf435},
pmid = {41239869},
pmcid = {PMC13232044}
}

RIS

TY - JOUR
AU - Moyer, Mitchell B
AU - Ivanova, Svetlana
AU - Keledjian, Kaspar
AU - Kreinbrink, Matthew
AU - Langbein, Jenna
AU - Yang, Penghua
AU - McAfee, Darrian
AU - Boddeti, Ujwal
AU - Khan, Ziam
AU - Olu-Owotade, Jemima
AU - Zhang, Timothy
AU - Benavides, David R
AU - Diamond, Joshua
AU - Zaghloul, Kareem
AU - Bachani, Muznabanu
AU - Gerzanich, Volodymyr
AU - Simard, J Marc
AU - Ksendzovsky, Alexander
TI - SUR1-TRPM4 is expressed in human epilepsy and promotes neuron hyperactivity and seizures in rodents
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/06/01
VL - 149
IS - 6
SP - 2124
EP - 2138
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awaf435
UR - https://doi.org/10.1093/brain/awaf435
LA - en
ER -

CSL-JSON

{
"id": "10.1093/brain/awaf435",
"type": "article-journal",
"title": "SUR1-TRPM4 is expressed in human epilepsy and promotes neuron hyperactivity and seizures in rodents",
"container-title": "Brain : a journal of neurology",
"author": [
{
"family": "Moyer",
"given": "Mitchell B"
},
{
"family": "Ivanova",
"given": "Svetlana"
},
{
"family": "Keledjian",
"given": "Kaspar"
},
{
"family": "Kreinbrink",
"given": "Matthew"
},
{
"family": "Langbein",
"given": "Jenna"
},
{
"family": "Yang",
"given": "Penghua"
},
{
"family": "McAfee",
"given": "Darrian"
},
{
"family": "Boddeti",
"given": "Ujwal"
},
{
"family": "Khan",
"given": "Ziam"
},
{
"family": "Olu-Owotade",
"given": "Jemima"
},
{
"family": "Zhang",
"given": "Timothy"
},
{
"family": "Benavides",
"given": "David R"
},
{
"family": "Diamond",
"given": "Joshua"
},
{
"family": "Zaghloul",
"given": "Kareem"
},
{
"family": "Bachani",
"given": "Muznabanu"
},
{
"family": "Gerzanich",
"given": "Volodymyr"
},
{
"family": "Simard",
"given": "J Marc"
},
{
"family": "Ksendzovsky",
"given": "Alexander"
}
],
"container-title-short": "Brain",
"volume": "149",
"issue": "6",
"page": "2124-2138",
"DOI": "10.1093/brain/awaf435",
"PMID": "41239869",
"PMCID": "PMC13232044",
"ISSN": "0006-8950",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/brain/awaf435",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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