OSCR

Assessing adverse effects and unspecific effects of transcutaneous spinal direct current stimulation (tsDCS).

Code ↔ Paper

5 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 5 matches
  1. [1] § Materials and Methods › Data processing › UEs › Respiratory activity ↔ UE_analysis_4_respiration.py, lines 4–63 · score 0.94 · representing maximum inhalation, breathing cycle, consecutive breaths, standard deviation, breathing rate variability, signal minima
  2. [2] § Materials and Methods › Data processing › UEs › Skin conductance fluctuations (SCF) ↔ UE_analysis_2_SCF.py, lines 4–63 · score 0.90 · Butterworth bandpass, baseline signal, skin conductance, SCF, 0.0159 Hz, quantified
  3. [3] § Materials and Methods › Data processing › UEs › Electrocardiographic (ECG) activity ↔ UE_analysis_3_ECG.py, lines 4–73 · score 0.76 · heart beats, heart rate variability, py, square, root, ecg
  4. [4] § Materials and Methods › Statistical analysis › UEs ↔ UE_analysis_5_outlier.py, lines 4–28 · score 0.75 · abnormal signal fluctuations, participants autonomic, inadvertently, instructed, administration, tsDCS
  5. [5] § Materials and Methods › Data processing › UEs › Electrocardiographic (ECG) activity ↔ UE_analysis_1_prep.py, lines 4–68 · score 0.62 · Pan Tompkins Algorithm, beats, ecg, peaks

