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Flexibility of internal attention: divergent spatiotemporal profiles for feature and object cueing.

Code ↔ Paper

6 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 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › MEG data analysis › Source analysis ↔ code/f_make_stc_tf_sloreta_di.py, lines 35–102 · score 0.80 · inverse operator, TFR epoch, cps, depth, lambda2, loose
  2. [2] § Materials and methods › MEG data analysis › Source analysis ↔ code/f_make_stc_tf_sloreta_ob.py, lines 54–106 · score 0.80 · inverse operator, TFR epoch, cps, depth, lambda2, loose
  3. [3] § Materials and methods › MEG data analysis › Preprocessing ↔ code/a_maxfiltered.sh, lines 41–99 · score 0.73 · head movement, head position, Maxfilter, tools, Python, filtered
  4. [4] § Materials and methods › MEG data analysis › Source analysis ↔ code/f_make_stc_tf_sloreta_ob.py, lines 54–106 · score 0.66 · flat criteria, 2.5 s, Morlet, cropped, epochs, evoked
  5. [5] § Materials and methods › MEG data analysis › Source analysis ↔ code/f_make_stc_tf_sloreta_di.py, lines 35–102 · score 0.65 · flat criteria, 2.5 s, Morlet, cropped, epochs, evoked
  6. [6] § Materials and methods › MEG data analysis › Source analysis ↔ code/e_fwd.py, the whole file · a weak match · score 0.63 · single layer, ico, mindist, model, MNE, MRI

Paper

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The authors' code

Python · 106 lines · 4.4 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Created on Thu Oct 31 11:50:44 2024
  6. @author: kpultsin
  7. """
  9. import mne
  10. import numpy as np
  11. from os.path import join
  14. freqmin=8
  15. freqmax=14
  16. freqs = np.arange(freqmin, freqmax+1, 1)
  18. n_cycles = 5
  20. sample_path = '/projects/mindeye/MRI'
  21. conditions = ['color', 'orient', 'random'] # List of conditions
  22. #conditions = ['color']
  23. # Loop through subjects 01 to 47
  24. for subject_num in range(1, 47):
  25. if subject_num in [1,2,3,17,32,31,39]:
  26. continue
  28. subject = f"{subject_num:02d}"
  29. subjects_dir = join(sample_path, subject)
  30. fname_fwd = join(subjects_dir, 'bem', f"{subject}-ob-fwd.fif")
  32. # Load raw data
  33. raw = mne.io.read_raw_fif(join(sample_path, 'wiica', f"{subject}_ica_di_raw.fif"), preload=True)
  35. #--------------Load events and set event IDs for the current condition
  37. events = mne.find_events(raw, shortest_event=1)
  38. if subject_num == 18:
  39. events[:, 2] = events[:, 2] - events[:, 1]
  40. true_event = []
  41. for i in events[:, 2]:
  42. if i == 8192:
  43. true_event.append(256)
  44. elif i == 4096:
  45. true_event.append(512)
  46. else:
  47. true_event.append(i)
  49. events[:, 2] = true_event
  51. if subject_num in {2, 4, 7, 11, 12}:
  52. events[:, 2] = events[:, 2] - 3072
  53. events = mne.merge_events(events, [14, 18], 14) # color events will be "14"
  54. events = mne.merge_events(events, [12, 16], 16) # orient events will be "16
  55. events = mne.merge_events(events, [11, 13, 15, 17], 11)
  57. reject_criteria = dict(
  58. mag=4000e-15, # 4000 fT
  59. grad=3000e-13) # 600 µV
  61. flat_criteria = dict(mag=1e-15, grad=1e-13)
  64. for condition in conditions:
  65. event_id = {condition: 14 if condition == 'color' else 16 if condition == 'orient' else 11}
  67. epochs = mne.Epochs(raw, events,
  68. event_id=event_id,
  69. tmin=-2, tmax=2.5, baseline=None,reject_tmax=0,
  70. flat=flat_criteria, reject=reject_criteria, preload=True)
  71. epochs = mne.Epochs.subtract_evoked(epochs,evoked=None)
  72. epochs_tfr = mne.time_frequency.tfr_morlet(epochs, freqs, n_cycles=n_cycles, return_itc=False,
  73. output="complex", average=False)
  74. epochs_tfr.crop(tmin=-0.5, tmax=2)
  76. fwd = mne.read_forward_solution(fname_fwd)
  78. noise_cov = mne.read_cov(f'/projects/mindeye/10_14_hz_sLoreta/{subject}_er-cov.fif', verbose=None)
  79. #noise_cov= mne.compute_covariance(epochs, keep_sample_mean=True, tmin=tmins[i], tmax=tmaxs[i], method='empirical', cv=3)
  80. inv = mne.minimum_norm.make_inverse_operator(raw.info, fwd, noise_cov, loose='auto', depth=0.8, fixed='auto', rank=None, use_cps=True, verbose=None)
  82. epochs_stcs= mne.minimum_norm.apply_inverse_tfr_epochs(epochs_tfr , inv, lambda2=1/3**2,method='sLORETA', prepared=False, method_params=None, use_cps=True, verbose=None)
  83. sum_array = [np.array([array.data for array in array_bl]) for array_bl in epochs_stcs]
  84. sum_freq = 10*np.log10(np.array(sum_array).sum(axis=0))
  86. tmins=[-0.5]
  87. tmaxs=[0]
  88. for i in range(1):
  89. epochs_tfr_bl = epochs_tfr.copy().crop(tmin=tmins[i], tmax=tmaxs[i], include_tmax=True)
  90. epochs_stcs_bl= mne.minimum_norm.apply_inverse_tfr_epochs(epochs_tfr_bl , inv, lambda2=1/3**2,method='sLORETA', prepared=False, method_params=None, use_cps=True, verbose=None)
  91. sum_array_bl = [np.array([array.data for array in array_bl]) for array_bl in epochs_stcs_bl]
  92. sum_freq_bl = np.array(sum_array_bl).sum(axis=0)
  93. sum_freq_bl = 10*np.log10(sum_freq_bl).mean(axis=(0,2))
  95. stc_with_bl= sum_freq - sum_freq_bl[np.newaxis,:,np.newaxis]
  97. temp = epochs_stcs[0][0]
  98. temp.data = np.mean(stc_with_bl, axis=0)
  99. # Save the results
  100. save_dir ='/projects/mindeye/8_14_hz_sLoreta/'
  101. output_fname = join(save_dir, f'{subject}-di-{condition}-{freqmin}-{freqmax}hz_sLoreta_base{tmins[i]}-{tmaxs[i]}_noier_concorr')
  102. temp.save(output_fname, overwrite=True)
  104. # Restore the default streams
  105. #sys.stdout = sys.__stdout__
  106. #sys.stderr = sys.__stderr__

