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Sender-receiver subdivisions of the default mode network in perceptual and memory-guided cognition.

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  1. [1] § Materials and Methods › Procedure. ↔ n-back_task_script.py, lines 205–246 · score 0.67 · 1500–2500 ms, fixation cross, red box, 1500 ms

Paper

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The authors' code

Python · 325 lines · 13 KB · no license · 1 match

  1. #!/usr/bin/env python
  2. # SUPPLIED WITH NO GUARANTEES!!
  4. ## For more information on this script please read the README.md file
  6. from numpy import random
  7. from psychopy import visual, core, event, gui
  8. import numpy as np
  9. import glob, pygame, sys
  10. from psychopy.misc import fromFile
  11. import csv, time
  12. from random import shuffle
  13. from src.experiment import Question, instructions
  14. from src.fileIO import load_conditions_dict, write_csv
  16. ########################################## Log participant details ##################################################
  18. #collect participant info, create logfile
  19. info = {'Subject':'test','datestr':time.strftime('%Y%m%d%H%M'),'In Scanner?':['Yes','No'],'Run':['1','2','3','4'],'Input method':['keyboard']}
  20. infoDlg = gui.DlgFromDict(info, title = 'Subject details:', order = ['Subject',], fixed=['datestr'])
  22. Subject = info.get('Subject')
  23. Input = info.get('Input method')
  24. Run = info.get('Run')
  26. #let the script know if we are in the scanner or not (true/false)
  27. In_scanner = info.get('In Scanner?')
  28. if In_scanner =='Yes':
  29. In_scanner=True
  30. slices_per_vol = 60
  31. dummy_vol = 3
  32. tr = 2
  33. # import serial
  34. # resp_device = serial
  35. #Create dummy period (time needed for the scanner to collect 2 volumes)
  36. # Dummy_timer=(dummy_vol*tr)
  37. else:
  38. In_scanner=False
  39. # resp_device = None
  40. # Dummy_timer=0
  41. print(In_scanner)
  43. #If use clicks OK continue with experiment and create a logfile with their details #else quite experiment
  44. if infoDlg.OK:
  45. logpath = 'data/%s_Run%s_%s_MRI_log.csv' %(info['Subject'],info['Run'],info['datestr'])
  46. f = open(logpath, 'w+') # create log file
  47. # f.write('%s\n' %(info['Subject'])) # write participant info
  48. f.write('StartTrial,Duration_Item,Image1,Image2,ConditionType,TrialType,RedBox,CorrectAnswer,Response,RT,Accuracy,StartFixation,ITI\n') # write headers
  49. else:
  50. print 'User Cancelled'
  51. core.quit()
  53. confirm = gui.Dlg(title="Confirm details")
  54. confirm.addText("Are you happy with your selection?",color='Blue')
  55. confirm.addText("Subject = %s" % (Subject))
  56. confirm.addText("In Scanner? = %s" % (Input))
  57. confirm.show()
  59. if confirm.OK:
  60. print 'correct details'
  61. else:
  62. print 'incorrect details'
  63. core.quit()
  65. ############################################Define all variables###############################################
  66. #create window to draw in - for full screen
  67. #win = visual.Window(size=(1680, 1050), fullscr=True,screen=0,monitor=u'testMonitor',units="deg")
  68. win = visual.Window(fullscr = True, monitor="testMonitor", units="pix")
  69. #win = visual.Window([1000, 800], monitor="testMonitor", units="pix")
  71. #create a fixation cross
  72. fixation = visual.TextStim(win,text='+',height=40)
  73. fixation_red = visual.TextStim(win,text='+',color=(1.0,-1,-1))
  76. #create image template you can call image.png too
  77. im = visual.ImageStim(win, image = None, size = (200, 200), units = 'pix')
  79. #create general instructions
  80. #create general instructions
  81. startmessage1 = ["""In this experiment you will be presented with pairs of images that represent either faces, scenes or objects. On certain trials the box outlining one of images will change colour, and you are to indicate which category that image is from.
  83. Press 1 for Faces, 2 for Scenes and 3 for Objects.
  85. On some trials the box will change color but there will be no image in this box. On these trials you must reponsd with what item was in that box on the previous trial.
  87. Once you make a selection please wait for the next trial to appear.
  89. Press any button to continue to the experiment :)
  91. """]
  93. startTxt = visual.TextStim(win, text = '',height=25)
  94. finishTxt = visual.TextStim(win, text = 'End of Experiment!',height=40)
  95. readyTxt = visual.TextStim(win, text = 'Waiting for scanner......',height=40)
  98. ############################################# Setup #############################################################
  100. def setup_input(input_method):
