OSCR

Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing.

Code ↔ Paper

9 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 9 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Kinematic recordings and analysis ↔ Fig2_F_G_Amplitude.m, lines 4–58 · score 0.82 · DeepLabCut, upper lip, lower lip, facial tasks, nose, velocity
  2. [2] § Methods › Analysis of videofluoroscopic swallow study ↔ InterraterReliabilityMBS.m, lines 1–20 · score 0.79 · interrater reliability, quadratic weighted Cohen, scores, agreement, kappa, Swallowing
  3. [3] § Methods › Speech assessment › Formant frequencies ↔ fig4C_formantChangePlots.m, lines 1–38 · score 0.71 · vowel phoneme, median filter, formant, spline, warping, pre
  4. [4] § Methods › Motor-evoked potential experiments ↔ Fig2_B_C_TMS_MEP.m, lines 1–52 · score 0.66 · motor evoked potentials, pulse, TMS, MASS, oris, MEPs
  5. [5] § Methods › Motor-evoked potential experiments ↔ Fig1_intraOp_comparison_contra_vs_ipsi_muscle.m, lines 1–56 · score 0.64 · stimulation frequencies, MENT, MYLO, CRICO, EMG, MASS
  6. [6] § Methods › Kinematic recordings and analysis ↔ Fig2_H_plotDLC_area_20230630.m, lines 61–117 · score 0.63 · upper lip, lower lip, nose, cheek, chin, jaw
  7. [7] § Methods › Motor-evoked potential experiments ↔ Fig2_B_C_TMS_MEP.m, lines 1–52 · score 0.62 · low pass filtered, pulses, TMS, evoke, MEPs, stimulus
  8. [8] § Methods › Statistical procedures ↔ utilities/bootstrapCompMeans.m, the whole file · a weak match · score 0.60 · confidence interval, replacement, resampling, rejected, alphas, bootstrap
  9. [9] § Methods › Statistical procedures ↔ Fig2_H_plotDLC_area_20230630.m, lines 327–362 · score 0.59 · confidence interval, replacement, resampling, rejected, alphas, bootstrap

