OSCR

Development of functional topography of the default mode subnetworks revealed by precision mapping.

Code ↔ Paper

4 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 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Sharpness of the boundary between DMN subnetworks ↔ analysis/calculate_sharpness.py, the whole file · a weak match · score 0.92 · spatially contiguous clusters, nearby vertices, boundary sharpness, clusters belonging, border vertex, fewer
  2. [2] § Materials and methods › Precision mapping ↔ cortex_mapping/mapping.py, lines 80–173 · score 0.75 · clusters smaller, small clusters, spatial contiguity, nearest, command, Precision
  3. [3] § Materials and methods › Precision mapping ↔ cortex_mapping/mapping.py, lines 80–173 · score 0.70 · template matching, precision mapping, functional connectivity, Dice, profile, matrix
  4. [4] § Materials and methods › Precision mapping ↔ analysis/calculate_sharpness.py, the whole file · a weak match · score 0.54 · neighboring network, spatial contiguity, clusters

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 128 lines · 5.6 KB · no license · 2 matches

  1. import warnings
  2. import numpy as np
  3. import pandas as pd
  4. import nibabel as nib
  6. from feature_extraction import utils
  7. from cortex_mapping.mapping import get_template_info
  9. from scipy.stats import mode
  10. warnings.filterwarnings('ignore')
  13. def get_boundary_sharpness(args):
  14. '''Calculates the boundary-sharnpess of each network.
  16. 1. Identify the spatially contiguous clusters that make up the 'networks' label file.
  17. 2. Take the mean BOLD time-series across vertices within the cluster.
  18. 3. At each vertex on the border of the spatial cluster:
  19. a. Find all the vertices within 'distance' (default=5mm) of this border-vertex.
  20. b. Calculate the correlation between each of these neighbouring vertex and the cluster time-series.
  21. c. For each neighbouring network seprately, compare how correlated the correlations within the
  22. cluster are to the correlations outside the cluster (cohen's d).
  23. d. Define this as the network-specific boundary sharpness for the current spatial cluster.
  24. 4. Take the average boundary sharpness across border vertices, write to .csv
  25. '''
  27. # Set-up.
  28. func = args.func
  29. surf = args.surf
  30. hemi = args.hemi
  31. networks = args.networks
  32. output = args.output
  33. tmp = f'{args.output}/tmp'
  35. network_indices, network_labels, _ = get_template_info()
  36. time_series = utils.get_time_series(func)
  37. coords = utils.get_surf_coords(surf)
  38. tree = utils.get_kdtree(surf)
  39. network_data = nib.load(networks).darrays[0].data
  41. # Get spatial clusters.
  42. clusters_gii = nib.load(f'{tmp}/clusters.{hemi}.func.gii')
  43. borders_gii = nib.load(f'{tmp}/borders.{hemi}.func.gii')
  46. # --- Define function for calculating sharpness of single cluster ---
  47. def get_cluster_sharpness(cluster_data, border_data, distance=5):
  48. '''
  49. Input:
  50. cluster_data: boolean array (shape:[n_vertices]) of spatial cluster.
  51. border_data: boolean array (shape:[n_vertices]) of border vertices.
  52. distance: Maximum distance for vertices to be considered in sharnpoess calculation (mm).
  54. Output:
  55. cluster_sharpness: dictionary with the mean boundary sharpness across vertices for each network
  56. cluster_network: the network label with which the current cluster belongs.
  57. '''
  59. cluster_data_bool = np.array([bool(x) for x in cluster_data])
  61. border_vertices = np.argwhere(border_data).flatten()
  62. cluster_network, _ = mode(network_data[cluster_data_bool])
  64. # Get cluster time-series (mean BOLD signal across vertices within the cluster).
  65. cluster_xs = time_series[:,cluster_data_bool].mean(axis=1)
  67. # Get list of nearby vertices (i.e., those +/- the specified distance) for each border vertex.
  68. all_nearby_vertices = [np.array(tree.query_ball_point(coords[border_vertex], r=distance)) for border_vertex in border_vertices]
  69. relevant_vertices = np.unique(np.hstack(all_nearby_vertices))
  71. # Correlate the time-series of each vertex on the surface with the cluster time-series.
  72. r_vals = np.zeros(network_data.shape[0])
  73. r_vals[relevant_vertices] = np.array([np.corrcoef(cluster_xs, vertex_xs)[0,1] for vertex_xs in time_series.T[relevant_vertices]])
  75. # Get network-wise border-sharpness.
  76. cluster_sharpness_vertex = {label:[] for label in network_labels}
  77. for vertex_idx, _ in enumerate(border_vertices):
  79. nearby_vertices = all_nearby_vertices[vertex_idx]
  80. vertex_networks = network_data[nearby_vertices]
  81. inside_corrs = r_vals[nearby_vertices][vertex_networks == cluster_network]
  83. # Get efect-size difference between r_vals in current network and r_vals in other networks.
  84. for net_idx, net_label in zip(network_indices, network_labels):
  86. outside_corrs = r_vals[nearby_vertices][vertex_networks == net_idx]
  88. # Excude network-network borders with fewer than 5 vertices.
  89. if len(outside_corrs) < 5:
  90. continue
  92. seg = utils.get_cohens_d(inside_corrs, outside_corrs)
  93. cluster_sharpness_vertex[net_label].append(seg)
  95. cluster_sharpness = {label: np.nanmean(cluster_sharpness_vertex[label]) for label in network_labels}
  97. return cluster_sharpness, cluster_network
  100. # Get boundary sharpness of each cluster.
  101. cluster_sharpness = {idx:[] for idx in network_indices}
  103. for cluster_darray, border_darray in zip(clusters_gii.darrays, borders_gii.darrays):
  105. cluster_data = cluster_darray.data
  106. border_data = border_darray.data
  108. sharpness, net_idx = get_cluster_sharpness(cluster_data, border_data)
  109. cluster_sharpness[net_idx].append(sharpness)
  111. # Combine into single dataframe and save to output.
  112. all_df = [pd.DataFrame(cluster_sharpness[idx]).assign(network_idx=idx) for idx in network_indices]
  113. sharpness_df = pd.concat(all_df, ignore_index=True)
  114. idx_to_label = dict(zip(network_indices, network_labels))
  115. sharpness_df['network_label'] = sharpness_df['network_idx'].map(idx_to_label)
  117. feature_df = pd.read_csv(f'{output}/features_{hemi}.csv')
  118. df = pd.concat([feature_df, sharpness_df], axis=1)
  120. # Reorder columns.
  121. front_cols = ['cluster_idx', 'network_idx', 'network_label']
  122. existing_front_cols = [col for col in front_cols if col in df.columns]
  123. other_cols = [col for col in df.columns if col not in existing_front_cols]
  124. df = df[existing_front_cols + other_cols]
  126. # Rename columns.
  127. df = df.rename(columns={col: f'{col}__sharpness' for col in network_labels})
  128. df.to_csv(f'{output}/features_{hemi}.csv', index=False)