  102. #If input_method is 'keyboard', we don't do anything
  103. #If input_method is 'serial', we set up the serial port for fMRI responses
  105. if input_method == 'keyboard':
  106. # Don't do anything
  107. resp_device = None
  108. elif input_method == 'serial':
  109. # Serial - we need to set it up
  110. import serial
  111. resp_device = serial
  112. #Create dummy period (time needed for the scanner to collect 2 volumes)
  113. else:
  114. raise Exception('Unknown input method')
  115. return resp_device
  118. def clear_buffer(input_method, resp_device):
  119. """
  120. Clear whichever buffer is appropriate for our input method
  121. """
  122. if input_method == 'keyboard':
  123. # Clear the keyboard buffer
  124. event.clearEvents()
  125. else:
  126. # Clear the serial buffer
  127. resp_device.flushInput()
  129. def get_response(input_method, resp_device, timeStamped):
  130. #if participants don't respond we will set up a null value so we don't get an error
  131. thisResp = None
  132. thisRT = np.nan
  134. if input_method == 'keyboard':
  135. for key, RT in event.getKeys(keyList = ['escape', 'q', 'left', 'right','1','2','3'], timeStamped = timeStamped):
  136. if key in ['escape','q']:
  137. print 'User Cancelled'
  138. print key
  139. core.quit()
  140. else:
  141. thisResp = key
  142. thisRT = myClock.getTime()
  143. else:
  144. thisResp = resp_device.read(1)
  145. thisRT = timeStamped.getTime()
  146. if len(thisResp) == 0:
  147. thisResp = None
  148. thisRT = np.nan
  149. # else:
  150. # # Map button numbers to side
  151. # ## Blue == 1, Green == 3
  152. # if thisResp in ['1', '3']:
  153. # thisResp = '1'
  154. # elif thisResp in ['2', '4']:
  155. # thisResp = '2'
  157. # Quickly check for a 'q' response on the keyboard to quit
  158. for key, RT in event.getKeys(keyList = ['escape', 'q'], timeStamped = timeStamped):
  159. if key in ['escape', 'q']:
  160. print 'User cancelled'
  161. core.quit()
  162. return thisResp, thisRT# This needs to be either keyboard or serial - we then setup the response device
  164. input_method = Input
  165. resp_device = setup_input(input_method)
  169. #if participants don't respond we will set up a null value so we don't get an error
  170. thisResp = ''
  171. thisRT = np.nan
  173. # read in csv file with conditions on
  174. ds = 'conds/Run%s_new.csv' %(Run)
  175. dataFile = open(ds, 'rb')
  176. #ds = dataFile.sample(frac=1)
  177. reader = csv.reader(dataFile, delimiter = ',')
  178. header = dataFile.readline() #read in first line of the csv file and assign this to the variable header
  179. #strip the header to remove \n from the end and split this line into as many entries as there are columns in the header file (i.e., into each of the columns headers)
  180. hdr = header.strip().split(',')
  181. lines = dataFile.readlines() #assign all other information into the variable lines
  182. #shuffle lines so they are called in a random order.
  183. #shuffle(lines)
  186. # experience sampling questions
  187. RSQ_txt = './instructions/RSQ_instr.txt'
  188. ES_PATH = './resting_Q.csv'
  189. questions, q_headers = load_conditions_dict(ES_PATH)
  190. q_headers += ['StartTime', 'Rating', 'RT', 'IDNO']
  191. shuffle(questions)
  192. # my question module
  193. ESQ_instructions = instructions(window=win, instruction_txt=RSQ_txt)
  194. question = Question(window=win, questions=questions, color='white', font=1)
  196. ######################################Experiment begins##############################################################
  197. #present instructions
  198. for inst in startmessage1:
  199. startTxt.setText(inst)
  200. startTxt.draw()
  201. event.Mouse(visible=False)
  202. win.flip()
  203. event.waitKeys(keyList=['space', 'left', 'right', 'return','1', '2','3','esc'])
  205. #present the ready screen and wait for a '5' to sync with triggers
  206. readyTxt.draw()
  207. win.flip()
  208. event.waitKeys(keyList=['5'])
  210. #Dummy files this will wait 6 seconds before clock is initated and experiment starts to account for 3 x 2TR of dummy trials if in the scanner or 0 in the lab
  211. fixation.draw()
  212. win.flip()
  213. core.wait(6)
  215. #set up a clock from which we can getTime() to measure length of experiment and trials and present a fixation cross for 1 second before trials begin
  216. myClock = core.Clock()
  217. fixation.draw()
  218. win.flip()
  219. core.wait(1)
  220. ############################################ Run 1 ################################################################
  221. for line in lines: # read in row by row from csv file
  222. data = line.strip().split(',')
