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Spatiotemporal characteristics of visual cortical responses to transpalpebral electrical stimulation.

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The authors' code

Python · 231 lines · 10 KB · CC-BY-4.0

  1. from PyQt5.QtWidgets import *
  2. from PyQt5.QtCore import *
  3. from PyQt5.QtGui import *
  4. import sys
  6. class ChannelparametersSet(QWidget):
  7. def __init__(self):
  8. super(ChannelparametersSet, self).__init__()
  9. self.setWindowTitle("通道参数设置")
  10. self.setFixedSize(900,400)
  11. self.setWindowIcon(QIcon('./images/channels.png'))
  12. self.setFont(QFont('等线',12))
  13. self.LabelFont = QFont('等线',13)
  14. self.initUI()
  16. def initUI(self):
  17. ChannelparametersSetLayout = QGridLayout()
  18. self.Amplitudephase1Label = QLabel('刺 激 幅 值1')
  19. self.Durationphase1Label = QLabel('刺 激 时 间1')
  20. self.Amplitudephase2Label = QLabel('刺 激 幅 值2')
  21. self.Durationphase2Label =QLabel('刺 激 时 间2')
  22. self.PeriodLabel = QLabel('刺 激 周 期')
  23. self.RepeatLabel = QLabel('重复次数')
  24. self.Amplitudephase1Label.setMaximumSize(160, 20)
  25. self.Durationphase1Label.setMaximumSize(160, 20)
  26. self.Amplitudephase2Label.setMaximumSize(160, 20)
  27. self.Durationphase2Label.setMaximumSize(160, 20)
  28. self.PeriodLabel.setMaximumSize(160, 20)
  29. self.RepeatLabel.setMaximumSize(100, 20)
  31. self.Amplitudephase1Label.setAlignment(Qt.AlignCenter)
  32. self.Durationphase1Label.setAlignment(Qt.AlignCenter)
  33. self.Amplitudephase2Label.setAlignment(Qt.AlignCenter)
  34. self.Durationphase2Label.setAlignment(Qt.AlignCenter)
  35. self.PeriodLabel.setAlignment(Qt.AlignCenter)
  36. self.RepeatLabel.setAlignment(Qt.AlignCenter)
  38. self.Amplitudephase1Label.setFont(self.LabelFont)
  39. self.Durationphase1Label.setFont(self.LabelFont)
  40. self.Amplitudephase2Label.setFont(self.LabelFont)
  41. self.Durationphase2Label.setFont(self.LabelFont)
  42. self.PeriodLabel.setFont(self.LabelFont)
  43. self.RepeatLabel.setFont(self.LabelFont)
  45. Labelslayout = QHBoxLayout()
  46. Labelslayout.addWidget(self.Amplitudephase1Label)
  47. Labelslayout.addWidget(self.Durationphase1Label)
  48. Labelslayout.addWidget(self.Amplitudephase2Label)
  49. Labelslayout.addWidget(self.Durationphase2Label)
  50. Labelslayout.addWidget(self.PeriodLabel)
  51. Labelslayout.addWidget(self.RepeatLabel)
  52. ChannelparametersSetLayout.addLayout(Labelslayout,0,1,1,11)
  55. self.Amplitude_Lineedit = {}
  56. self.Amplitude_ComboBox ={}
  57. self.Duration_Lineedit = {}
  58. self.Duration_ComboBox = {}
  59. for i in range(8):
  60. self.Amplitude_Lineedit[i] = QLineEdit('0')
  61. self.Amplitude_Lineedit[i].setAlignment(Qt.AlignCenter)
  62. self.Amplitude_ComboBox[i] = QComboBox()
  63. self.Amplitude_ComboBox[i].addItems(['mA', 'μA'])
  64. self.Duration_Lineedit[i] = QLineEdit('500')
  65. self.Duration_Lineedit[i].setAlignment(Qt.AlignCenter)
  66. self.Duration_ComboBox[i] = QComboBox()
  67. self.Duration_ComboBox[i].addItems(['ms','μs'])
  68. self.Duration_ComboBox[i].setCurrentIndex(1)
  69. if i != 0 and i != 1:
  70. self.Amplitude_Lineedit[i].setEnabled(False)