Paper

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The authors' code

Python · 480 lines · 22 KB · no license · 1 match

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  4. """
  6. Description:
  7. ------------
  8. This script analyzes the respiratory activity.
  9. First we cut the data according to the information saved using a_tsdcs_UE_analysis_prep.py.
  10. The time points that mark the beginning of a new breathing cycle are automatically detected
  11. as signal minima represent maximum inhalation.
  12. We then extract the breathing rate (breaths per minute) and breathing rate variability
  13. (standard deviation of the interval between consecutive breaths) over the whole interval
  14. and within the 4 quartiles.
  15. In the unblinding step the session-condition mapping is read and
  16. outliers are excluded. The resulting table BR_BDev_unblind.csv can be used
  17. for statistical analysis and further plotting.
  18. The within-subject plots (fig 4D and 4E) are created.
  19. Additionally, an interaction plot (fig 4F) is created.
  21. Authors:
  22. --------
  23. Ulrike Horn
  25. Contact:
  26. --------
  27. [email hidden]
  29. Date:
  30. -----
  31. 20th November 2023
  32. """
  34. import mne
  35. import numpy as np
  36. import pandas as pd
  37. import os
  38. import glob
  39. import json
  40. from scipy import signal, stats
  41. import matplotlib as mpl
  42. mpl.use('TkAgg')
  43. import matplotlib.pyplot as plt
  44. import seaborn as sns
  46. raw_path = '/data/pt_02582/tsDCS_BIDS/'
  47. output_path = '/data/pt_02582/tsDCS_processed/'
  48. result_path = '/data/pt_02582/tsDCS_results/Resp/'
  50. if not os.path.exists(output_path):
  51. os.makedirs(output_path)
  53. if not os.path.exists(result_path):
  54. os.makedirs(result_path)
  56. new_sr = 100 # Hz that data will be downsampled to
  58. tasks = ['tsDCS']
  60. subjects = ['SR01', 'SR02', 'SR03', 'SR04', 'SR05', 'SR06', 'SR07', 'SR08', 'SR09', 'SR10',
  61. 'SR11', 'SR12', 'SR13', 'SR14', 'SR15', 'SR16', 'SR17', 'SR18', 'SR19', 'SR20']
  63. sessions = [1, 2, 3]
  65. preprocess = True
  66. save_markers = True
  67. unblinding = True
  68. within_sub_plot = True
  69. interaction_plot = True
  71. np.random.seed(1990) # for jitter
  73. color_blue = [0, 128/255, 255/255]
  74. color_red = [227/255, 0, 15/255]
  75. color_orange = [239/255, 138/255, 16/255]
  77. new_rc_params = {"font.family": 'Arial', "font.size": 12, "font.serif": [],
  78. "svg.fonttype": 'none'}
  79. mpl.rcParams.update(new_rc_params)
  81. bpm_all = []
  82. dev_all = []
  83. bpm_Q1 = []
  84. dev_Q1 = []
  85. bpm_Q2 = []
  86. dev_Q2 = []
  87. bpm_Q3 = []
  88. dev_Q3 = []
  89. bpm_Q4 = []
  90. dev_Q4 = []
  92. for isub in subjects:
  93. sub = 'sub-' + isub
  94. print('subject {}'.format(isub))
  96. for ses in sessions:
  97. for task in tasks:
  98. physio_files = glob.glob(raw_path + sub + os.sep + 'ses-' + str(ses) + os.sep + 'beh' + os.sep + '*' + task + '*_emg.vhdr')
  99. physio_files = np.array(sorted(physio_files))
  100. if len(physio_files) > 1:
  101. print('Warning! More than one file found!')
  103. sub_out_path = output_path + sub + os.sep + 'ses-' + str(ses) + os.sep + 'Resp'
  104. if not os.path.exists(sub_out_path):
  105. os.makedirs(sub_out_path)
  107. if preprocess:
  108. p = physio_files[0]
  109. # import raw data with MNE
  110. raw = mne.io.read_raw_brainvision(p, preload=True)
  111. sr = raw.info['sfreq']
  112. # pick respiration channel
  113. raw.pick_channels(ch_names=["RES"])
  115. # get events file
  116. tmp = p.split('_emg')