Paper

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

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The authors' code

MATLAB · 311 lines · 9.7 KB · MIT · 2 matches

  1. %% Fig2_B_C_TMS_MEP
  2. % TMS motor evoked potential (MEP) analysis comparing no-stimulation and
  3. % DBS conditions across facial and hand muscles.
  4. %
  5. % Creates figures 2B and 2C for the manuscript.
  6. %
  7. % Generates the following figures:
  8. % F Per-muscle QC traces: raw vs. low-pass filtered, one figure per condition (not saved)
  9. % T All traces overlaid per condition, raw and filtered (not saved)
  10. % G Peak-to-peak and AUC boxplots across conditions
  11. % V Percentage increase in AUC from no-stim baseline (saved)
  12. %
  13. % Required Data:
  14. % Fig2_B_Nostim.mat TMS recording during no-stimulation condition
  15. % Fig2_B_55Hz.mat TMS recording during 55 Hz DBS condition
  16. % Each file contains per-muscle snip arrays and a snipTime vector.
  17. % MEP data is stored as: Data.(muscleName) — cell array of {avg, snips}
  18. %
  19. % User Configuration:
  20. % pathName Path to the folder containing the .mat data files
  21. % savePATH Path to the folder where figures will be saved
  22. % Trial2Load Which body part to analyze: 'Face' or 'Hand'
  23. % savePlots Set to 1 to save figures as PDF, 0 to skip saving
  24. %
  25. % Created by Lilly Tang, Erinn Grigsby, and Arianna Damiani
  26. % Copyright (C) 2026
  28. clear, close all
  30. % Data path information
  31. curPath = pwd; addpath(genpath(curPath));
  32. pathName = '/Users/zira/Data/mThal_NatComm_2026';
  33. savePATH = '/Users/zira/analysisFigures/mThal_NatComm_2026_figs';
  34. savePlots = 0; % Set to 1 to save figures as PDF
  36. % General variables for the function
  37. muscles = {'L_APB' 'L_MASS' 'L_ORIS' 'R_APB' 'R_MASS' 'R_ORIS'};
  38. stim = {'noStim' '55Hz Tremor'};
  39. Fs = 2500; % Sampling rate (Hz)
  41. % Trial2Load selects the body part and sets the MEP time window (in ms)
  42. % and the .mat filenames to load.
  43. Trial2Load = 'Face';
  45. switch Trial2Load
  46. case 'Face'
  47. Trial = {'Fig2_B_Nostim','Fig2_B_55Hz'};
  48. TimeWindow = [75 100]; % MEP window in ms post-TMS pulse
  49. case 'Hand' % Not used in the current manuscript but code is set up to easily switch to hand data if desired
  50. Trial = {'5' '13'};
  51. TimeWindow = [100 140];
  52. end
  54. %% Load data
  55. D = dir(fullfile(pathName,'*.mat'));
  57. for s = 1:length(Trial)
  58. for d = 1:size(D,1)
  59. if contains(D(d).name, Trial{s})
  60. Data(s) = load(fullfile(D(d).folder, D(d).name));
  61. end
  62. end
  63. end
  65. % Extract MEP snip traces from the loaded data structs.
  66. % Each muscle field contains {avg, snips} — index 2 gives the individual trial snips.
  67. fieldN = fieldnames(Data);
  69. for m = 1:length(muscles)
  70. idxM = find(contains(fieldN, muscles{m}));
  71. if ~isempty(idxM)
  72. for s = 1:length(Data)
  73. for i = 1:length(idxM)
  74. MEPsnips{i} = Data(s).(fieldN{idxM(i)});
  75. end
  76. MEPtraces{s,m} = MEPsnips{2}; % Individual trial snips (not the average)
  77. snipTime{s} = Data(s).snipTime;
  78. end
  79. end
  80. end
  82. %% Low-pass filter and QC traces
  83. % Creates one figure per (stim × muscle) for visual inspection. Not saved.
  84. close all
  86. count = 1;
  88. % Compute filter coefficients once
  89. [Bbp, Abp] = butter(4, 100/(Fs/2), 'low');