  71. self.Duration_Lineedit[i].setEnabled(False)
  73. self.PeriodLineedit = {}
  74. self.PeriodComboBox = {}
  75. self.RepeatLineedit ={}
  76. for i in range(4):
  77. self.PeriodLineedit[i] = QLineEdit('1')
  78. self.PeriodLineedit[i].setAlignment(Qt.AlignCenter)
  79. self.PeriodComboBox[i] = QComboBox()
  80. self.PeriodComboBox[i].addItems(['ms', 'μs'])
  81. self.RepeatLineedit[i] = QLineEdit('0')
  82. self.RepeatLineedit[i].setAlignment(Qt.AlignCenter)
  83. if i !=0:
  84. self.PeriodLineedit[i].setEnabled(False)
  85. self.RepeatLineedit[i].setEnabled(False)
  87. self.ChannelCheckbox = {}
  88. for i in range(4):
  89. self.ChannelCheckbox[i] = QCheckBox("通道%d" % (i + 1))
  90. self.ChannelCheckbox[i].stateChanged.connect(self.ChannelCheckbox_State)
  91. self.ChannelCheckbox[0].setChecked(True)
  94. ChannelLayout = {}
  95. for i in range (4):
  96. ChannelLayout[i] = QHBoxLayout()
  97. ChannelLayout[i].addWidget(self.ChannelCheckbox[i],2)
  98. ChannelLayout[i].addWidget(self.Amplitude_Lineedit[2*i],2)
  99. ChannelLayout[i].addWidget(self.Amplitude_ComboBox[2*i],1)
  100. ChannelLayout[i].addWidget(self.Duration_Lineedit[2*i],2)
  101. ChannelLayout[i].addWidget(self.Duration_ComboBox[2*i],1)
  102. ChannelLayout[i].addWidget(self.Amplitude_Lineedit[2*i+1],2)
  103. ChannelLayout[i].addWidget(self.Amplitude_ComboBox[2*i+1],1)
  104. ChannelLayout[i].addWidget(self.Duration_Lineedit[2*i+1],2)
  105. ChannelLayout[i].addWidget(self.Duration_ComboBox[2*i+1],1)
  106. ChannelLayout[i].addWidget(self.PeriodLineedit[i],2)
  107. ChannelLayout[i].addWidget(self.PeriodComboBox[i],1)
  108. ChannelLayout[i].addWidget(self.RepeatLineedit[i],2)
  109. ChannelparametersSetLayout.addLayout(ChannelLayout[i],i+1,0,1,12)
  112. self.ChannelparameterssaveButton = QPushButton("保存")
  113. self.ChannelparametersresetButton = QPushButton("重置")
  115. self.ChannelparameterssaveButton.setFont(QFont('黑体', 11))
  116. self.ChannelparametersresetButton.setFont(QFont('黑体', 11))
  117. self.ChannelparameterssaveButton.setMinimumHeight(30)
  118. self.ChannelparametersresetButton.setMinimumHeight(30)
  119. self.ChannelparameterssaveButton.clicked.connect(self.On_ChannelparameterssaveButton_Clicked)
  120. self.ChannelparametersresetButton.clicked.connect(self.On_ChannelparametersresetButton_Clicked)
  122. ChannelparametersSetLayout.addWidget(self.ChannelparameterssaveButton, 5, 8, 1, 1)
  123. ChannelparametersSetLayout.addWidget(self.ChannelparametersresetButton, 5, 9, 1, 1)
  125. self.setLayout(ChannelparametersSetLayout)
  127. def ChannelCheckbox_State(self):
  128. for i in range(4):
  129. if self.ChannelCheckbox[i].isChecked() ==1:
  130. self.Amplitude_Lineedit[2*i].setEnabled(True)
  131. self.Amplitude_Lineedit[2*i+1].setEnabled(True)
  132. self.Duration_Lineedit[2*i].setEnabled(True)
  133. self.Duration_Lineedit[2*i+1].setEnabled(True)
  134. self.PeriodLineedit[i].setEnabled(True)
  135. self.RepeatLineedit[i].setEnabled(True)
  136. else:
  137. self.Amplitude_Lineedit[2*i].setText('0')
  138. self.Amplitude_Lineedit[2*i+1].setText('0')
  139. self.PeriodLineedit[i].setText('1')
  140. self.RepeatLineedit[i].setText('0')