  117. event_file = tmp[0] + '_events.tsv'
  118. events = pd.read_table(event_file, sep='\t')
  120. # cut data from manually inserted event
  121. onset = events.loc[events['value'] == 'manual', 'onset'].values[0]
  122. duration = events.loc[events['value'] == 'manual', 'duration'].values[0]
  123. if onset + duration >= len(raw) / sr:
  124. raw.crop(tmin=onset, tmax=None, include_tmax=True)
  125. else:
  126. raw.crop(tmin=onset, tmax=onset + duration, include_tmax=True)
  128. raw_plot = raw.get_data() # convert object to numpy array of shape (n_channels, n_times)
  129. raw_plot = np.squeeze(raw_plot) # get rid of dimension "n_channels" -> (rows: time points)
  131. # inhalation starts = min peaks, so invert data
  132. raw_plot = 1 - raw_plot
  133. median_height = np.median(raw_plot)
  134. inhale_peaks, properties = signal.find_peaks(raw_plot, height=median_height, distance=1.0 * sr, width=0.5 * sr)
  136. # how many breaths per minute?
  137. bpm = len(inhale_peaks) / (len(raw_plot) / sr / 60)
  138. bpm_all.append(bpm)
  139. # how much deviation is in the peaks
  140. dev = np.std(np.diff(inhale_peaks))/sr
  141. dev_all.append(dev)
  143. # same for 5 minute blocks
  144. len_quart = int(np.floor(len(raw_plot)/4))
  145. inhale_peaks_1 = np.delete(inhale_peaks, inhale_peaks > len_quart)
  146. bpm_1 = len(inhale_peaks_1) / ((len(raw_plot)/4) / sr / 60)
  147. bpm_Q1.append(bpm_1)
  148. dev_1 = np.std(np.diff(inhale_peaks_1)) / sr
  149. dev_Q1.append(dev_1)
  151. inhale_peaks_2 = np.delete(inhale_peaks, (inhale_peaks < len_quart) | (inhale_peaks > 2 * len_quart))
  152. bpm_2 = len(inhale_peaks_2) / ((len(raw_plot) / 4) / sr / 60)
  153. bpm_Q2.append(bpm_2)
  154. dev_2 = np.std(np.diff(inhale_peaks_2)) / sr
  155. dev_Q2.append(dev_2)
  157. inhale_peaks_3 = np.delete(inhale_peaks, (inhale_peaks < 2 * len_quart) | (inhale_peaks > 3 * len_quart))
  158. bpm_3 = len(inhale_peaks_3) / ((len(raw_plot) / 4) / sr / 60)
  159. bpm_Q3.append(bpm_3)
  160. dev_3 = np.std(np.diff(inhale_peaks_3)) / sr
  161. dev_Q3.append(dev_3)
  163. inhale_peaks_4 = np.delete(inhale_peaks, inhale_peaks < 3 * len_quart)
  164. bpm_4 = len(inhale_peaks_4) / ((len(raw_plot) / 4) / sr / 60)
  165. bpm_Q4.append(bpm_4)
  166. dev_4 = np.std(np.diff(inhale_peaks_4)) / sr
  167. dev_Q4.append(dev_4)
  169. if save_markers:
  170. rep_subjects = subjects * 3
  171. rep_subjects_prefix = ['sub-' + subject for subject in rep_subjects]
  173. markers = pd.DataFrame({'BPM': bpm_all, 'BDev': dev_all,
  174. 'BPM_Q1': bpm_Q1, 'BPM_Q2': bpm_Q2, 'BPM_Q3': bpm_Q3, 'BPM_Q4': bpm_Q4,
  175. 'BDev_Q1': dev_Q1, 'BDev_Q2': dev_Q2, 'BDev_Q3': dev_Q3, 'BDev_Q4': dev_Q4,
  176. 'Subject': rep_subjects_prefix,
  177. 'Session': np.resize(np.arange(1, 4), len(subjects)*3)})
  178. markers.to_csv(result_path + 'BPM.csv', sep=',', index=False)
  180. if unblinding:
  181. def adjust_box_widths(g, fac):
  182. """
  183. Adjust the withs of a seaborn-generated boxplot.
  184. """
  186. # iterating through Axes instances
  187. for ax in g.axes:
  189. # iterating through axes artists:
  190. for c in ax.get_children():
  192. # searching for PathPatches
  193. if isinstance(c, PathPatch):
  194. # getting current width of box:
  195. p = c.get_path()
  196. verts = p.vertices
  197. verts_sub = verts[:-1]
  198. xmin = np.min(verts_sub[:, 0])
  199. xmax = np.max(verts_sub[:, 0])
  200. xmid = 0.5 * (xmin + xmax)
  201. xhalf = 0.5 * (xmax - xmin)
  203. # setting new width of box
  204. xmin_new = xmid - fac * xhalf
  205. xmax_new = xmid + fac * xhalf
  206. verts_sub[verts_sub[:, 0] == xmin, 0] = xmin_new
