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Cortical Contributions to Attentional Orienting and Response Cancellation in Action Stopping.

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The authors' code

Jupyter notebook · 577 lines · 21 KB · no license

  1. # %%
  2. ############################
  3. ## import modules
  4. ############################
  6. import numpy as np, os, glob, pandas as pd, warnings
  7. import scipy as sp, seaborn as sns, matplotlib.pyplot as plt
  8. import statsmodels.api as sm, statsmodels.formula.api as smf
  10. from scipy.stats import iqr
  11. from sklearn import preprocessing
  14. # %%
  16. # set directories
  17. workdir = 'F:/projects/okapi/fnirs/fnirs data/homer3/2024-averaging/'
  18. savedir = 'C:/Users/sarah/OneDrive - University of Tasmania/projects/okapi/data/'
  21. ## define some variables for the response window
  23. f=7.8125 # sampling frequency
  24. ETL=9.5 # trial length
  25. time = np.arange(0, ETL + 1/f, 1/f)
  27. # this for some reason is 1 longer than matlab so let's remove the last number
  28. # (first 75 values line up)
  29. new_time = time[:-1]
  31. # find segments of the column that's within the time window
  32. time_window = (new_time >= 2) & (new_time <= 7)
  34. print(time_window)
  35. print(time_window.astype(int))
  37. # so this is the time window in which we will look for peaks
  40. # %%
  41. ##
  42. ## create foldies
  43. ##
  45. # Want some nice subfolders to save this data into to keep it organised
  46. # If these folders don't exist, this module creates them
  48. # NB the next module assumes they exist with this structure - if you don't have your data organised this way, the saving at the end
  49. # will not work and that module will need to be modified (they're in subfolders because I was playing around with different ways
  50. # to do outlier removal). I do think this is a nice clean folder structure and lets you keep the raw + normalised data without risking
  51. # confusion.
  54. for subject in os.listdir(savedir):
  55. if 'sub-0' in subject:
  56. iqr_folder = 'iqr3'
  57. behav = 'behav'
  58. emg = 'emg'
  60. path = os.path.join(savedir, subject, iqr_folder)
  61. path1 = os.path.join(savedir, subject, iqr_folder, behav)
  62. path2 = os.path.join(savedir, subject, iqr_folder, emg)
  64. if not os.path.exists(path):
  65. os.makedirs(path)
  66. os.makedirs(path1)
  67. os.makedirs(path2)
  70. # %% [markdown]
  71. # ## Behavioural data crunching
  73. # %%
  74. # for each participant and region, we need to go through and the files that are relevant
  75. # then need to find the IQR and exclude, and probably let's save all the images
  77. # this is the BEHAVIOURAL DATA, ie this does not access the dataframes that have
  78. # information about partial burst splits
  80. # go through each participant and exclude the IQR per region
  82. regions = ['LIFG', 'RIFG', 'preSMA']
  84. for subject in os.listdir(workdir):
  85. if 'sub-0' in subject: # if it's an actual subject
  86. print('Processing', subject, '...')
  87. for region in regions:
  88. # load the files
  89. alltrials = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_ALL.csv')
  90. alltrials = alltrials.dropna(axis=1) # get rid of nans
  91. fs = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_FS.csv')
  92. ss = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_SS.csv')
  93. ig = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_I.csv')
  94. go = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_GO.csv')
  96. stops = pd.concat([ss, fs], axis=1)
  97. stops = stops.dropna(axis=1)
  98. ig = ig.dropna(axis=1)
  99. go = go.dropna(axis=1)
  102. dfs = [stops, go, ig]
  103. df_names = ['stops', 'go', 'ig']
  105. # dictionary to store filtered dfs
  106. filtered_dfs = {}
  108. for i, df in enumerate(dfs):
  109. # Find the IQR - find the highest absolute value in each column and IQR from there
  110. highest_abs = [df[column].abs().max() for column in df.columns]
  111. iqr_value = iqr(highest_abs)
  113. # Filter columns
