OSCR

Encoding of speech modes and loudness in ventral precentral gyrus.

Code ↔ Paper

13 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 13 matches
  1. [1] § Results › Loudness representation in neural ensemble activity ↔ plotting_scripts/dpca.py, lines 240–288 · score 0.73 · pairwise cosine, dPCs, upper triangle, orthogonality, dPCA, word
  2. [2] § Results › Loudness encoding in ventral precentral gyrus ↔ analyses_scripts/ch_encoding_word.py, lines 150–174 · score 0.72 · post hoc, way ANOVA, word tuning, word pairs, Firing rate, Tukey
  3. [3] § Methods › Data recording and processing › Breath analysis ↔ analyses_scripts/ol_classification.py, lines 140–206 · score 0.69 · trained logistic regression, cross validation, neural features, shuffled, fold, classifiers
  4. [4] § Methods › Data recording and processing › Offline loudness decoder training and analyses ↔ analyses_scripts/ol_striding_classification.py, lines 234–304 · score 0.68 · cross validation, logistic regression, decoding accuracy, stride, permutation, trained
  5. [5] § Methods › Data recording and processing › Firing rate analyses ↔ analyses_scripts/ch_encoding_word.py, lines 150–174 · score 0.66 · post hoc, way ANOVA, firing rate, Tukey, encodes, electrode
  6. [6] § Methods › Data recording and processing › Firing rate analyses ↔ analyses_scripts/ch_encoding_loudness.py, lines 150–175 · score 0.65 · post hoc, way ANOVA, firing rate, Tukey, encodes, electrode
  7. [7] § Results › Loudness representation in neural ensemble activity ↔ plotting_scripts/dpca.py, lines 240–288 · score 0.63 · dot product, Explained variance, orthogonal, triangle, PCs, axis
  8. [8] § Methods › Data recording and processing › Breath analysis ↔ analyses_scripts/ol_striding_classification.py, lines 549–626 · score 0.62 · trained logistic regression, cross validation, neural features, fold, classifiers, accuracy
  9. [9] § Methods › Data recording and processing › Breath analysis ↔ plotting_scripts/speech_breath_analyses.py, lines 120–228 · score 0.60 · minimum breath belt, breath cycle
  10. [10] § Methods › Data recording and processing › Spike sorting and single-unit analyses ↔ analyses_scripts/ch_encoding_loudness.py, lines 150–175 · score 0.59 · post hoc, way ANOVA, firing rate, Tukey, tuned, loudness
  11. [11] § Results › Offline decoding of loudness ↔ plotting_scripts/ol_striding_classification.py, lines 42–159 · score 0.58 · cluster permutation, cue onset, Loudness decoders, vertical, stride, window
  12. [12] § Methods › Behavioral tasks › Instructed breathing task ↔ plotting_scripts/speech_breath_analyses.py, lines 120–228 · score 0.57 · instructed breathing, breath belt, attempted loudness
  13. [13] § Methods › Data recording and processing › Behavioral data ↔ analyses_scripts/psth.py, lines 136–198 · score 0.54 · Gaussian smoothing, go period, binned