  207. verts_sub[verts_sub[:, 0] == xmax, 0] = xmax_new
  209. # setting new width of median line
  210. for l in ax.lines:
  211. if np.all(l.get_xdata() == [xmin, xmax]):
  212. l.set_xdata([xmin_new, xmax_new])
  214. df = pd.read_csv(result_path + 'BPM.csv', sep=',', header=0)
  215. rand_df = pd.read_csv('/data/pt_02582/tsDCS_BIDS/participants.tsv', sep='\t')
  217. def replace_cond(row):
  218. this_sub = row['Subject']
  219. this_rand = rand_df[rand_df['participant_id'] == this_sub]
  220. if row['Session'] == 1:
  221. val = this_rand['condition_ses-1'].values[0]
  222. elif row['Session'] == 2:
  223. val = this_rand['condition_ses-2'].values[0]
  224. elif row['Session'] == 3:
  225. val = this_rand['condition_ses-3'].values[0]
  226. else:
  227. print('something weird happened')
  228. return val
  230. df['Condition'] = df.apply(replace_cond, axis=1)
  232. # exclude the subjects' sessions that we decided on:
  233. df.loc[(df['Subject'] == 'sub-SR01') & (df['Session'] == 2), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  234. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  235. df.loc[(df['Subject'] == 'sub-SR08') & (df['Session'] == 2), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  236. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  237. df.loc[(df['Subject'] == 'sub-SR09'), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  238. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  239. df.loc[(df['Subject'] == 'sub-SR10') & (df['Session'] == 3), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  240. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  241. df.loc[(df['Subject'] == 'sub-SR13'), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  242. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  243. df.loc[(df['Subject'] == 'sub-SR14'), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  244. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  245. df.loc[(df['Subject'] == 'sub-SR16') & (df['Session'] == 3), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  246. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  247. df.loc[(df['Subject'] == 'sub-SR17') & (df['Session'] == 3), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  248. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  249. df.loc[(df['Subject'] == 'sub-SR19') & (df['Session'] == 1), ['BPM', 'BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  250. 'BDev', 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4']] = np.nan
  252. # make a plot
  253. sns.set_context("talk")
  254. hue_order = ['A', 'C', 'B']
  255. fig, axes = plt.subplots(2, 1)
  256. fig.suptitle('Breathing rate and breathing rate variability', fontsize=16)
  257. sns.boxplot(df, x='Condition', y='BPM', order=hue_order,
  258. ax=axes[0], boxprops={'alpha': 0.4}, showfliers=False)
  259. sns.stripplot(data=df, x="Condition", y="BPM", order=hue_order, hue='Condition', hue_order=hue_order,
  260. dodge=False, ax=axes[0])
  261. handles, labels = axes[0].get_legend_handles_labels()
  262. axes[0].legend(handles=handles, labels=['A (Anodal)', 'C (Cathodal)', 'B (Sham)'], loc='upper right')
  263. sns.boxplot(df, x='Condition', y='BDev', order=hue_order,
  264. ax=axes[1], boxprops={'alpha': 0.4}, showfliers=False)
  265. sns.stripplot(data=df, x="Condition", y="BDev", order=hue_order, hue='Condition', hue_order=hue_order,
  266. dodge=False, ax=axes[1], legend=False)
  267. handles, labels = axes[0].get_legend_handles_labels()
  268. plt.show()
  270. # create tables for the stats
  271. df = df.drop(['Session'], axis=1)