  91. % Iterate through all the muscles and simtimulation parameters
  92. for s = 1:size(MEPtraces,1)
  93. for m = 1:size(MEPtraces,2)
  96. F(count) = figure;
  97. F(count).Name = sprintf('%s %s Lowpass 100Hz', stim{s}, muscles{m});
  99. for ii = 1:size(MEPtraces{s,m},1)
  100. filtMEP{s,m}(ii,:) = filtfilt(Bbp, Abp, MEPtraces{s,m}(ii,:));
  102. ax(1) = subplot(2,1,1); hold on
  103. plot(snipTime{s}, MEPtraces{s,m}(ii,:))
  104. title('Raw')
  106. ax(2) = subplot(2,1,2); hold on
  107. plot(snipTime{s}, filtMEP{s,m}(ii,:))
  108. title('Low-pass (100 Hz)')
  109. xlabel('Time (ms)')
  111. % Overlay the trial mean in black on the last rep
  112. if ii == size(MEPtraces{s,m},1)
  113. subplot(2,1,1), hold on
  114. plot(snipTime{s}, mean(MEPtraces{s,m},1), 'LineWidth',2, 'Color','k')
  115. subplot(2,1,2), hold on
  116. plot(snipTime{s}, mean(filtMEP{s,m},1), 'LineWidth',2, 'Color','k')
  117. end
  118. sgtitle(sprintf('%s %s', muscles{m}, stim{s}), 'Interpreter','none')
  119. end
  120. linkaxes(ax, 'xy')
  121. xlim([60 200])
  122. count = count + 1;
  123. end
  124. end
  126. %% All traces overlaid per condition
  127. % Layout: rows = muscles, columns = raw | filtered. Not saved.
  128. close all
  130. tracefilt = {'Raw' 'Filtered'};
  132. for s = 1:size(MEPtraces,1)
  133. T(s) = figure; hold on
  134. T(s).Name = sprintf('MEP traces %s', stim{s});
  136. for f = 1:2
  137. if f == 1
  138. traces2Plot = MEPtraces;
  139. else
  140. traces2Plot = filtMEP;
  141. end
  143. for m = 1:size(traces2Plot,2)
  144. % figIdx maps (muscle, filter type) to subplot index in a
  145. % nMuscle × 2 grid (columns = raw/filtered)
  146. figIdx = (m-1)*2 + f;
  147. subplot(size(traces2Plot,2), size(traces2Plot,1), figIdx); hold on
  149. for ii = 1:size(traces2Plot{s,m},1)
  150. plot(snipTime{s}, traces2Plot{s,m}(ii,:))
  151. end
  152. plot(snipTime{s}, mean(traces2Plot{s,m}), 'LineWidth',2, 'Color','k')
  153. xlabel('Time (ms)')
  155. if f == 1
  156. ylabel(muscles{m}, 'Interpreter','none', 'FontWeight','bold')
  157. end
  158. if m == 1
  159. title(tracefilt{f})
  160. end
  161. end
  162. end
  163. sgtitle(stim{s})
  164. end
  166. %% No-stim vs. stim comparison traces
  167. % Layout: rows = muscles, columns = stim conditions. Not saved.
  168. close all
  170. for f = 1:2
  171. T(f) = figure; hold on
  172. T(f).Name = sprintf('MEP traces %s', tracefilt{f});
  174. if f == 1
  175. traces2Plot = MEPtraces;
  176. else
  177. traces2Plot = filtMEP;
  178. end
  180. for s = 1:size(traces2Plot,1)
  181. for m = 1:size(traces2Plot,2)
  182. % figIdx maps (muscle, stim) to subplot index in a nMuscle × nStim grid
  183. figIdx = (m-1)*2 + s;
  184. Tx(figIdx) = subplot(size(traces2Plot,2), size(traces2Plot,1), figIdx); hold on
  186. for ii = 1:size(traces2Plot{s,m},1)
  187. plot(snipTime{s}, traces2Plot{s,m}(ii,:))
  188. end
  189. plot(snipTime{s}, mean(traces2Plot{s,m}), 'LineWidth',2, 'Color','k')
  190. xlabel('Time (ms)')
  191. ylabel(muscles{m}, 'Interpreter','none', 'FontWeight','bold')
  192. xlim([60 120])
  194. if m == 1
  195. title(stim{s})
  196. end
  197. end
  198. end
  200. % Link axes for each muscle across stim conditions so zoom/pan stays in sync
  201. nMuscle = size(traces2Plot,2);
  202. nStim = size(traces2Plot,1);
  203. for m = 1:nMuscle
  204. linkaxes(Tx((m-1)*nStim + (1:nStim)));
  205. end
  207. sgtitle(tracefilt{f})
  208. end