Paper

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The authors' code

Python · 288 lines · 13 KB · CC-BY-NC-SA-4.0 · 2 matches

  1. # This script plots dPCA results: projections, explained variance, correlation matrix.
  3. import argparse
  4. import numpy as np
  5. import scipy
  6. import math
  7. import os
  8. from datetime import datetime
  9. import matplotlib.pyplot as plt
  10. from matplotlib.colors import LinearSegmentedColormap
  12. '''
  13. Example cmd (when run from this directory; provide python script path appropriately if run from different directory):
  14. For t15,
  15. python dpca.py --participant t15 --session word-loudness --nbins_before_onset 75 --nbins_after_onset 75 --savepath_data ../plotting_data/t15/word-loudness/dPCA/ --savepath_fig ../plotting_figures/t15/word-loudness/dPCA/
  16. For t16,
  17. python dpca.py --participant t16 --session word-loudness --nbins_before_onset 75 --nbins_after_onset 75 --savepath_data ../plotting_data/t16/word-loudness/dPCA/ --savepath_fig ../plotting_figures/t16/word-loudness/dPCA/
  19. Data will be loaded from the specified savepath_data directory.
  20. Figures will be saved in the specified savepath_fig directory.
  21. '''
  23. #-----------------------
  24. # global variables
  25. #-----------------------
  26. amplitudes = ['MIME', 'WHISPER', 'NORMAL', 'LOUD']
  27. words = ['be', 'my', 'know', 'do', 'have', 'going']
  29. marg_names = ['Word', 'Loudness', 'Condition\nindependent', 'Interaction']
  30. num_pcs_plot = 3
  32. # plotting
  33. marg_plotting_order = ['Condition\nindependent', 'Word', 'Loudness', 'Interaction']
  34. marg_plotting_order_ind = [2, 0, 1, 3]
  35. target_color = [ # word colors
  36. (0.9254902, 0.12156863, 0.14117647),
  37. (0.98431373, 0.72941176, 0.07058824),
  38. (0.384, 0.682, 0.2),
  39. (0.43137255, 0.79607843, 0.85490196),
  40. [0.45568627, 0.31764706, 0.63529412],
  41. (0.84705882, 0.2627451, 0.59215686)
  42. ]
  43. hue = [0.25, 0.5, 0.75, 1]
  44. linewidth = [0.5, 1, 1.5, 2]
  45. scatter_size = 30
  46. fontsize = 15
  47. my_explode = [0.1] * len(marg_names)
  48. my_color = [ # ordered similar to marg names
  49. (150/255, 54/255, 34/255), # word, red
  50. (34/255, 54/255, 150/255), # loudness, blue
  51. (128/255, 128/255, 128/255), # condition independent, gray
  52. (150/255, 34/255, 150/255), # interaction, purple
  53. ]
  55. #-----------------------
  56. # functions
  57. #-----------------------
  58. def plot_projections(projections, which_marg, explained_var):
  60. fig, ax = plt.subplots(len(marg_names), num_pcs_plot, figsize = (12,6))
  61. for marg in range(len(marg_names)):
  63. marg_ind_to_plot = marg_plotting_order_ind[marg]
  64. components = [i for i, val in enumerate(which_marg.squeeze()) if val == marg_ind_to_plot]
  65. comp_min = 1000; comp_max = -1
  67. for comp in range(min(num_pcs_plot, len(components))):
  68. for d1 in range(len(words)):
  69. for d2 in range(len(amplitudes)):
  71. # Plot the data
  72. ax[marg, comp].plot(
  73. time[args.participant],
  74. np.squeeze(projections[components[comp], d1, d2, :]),
  75. linestyle = '-',
  76. color=target_color[d1],
  77. alpha = hue[d2],
  78. linewidth = linewidth[d2],
  79. label=f"{amplitudes[d2]} {words[d1]}"
  80. )
  82. comp_min = min(comp_min, np.min(np.squeeze(projections[components[comp], d1, d2, :])))
  83. comp_max = max(comp_max, np.max(np.squeeze(projections[components[comp], d1, d2, :])))