  272. wide_df = df.pivot(index='Subject', columns='Condition', values=['BPM', 'BDev', 'BPM_Q1',
  273. 'BPM_Q2', 'BPM_Q3', 'BPM_Q4',
  274. 'BDev_Q1', 'BDev_Q2', 'BDev_Q3', 'BDev_Q4'])
  275. wide_df.columns = ['_'.join(col).strip() for col in wide_df.columns.values]
  276. wide_df.reset_index('Subject', inplace=True)
  277. wide_df.to_csv(result_path + 'BPM_BDev_unblind.csv', sep=',', index=None)
  279. if within_sub_plot:
  280. df = pd.read_csv(result_path + 'BPM_BDev_unblind.csv', sep=',')
  282. # 1. Breathing rate BPM
  283. # cathodal comparison
  284. cathodal = df.drop(labels=['BPM_A', 'BDev_A', 'BDev_B', 'BDev_C',
  285. 'BPM_Q1_A', 'BPM_Q2_A', 'BPM_Q3_A', 'BPM_Q4_A',
  286. 'BDev_Q1_A', 'BDev_Q2_A', 'BDev_Q3_A', 'BDev_Q4_A'
  287. ], axis=1)
  288. cathodal.dropna(axis=0, inplace=True)
  289. cathodal.reset_index(inplace=True, drop=True)
  290. cathodal.drop('Subject', axis=1, inplace=True)
  292. sns.set_context("poster")
  293. sns.set_style("white")
  294. jitter = 0.05
  295. df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=(cathodal.values.shape[0], 2)),
  296. columns=['Sham', 'Cathodal'])
  297. df_x_jitter += np.arange(2)
  298. fig, ax = plt.subplots(1, 2, figsize=(12, 8), sharey=True)
  299. fig.suptitle("Breathing rate", fontsize=30)
  300. b = sns.boxplot(ax=ax[0], x=np.repeat(0, len(cathodal)), y=cathodal['BPM_B'], color=color_blue, width=0.5,
  301. native_scale=True, showfliers=False)
  302. for patch in b.patches:
  303. r, g, b, a = patch.get_facecolor()
  304. patch.set_facecolor((r, g, b, 0.7))
  305. c = sns.boxplot(ax=ax[0], x=np.repeat(1, len(cathodal)), y=cathodal['BPM_C'], color=color_red, width=0.5,
  306. native_scale=True, showfliers=False)
  307. for patch in c.patches:
  308. r, g, b, a = patch.get_facecolor()
  309. patch.set_facecolor((r, g, b, 0.7))
  310. df_sham = pd.DataFrame({'jitter': df_x_jitter['Sham'], 'bpm': cathodal['BPM_B']})
  311. df_cath = pd.DataFrame({'jitter': df_x_jitter['Cathodal'], 'bpm': cathodal['BPM_C']})
  312. sns.scatterplot(ax=ax[0], data=df_sham, x='jitter', y='bpm', color=color_blue, zorder=100, edgecolor="black")
  313. sns.scatterplot(ax=ax[0], data=df_cath, x='jitter', y='bpm', color=color_red, zorder=100, edgecolor="black")
  314. ax[0].set_xticks(range(2))
  315. ax[0].set_xticklabels(['Sham', 'Cathodal'])
  316. ax[0].tick_params(axis='x', which='major', labelsize=25)
  317. ax[0].set_xlim(-1.0, 2)
  318. sns.despine()
  319. ax[0].set_xlabel('')
  320. ax[0].set_ylabel('Breaths per minute', fontsize=25)
  321. plt.subplots_adjust(bottom=0.2, left=0.15)
  322. ax[0].set_title('Cathodal', fontsize=25)
  323. for idx in cathodal.index:
  324. ax[0].plot(df_x_jitter.loc[idx, ['Sham', 'Cathodal']], cathodal.loc[idx, ['BPM_B', 'BPM_C']], color='grey',
  325. linewidth=0.5, linestyle='--', zorder=-1)
  327. # anodal comparison
  328. anodal = df.drop(labels=['BPM_C', 'BDev_A', 'BDev_B', 'BDev_C',
  329. 'BPM_Q1_C', 'BPM_Q2_C', 'BPM_Q3_C', 'BPM_Q4_C',
  330. 'BDev_Q1_C', 'BDev_Q2_C', 'BDev_Q3_C', 'BDev_Q4_C'
  331. ], axis=1)
  332. anodal.dropna(axis=0, inplace=True)
  333. anodal.reset_index(inplace=True, drop=True)
  334. anodal.drop('Subject', axis=1, inplace=True)
  336. df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=(anodal.values.shape[0], 2)),
  337. columns=['Sham', 'Anodal'])
  338. df_x_jitter += np.arange(2)
  339. b = sns.boxplot(ax=ax[1], x=np.repeat(0, len(anodal)), y=anodal['BPM_B'], color=color_blue, width=0.5,
  340. native_scale=True, showfliers=False)
  341. for patch in b.patches:
  342. r, g, b, a = patch.get_facecolor()
  343. patch.set_facecolor((r, g, b, 0.7))
