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Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions.

Code ↔ Paper

2 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 2 matches
  1. [1] § Results › Relationship between information dynamics changes and cognitive terms ↔ Code_NCOMMS/Python/05_metaanalysis_neurosynth_myanalysis.ipynb, lines 40–60 · score 0.94 · verbal semantics, numerical cognition, social cognition, visual semantics, affective processing, visual perception
  2. [2] § Methods › NeuroSynth-based cognitive mapping analysis › Cognitive mapping of t-value derived abnormality maps ↔ Code_NCOMMS/Python/05_metaanalysis_neurosynth_myanalysis.ipynb, lines 40–60 · score 0.74 · social cognition, visual perception, multisensory processing, Python, threshold, NeuroSynth

Paper

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The authors' code

Jupyter notebook · 124 lines · 3.4 KB · Apache-2.0 · 2 matches

  1. # %%
  2. % matplotlib inline
  4. from neurosynth.base.dataset import Dataset
  5. from neurosynth.analysis import decode
  6. import pandas as pd
  7. import numpy as np
  8. import seaborn as sns
  9. import matplotlib as mpl
  10. import matplotlib.pyplot as plt
  11. mpl.rcParams['svg.fonttype'] = 'none'
  13. # %%
  14. def getOrder(d, thr):
  15. dh = []
  16. for i in range(0,len(d)):
  17. di = d[i]
  18. dh.append(np.average(np.array(xrange(0,len(d[i]))) + 1, weights=di))
  19. heatmapOrder = np.argsort(dh)
  20. return heatmapOrder
  22. # %%
  23. # Create a new Dataset instance
  24. dataset = Dataset('database_feb_2015/database.txt')
  25. # Add some features
  26. dataset.add_features('database_feb_2015/features.txt')
  27. dataset.save('database_feb_2015/dataset.pkl')
  28. #dataset
  29. #OR
  30. # Import neurosynth database:
  31. #pickled_dataset='database_feb_2015/dataset.pkl'
  32. #dataset=Dataset.load(pickled_dataset)
  34. # %%
  35. dataset
  37. # %%
  40. # %%
  41. # Analysis with 24 terms:
  42. features = pd.read_csv('database_feb_2015/v3-topics-50.txt', sep='\t', index_col=0)
  43. topics_to_keep = [ 1, 4, 6, 14,
  44. 18, 19, 23, 25,
  45. 20, 21, 27, 29,
  46. 30, 31, 33, 35,
  47. 36, 38, 37, 41,
  48. 44, 45, 48, 49]
  49. labels = ['face/affective processing', ' verbal semantics', 'cued attention', 'working memory',
  50. 'autobiographical memory', 'reading', 'inhibition', 'motor',
  51. 'visual perception', 'numerical cognition', 'reward-based decision making', 'visual attention',
  52. 'multisensory processing', 'visuospatial','eye movements', 'action',
  53. 'auditory processing', 'pain', 'language', 'declarative memory',
  54. 'visual semantics', 'emotion', 'cognitive control', 'social cognition']
  55. features = features.iloc[:, topics_to_keep]
  56. features.columns = labels
  57. dataset.add_features(features, append=False)
  59. # removed_as_noise = [0,5,9,12,17,40] # from 30 terms that were above threshold
  60. # labels_noise = ['resting-state', 'dementia', 'development', 'misc', 'task timing', 'lateralization']
  62. # %%
  63. # Gradient 1
  65. decoder = decode.Decoder(dataset, method='roi')
  67. # Set threshold:
  68. thr = 3.1
  69. vmin = 5
  70. vmax = 12
  72. tot = 5
  73. data = decoder.decode([str('my_masks/56subjs_SDI_%02d.nii' % (i))
  74. for i in xrange(1,21)],save='decoding_results_SDI_56subjs_.txt')
  75. #data = decoder.decode([str('../Margulies_paper_code/NeuroanatomyAndConnectivity-gradient_analysis-5b2ac63/gradient_data/masks/volume_%02d_%02d.nii.gz' % (i * tot, (i * tot) + tot))
  76. # for i in xrange(0,100/tot)], save='decoding_results_Margulies.txt')
  77. df = []
  78. df = data.copy()
  79. newnames = []
  80. [newnames.append(('%s-%s' % (str(i * tot), str((i*tot) + tot)))) for i in xrange(0,len(df.columns))]
  81. df.columns = newnames
  82. df[df<thr] = 0
  83. heatmapOrder = getOrder(np.array(df), thr)
  85. sns.set(context="paper", font="sans-serif", font_scale=2)
  86. f, (ax1) = plt.subplots(nrows=1,ncols=1,figsize=(15, 10), sharey=True)
  87. plotData = df.reindex(df.index[heatmapOrder])
  88. cax = sns.heatmap(plotData, linewidths=1, square=True, cmap='Greys', robust=False,
  89. ax=ax1, vmin=0.5, vmax=vmax, mask=plotData == 0)
  90. #sns.axlabel('Percentile along gradient', 'NeuroSynth topics terms')
  91. cbar = cax.collections[0].colorbar
  92. cbar.set_label('z-stat', rotation=270)
  93. cbar.set_ticks(ticks=[thr,vmax])
  94. cbar.set_ticklabels(ticklabels=[thr,vmax])
  95. cbar.outline.set_edgecolor('black')
  96. cbar.outline.set_linewidth(0.5)
  98. plt.draw()
  99. #f.savefig('fig_56subjs_ratioLA_0-5_15.neurosynth.svg', format='svg')
  101. # %%
  104. # %%
  107. # %%
  110. # %% [markdown]
  111. # cax
  112. #
  114. # %%
  117. # %%
  120. # %%
  123. # %%