  85. ax[marg, comp].set_ylim([math.floor(comp_min), math.ceil(comp_max)])
  86. ax[marg, comp].scatter(0, math.floor(comp_min) + (math.ceil(comp_max) * 2/20), color='black', s=scatter_size) # speech onset marker
  87. ax[marg, comp].text((args.nbins_after_onset/100)- 0.05, math.ceil(comp_max), f"{explained_var[marg_ind_to_plot,components[comp]]:.1f}%", fontsize = fontsize, ha = 'center') # explained variance % at top right
  88. ax[marg, comp].text(-((args.nbins_before_onset/100) - 0.1), math.floor(comp_min) + 0.07 * (math.ceil(comp_max) - math.floor(comp_min)), f"{components[comp] + 1}", fontsize = fontsize, ha = 'center')
  89. ax[marg, comp].scatter(-((args.nbins_before_onset/100) - 0.1), math.floor(comp_min) + 0.16 * (math.ceil(comp_max) - math.floor(comp_min)), s = 400, color = 'black', facecolor = 'none')
  91. if comp != 0:
  92. for pos in ['right', 'top', 'bottom']:
  93. ax[marg, comp].spines[pos].set_visible(False)
  94. ax[marg, comp].set_xticks([])
  95. ax[marg, comp].set_yticks([])
  97. if comp == 0:
  98. for pos in ['right', 'top', 'bottom',]:
  99. ax[marg, comp].spines[pos].set_visible(False)
  100. ax[marg, comp].set_yticks([math.floor(comp_min), 0, math.ceil(comp_max)],
  101. [math.floor(comp_min), 0, math.ceil(comp_max)], fontsize = fontsize)
  102. ax[marg, comp].set_xticks([])
  103. ax[marg, comp].set_ylabel(marg_names[marg_ind_to_plot], fontsize = fontsize)
  105. if marg == 3 and comp == 2:
  106. ax[marg, comp].hlines(-math.ceil(comp_max), xmin = args.nbins_after_onset/100 - 0.5, xmax = args.nbins_after_onset/100, linewidth = 3, color = 'black')
  107. ax[marg, comp].text(args.nbins_after_onset/100 - 0.25, -1.5, '500 ms', color = 'black', ha = 'center', fontsize = fontsize)
  109. if marg == 3 and comp == 0:
  110. ax[marg, comp].text(0, -1.5, 'Speech onset', color = 'black', ha = 'center', fontsize = fontsize)
  112. if marg == 0 and comp == 0:
  113. ax[marg, comp].text((args.nbins_after_onset/100) - 0.05, math.ceil(comp_max) + 3, 'Explained variance', color = 'black', ha = 'center', fontsize = fontsize)
  115. fig.tight_layout()
  116. plt.subplots_adjust(wspace=0.05)
  117. # plt.show()
  119. # save figure
  120. plt.savefig(f'{args.savepath_fig}{args.participant}_dpca_projections_{formatted_datetime}_{args.nbins_before_onset}_{args.nbins_after_onset}.png', format='png')
  122. return
  125. def plot_explained_variance(explained_var):
  127. fontsize = 13
  128. fig = plt.figure(figsize=(5,5))
  129. plt.subplot(2,1,1)
  131. # pie chart
  132. expl_var_comp = np.sum(explained_var, axis = -1)
  133. marg_names_with_percent = []
  134. for i in range(len(marg_names)):
  135. marg_names_with_percent.append(f'{marg_names[i]}\n({(expl_var_comp[i]/sum(expl_var_comp))*100:.1f}%)')
  136. plt.pie(expl_var_comp, labels = marg_names_with_percent, #autopct='%1.1f%%',
  137. colors = my_color, explode = my_explode, startangle = 140, shadow = False, textprops={'fontsize': fontsize}, labeldistance=1.3)
  139. # stacked bar plot
  140. plt.subplot(2, 1, 2)
  141. num_comps_to_plot = 15
  142. bottom = np.zeros(num_comps_to_plot) # Initialize bottom for stacking
  143. for i in range(len(marg_names)):
  144. plt.bar(np.arange(num_comps_to_plot), explained_var[i, :num_comps_to_plot], label=marg_names[i], color=my_color[i], bottom=bottom)
  145. bottom += explained_var[i, :num_comps_to_plot]
  147. for pos in ['right', 'top']:
  148. plt.gca().spines[pos].set_visible(False)