  344. c = sns.boxplot(ax=ax[1], x=np.repeat(1, len(anodal)), y=anodal['BPM_A'], color=color_orange, width=0.5,
  345. native_scale=True, showfliers=False)
  346. for patch in c.patches:
  347. r, g, b, a = patch.get_facecolor()
  348. patch.set_facecolor((r, g, b, 0.7))
  349. df_sham = pd.DataFrame({'jitter': df_x_jitter['Sham'], 'bpm': anodal['BPM_B']})
  350. df_anod = pd.DataFrame({'jitter': df_x_jitter['Anodal'], 'bpm': anodal['BPM_A']})
  351. sns.scatterplot(ax=ax[1], data=df_sham, x='jitter', y='bpm', color=color_blue, zorder=100, edgecolor="black")
  352. sns.scatterplot(ax=ax[1], data=df_anod, x='jitter', y='bpm', color=color_orange, zorder=100, edgecolor="black")
  353. ax[1].set_xticks(range(2))
  354. ax[1].set_xticklabels(['Sham', 'Anodal'])
  355. ax[1].tick_params(axis='x', which='major', labelsize=25)
  356. ax[1].set_xlim(-1.0, 2)
  357. sns.despine()
  358. ax[1].set_xlabel('')
  359. ax[1].set_ylabel('Breaths per minute', fontsize=25)
  360. plt.subplots_adjust(bottom=0.2, left=0.15)
  361. ax[1].set_title('Anodal', fontsize=25)
  362. for idx in anodal.index:
  363. ax[1].plot(df_x_jitter.loc[idx, ['Sham', 'Anodal']], anodal.loc[idx, ['BPM_B', 'BPM_A']], color='grey',
  364. linewidth=0.5, linestyle='--', zorder=-1)
  366. plt.savefig(result_path + 'BR_within_sub.png')
  367. plt.show()
  369. # 2. Breathing rate variability HRV
  370. # cathodal comparison
  371. cathodal = df.drop(labels=['BDev_A', 'BPM_A', 'BPM_B', 'BPM_C',
  372. 'BPM_Q1_A', 'BPM_Q2_A', 'BPM_Q3_A', 'BPM_Q4_A',
  373. 'BDev_Q1_A', 'BDev_Q2_A', 'BDev_Q3_A', 'BDev_Q4_A'
  374. ], axis=1)
  375. cathodal.dropna(axis=0, inplace=True)
  376. cathodal.reset_index(inplace=True, drop=True)
  377. cathodal.drop('Subject', axis=1, inplace=True)
  379. sns.set_context("poster")
  380. sns.set_style("white")
  381. jitter = 0.05
  382. df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=(cathodal.values.shape[0], 2)),
  383. columns=['Sham', 'Cathodal'])
  384. df_x_jitter += np.arange(2)
  385. fig, ax = plt.subplots(1, 2, figsize=(12, 8), sharey=True)
  386. fig.suptitle("Breathing rate variability", fontsize=30)
  387. b = sns.boxplot(ax=ax[0], x=np.repeat(0, len(cathodal)), y=cathodal['BDev_B'], color=color_blue, width=0.5,
  388. native_scale=True, showfliers=False)
  389. for patch in b.patches:
  390. r, g, b, a = patch.get_facecolor()
  391. patch.set_facecolor((r, g, b, 0.7))
  392. c = sns.boxplot(ax=ax[0], x=np.repeat(1, len(cathodal)), y=cathodal['BDev_C'], color=color_red, width=0.5,
  393. native_scale=True, showfliers=False)
  394. for patch in c.patches:
  395. r, g, b, a = patch.get_facecolor()
  396. patch.set_facecolor((r, g, b, 0.7))
  397. df_sham = pd.DataFrame({'jitter': df_x_jitter['Sham'], 'bdev': cathodal['BDev_B']})
  398. df_cath = pd.DataFrame({'jitter': df_x_jitter['Cathodal'], 'bdev': cathodal['BDev_C']})
  399. sns.scatterplot(ax=ax[0], data=df_sham, x='jitter', y='bdev', color=color_blue, zorder=100, edgecolor="black")
  400. sns.scatterplot(ax=ax[0], data=df_cath, x='jitter', y='bdev', color=color_red, zorder=100, edgecolor="black")
  401. ax[0].set_xticks(range(2))
  402. ax[0].set_xticklabels(['Sham', 'Cathodal'])
  403. ax[0].tick_params(axis='x', which='major', labelsize=25)
  404. ax[0].set_xlim(-1.0, 2)
  405. sns.despine()
  406. ax[0].set_xlabel('')
  407. ax[0].set_ylabel('SD of breath intervals', fontsize=25)
  408. plt.subplots_adjust(bottom=0.2, left=0.15)
  409. ax[0].set_title('Cathodal', fontsize=25)
  410. for idx in cathodal.index:
  411. ax[0].plot(df_x_jitter.loc[idx, ['Sham', 'Cathodal']], cathodal.loc[idx, ['BDev_B', 'BDev_C']], color='grey',