calculate_sharpness.py at commit 8548e2d, no license · at the source

Overview

Authors: Ying He1,2,3, Jonah Kember3, Hongxiu Jiang3, Xiaoqian J Chai3
ORCID iDs: Ying He
  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 Neurology and Neurosurgery, McGill University, Montréal, QC, H3A 2B4, Canada
Institutions: Chinese Academy of Sciences (China); Institute of Psychology, Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); McGill University (Canada)
Journal: Developmental cognitive neuroscience, volume 81, article 101808
Dates: received 18 May 2026; accepted 27 August 2026; published online 29 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.dcn.2026.101808 · PMID 42697126 · PMCID PMC13572014 · OpenAlex W7131412575
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.02.24.707618
Status: code verified
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Machine learning, fMRI & imaging
Keywords: Default mode subnetwork, Precision mapping, Memory, Functional topography
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs Program; Quebec Research Fund Nature and Technology (2021-NC-283571); Healthy Brains Healthy Lives; Santé
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The default mode network (DMN) is a functionally and anatomically heterogeneous network comprising distinct subsystems that support internally directed processes such as memory and self-related thought. The development of functional brain networks is critical for cognitive maturation, yet how DMN subnetworks develop remains unclear. Group-average approaches obscure individual variability and blur spatial details, limiting precise characterization of network topography. Using precision mapping in 547 participants aged 5–21 years, we delineated individualized DMN subnetworks and examined their development across multiple aspects, including functional segregation and spatial topography. We found that DMN subnetworks become increasingly functionally segregated from each other and from non-DMN networks with age, and exhibit greater topographic distinctiveness through boundary sharpening and reduced spatial overlap. Furthermore, the memory-related subnetwork showed a negative association between age and spatial extent, with a smaller surface area associated with better episodic memory performance. Our findings underscore the importance of characterizing multidimensional functional and topographical features at the individual level to better understand typical neurodevelopment and neurodevelopmental and psychiatric conditions.

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 4 matches between paragraphs and lines of code.

Chai-Neuro-Lab/Segregation-of-DMN-subnetworks

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8548e2d8646a6d8c7e95f36b182d69a892d247f8, 29 October 2025
Languages: Python (10)
Size: 13 files, 10 scripts
Software Heritage: not archived
Found in: “Data and materials availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NiBabel (9 files), NumPy (9 files), pandas (8 files), SciPy (4 files), Connectome Workbench (2 files), Matplotlib (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/Chai-Neuro-Lab/Segregation-of-DMN-subnetworks

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;
  • 10 scripts, each with its path and the digest of its content;
  • 4 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.