  141. self.Amplitude_Lineedit[2 * i].setEnabled(False)
  142. self.Amplitude_Lineedit[2 * i + 1].setEnabled(False)
  143. self.Duration_Lineedit[2 * i].setEnabled(False)
  144. self.Duration_Lineedit[2 * i + 1].setEnabled(False)
  145. self.PeriodLineedit[i].setEnabled(False)
  146. self.RepeatLineedit[i].setEnabled(False)
  149. def On_ChannelparameterssaveButton_Clicked(self):
  150. self.CorrectDuration_and_Period() # 当刺激时间和周期是μs的时候，转化为100的整数倍
  151. self.CorrectAmplitude() # 当刺激幅值是μA的时候，转化为10的整倍数
  152. QMessageBox.information(self, ' ', '电刺激通道参数设置完成', QMessageBox.Yes)
  153. self.ChannelparameterssaveButton.setEnabled(False)
  155. def On_ChannelparametersresetButton_Clicked(self):
  156. self.ChannelparameterssaveButton.setEnabled(True)
  158. def CorrectDuration_and_Period(self):
  160. for t in range(8):
  161. if self.Duration_ComboBox[t].currentIndex() ==1:
  162. duration = int(self.Duration_Lineedit[t].text())
  163. if duration % 100!=0:
  164. duration = 100 * (int(duration / 100) + 1)
  165. self.Duration_Lineedit[t].setText(str(duration))
  166. # PeriodComBox = [self.Period1ComboBox, self.Period2ComboBox, self.Period3ComboBox, self.Period4ComboBox]
  167. # PeriodLineedit = [self.Period1Lineedit, self.Period2Lineedit, self.Period3Lineedit, self.Period4Lineedit]
  168. for i in range(4):
  169. if self.PeriodComboBox[i].currentIndex() == 1:
  170. period = int(self.PeriodLineedit[i].text())
  171. if period % 100 != 0:
  172. period = 100 * (int(period / 100) + 1)
  173. self.PeriodLineedit[i].setText(str(period))
  175. def CorrectAmplitude(self):
  176. for i in range(8):
  177. if self.Amplitude_ComboBox[i].currentIndex() == 1:
  178. Amplitude = int(self.Amplitude_Lineedit[i].text())
  179. if Amplitude % 10 !=0:
  180. Amplitude = 10 * round(Amplitude / 10)
  181. self.Amplitude_Lineedit[i].setText(str(Amplitude))
  183. def GetAmplitude(self):
  184. Amplitude={}
  185. for i in range(8):
  186. Amplitude[i] = float(self.Amplitude_Lineedit[i].text())
  187. return Amplitude
  189. def GetAmplitudeunit(self):
  190. Amplitudeunit={}
  191. for i in range(8):
  192. Amplitudeunit[i] = self.Amplitude_ComboBox[i].currentText()
  193. return Amplitudeunit
  195. def GetDuration(self):
  196. Duration = {}
  197. for i in range(8):
  198. Duration[i] = int(self.Duration_Lineedit[i].text())
  199. return Duration
  201. def GetDurationunit(self):
  202. Durationunit = {}
  203. for i in range(8):
  204. Durationunit[i] = self.Duration_ComboBox[i].currentText()
  205. return Durationunit
  207. def GetPeriod(self):
  208. Period = {}
  209. for i in range(4):
  210. Period[i] = int(self.PeriodLineedit[i].text())
  211. return Period
  213. def GetPeriodunit(self):
  214. Periodunit = {}
  215. for i in range(4):
  216. Periodunit[i] = self.PeriodComboBox[i].currentText()
  217. return Periodunit
  219. # 获得各通道的重复次数
  220. def GetRepeat(self):
  221. Repeat = {}
  222. for i in range(4):
  223. Repeat[i] = int(self.RepeatLineedit[i].text())
  224. return Repeat
  227. if __name__ == '__main__':
  228. app=QApplication(sys.argv)
  229. main=ChannelparametersSet()
  230. main.show()
  231. sys.exit(app.exec())