  210. %% Peak-to-peak and AUC within the MEP window
  211. close all
  213. for s = 1:size(filtMEP,1)
  214. for m = 1:size(filtMEP,2)
  215. % Find time indices corresponding to the MEP window
  216. [~, startIdx] = min(abs(snipTime{s} - TimeWindow(1)));
  217. [~, stopIdx] = min(abs(snipTime{s} - TimeWindow(2)));
  219. for ii = 1:size(filtMEP{s,m},1)
  220. peakwindow = filtMEP{s,m}(ii, startIdx:stopIdx);
  222. % Subtract the mean of the first 3 samples as a local baseline,
  223. % then take absolute value so peak-to-peak is always positive
  224. adj_peakwindow = abs(peakwindow - mean(peakwindow(1:3)));
  226. aucMat{s,m}(ii,:) = trapz(adj_peakwindow);
  227. p2pMat{s,m}(ii,:) = peak2peak(adj_peakwindow);
  228. end
  230. % Remove statistical outliers before plotting and computing group stats
  231. p2pCleaned{s,m} = rmoutliers(p2pMat{s,m});
  232. aucCleaned{s,m} = rmoutliers(aucMat{s,m});
  233. end
  234. end
  236. ColorMap = {'#0072BD','#D95319','#EDB120','#7E2F8E','#77AC30','#4DBEEE','#A2142F'};
  238. G(1) = figure;
  239. G(1).Name = sprintf('P2P %s', Trial2Load);
  240. for m = 1:size(p2pMat,2)
  241. Gx(m) = subplot(2,3,m); hold on
  242. myboxplot(p2pCleaned(:,m), 'box', ColorMap, 1, Gx(m))
  243. xticks(1:size(p2pMat,1))
  244. xticklabels(stim)
  245. title(muscles{m}, 'Interpreter','none')
  246. end
  247. sgtitle('Peak-to-Peak MEPs')
  249. G(2) = figure;
  250. G(2).Name = sprintf('AUC %s', Trial2Load);
  251. for m = 1:size(aucMat,2)
  252. Dx(m) = subplot(2,3,m); hold on
  253. myboxplot(aucCleaned(:,m), 'box', ColorMap, 1, Dx(m))
  254. xticks(1:size(aucMat,1))
  255. xticklabels(stim)
  256. title(muscles{m}, 'Interpreter','none')
  257. end
  258. sgtitle('AUC MEPs')
  260. %% Percentage increase in AUC from no-stim baseline (Fig 2B, 2C)
  261. close all
  263. % Compute mean AUC per (stim, muscle) for normalization
  264. for s = 1:size(aucCleaned,1)
  265. for m = 1:size(aucCleaned,2)
  266. meanAUC(s,m) = mean(aucCleaned{s,m});
  267. end
  268. end
  270. % Individual trial scatter with condition mean
  271. V(1) = figure;
  272. V(1).Name = sprintf('Percentage Increase %s pts', Trial2Load);
  274. for s = 1:size(aucCleaned,1)
  275. for m = 1:size(aucCleaned,2)
  276. baselineAUC = meanAUC(1,m); % No-stim mean as baseline
  277. varAUCmean = (meanAUC(s,m) - baselineAUC) / baselineAUC * 100;
  279. subplot(2,3,m); hold on
  280. plot(s, varAUCmean, '-o', 'Color', ColorMap{s})
  282. for r = 1:length(aucCleaned{s,m})
  283. varAUC{s,m}(r,1) = (aucCleaned{s,m}(r) - baselineAUC) / baselineAUC * 100;
  284. plot(s, varAUC{s,m}(r,1), '.', 'Color', ColorMap{s})
  285. end
  287. xticks(1:size(aucMat,1))
  288. xticklabels(stim)
  289. ylabel('Percentage Increase')
  290. title(muscles{m}, 'Interpreter','none')
  291. end
  292. end
  293. sgtitle('Percentage Increase TMS')
  295. % Summary boxplot with bootstrap statistics
  296. V(2) = figure;
  297. V(2).Name = sprintf('Percentage Increase %s Stats', Trial2Load);
  298. for m = 1:size(aucMat,2)
  299. Vx(m) = subplot(2,3,m); hold on
  300. myboxplot(varAUC(:,m), 'box', ColorMap, 1, Vx(m))
  301. xticks(1:size(aucMat,1))
  302. xticklabels(stim)
  303. title(muscles{m}, 'Interpreter','none')
  304. end
  305. sgtitle('Percentage Increase TMS')
  307. %% Save
  308. if savePlots
  309. if ~exist(savePATH,'dir'), mkdir(savePATH); end
  310. saveFigurePDF(V, fullfile(savePATH, Trial2Load))
  311. end