f_make_stc_tf_sloreta_di.py, no license · at the source

Overview

Authors: Xiaoshu Lin1,2, Kristina Pultsina1,2, Simo Monto1,2, Chaoxiong Ye1,3, Tiina Parviainen1,2
ORCID iDs: Xiaoshu Lin, Chaoxiong Ye, Tiina Parviainen
  1. Department of Psychology, University of Jyväskylä, Ruusupuisto (RUU Building), Alvar Aallon katu 9, FI-40014 Jyväskylä, Finland
  2. Centre for Interdisciplinary Brain Research, University of Jyväskylä, Kärki Building, Mattilanniemi 6, FI-40014 Jyväskylä, Finland
  3. School of Education, Anyang Normal University, No. 436 Xian'ge Avenue, Anyang, Henan Province 455000, China
Institutions: University of Jyväskylä (Finland); Anyang Normal University (China)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 9, article bhag110
Dates: received 4 March 2026; accepted 1 July 2026; published online 17 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag110 · PMID 42752845 · PMCID PMC13584068 · OpenAlex W7213517255
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: alpha oscillations, internal attention, magnetoencephalography (MEG), spatiotemporal dynamics, working memory
MeSH: Attention*, Brain*, Cues*, Memory, Short-Term*, Adult, Alpha Rhythm, Brain Mapping, Female, Humans, Magnetoencephalography, Male, Photic Stimulation, Reaction Time, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Finnish Cultural Foundation (00231373); China Scholarship Council (202208210090); Research Council of Finland (former Academy of Finland) Academy Research Fellow project (355369)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Internal attention efficiently prioritizes information in working memory (WM), yet it remains unclear whether it reflects a unified, domain-general mechanism, or a flexible set of operations tailored to the format of selected information. Using magnetoencephalography (MEG), we characterized the spatiotemporal alpha-band dynamics underlying feature-based (color, orientation) and object-based (left, right) retro-cues that direct internal attention within WM. Behaviorally, both cue types improved performance relative to neutral conditions, with object-based cues yielding a modest advantage. Neurally, both formats elicited robust modulations of induced alpha-band (8 to 14 Hz) activity distributed across visual, frontoparietal control, and memory-related networks. These effects followed a common temporal progression from early weak modulation to mid-latency reconfiguration and sustained late activity. However, object-based cueing produced more sustained and spatially widespread alpha suppression than feature-based cueing, indicating format-dependent differences in the dynamics of internal selection. Although neural effects closely tracked behavioral benefits at the condition level, no reliable brain–behavior correlations were observed across individuals. Together, these findings suggest that internal attention operates through a shared large-scale control architecture while flexibly adapting its temporal dynamics to representational format, with alpha-band activity playing a central role in the prioritization of WM content.

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 6 matches between paragraphs and lines of code.

OSF vxpsb

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: Python (15), Shell (1)
Size: 16 files, 16 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (15 files), NumPy (11 files), Matplotlib (4 files), pandas (2 files), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
16 files
At the source: osf.io/vxpsb/files/osfstorage

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

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  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions and lack of participant consent, but the analysis code is available at https://osf.io/vxpsb/files/osfstorage.

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, issue, pages, dates, 5 authors, 5 keywords, 14 MeSH terms, 3 funders, 63 references.

Cite

This paper

Lin, X., Pultsina, K., Monto, S., Ye, C., & Parviainen, T. (2026). Flexibility of internal attention: divergent spatiotemporal profiles for feature and object cueing. Cerebral cortex (New York, N.Y. : 1991), 36(9), bhag110. https://doi.org/10.1093/cercor/bhag110

BibTeX

@article{lin2026flexibility,
author = {Lin, Xiaoshu and Pultsina, Kristina and Monto, Simo and Ye, Chaoxiong and Parviainen, Tiina},
title = {{Flexibility of internal attention: divergent spatiotemporal profiles for feature and object cueing}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = sep,
volume = {36},
number = {9},
pages = {bhag110},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag110},
url = {https://doi.org/10.1093/cercor/bhag110},
pmid = {42752845},
pmcid = {PMC13584068}
}

RIS

TY - JOUR
AU - Lin, Xiaoshu
AU - Pultsina, Kristina
AU - Monto, Simo
AU - Ye, Chaoxiong
AU - Parviainen, Tiina
TI - Flexibility of internal attention: divergent spatiotemporal profiles for feature and object cueing
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/09/01
VL - 36
IS - 9
SP - bhag110
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag110
UR - https://doi.org/10.1093/cercor/bhag110
LA - en
ER -

CSL-JSON

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