ElectricalstimulusChannelSet.py, under CC-BY-4.0 · at the source

Overview

Authors: Meixuan Zhou1, Yiheng Xu1,2, Tianyue Meng1, Tianruo Guo3, Yanyang Zhang4,5, Liqing Di6, Liming Li1, Heng Li2, Xinyu Chai1
ORCID iDs: Meixuan Zhou, Heng Li
  1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  2. Department of Physical Education, Shanghai Jiao Tong University, Shanghai, China
  3. School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia
  4. Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
  5. Neurosurgery Institute, Chinese PLA General Hospital, Beijing, China
  6. Department of Orthopedics, Shanghai Pudong Hospital, Shanghai, China
Institutions: Shanghai Jiao Tong University (China); UNSW Sydney (Australia); Chinese PLA General Hospital (China); Pudong Medical Center (China)
Journal: iScience, volume 29, issue 8, article 116705
Dates: received 22 December 2025; accepted 19 June 2026; published online 10 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116705 · PMID 42472104 · PMCID PMC13380434 · OpenAlex W7167943810
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), computational (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: retinal degeneration, transpalpebral electrical stimulation, visual cortical responses, intrinsic optical signal imaging, electrical stimulation site, computational modeling
Topic: Spatial Neglect and Hemispheric Dysfunction (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Shanghai Municipal Natural Science Foundation; National Natural Science Foundation of China; Key Technologies Research and Development Program
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

Transpalpebral electrical stimulation (TpES) has comparable therapeutic efficacy to transcorneal electrical stimulation (TcES) for retinal neurodegenerative disorders. Characterizing TpES-evoked visual cortical responses is critical to expand the clinical application of minimally invasive neuromodulation. We performed intrinsic optical signal imaging in the cat visual cortex to characterize spatiotemporal neurovascular coupling responses to independent-channel TpES, analyzed retinal electric field distribution via a human head computational model, with TcES as a control in both in vivo and simulation experiments. TpES evoked peripheral visual field cortical responses and retinal electric fields consistent with TcES patterns, with similar temporal dynamics. TpES amplitudes were comparable or significantly higher, indicating more efficient visual pathway activation. Our findings provide important evidence supporting the advancement and optimization of non-invasive stimulation techniques for the treatment of retinal neurodegenerative diseases.

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

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 20730362

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (27 files), OpenCV (23 files), Pillow (6 files), SciPy (4 files), Matplotlib (1 file), PsychoPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
37 files
At the source: doi.org/10.5281/zenodo.20730362

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;
  • 36 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 data reported in this paper will be shared by the lead contact upon request.

The code used for IOS imaging and analysis, as well as retinal electric fields analysis, is available in Zenodo at https://doi.org/10.5281/zenodo.20730362.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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 2, 28 September 2026

  • Authors: added Meixuan Zhou (0009-0002-1175-4118); Heng Li (0000-0002-1303-8898); removed Meixuan Zhou; Heng Li

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 3 funders, 101 references.

Cite

This paper

Zhou, M., Xu, Y., Meng, T., Guo, T., Zhang, Y., Di, L., Li, L., Li, H., & Chai, X. (2026). Spatiotemporal characteristics of visual cortical responses to transpalpebral electrical stimulation. iScience, 29(8), 116705. https://doi.org/10.1016/j.isci.2026.116705

BibTeX

@article{zhou2026spatiotemporal,
author = {Zhou, Meixuan and Xu, Yiheng and Meng, Tianyue and Guo, Tianruo and Zhang, Yanyang and Di, Liqing and Li, Liming and Li, Heng and Chai, Xinyu},
title = {{Spatiotemporal characteristics of visual cortical responses to transpalpebral electrical stimulation}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {116705},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116705},
url = {https://doi.org/10.1016/j.isci.2026.116705},
pmid = {42472104},
pmcid = {PMC13380434}
}

RIS

TY - JOUR
AU - Zhou, Meixuan
AU - Xu, Yiheng
AU - Meng, Tianyue
AU - Guo, Tianruo
AU - Zhang, Yanyang
AU - Di, Liqing
AU - Li, Liming
AU - Li, Heng
AU - Chai, Xinyu
TI - Spatiotemporal characteristics of visual cortical responses to transpalpebral electrical stimulation
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/10
VL - 29
IS - 8
SP - 116705
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116705
UR - https://doi.org/10.1016/j.isci.2026.116705
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Spatiotemporal characteristics of visual cortical responses to transpalpebral electrical stimulation",
"container-title": "iScience",
"author": [
{
"family": "Zhou",
"given": "Meixuan"
},
{
"family": "Xu",
"given": "Yiheng"
},
{
"family": "Meng",
"given": "Tianyue"
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{
"family": "Guo",
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{
"family": "Zhang",
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},
{
"family": "Di",
"given": "Liqing"
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{
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"given": "Liming"
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"given": "Xinyu"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "8",
"page": "116705",
"DOI": "10.1016/j.isci.2026.116705",
"PMID": "42472104",
"PMCID": "PMC13380434",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116705",
"language": "en",
"issued": {
"date-parts": [
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2026,
7,
10
]
]
}
}

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