  223. trialType = (data[0])
  224. item1 = str(data[1])
  225. item2 = str(data[2])
  226. redBox = str(data[3])
  227. correctAns = str(data[4])
  228. cond = str(data[5])
  230. # define rectangles and image stim.
  231. #rect1_black = visual.Rect(win, units='pix', width=300, height=300, pos=(-250,0),lineColor='black')
  232. #rect2_black = visual.Rect(win, units='pix', width=300, height=300, pos=(250,0),lineColor='black')
  233. #rect1_red = visual.Rect(win, units='pix', width=300, height=300, pos=(-250,0),lineColor='red')
  234. #rect2_red = visual.Rect(win, units='pix', width=300, height=300, pos=(250,0),lineColor='red')
  235. #img1 = visual.ImageStim(win,units='pix',image=item1,size=[200,200], pos =(-250,0))
  236. #img2 = visual.ImageStim(win,units='pix',image=item2,size=[200,200], pos =(250,0))
  237. rect1_black = visual.Rect(win, units='pix', width=400, height=400, pos=(-300,0),lineColor='black')
  238. rect2_black = visual.Rect(win, units='pix', width=400, height=400, pos=(300,0),lineColor='black')
  239. rect1_red = visual.Rect(win, units='pix', width=400, height=400, pos=(-300,0),lineColor='red')
  240. rect2_red = visual.Rect(win, units='pix', width=400, height=400, pos=(300,0),lineColor='red')
  241. img1 = visual.ImageStim(win,units='pix',image=item1,size=[300,300], pos =(-300,0))
  242. img2 = visual.ImageStim(win,units='pix',image=item2,size=[300,300], pos =(300,0))
  244. # define ITI, duration of items.
  245. ITI = np.random.uniform(1.5, 2.5)
  246. duration_item = 2.5
  248. # draw boxes to dictate whether participant make no response ('none') or have to respond to the left or right image
  249. if redBox == 'none':
  250. rect1_black.draw()
  251. rect2_black.draw()
  252. elif redBox == 'left':
  253. rect1_red.draw()
  254. rect2_black.draw()
  255. elif redBox == 'right':
  256. rect1_black.draw()
  257. rect2_red.draw()
  259. # for 0-back and non-response trials two items will be presented on the screen. For 1-back trials we will skip this and just draw fixation and boxes
  260. if cond == '2':
  261. fixation.draw()
  262. img1.draw()
  263. img2.draw()
  264. elif cond == '0':
  265. fixation.draw()
  266. img1.draw()
  267. img2.draw()
  268. elif cond == '1':
  269. fixation.draw()
  271. #start presentation of target and collect responses.
  272. RT = 0 #need this to be 0 in case no response is made
  273. t_remain = 0
  274. contTrial = True #create variable contTrial
  275. event.clearEvents() #start each trial by clearing event buffer to prevent any previous keys interfering with the current trial
  276. isCorrect = 0
  277. thisResp = 'none'
  278. # take time stamp and present trial
  279. t1 = myClock.getTime()
  280. win.flip()
  282. #Wait for response, if no response is made in the alloted time move in, if a response is made then present fixation for the remainder of this time
  283. while contTrial and myClock.getTime()-t1 <=duration_item:
  284. thisResp, thisRT = get_response(input_method, resp_device, myClock)
  285. if thisResp is not None:
  286. contTrial = False
  287. RT = float(thisRT - t1)# duration = time participants took to identify
  288. t_remain = duration_item - RT
  289. ITI = t_remain + ITI
  290. #draw fixation for the ITI
  291. thisResp = str(thisResp)
  292. isCorrect = int(thisResp == correctAns) #determine whether participants response matches the corrAns []
  295. t2 = myClock.getTime()
  296. fixation.draw()
  297. win.flip()
  298. core.wait(ITI)
  299. #f.write('StartTrial,Duration_Item,Image1,Image2,ConditionType,TrialType,RedBox,CorrectAnswer,Response,RT,Accuracy,StartFixation,ITI\n') # write headers
  300. f.write('%f,%f,%s,%s,%s,%s,%s,%s,%s,%f,%f,%f,%f\n' %(t1, duration_item, item1, item2, cond, trialType, redBox, correctAns, thisResp, RT, isCorrect, t2, ITI))
  301. f.flush()
  302. clear_buffer(input_method, resp_device)
  305. #############################################Questions###############################################################
  306. #ESQ_instructions.show(duration=10)# changed this from 20
  307. ## show experience sampling questions
  308. #for q in questions:
  309. # question.set(q)
  310. # start_trial, score, rt = question.show(myClock)
  311. #
  312. # # save this trial
  313. # q['StartTime'] = start_trial
  314. # q['Rating'] = score
  315. # q['RT'] = rt
  316. # q['IDNO'] = info['Subject']
  317. # write_csv('./data/{}_ES_{}.csv'.format(info['Subject'], info['datestr']), q_headers, q)
  319. ############################################End Experiment###############################################################
  320. finishTxt.draw()
  321. win.flip()
  322. event.waitKeys(maxWait = 5)
  323. f.close()
  324. core.quit()