  114. filtered_columns = [column for column in df.columns if not (df[column].abs() > 3 * iqr_value).any()]
  115. filtered_dfs[df_names[i]] = df[filtered_columns]
  117. # print(f"{df_names[i].capitalize()}:")
  118. # print(f"Original number of columns: {len(df.columns)}")
  119. # print(f"Filtered number of columns: {len(filtered_dfs[df_names[i]].columns)}\n")
  121. stops_filtered = filtered_dfs['stops']
  122. go_filtered = filtered_dfs['go']
  123. ig_filtered = filtered_dfs['ig']
  125. # separate stops out again
  126. fs_columns = [col for col in stops_filtered.columns if "FS" in col]
  127. fs_filtered = stops[fs_columns]
  128. ss_columns = [col for col in stops_filtered.columns if "SS" in col]
  129. ss_filtered = stops[ss_columns]
  132. ## NORMALISE
  133. # Now each trial is going to get normalised by that person's average SD
  134. # First finds the SD of each trial, then finds the average SD, then divides all values by that avg SD
  135. individual_sds = alltrials.apply(np.std)
  136. avg_sd = np.mean(individual_sds)
  137. alltrials_normalised = alltrials.div(avg_sd)
  140. ## GET TRIALS
  141. # now we've normalised all the data, we want to retain only the columns that weren't outliers
  142. # Really tediously the headings aren't named consistently so let's rename them.
  143. fs_filtered.columns = fs_filtered.columns.str.replace('FS', 'ALL')
  144. ss_filtered.columns = ss_filtered.columns.str.replace('SS', 'ALL')
  145. ig_filtered.columns = ig_filtered.columns.str.replace('_I', '_ALL')
  146. go_filtered.columns = go_filtered.columns.str.replace('GO', 'ALL')
  148. #extract which headings are which trial type
  149. fs_headings = fs_filtered.columns
  150. ss_headings = ss_filtered.columns
  151. ig_headings = ig_filtered.columns
  152. go_headings = ig_filtered.columns
  154. # now grab the trials we want from the big normalised dataframe
  155. fs_norm = alltrials_normalised[[col for col in fs_headings if col in alltrials_normalised.columns]]
  156. ss_norm = alltrials_normalised[[col for col in ss_headings if col in alltrials_normalised.columns]]
  157. ig_norm = alltrials_normalised[[col for col in ig_headings if col in alltrials_normalised.columns]]
  158. go_norm = alltrials_normalised[[col for col in go_headings if col in alltrials_normalised.columns]]
  160. fs_norm.to_csv(savedir + subject + '/iqr3/behav/' + region + '_FS_norm.csv', index=False)
  161. ss_norm.to_csv(savedir + subject + '/iqr3/behav/' + region + '_SS_norm.csv', index=False)
  162. ig_norm.to_csv(savedir + subject + '/iqr3/behav/' + region + '_IG_norm.csv', index=False)
  163. go_norm.to_csv(savedir + subject + '/iqr3/behav/' + region + '_GO_norm.csv', index=False)
  166. print('finito')
  168. # %%
  169. ## EXTRACT PEAK VALUES >>>
  171. # for each trial, we want to get the value of the local maximum
  172. ## now let's find all the peaks and save them to a df
  173. warnings.filterwarnings("ignore")
  175. behavpeaks = pd.DataFrame(columns=['subject', 'region', 'peak_value'])
  176. timeseries = pd.DataFrame()
  178. for subject in os.listdir(savedir):
  179. number = 1
  180. if 'sub-0' in subject:
  181. print('Getting peaks for ', subject)
  183. # look through the subject's folder and find any CSV files
  184. csvs = glob.glob(savedir + subject +'/iqr3/behav/*norm.csv')
  185. for csv in csvs:
  187. # find the filename
  188. filename = os.path.basename(csv)
  189. filename = os.path.splitext(filename)[0]
  191. # load the file
  192. df = pd.read_csv(str(csv))
  194. # now look through the file
  195. for column in df.columns:
  196. if df[column].isnull().all(): # check it's not a null
  197. continue
  198. elif (df[column] == 0).all():
  199. continue
  201. # if the column has stuff in it, find the peak :)
  202. else:
  203. # find relevant segment in the time window
  204. relevant_segment = df.loc[time_window, column].values
  206. # find the peak of this
  207. peak_location = sp.signal.find_peaks(relevant_segment)
  209. # need to add an if statement checking that PL[0] is not empty
  210. # then find the value of the peak
  211. if not peak_location[0].any():