  412. linewidth=0.5, linestyle='--', zorder=-1)
  414. # anodal comparison
  415. anodal = df.drop(labels=['BDev_C', 'BPM_A', 'BPM_B', 'BPM_C',
  416. 'BPM_Q1_C', 'BPM_Q2_C', 'BPM_Q3_C', 'BPM_Q4_C',
  417. 'BDev_Q1_C', 'BDev_Q2_C', 'BDev_Q3_C', 'BDev_Q4_C'
  418. ], axis=1)
  419. anodal.dropna(axis=0, inplace=True)
  420. anodal.reset_index(inplace=True, drop=True)
  421. anodal.drop('Subject', axis=1, inplace=True)
  423. df_x_jitter = pd.DataFrame(np.random.normal(loc=0, scale=jitter, size=(anodal.values.shape[0], 2)),
  424. columns=['Sham', 'Anodal'])
  425. df_x_jitter += np.arange(2)
  426. b = sns.boxplot(ax=ax[1], x=np.repeat(0, len(anodal)), y=anodal['BDev_B'], color=color_blue, width=0.5,
  427. native_scale=True, showfliers=False)
  428. for patch in b.patches:
  429. r, g, b, a = patch.get_facecolor()
  430. patch.set_facecolor((r, g, b, 0.7))
  431. c = sns.boxplot(ax=ax[1], x=np.repeat(1, len(anodal)), y=anodal['BDev_A'], color=color_orange, width=0.5,
  432. native_scale=True, showfliers=False)
  433. for patch in c.patches:
  434. r, g, b, a = patch.get_facecolor()
  435. patch.set_facecolor((r, g, b, 0.7))
  436. df_sham = pd.DataFrame({'jitter': df_x_jitter['Sham'], 'bdev': anodal['BDev_B']})
  437. df_anod = pd.DataFrame({'jitter': df_x_jitter['Anodal'], 'bdev': anodal['BDev_A']})
  438. sns.scatterplot(ax=ax[1], data=df_sham, x='jitter', y='bdev', color=color_blue, zorder=100, edgecolor="black")
  439. sns.scatterplot(ax=ax[1], data=df_anod, x='jitter', y='bdev', color=color_orange, zorder=100, edgecolor="black")
  440. ax[1].set_xticks(range(2))
  441. ax[1].set_xticklabels(['Sham', 'Anodal'])
  442. ax[1].tick_params(axis='x', which='major', labelsize=25)
  443. ax[1].set_xlim(-1.0, 2)
  444. sns.despine()
  445. ax[1].set_xlabel('')
  446. ax[1].set_ylabel('SD of breath intervals', fontsize=25)
  447. plt.subplots_adjust(bottom=0.2, left=0.15)
  448. ax[1].set_title('Anodal', fontsize=25)
  449. for idx in anodal.index:
  450. ax[1].plot(df_x_jitter.loc[idx, ['Sham', 'Anodal']], anodal.loc[idx, ['BDev_B', 'BDev_A']], color='grey',
  451. linewidth=0.5, linestyle='--', zorder=-1)
  453. plt.savefig(result_path + 'BRV_within_sub.png')
  454. plt.show()
  456. if interaction_plot:
  457. df = pd.read_csv(result_path + 'BPM_BDev_unblind.csv', sep=',')
  458. # breathing rate
  459. br = df[df.columns.drop(list(df.filter(regex='BDev_')))]
  460. br = br[br.columns.drop(['BPM_A', 'BPM_B', 'BPM_C'])]
  461. df_q_long = pd.wide_to_long(br, stubnames=['BPM_Q1', 'BPM_Q2', 'BPM_Q3', 'BPM_Q4'], i='Subject', j='condition',
  462. sep='_', suffix=r'\w+').reset_index()
  463. df_q_longer = pd.wide_to_long(df_q_long, stubnames=['BPM'], i=['Subject', 'condition'], j='Quartile',
  464. sep='_Q', suffix=r'\w+').reset_index()
  465. sns.set_context("poster")
  466. sns.set_style("white")
  467. fig, ax = plt.subplots(1, 1, figsize=(12, 8))
  468. fig.suptitle("Breathing rate over time", fontsize=30)
  469. sns.lineplot(data=df_q_longer, x='Quartile', y='BPM', hue='condition', palette=[color_blue, color_red, color_orange],
  470. hue_order=['B', 'C', 'A'], errorbar='se')
  471. handles, labels = ax.get_legend_handles_labels()
  472. ax.xaxis.get_major_locator().set_params(integer=True)
  473. plt.ylim(14, 19)
  474. sns.despine()
  475. plt.subplots_adjust(bottom=0.2, left=0.15)
  476. ax.set_ylabel('Quartile', fontsize=25)
  477. ax.set_ylabel('Breaths per minute', fontsize=25)
  478. plt.legend(handles=handles, labels=['Sham', 'Cathodal', 'Anodal'])
  479. plt.savefig(result_path + 'BR_Quartile_interaction.png')
  480. plt.show()