  150. plt.ylim([0, math.ceil(bottom[0])])
  151. plt.yticks(np.arange(0, math.ceil(bottom[0]), 10), np.arange(0, math.ceil(bottom[0]), 10), fontsize = fontsize)
  152. plt.xticks([0, 4, 9, 14], [1, 5, 10, 15], fontsize = fontsize)
  153. plt.xlabel('dPCA component', fontsize = fontsize)
  154. plt.ylabel('Variance (%)', fontsize = fontsize)
  156. fig.tight_layout()
  157. # plt.show()
  159. # save figure
  160. plt.savefig(f'{args.savepath_fig}{args.participant}_dpca_variance_plot_{formatted_datetime}_{args.nbins_before_onset}_{args.nbins_after_onset}.png', format='png')
  162. return
  165. def plot_correlation(corr_matrix, sig_ind):
  167. fontsize = 19
  168. fig = plt.figure(figsize = (5,5))
  169. num_comps_to_plot = 15
  170. custom_cmap = LinearSegmentedColormap.from_list("dark_bwr", ["#002147", "white", "#800000"]) # Dark navy blue → White → Maroon
  171. img = plt.imshow(corr_matrix[:num_comps_to_plot, :num_comps_to_plot], cmap = custom_cmap, vmin = -1, vmax = 1)
  172. ind_to_plot = []
  173. for k in range(len(sig_ind[0])):
  174. if sig_ind[0][k] < num_comps_to_plot and sig_ind[1][k] < num_comps_to_plot:
  175. ind_to_plot.append(k)
  176. plt.scatter(sig_ind[1][ind_to_plot], sig_ind[0][ind_to_plot], color='k', marker='*')
  177. plt.xticks([0, 4, 9, 14], [1, 5, 10, 15], fontsize = fontsize)
  178. plt.yticks([0, 4, 9, 14], [1, 5, 10, 15], fontsize = fontsize)
  179. plt.xlabel('dPCA component', fontsize = fontsize)
  180. plt.ylabel('dPCA component', fontsize = fontsize)
  181. cbar = fig.colorbar(img, fraction=0.046, pad=0.04)
  182. cbar.outline.set_visible(False)
  183. cbar.ax.tick_params(labelsize=fontsize)
  184. cbar.set_ticks([-1, 0, 1])
  185. cbar.ax.set_title('Similarity', fontsize=fontsize)
  186. for pos in ['right', 'top', 'left', 'bottom']:
  187. plt.gca().spines[pos].set_visible(False)
  189. fig.tight_layout()
  190. # plt.show()
  192. # save figure
  193. plt.savefig(f'{args.savepath_fig}{args.participant}_dpca_correlation_matrix_{formatted_datetime}_{args.nbins_before_onset}_{args.nbins_after_onset}.png', format='png')
  195. return
  197. if __name__ == "__main__":
  199. current_datetime = datetime.now()
  200. formatted_datetime = current_datetime.strftime("%Y%m%d_%H%M%S")
  202. parser = argparse.ArgumentParser()
  203. parser.add_argument('--participant', type=str, default=None, help='participant id')
  204. parser.add_argument('--session', type=str, default=None, help = 'session id')
  205. parser.add_argument('--nbins_before_onset', type=int, default = None, help = 'number of bins before speech onset')
  206. parser.add_argument('--nbins_after_onset', type=int, default = None, help = 'number of bins after speech onset')
  207. parser.add_argument('--savepath_data', type=str, default='../figures_data/', help = 'path to save processed data from this script')
  208. parser.add_argument('--savepath_fig', type=str, default='../figures/', help = 'path to save figures from this script')
  209. args = parser.parse_args()
  211. if not os.path.exists(args.savepath_fig):
  212. os.makedirs(args.savepath_fig, exist_ok=True)
  214. if args.participant in 't15':
  215. n_channels = 256
  216. elif args.participant == 't16':
  217. n_channels = 128
  219. time = {
  220. 't15': np.arange(-args.nbins_before_onset/100, args.nbins_after_onset/100, 0.01),
  221. 't16': np.arange(-args.nbins_before_onset/100, args.nbins_after_onset/100, 0.01),
  222. }
  224. print('Running plot_dPCA_results.py')