n-back_task_script.py, no license · at the source

Overview

Authors: Meichao Zhang1,2,3, Casey Paquola4, Katya Krieger-Redwood3, Brontë Mckeown5, Charlotte Murphy3, Chang Liu1,2, Daniel S. Margulies6, Robert Leech7, Jonathan Smallwood5, Elizabeth Jefferies3
ORCID iDs: Meichao Zhang, Katya Krieger-Redwood, Brontë Mckeown, Daniel S. Margulies, Jonathan Smallwood, Elizabeth Jefferies
  1. State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
  2. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
  3. Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
  4. Institute for Neuroscience and Medicine, Forschungszentrum Juelich, Juelich 52425, Germany
  5. Department of Psychology, Queen’s University, Kingston, ON K7L 3N6, Canada
  6. Integrative Neuroscience and Cognition Centre, Centre National de la Recherche Scientifique and Université de Paris, Paris 75006, France
  7. Institute of Psychiatry, Psychology & Neuroscience, King‘s College London, London SE5 8AF, United Kingdom
Institutions: Chinese Academy of Sciences (China); Institute of Psychology, Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); University of York (United Kingdom); Forschungszentrum Jülich (Germany); Queen's University (Canada); Centre National de la Recherche Scientifique (France); Université Paris Cité (France); Centre Neurosciences intégratives et Cognition (France); King's College London (United Kingdom)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 15, article e2528851123
Dates: received 13 October 2025; accepted 22 February 2026; published online 7 April 2026; in print 14 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2528851123 · PMID 41945445 · PMCID PMC13079981 · OpenAlex W7151285219
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, fMRI & imaging, Machine learning
Keywords: default mode network, memory, perception, functional connectivity, internal-external attention
MeSH: Brain*, Cognition*, Default Mode Network*, Memory*, Adult, Brain Mapping, Decision Making, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Young Adult (* major topic)
Journal subjects: Biological Sciences, Psychological and Cognitive Sciences, Social Sciences
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (771863 - FLEXSEM, 646927- WANDERINGMINDS); National Natural Science Foundation of China (National Science Foundation of China) (32300882)
Citations: cited by 2 papers (Europe PMC); 71 references in the paper

Abstract

Everyday cognition depends on the brain’s capacity to shift between sensing the external world and constructing it from memory. To achieve this, large-scale cortical systems must flexibly integrate incoming sensory signals with internally generated representations. Here, we show that this flexibility is reflected in the macroscale architecture of the default mode network (DMN). Using convergent analyses across three independent fMRI datasets spanning directional connectivity, intrinsic organization, and task-evoked responses, we identify spatially distinct DMN subregions that are preferentially engaged during perceptual decisions about faces or memory-guided decisions based on previously seen images. These subregions correspond to a microarchitectural distinction, captured via directional and intrinsic connectivity profiles: regions preferentially engaged during face perception align with receiver-like, afferent-biased zones that show strong intrinsic connectivity across the heteromodal cortex, a profile that might support information integration during perceptually guided decision-making. In contrast, memory-guided, perceptually decoupled decisions differentially engage sender-like, efferent-biased zones that show broader connectivity with perceptual-motor and attentional systems beyond the DMN. This double dissociation demonstrates a systematic association between DMN connectivity and engagement during perceptually coupled versus memory-guided cognitive processes, providing an organizational account of how DMN architecture relates to flexible human thought.