Data and materials availability

Raw data are freely available through the Human Connectome Project, upon completion of a data-usage agreement: [https://nda.nih.gov/general-query.html?q=query=featured-datasets:HCP%20Aging%20and%20Development (https://nda.nih.gov/general-query.html?q=query=featured-datasets:HCP Aging and Development)].

All the analysis and visualization code are available on GitHub: https://github.com/Chai-Neuro-Lab/Segregation-of-DMN-subnetworks

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

Data availability

Raw data are freely available through the Human Connectome Project. All the analysis and visualization code are available on GitHub: https://github.com/Chai-Neuro-Lab/Segregation-of-DMN-subnetworks

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 6 funders, 51 references.

Cite

This paper

He, Y., Kember, J., Jiang, H., & Chai, X. J. (2026). Development of functional topography of the default mode subnetworks revealed by precision mapping. Developmental cognitive neuroscience, 81, 101808. https://doi.org/10.1016/j.dcn.2026.101808

BibTeX

@article{he2026development,
author = {He, Ying and Kember, Jonah and Jiang, Hongxiu and Chai, Xiaoqian J},
title = {{Development of functional topography of the default mode subnetworks revealed by precision mapping}},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = aug,
volume = {81},
pages = {101808},
publisher = {Elsevier},
issn = {1878-9293},
doi = {10.1016/j.dcn.2026.101808},
url = {https://doi.org/10.1016/j.dcn.2026.101808},
pmid = {42697126},
pmcid = {PMC13572014}
}

RIS

TY - JOUR
AU - He, Ying
AU - Kember, Jonah
AU - Jiang, Hongxiu
AU - Chai, Xiaoqian J
TI - Development of functional topography of the default mode subnetworks revealed by precision mapping
T2 - Developmental cognitive neuroscience
J2 - Dev Cogn Neurosci
PY - 2026
DA - 2026/08/29
VL - 81
SP - 101808
SN - 1878-9293
PB - Elsevier
DO - 10.1016/j.dcn.2026.101808
UR - https://doi.org/10.1016/j.dcn.2026.101808
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.dcn.2026.101808",
"type": "article-journal",
"title": "Development of functional topography of the default mode subnetworks revealed by precision mapping",
"container-title": "Developmental cognitive neuroscience",
"author": [
{
"family": "He",
"given": "Ying"
},
{
"family": "Kember",
"given": "Jonah"
},
{
"family": "Jiang",
"given": "Hongxiu"
},
{
"family": "Chai",
"given": "Xiaoqian J"
}
],
"container-title-short": "Dev Cogn Neurosci",
"volume": "81",
"page": "101808",
"DOI": "10.1016/j.dcn.2026.101808",
"PMID": "42697126",
"PMCID": "PMC13572014",
"ISSN": "1878-9293",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.dcn.2026.101808",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
29
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/psyrad/kkag013 [code]
Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins.
Journal: Psychoradiology
In common: Connectome Workbench, scikit-learn, SciPy, 1 other tool, 11 references
[2] doi:10.1038/s41467-026-74572-1 [code]
The hippocampus becomes topographically and functionally specialized along the longitudinal axis with development.
Journal: Nature communications
In common: Connectome Workbench, NiBabel, scikit-learn, 4 other tools, 7 references
[3] doi:10.1002/advs.202523009 [code]
Personalized Network-Guided Neuromodulation Enhances Human Working Memory.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: Connectome Workbench, scikit-learn, pandas, 3 other tools, 8 references
[4] doi:10.1016/j.dcn.2026.101789 [code]
Feasibility of precision functional mapping in youth multi-echo fMRI data.
Journal: Developmental cognitive neuroscience
In common: Connectome Workbench, pandas, NumPy, 9 references
[5] doi:10.1038/s41467-026-75745-8 [code]
A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.
Journal: Nature communications
In common: NiBabel, scikit-learn, pandas, 3 other tools, 8 references
[6] doi:10.1162/imag.a.1222 [code]
Network-based near-scalp personalized brain stimulation targets.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Connectome Workbench, NiBabel, scikit-learn, 3 other tools, 7 references
[7] doi:10.1038/s41467-026-76011-7 [code]
Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.
Journal: Nature communications
In common: Connectome Workbench, NiBabel, SciPy, 2 other tools, 7 references
[8] doi:10.1002/hbm.70483 [code]
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.
Journal: Human brain mapping
In common: Connectome Workbench, NiBabel, scikit-learn, 3 other tools, 6 references
[9] doi:10.1162/imag.a.1285 [code]
Individualized mapping of functional brain networks in older adulthood.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Connectome Workbench, 9 references
[10] doi:10.1016/j.neuron.2026.04.011 [code]
Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.
Journal: Neuron
In common: Connectome Workbench, NiBabel, scikit-learn, 3 other tools, 6 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.