  225. print(args)
  227. # # load projections
  228. print('Loading projections, which marginals, explained variance...')
  229. projections = scipy.io.loadmat(f'{args.savepath_data}{args.participant}_{args.nbins_before_onset}_{args.nbins_after_onset}_avg_firingrates_projections.mat')
  230. projections = projections['ProjectionAverage']
  232. # load which margins the projections belong to
  233. which_marg = scipy.io.loadmat(f'{args.savepath_data}{args.participant}_{args.nbins_before_onset}_{args.nbins_after_onset}_which_marg.mat')
  234. which_marg = which_marg['whichMarg'] - 1 # subtract 1 as matlab indices are 1-indexed
  236. # # load explained variance by components
  237. explained_var = scipy.io.loadmat(f'{args.savepath_data}{args.participant}_{args.nbins_before_onset}_{args.nbins_after_onset}_explained_var.mat')
  238. explained_var = explained_var['margVar']
  240. # # plot projections
  241. print('Plotting projections...')
  242. plot_projections(projections, which_marg, explained_var)
  244. # # plot explained variance piechart and bar plot
  245. print('Plotting explained variance...')
  246. plot_explained_variance(explained_var)
  248. # load correlation, decoder dot product, kendall p-value
  249. print('Loading correlation, decoder dot product, kendall p-value matrices...')
  250. correlation = scipy.io.loadmat(f'{args.savepath_data}{args.participant}_{args.nbins_before_onset}_{args.nbins_after_onset}_correlations.mat')
  251. correlation = correlation['a']
  253. decoder_dot_product = scipy.io.loadmat(f'{args.savepath_data}{args.participant}_{args.nbins_before_onset}_{args.nbins_after_onset}_decoder_matrix_dot_product.mat')
  254. decoder_dot_product = decoder_dot_product['b']
  256. pval = scipy.io.loadmat(f'{args.savepath_data}{args.participant}_{args.nbins_before_onset}_{args.nbins_after_onset}_kendall_pvalue.mat')
  257. pval = pval['psp']
  259. map_matrix = np.tril(correlation, -1) + np.triu(decoder_dot_product) #lower triangle is correlation, upper triangle is decoder dot product
  260. # components with significant difference in dot product
  261. indices = np.where((abs(np.triu(decoder_dot_product, 1)) > 3.3 / np.sqrt(n_channels)) & (pval < 0.001))
  263. # plot correlation matrix
  264. print('Plotting correlation matrix...')
  265. plot_correlation(map_matrix, indices)
  267. # check how many pairs of of components in the dot product belong to word-loudness and how many are significantly orthogonal
  268. which_marg_word = np.where(which_marg.squeeze() == marg_names.index('Word'))[0]
  269. which_marg_loudness = np.where(which_marg.squeeze() == marg_names.index('Loudness'))[0]
  270. print('Word component indices:', which_marg_word)
  271. print('Loudness component indices:', which_marg_loudness)
  273. n_word_loudness_axis_pairs = len(which_marg_word) * len(which_marg_loudness)
  274. print(f'Number of word-loudness axis pairs: {n_word_loudness_axis_pairs}')
  276. # pairwise cosine similarity between top-X loudness dPCs and word dPCS
  277. n_top_dpcs = 5
  278. abs_dpc_cosine_sim = []
  279. for i in range(n_top_dpcs):
  280. word_dpc_ind = which_marg_word[i]
  281. loud_dpc_ind = which_marg_loudness[i]
  283. abs_dpc_cosine_sim.append(abs(decoder_dot_product[word_dpc_ind, loud_dpc_ind]))
  285. print(f'Mean cosine similarity top {n_top_dpcs}:', np.mean(abs_dpc_cosine_sim))
  286. print(f'Std dev cosine similarity top {n_top_dpcs}:', np.std(abs_dpc_cosine_sim))
  288. print('DONE!')