05_metaanalysis_neurosynth_myanalysis.ipynb at commit 16382be, under Apache-2.0 · at the source

Overview

Authors: Hinata Nago1,2, Hiroki Kojima1, Hiroyuki Yamaguchi1, Yuichi Yamashita1
  1. Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry,4–1-1 Ogawa-Higashi, Kodaira, 187–8502 Tokyo Japan
  2. College of Arts and Sciences, The University of Tokyo,3–8-1 Komaba, Meguro-ku, 153–8902 Tokyo Japan
Institutions: National Center of Neurology and Psychiatry (Japan); The University of Tokyo (Japan)
Journal: Brain informatics, volume 13, issue 1, article 25
Dates: received 20 February 2026; accepted 29 May 2026; published online 13 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s40708-026-00312-2 · PMID 42287597 · PMCID PMC13280263 · OpenAlex W7164643212
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), other condition (population), autism (population), schizophrenia / psychosis (population), ADHD (population), developmental (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Graphs, fMRI & imaging
Keywords: Neurodevelopmental disorders, Schizophrenia, Information theory, Resting-state fMRI
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (JP22K15777); Japan Agency for Medical Research and Development (JP24wm0625407); MEXT | JST | Core Research for Evolutional Science and Technology (CREST) (JPMJCR21P4)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

gpreti/GSP_StructuralDecouplingIndex

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 16382be93c3bae8e196934a8e05b7a9300a909c3, 10 September 2019
Languages: MATLAB (36), Jupyter (2)
Size: 69 files, 38 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), Matplotlib (2 files), NumPy (2 files), pandas (2 files), seaborn (2 files), FreeSurfer (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
40 files
At the source: github.com/gpreti/GSP_StructuralDecouplingIndex

nitrc.org/projects/conn

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: nitrc.org/projects/conn/

hinatanago/PhiID_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5d544c885e87e98a1a79b62cae08a867bdd94d08, 20 April 2026
Languages: Python (1), Jupyter (1)
Size: 29 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), Matplotlib (1 file), NiBabel (1 file), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/hinatanago/PhiID_analysis

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

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 40 scripts, each with its path and the digest of its content;
  • 2 matches 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

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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1186/s40708-026-00312-2.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 3 funders, 61 references.

Cite

This paper

Nago, H., Kojima, H., Yamaguchi, H., & Yamashita, Y. (2026). Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions. Brain informatics, 13(1), 25. https://doi.org/10.1186/s40708-026-00312-2

BibTeX

@article{nago2026synergistic,
author = {Nago, Hinata and Kojima, Hiroki and Yamaguchi, Hiroyuki and Yamashita, Yuichi},
title = {{Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions}},
journal = {Brain informatics},
year = {2026},
month = jun,
volume = {13},
number = {1},
pages = {25},
publisher = {Springer},
issn = {2198-4018},
doi = {10.1186/s40708-026-00312-2},
url = {https://doi.org/10.1186/s40708-026-00312-2},
pmid = {42287597},
pmcid = {PMC13280263}
}

RIS

TY - JOUR
AU - Nago, Hinata
AU - Kojima, Hiroki
AU - Yamaguchi, Hiroyuki
AU - Yamashita, Yuichi
TI - Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions
T2 - Brain informatics
J2 - Brain Inform
PY - 2026
DA - 2026/06/13
VL - 13
IS - 1
SP - 25
SN - 2198-4018
PB - Springer
DO - 10.1186/s40708-026-00312-2
UR - https://doi.org/10.1186/s40708-026-00312-2
LA - en
ER -

CSL-JSON

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