UE_analysis_4_respiration.py at commit 3cbcf57, no license · at the source

Overview

Authors: Hongyan Zhao1, Ulrike Horn1, Melanie Freund1, Anna Bujanow2, Christopher Gundlach3, Gesa Hartwigsen4,5, Falk Eippert1
ORCID iDs: Hongyan Zhao, Ulrike Horn, Christopher Gundlach, Gesa Hartwigsen, Falk Eippert
  1. Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  2. Methods and Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  3. Experimental Psychology and Methods, Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
  4. Cognitive and Biological Psychology, Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
  5. Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Institutions: Max Planck Society (Germany); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Leipzig University (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1292
Dates: received 2 February 2024; accepted 15 June 2026; published online 14 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1292 · PMID 42459499 · PMCID PMC13370751 · OpenAlex W4389752166
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2023.12.14.571694
Status: code verified
Categories: behavior only (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Physiology & signal measures, Smoothing, state filtering, decompositions
Keywords: spinal cord, transcutaneous spinal direct current stimulation, adverse effects, unspecific effects, structured questionnaire, autonomic nervous system
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: European Research Council (758974)
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

Transcutaneous spinal direct current stimulation (tsDCS) is a relatively recent method for non-invasively modulating neural activity in the spinal cord. Despite its growing prominence, comprehensive studies addressing its potential adverse effects (AEs) and unspecific effects (UEs) are lacking. In this study, we conducted a systematic investigation of the potential AEs and UEs of tsDCS in healthy volunteers (N=20) who underwent a double-blind within-participant design, employing anodal, cathodal, and sham tsDCS of the thoracolumbar spinal cord. Our approach involved utilizing a newly developed structured questionnaire (to assess subjectively reported AEs) in combination with tsDCS-concurrent recording of skin conductance, cardiac and respiratory activity (to assess UEs in bodily state). The most frequent participant-reported AEs were sensations of burning, tingling, and itching, although they were largely described as mild; skin redness (experimenter-reported) occurred even more frequently. Importantly, when comparing AEs between active and sham tsDCS via frequentist and Bayesian analysis approaches, the results were largely in favour of no difference between conditions (with the exception of skin redness). A similar picture emerged for most UE metrics, suggesting that tsDCS does not induce changes in bodily state, at least as measured by our autonomic nervous system metrics. We believe that the strategy employed here could serve as a starting point for a systematic AE and UE assessment in clinical populations, longitudinal designs, and when targeting different spinal sites. Taken together, our results contribute to assessing the tolerability and specificity of tsDCS, in order to further the application of this spinal neuromodulation method in health and disease.

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 5 matches between paragraphs and lines of code.

eippertlab/tsdcs-sideeffects

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3cbcf57529a400c7cc0b462dd7ebc68b289c2dc2, 25 June 2026
Languages: Python (7), MATLAB (3)
Size: 11 files, 10 scripts
Software Heritage: archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (7 files), NumPy (7 files), pandas (6 files), seaborn (5 files), MNE-Python (4 files), SciPy (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/eippertlab/tsdcs-sideeffects

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data and Code Availability

The underlying data are openly available (https://osf.io/f7spw/), as is all analysis code (https://github.com/eippertlab/tsdcs-sideeffects).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 6 keywords, 1 funder, 81 references.

Cite

This paper

Zhao, H., Horn, U., Freund, M., Bujanow, A., Gundlach, C., Hartwigsen, G., & Eippert, F. (2026). Assessing adverse effects and unspecific effects of transcutaneous spinal direct current stimulation (tsDCS). Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1292. https://doi.org/10.1162/imag.a.1292

BibTeX

@article{zhao2026assessing,
author = {Zhao, Hongyan and Horn, Ulrike and Freund, Melanie and Bujanow, Anna and Gundlach, Christopher and Hartwigsen, Gesa and Eippert, Falk},
title = {{Assessing adverse effects and unspecific effects of transcutaneous spinal direct current stimulation (tsDCS)}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1292},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1292},
url = {https://doi.org/10.1162/imag.a.1292},
pmid = {42459499},
pmcid = {PMC13370751}
}

RIS

TY - JOUR
AU - Zhao, Hongyan
AU - Horn, Ulrike
AU - Freund, Melanie
AU - Bujanow, Anna
AU - Gundlach, Christopher
AU - Hartwigsen, Gesa
AU - Eippert, Falk
TI - Assessing adverse effects and unspecific effects of transcutaneous spinal direct current stimulation (tsDCS)
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/14
VL - 4
SP - IMAG.a.1292
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1292
UR - https://doi.org/10.1162/imag.a.1292
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1292",
"type": "article-journal",
"title": "Assessing adverse effects and unspecific effects of transcutaneous spinal direct current stimulation (tsDCS)",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Zhao",
"given": "Hongyan"
},
{
"family": "Horn",
"given": "Ulrike"
},
{
"family": "Freund",
"given": "Melanie"
},
{
"family": "Bujanow",
"given": "Anna"
},
{
"family": "Gundlach",
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"given": "Gesa"
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{
"family": "Eippert",
"given": "Falk"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1292",
"DOI": "10.1162/imag.a.1292",
"PMID": "42459499",
"PMCID": "PMC13370751",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1292",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
14
]
]
}
}

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