dpca.py at commit 4f73ec4, under CC-BY-NC-SA-4.0 · at the source

Overview

Authors: Aparna Srinivasan1,2, Maitreyee Wairagkar1, Carrina Iacobacci1, Xianda Hou1,3, Nicholas S Card1, Brandon G Jacques4, Anna L Pritchard4, Payton H Bechefsky4, Leigh R Hochberg5,6,7, Nicholas AuYong4,8,9, Chethan Pandarinath4,8, David M Brandman1, Sergey D Stavisky1
ORCID iDs: Aparna Srinivasan, Maitreyee Wairagkar, Xianda Hou, Nicholas S Card, Anna L Pritchard, Leigh R Hochberg, Nicholas AuYong, David M Brandman, Sergey D Stavisky
  1. Department of Neurological Surgery, University of California Davis, Davis, CA USA
  2. Biomedical Engineering Graduate Group, University of California Davis, Davis, CA USA
  3. Computer Science Graduate Group, University of California Davis, Davis, CA USA
  4. Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA USA
  5. Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  6. Veterans Affairs Rehabilitation Research & Development Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI USA
  7. Robert J. & Nancy D. Carney Institute for Brain Science and School of Engineering, Brown University, Providence, RI USA
  8. Department of Neurosurgery, Emory University, Atlanta, GA USA
  9. Department of Cell Biology, Emory University, Atlanta, GA USA
Institutions: University of California, Davis (United States); Georgia Institute of Technology (United States); Emory University (United States); The Wallace H. Coulter Department of Biomedical Engineering (United States); Harvard University (United States); Brown University (United States); Providence VA Medical Center (United States); Massachusetts General Hospital (United States)
Journal: Nature communications, volume 17, issue 1, article 5301
Dates: received 27 May 2025; accepted 16 March 2026; published online 15 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71284-4 · PMID 41986329 · PMCID PMC13270037 · OpenAlex W4410926537
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.30.657105
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Single-unit activity, calcium imaging
Keywords: Brain-machine interface, Premotor cortex, Motor cortex, Neural decoding
MeSH: Frontal Lobe*, Loudness Perception*, Speech*, Speech Perception*, Adult, Electrodes, Implanted, Female, Humans, Male, Microelectrodes, Neurons (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIDCD NIH HHS (DP2 DC021055)
Citations: cited by 4 papers (Europe PMC); 44 references in the paper

Abstract

The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with 94% and 89% accuracy for the two participants, and corresponding neural preparatory activity at 640 ms and 270 ms before speech onset enabled 80% decoding accuracy, respectively. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.

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

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Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

Neuroprosthetics-Lab/srinivasan-speech-modes

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 4f73ec463ffe5a52366aab3257729976a8594fa3, 3 April 2026
Languages: Python (19)
Size: 131 files, 19 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (19 files), NumPy (19 files), SciPy (11 files), scikit-learn (4 files), seaborn (4 files), pandas (2 files), MNE-Python (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
21 files
At the source: github.com/Neuroprosthetics-Lab/srinivasan-speech-modes

Code availability

Code to implement the analyses described in this study is publicly available on GitHub (https://github.com/Neuroprosthetics-Lab/srinivasan-speech-modes).

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

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Data

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Data availability

De-identified neural data reported in this study are publicly available on Dryad (10.5061/dryad.2547d7x5w)44. Source data are provided with this paper.

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

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Srinivasan, A., Wairagkar, M., Iacobacci, C., Hou, X., Card, N. S., Jacques, B. G., Pritchard, A. L., Bechefsky, P. H., Hochberg, L. R., AuYong, N., Pandarinath, C., Brandman, D. M., & Stavisky, S. D. (2026). Encoding of speech modes and loudness in ventral precentral gyrus. Nature communications, 17(1), 5301. https://doi.org/10.1038/s41467-026-71284-4

BibTeX

@article{srinivasan2026encoding,
author = {Srinivasan, Aparna and Wairagkar, Maitreyee and Iacobacci, Carrina and Hou, Xianda and Card, Nicholas S and Jacques, Brandon G and Pritchard, Anna L and Bechefsky, Payton H and Hochberg, Leigh R and AuYong, Nicholas and Pandarinath, Chethan and Brandman, David M and Stavisky, Sergey D},
title = {{Encoding of speech modes and loudness in ventral precentral gyrus}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5301},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71284-4},
url = {https://doi.org/10.1038/s41467-026-71284-4},
pmid = {41986329},
pmcid = {PMC13270037}
}

RIS

TY - JOUR
AU - Srinivasan, Aparna
AU - Wairagkar, Maitreyee
AU - Iacobacci, Carrina
AU - Hou, Xianda
AU - Card, Nicholas S
AU - Jacques, Brandon G
AU - Pritchard, Anna L
AU - Bechefsky, Payton H
AU - Hochberg, Leigh R
AU - AuYong, Nicholas
AU - Pandarinath, Chethan
AU - Brandman, David M
AU - Stavisky, Sergey D
TI - Encoding of speech modes and loudness in ventral precentral gyrus
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/15
VL - 17
IS - 1
SP - 5301
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71284-4
UR - https://doi.org/10.1038/s41467-026-71284-4
LA - en
ER -

CSL-JSON

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