Fig2_B_C_TMS_MEP.m at commit 14655cc, under MIT · at the source

Overview

Authors: Lilly W Tang1,2, Erinn M Grigsby1,3,4, Arianna Damiani1,5, Jonathan C Ho1,2,6, Isabella M Montanaro1,5, Sirisha Nouduri1,2, Scott Ensel1,3, Sara Trant7, Theodora Constantine8, Gregory M Adams8, Denise V Balason9, Tracey L Thomas10, Kelly Zdrowak11, Jordyn Ting1,3,12, George F Wittenberg1,3,5,13,14,15, Kevin Franzese3, Bradford Z Mahon16,17, Julie A Fiez9,15,18,19, Donald J Crammond8, Kaila L Stipancic11, Jorge A Gonzalez-Martinez8,15,19,20, Elvira Pirondini1,3,5,12,15
ORCID iDs: Arianna Damiani, Bradford Z Mahon, Julie A Fiez, Kaila L Stipancic, Jorge A Gonzalez-Martinez, Elvira Pirondini
20 affiliations
  1. Rehab Neural Engineering Labs, University of Pittsburgh, 1622 Locust St., Floor 4, Pittsburgh, PA USA
  2. School of Medicine, University of Pittsburgh, 3550 Terrace St, Pittsburgh, PA USA
  3. Department of Physical Medicine and Rehabilitation, University of Pittsburgh, 3471 Fifth Avenue, Suite 910, Pittsburgh, PA USA
  4. Department of Neuroscience, University of Montreal, Pavillon Paul-G.-Desmarais, 2960, chemin de la Tour, Room 2140, Montreal, Quebec, Canada
  5. Department of Bioengineering, University of Pittsburgh, 151 Benedum Hall, Pittsburgh, PA USA
  6. Department of Neurosurgery, Johns Hopkins University, 1800 Orleans St Sheikh Zayed Tower, Baltimore, MD USA
  7. Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA USA
  8. Department of Neurological Surgery, University of Pittsburgh, Medical Center, 200 Lothrop Street, Suite B-400, Pittsburgh, PA USA
  9. Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
  10. UPMC Voice, Airway, & Swallow Center, UPMC Mercy Hospital, 1400 Locust St., Pittsburgh, PA USA
  11. Department of Communicative Disorders and Sciences, University at Buffalo, 122 Cary Hall, South Campus, Buffalo, NY USA
  12. University of Pittsburgh Clinical and Translational Science Institute (CTSI), Pittsburgh, PA USA
  13. Department of Neurology, University of Pittsburgh, 3471 Fifth Avenue Ste 802, Pittsburgh, PA USA
  14. TECH-GRECC & HERL, VA Pittsburgh Healthcare System, University Dr., Pittsburgh, PA USA
  15. Center for the Neural Basis of Cognition, 4400 Fifth Avenue, Suite 115, Pittsburgh, PA USA
  16. Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA USA
  17. Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA USA
  18. Department of Communication Sciences & Disorders, University of Pittsburgh, Pittsburgh, PA USA
  19. Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA USA
  20. Department of Neurobiology, University of Pittsburgh, 200 Lothrop Street, Room E1440, Pittsburgh, PA USA
Institutions: University of Pittsburgh (United States); Université de Montréal (Canada); Johns Hopkins University (United States); University of Pittsburgh Medical Center (United States); University at Buffalo, State University of New York (United States); Center for the Neural Basis of Cognition (United States); Geriatric Research Education and Clinical Center (United States); VA Pittsburgh Healthcare System (United States); Carnegie Mellon University (United States)
Journal: Nature communications, volume 17, issue 1, article 8794
Dates: received 4 March 2025; accepted 23 June 2026; published online 18 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75588-3 · PMID 42471326 · PMCID PMC13493936 · OpenAlex W7169676337
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), traumatic brain injury (population), cognitive (subfield)
Methods: Connectivity, Statistics, Spectral & time-frequency, fMRI & imaging, Physiology & signal measures
Keywords: White matter injury, Motor cortex
MeSH: Brain Injuries, Traumatic*, Deep Brain Stimulation*, Deglutition*, Deglutition Disorders*, Speech*, Thalamus*, Adult, Dysarthria, Female, Humans, Male, Middle Aged, Motor Cortex (* major topic)
Topic: Dysphagia Assessment and Management (Speech and Hearing, Health Professions), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS131428)
Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

emg27/mThal_Face_2026

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 14655cc1b626eb0c5f44914a1fbb765e31782e89, 27 May 2026
Languages: MATLAB (26)
Size: 30 files, 26 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (5 files), Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/emg27/mThal_Face_2026

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-75588-3.

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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;
  • 26 scripts, each with its path and the digest of its content;
  • 9 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 statement

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  • it points to a dataset: Zenodo 20091706
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-75588-3.