  212. peak_value = np.nan
  213. else:
  214. # this can either grab the largest of multiple points, or it can grab the first peak
  215. # I've got it getting the first peak but you might want to change it
  216. # just comment out the right bit
  218. first_peak = peak_location[0][0]
  219. peak_value = relevant_segment[first_peak]
  222. # highest_peak_index = np.argmax(relevant_segment[peak_location[0]])
  223. # highest_peak = peak_location[0][highest_peak_index]
  224. # peak_value = relevant_segment[highest_peak]
  226. # and grab that column for graphing later by appending time series
  227. timeseries[f'{subject}_{filename}_{number}'] = df[column]
  228. number = number + 1
  230. # save peak_value and add this new row to allpeaks
  231. new_row = pd.DataFrame({
  232. 'subject': [subject],
  233. 'region': [filename],
  234. 'peak_value': [peak_value]})
  235. behavpeaks = pd.concat([behavpeaks, new_row], ignore_index=True)
  237. #behavpeaks.to_csv(savedir + 'fnirs_behav.csv', index=False)
  239. # let's make the file called behavpeaks a bit prettier so we can analyse it
  241. def findTrialType (s):
  242. if 'SS' in s:
  243. return 'Successful stop'
  244. elif 'FS' in s:
  245. return 'Failed stop'
  246. elif 'IG' in s:
  247. return 'Ignore'
  248. elif 'GO' in s:
  249. return 'Go'
  250. else:
  251. return 'error'
  254. behavpeaks['trial'] = behavpeaks['region'].apply(findTrialType)
  255. behavpeaks['roi'] = behavpeaks['region'].apply(lambda x: x.split('_')[0]) # get the name before the first _
  256. behavpeaks.to_csv(savedir + 'fnirs_behav_norm.csv', index=False)
  257. print('done')
  263. # %% [markdown]
  264. # # graph data
  266. # %%
  267. fs = pd.read_csv(savedir + 'sub-009/behav/LIFG_FS_raw.csv')
  268. ss = pd.read_csv(savedir + 'sub-009/behav/LIFG_SS_raw.csv')
  269. ig = pd.read_csv(savedir + 'sub-009/behav/LIFG_IG_raw.csv')
  270. go = pd.read_csv(savedir + 'sub-009/behav/LIFG_GO_raw.csv')
  272. data_list = [(fs, 'Failed stops'),
  273. (ss, 'Successful stops'),
  274. (ig, 'Ignore trials'),
  275. (go, 'Go')]
  277. # Loop through each dataset and create a new figure for each plot
  278. for data, label in data_list:
  279. plt.figure()
  280. #plt.plot(data, linewidth=0.7)
  281. data.iloc[:, :8].plot()
  282. plt.title(label) # Optionally, add a title to each figure
  283. plt.show()
  287. # %%
  288. fs = pd.read_csv(savedir + 'sub-022/LIFG_FS_norm.csv')
  289. ss = pd.read_csv(savedir + 'sub-022/LIFG_SS_norm.csv')
  290. ig = pd.read_csv(savedir + 'sub-022/LIFG_IG_norm.csv')
  291. go = pd.read_csv(savedir + 'sub-022/LIFG_GO_norm.csv')
  293. data_list = [(fs, 'Failed stops'),
  294. (ss, 'Successful stops'),
  295. (ig, 'Ignore trials'),
  296. (go, 'Go')]
  298. # Loop through each dataset and create a new figure for each plot
  299. for data, label in data_list:
  300. plt.figure()
  301. plt.plot(data, linewidth=0.7)
  302. plt.title(label) # Optionally, add a title to each figure
  303. plt.show()
  306. # %% [markdown]
  307. # # EMG contrast
  308. # Now do the same thing I just did with the behavioural contrast, but using the EMG
  309. #
  310. # So in this instance we are just going to have successful stop and ignore trials and split them by partial burst presence
  312. # %%
  313. # I don't want this to rely on the behavioural portion of this script - want to be able to do this individually
  314. # therefore have some repetition here but we'll live with this
  316. # note to self probably an idea to try and use process at some point, this is a bit slow
  318. regions = ['LIFG', 'RIFG', 'preSMA']
  320. for subject in os.listdir(workdir):
  321. if 'sub-0' in subject: # if it's an actual subject
  322. print('Processing', subject, '...')
  323. for region in regions:
  325. # trial types:
  326. # s_pr > stop with partial response
  327. # s_nr > stop no partial response
  328. # i_pr > ignore with partial response
  329. # i_nr > ignore no partial response
  331. # load the files
  332. alltrials = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_ALL.csv')
  333. alltrials = alltrials.dropna(axis=1) # get rid of nans