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 1 match between paragraphs and lines of code.

OSF 9rk6m

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 6 files, 1 script
Software Heritage: not checked
Found in: “Data, Materials, and Software Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), PsychoPy (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/9rk6m/

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;
  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data, Materials, and Software Availability

Neuroimaging data at the group-level are openly available in Neurovault at https://neurovault.org/collections/13326/ (69). Materials and the script for the task are accessible in the Open Science Framework at https://osf.io/9rk6m/ (70). All brain figures were created using BrainNet Viewer [http://www.nitrc.org/projects/bnv/; (71)]. Raw data are not publicly accessible as we do not have sufficient consent from participants. Researchers who wish to access the raw data should contact the Research Ethics and Governance Committee of the York Neuroimaging Centre, University of York, or the corresponding authors. Data will be released to researchers when this is possible under the terms of the GDPR (General Data Protection Regulation).

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 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 13 MeSH terms, 2 funders, 62 references.

Cite

This paper

Zhang, M., Paquola, C., Krieger-Redwood, K., Mckeown, B., Murphy, C., Liu, C., Margulies, D. S., Leech, R., Smallwood, J., & Jefferies, E. (2026). Sender-receiver subdivisions of the default mode network in perceptual and memory-guided cognition. Proceedings of the National Academy of Sciences of the United States of America, 123(15), e2528851123. https://doi.org/10.1073/pnas.2528851123

BibTeX

@article{zhang2026sender,
author = {Zhang, Meichao and Paquola, Casey and Krieger-Redwood, Katya and Mckeown, Brontë and Murphy, Charlotte and Liu, Chang and Margulies, Daniel S. and Leech, Robert and Smallwood, Jonathan and Jefferies, Elizabeth},
title = {{Sender-receiver subdivisions of the default mode network in perceptual and memory-guided cognition}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = apr,
volume = {123},
number = {15},
pages = {e2528851123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2528851123},
url = {https://doi.org/10.1073/pnas.2528851123},
pmid = {41945445},
pmcid = {PMC13079981}
}

RIS

TY - JOUR
AU - Zhang, Meichao
AU - Paquola, Casey
AU - Krieger-Redwood, Katya
AU - Mckeown, Brontë
AU - Murphy, Charlotte
AU - Liu, Chang
AU - Margulies, Daniel S.
AU - Leech, Robert
AU - Smallwood, Jonathan
AU - Jefferies, Elizabeth
TI - Sender-receiver subdivisions of the default mode network in perceptual and memory-guided cognition
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/04/07
VL - 123
IS - 15
SP - e2528851123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2528851123
UR - https://doi.org/10.1073/pnas.2528851123
LA - en
ER -

CSL-JSON

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"title": "Sender-receiver subdivisions of the default mode network in perceptual and memory-guided cognition",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[5] doi:10.1093/brain/awag032 [code]
A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia.
Journal: Brain : a journal of neurology
In common: NumPy, fMRI, 6 references
[6] doi:10.1038/s41467-026-73153-6 [code]
Latent neural architecture organising shared aesthetic evaluations of visual artworks.
Journal: Nature communications
In common: NumPy, fMRI, cognitive, 6 references
[7] doi:10.1073/pnas.2512071123 [code]
Narrative "twist" shifts within-individual neural representations of dissociable story features.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: NumPy, fMRI, cognitive, 4 references
[8] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: NumPy, cognitive, 6 references
[9] doi:10.1186/s40708-026-00312-2 [code]
Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions.
Journal: Brain informatics
In common: NumPy, fMRI, 6 references
[10] doi:10.1111/ejn.70511 [code]
The Effect of Caffeine Consumption and Acute Withdrawal on Resting-State fMRI Brain Connectivity, Mood and Cognition.
Journal: The European journal of neuroscience
In common: fMRI, cognitive, 5 references

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