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, 22 authors, 2 keywords, 13 MeSH terms, 1 funder, 90 references.

Cite

This paper

Tang, L. W., Grigsby, E. M., Damiani, A., Ho, J. C., Montanaro, I. M., Nouduri, S., Ensel, S., Trant, S., Constantine, T., Adams, G. M., Balason, D. V., Thomas, T. L., Zdrowak, K., Ting, J., Wittenberg, G. F., Franzese, K., Mahon, B. Z., Fiez, J. A., Crammond, D. J., . . . Pirondini, E. (2026). Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing. Nature communications, 17(1), 8794. https://doi.org/10.1038/s41467-026-75588-3

BibTeX

@article{tang2026frequency,
author = {Tang, Lilly W and Grigsby, Erinn M and Damiani, Arianna and Ho, Jonathan C and Montanaro, Isabella M and Nouduri, Sirisha and Ensel, Scott and Trant, Sara and Constantine, Theodora and Adams, Gregory M and Balason, Denise V and Thomas, Tracey L and Zdrowak, Kelly and Ting, Jordyn and Wittenberg, George F and Franzese, Kevin and Mahon, Bradford Z and Fiez, Julie A and Crammond, Donald J and Stipancic, Kaila L and Gonzalez-Martinez, Jorge A and Pirondini, Elvira},
title = {{Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8794},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75588-3},
url = {https://doi.org/10.1038/s41467-026-75588-3},
pmid = {42471326},
pmcid = {PMC13493936}
}

RIS

TY - JOUR
AU - Tang, Lilly W
AU - Grigsby, Erinn M
AU - Damiani, Arianna
AU - Ho, Jonathan C
AU - Montanaro, Isabella M
AU - Nouduri, Sirisha
AU - Ensel, Scott
AU - Trant, Sara
AU - Constantine, Theodora
AU - Adams, Gregory M
AU - Balason, Denise V
AU - Thomas, Tracey L
AU - Zdrowak, Kelly
AU - Ting, Jordyn
AU - Wittenberg, George F
AU - Franzese, Kevin
AU - Mahon, Bradford Z
AU - Fiez, Julie A
AU - Crammond, Donald J
AU - Stipancic, Kaila L
AU - Gonzalez-Martinez, Jorge A
AU - Pirondini, Elvira
TI - Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/18
VL - 17
IS - 1
SP - 8794
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75588-3
UR - https://doi.org/10.1038/s41467-026-75588-3
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-75588-3",
"type": "article-journal",
"title": "Frequency-dependent effects of motor thalamus deep brain stimulation on speech and swallowing",
"container-title": "Nature communications",
"author": [
{
"family": "Tang",
"given": "Lilly W"
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{
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{
"family": "Damiani",
"given": "Arianna"
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{
"family": "Ho",
"given": "Jonathan C"
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{
"family": "Montanaro",
"given": "Isabella M"
},
{
"family": "Nouduri",
"given": "Sirisha"
},
{
"family": "Ensel",
"given": "Scott"
},
{
"family": "Trant",
"given": "Sara"
},
{
"family": "Constantine",
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{
"family": "Adams",
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{
"family": "Balason",
"given": "Denise V"
},
{
"family": "Thomas",
"given": "Tracey L"
},
{
"family": "Zdrowak",
"given": "Kelly"
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{
"family": "Ting",
"given": "Jordyn"
},
{
"family": "Wittenberg",
"given": "George F"
},
{
"family": "Franzese",
"given": "Kevin"
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{
"family": "Mahon",
"given": "Bradford Z"
},
{
"family": "Fiez",
"given": "Julie A"
},
{
"family": "Crammond",
"given": "Donald J"
},
{
"family": "Stipancic",
"given": "Kaila L"
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{
"family": "Gonzalez-Martinez",
"given": "Jorge A"
},
{
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"given": "Elvira"
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],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8794",
"DOI": "10.1038/s41467-026-75588-3",
"PMID": "42471326",
"PMCID": "PMC13493936",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75588-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
18
]
]
}
}

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