  334. s_pr = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_SS_PB.csv')
  335. s_nr = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_SS_NB.csv')
  336. i_pr = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_I_PB.csv')
  337. i_nr = pd.read_csv(workdir + subject + '/derivatives/homer/' + region + '_I_NB.csv')
  339. # put stops and ignores together
  340. stops = pd.concat([s_pr, s_nr], axis=1)
  341. ignores = pd.concat([i_pr, i_nr], axis=1)
  342. stops = stops.dropna(axis=1)
  343. ignores = ignores.dropna(axis=1)
  345. dfs = [stops, ignores]
  346. df_names = ['stops', 'ignores']
  347. filtered_dfs = {}
  349. for i, df in enumerate(dfs):
  350. highest_abs = [df[column].abs().max() for column in df.columns]
  351. iqr_value = iqr(highest_abs)
  353. filtered_columns = [column for column in df.columns if not (df[column].abs() > 3 * iqr_value).any()]
  354. filtered_dfs[df_names[i]] = df[filtered_columns]
  356. stops_filtered = filtered_dfs['stops']
  357. ig_filtered = filtered_dfs['ignores']
  359. s_pr_columns = [col for col in stops_filtered.columns if "_PB" in col]
  360. s_pr_filtered = stops[s_pr_columns]
  361. s_nr_columns = [col for col in stops_filtered.columns if "_NB" in col]
  362. s_nr_filtered = stops[s_nr_columns]
  364. i_pr_columns = [col for col in ig_filtered.columns if "_PB" in col]
  365. i_pr_filtered = ignores[i_pr_columns]
  366. i_nr_columns = [col for col in ig_filtered.columns if "_NB" in col]
  367. i_nr_filtered = ignores[i_nr_columns]
  370. ## NORMALISE
  371. # First finds the SD of each trial (for that ROI)
  372. # then calculates the average SD, then divides all values by that avg SD
  373. individual_sds = alltrials.apply(np.std)
  374. avg_sd = np.mean(individual_sds)
  375. alltrials_normalised = alltrials.div(avg_sd)
  378. ## GET TRIALS
  379. # Rename the columns first
  380. s_pr_filtered.columns = s_pr_filtered.columns.str.replace('SS_PB', 'ALL')
  381. s_nr_filtered.columns = s_nr_filtered.columns.str.replace('SS_NB', 'ALL')
  382. i_pr_filtered.columns = i_pr_filtered.columns.str.replace('I_PB', 'ALL')
  383. i_nr_filtered.columns = i_nr_filtered.columns.str.replace('I_NB', 'ALL')
  386. #extract which headings are which trial type
  387. s_pr_heads = s_pr_filtered.columns
  388. s_nr_heads = s_nr_filtered.columns
  389. i_pr_heads = i_pr_filtered.columns
  390. i_nr_heads = i_nr_filtered.columns
  392. # now grab the trials we want from the now-normalised data
  393. s_pr_norm = alltrials_normalised[[col for col in s_pr_heads if col in alltrials_normalised.columns]]
  394. s_nr_norm = alltrials_normalised[[col for col in s_nr_heads if col in alltrials_normalised.columns]]
  395. i_pr_norm = alltrials_normalised[[col for col in i_pr_heads if col in alltrials_normalised.columns]]
  396. i_nr_norm = alltrials_normalised[[col for col in i_nr_heads if col in alltrials_normalised.columns]]
  398. # now save the normed data
  399. s_pr_norm.to_csv(savedir + subject + '/iqr3/emg/' + region + '_S_PR_norm.csv', index=False)
  400. s_nr_norm.to_csv(savedir + subject + '/iqr3/emg/' + region + '_S_NR_norm.csv', index=False)
  401. i_pr_norm.to_csv(savedir + subject + '/iqr3/emg/' + region + '_I_PR_norm.csv', index=False)
  402. i_nr_norm.to_csv(savedir + subject + '/iqr3/emg/' + region + '_I_NR_norm.csv', index=False)
  404. print('done!')
  408. # %%
  409. ## EXTRACT PEAK VALUES >>>
  411. # for each trial, we want to get the value of the local maximum
  412. ## now let's find all the peaks and save them to a df
  413. warnings.filterwarnings("ignore")
  415. emgpeaks = pd.DataFrame(columns=['subject', 'region', 'peak_value'])
  416. timeseries = pd.DataFrame()
  418. for subject in os.listdir(savedir):
  419. number = 1
  420. if 'sub-0' in subject:
  421. print('Getting peaks for ', subject)
  423. # look through the subject's folder and find any CSV files
  424. csvs = glob.glob(savedir + subject +'/iqr3/emg/*norm.csv')
  425. for csv in csvs:
  427. # find the filename
  428. filename = os.path.basename(csv)
  429. filename = os.path.splitext(filename)[0]
  431. # load the file
  432. df = pd.read_csv(str(csv))
  434. # now look through the file
  435. for column in df.columns:
  436. if df[column].isnull().all(): # check it's not a null
  437. continue
  438. elif (df[column] == 0).all():
  439. continue
  441. # if the column has stuff in it, find the peak :)
  442. else:
  443. # find relevant segment in the time window
  444. relevant_segment = df.loc[time_window, column].values
  446. # find the peak of this
  447. peak_location = sp.signal.find_peaks(relevant_segment)
  449. # need to add an if statement checking that PL[0] is not empty
  450. # then find the value of the peak
  451. if not peak_location[0].any():
  452. peak_value = np.nan
  453. else:
  454. # highest_peak_index = np.argmax(relevant_segment[peak_location[0]])
  455. # highest_peak = peak_location[0][highest_peak_index]
  456. # peak_value = relevant_segment[highest_peak]
  457. first_peak = peak_location[0][0]
  458. peak_value = relevant_segment[first_peak]
  460. # and grab that column for graphing later by appending time series
  461. timeseries[f'{subject}_{filename}_{number}'] = df[column]
  462. number = number + 1
  464. # save peak_value and add this new row to allpeaks
  465. new_row = pd.DataFrame({
  466. 'subject': [subject],
  467. 'region': [filename],
  468. 'peak_value': [peak_value]})
  469. emgpeaks = pd.concat([emgpeaks, new_row], ignore_index=True)
  471. # emgpeaks.to_csv(savedir + 'fnirs_emg_firstpeak.csv', index=False)
  472. # print('saved - now beautifying')
  475. # let's make the file called emgpeaks a bit prettier so we can analyse it
  477. def findTrialType (s):
  478. if '_S_' in s:
  479. return 'Stop'
  480. elif '_I_' in s:
  481. return 'Ignore'
  482. else:
  483. return 'error'
  485. def findPartialResponse (s):
  486. if '_PR_' in s:
  487. return 'Present'
  488. elif '_NR_' in s:
  489. return 'Absent'
  490. else:
  491. return 'error'
  496. emgpeaks['trial'] = emgpeaks['region'].apply(findTrialType)
  497. emgpeaks['partial_response'] = emgpeaks['region'].apply(findPartialResponse)
  498. emgpeaks['roi'] = emgpeaks['region'].apply(lambda x: x.split('_')[0]) # get the name before the first _
  499. emgpeaks.to_csv(savedir + 'fnirs_emg_norm.csv', index=False)
  500. print('done')
  503. # %% [markdown]
  504. # # graph data
  506. # %%
  507. # let's see if we have data
  509. s_pr = pd.read_csv(savedir + 'sub-002/emg/LIFG_S_PR_norm.csv')
  510. s_nr = pd.read_csv(savedir + 'sub-002/emg/LIFG_S_NR_norm.csv')
  511. i_pr = pd.read_csv(savedir + 'sub-002/emg/LIFG_I_PR_norm.csv')
  512. i_nr = pd.read_csv(savedir + 'sub-002/emg/LIFG_I_NR_norm.csv')
  514. data_list = [(s_pr, 'Stop partial resp'),
  515. (s_nr, 'Stop no resp'),
  516. (i_pr, 'Ignore partial resp'),
  517. (i_nr, 'Ignore no resp')]
  519. # Loop through each dataset and create a new figure for each plot
  520. for data, label in data_list:
  521. plt.figure()
  522. data.iloc[:, :8].plot()
  523. plt.title(label) # Optionally, add a title to each figure
  524. plt.show()
  527. # %%
  528. emgpeaks.groupby(['roi', 'trial'])['peak_value'].median()
  529. #emgpeaks
  531. # %%
  532. # let's do some visualising
  534. sns.displot(behavpeaks, x='peak_value', hue='subject', kind='kde', col='trial')
  535. #plt.show()
  537. # %%
  538. # presma
  540. sns.set_theme('paper')
  543. sns.pointplot(data=emgpeaks[emgpeaks['roi'] == 'preSMA'],
  544. x='trial', y='peak_value', errorbar='se')
  551. # %%
  554. sns.pointplot(data=behavpeaks[behavpeaks['roi'] == 'preSMA'],
  555. x='trial', y='peak_value', errorbar='se')
  559. # %%
  560. sns.pointplot(data=allpeaks[allpeaks['roi'] == 'LIFG'],
  561. x='trial', y='peak_value', errorbar='se')
  563. # %%
  564. # let's look at everyone's raw data
  566. for subject in os.listdir(savedir):
  567. number = 1
  568. if 'sub-0' in subject:
  569. print('Getting peaks for ', subject)
  571. # look through the subject's folder and find any CSV files
  572. csvs = glob.glob(savedir + subject +'/*_norm.csv')
  573. for csv in csvs:
  574. print('hello')
  576. # %% [markdown]
  577. # # checking stuff

fnirs peaks and figures.ipynb, no license · at the source

Overview

Authors: Sarah A Kemp1,2, Sauro Salomoni3, Pierre‐Louis Bazin4, Luke Pash1, Rebecca J St George1, Mark R Hinder1
ORCID iDs: Sarah A Kemp, Sauro Salomoni, Pierre‐Louis Bazin, Rebecca J St George, Mark R Hinder
  1. School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
  2. Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
  3. Wicking Dementia and Research Education Centre, University of Tasmania, Hobart, Australia
  4. Full Brain Picture Analytics, Leiden, the Netherlands
Institutions: University of Tasmania (Australia)
Journal: Psychophysiology, volume 63, issue 9, article e70397
Dates: received 28 July 2025; accepted 27 August 2026; published online 7 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70397 · PMID 42704230 · PMCID PMC13548964 · OpenAlex W4404170055
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.11.08.622650
Status: code verified
Categories: other (modality), fNIRS (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials, Connectivity, fMRI & imaging, Physiology & signal measures
MeSH: Attention*, Inhibition, Psychological*, Motor Cortex*, Orientation*, Prefrontal Cortex*, Psychomotor Performance*, Adult, Electromyography, Female, Humans, Male, Spectroscopy, Near-Infrared, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Australian Research Council Discovery (DP200101696)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

Action cancellation involves the termination of planned or ongoing movement, likely initiated in the cortex by the pre‐supplementary motor area (preSMA) and inferior frontal gyrus (IFG). It is frequently examined using the stop‐signal task; electromyography (EMG) studies have shown action cancellation (in stop trials) can involve the partial activation of the responding muscles, which does not result in an overt behavioral response. The neural correlates of these partial responses are not well understood. Here, we combined functional near‐infrared spectroscopy (fNIRS) and EMG to examine the neural correlates of terminated actions (partial responses) in a response‐ and stimulus‐selective stop signal task, controlling for attentional confounds by comparing neural activity in stop and ignore trials. fNIRS analyses revealed increased preSMA activity in successful stop compared to ignore trials, but no such differences in the IFG, consistent with postulations that the preSMA is involved uniquely in action cancellation, while the IFG responds generically to unexpected stimuli. Critically, preSMA activity was greater in trials without partial muscle activation, potentially due to proactive inhibitory processes pre‐emptively suppressing motor output in these trials. These findings advance understanding of some of the neurophysiological dynamics involved in action cancellation and highlight the utility of combining fNIRS with EMG in this domain.

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

Repository

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OSF ztb5m

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: Jupyter (1)
Size: 10 files, 1 script
Software Heritage: not checked
Found in: the text, “fNIRS Data”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/ztb5m/

Tracing map

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  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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Data

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

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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

Versions

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Version 3, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 13 MeSH terms, 1 funder, 93 references.

Cite

This paper

Kemp, S. A., Salomoni, S., Bazin, P., Pash, L., St George, R. J., & Hinder, M. R. (2026). Cortical Contributions to Attentional Orienting and Response Cancellation in Action Stopping. Psychophysiology, 63(9), e70397. https://doi.org/10.1111/psyp.70397

BibTeX

@article{kemp2026cortical,
author = {Kemp, Sarah A and Salomoni, Sauro and Bazin, Pierre‐Louis and Pash, Luke and St George, Rebecca J and Hinder, Mark R},
title = {{Cortical Contributions to Attentional Orienting and Response Cancellation in Action Stopping}},
journal = {Psychophysiology},
year = {2026},
month = sep,
volume = {63},
number = {9},
pages = {e70397},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70397},
url = {https://doi.org/10.1111/psyp.70397},
pmid = {42704230},
pmcid = {PMC13548964}
}

RIS

TY - JOUR
AU - Kemp, Sarah A
AU - Salomoni, Sauro
AU - Bazin, Pierre‐Louis
AU - Pash, Luke
AU - St George, Rebecca J
AU - Hinder, Mark R
TI - Cortical Contributions to Attentional Orienting and Response Cancellation in Action Stopping
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/09/01
VL - 63
IS - 9
SP - e70397
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70397
UR - https://doi.org/10.1111/psyp.70397
LA - en
ER -

CSL-JSON

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