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Social disconnection in the brain: loneliness and age across networks using graph theory.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Loneliness and age – Graph-based brain functional connectivity statistical analysis ↔ fmri_stats_palm.m, the whole file · a weak match · score 0.95 · Exchangeability blocks, FWE corrected, Linear Models, MoCA, FSL, PALM
  2. [2] § Methods › Resting-state functional connectivity graph theoretical based measures ↔ fmri_network_analysis_absedges.py, lines 4–44 · score 0.86 · edge weight, graph metrics, eigenvector centrality, closeness centrality, clustering coefficient, participation coefficient
  3. [3] § Methods › Resting-state functional connectivity graph theoretical based measures ↔ fmri_network_analysis_negedges.py, lines 4–52 · score 0.82 · graph metrics, eigenvector centrality, closeness centrality, clustering coefficient, participation coefficient, functional connections
  4. [4] § Results › Loneliness and graph-based brain functional connectivity measures ↔ fmri_stats_palm.m, the whole file · a weak match · score 0.73 · age interaction, MoCA, marital status, clustering coefficient, GMV, linear
  5. [5] § Methods › MRI data acquisition and preprocessing ↔ behav_preproc_recode_variables.Rmd, lines 93–110 · score 0.59 · High motion frame, preprocessing, FD, fMRI
  6. [6] § Methods › Resting-state fMRI functional connectivity matrix construction ↔ fmri_network_analysis_posedges.py, lines 4–48 · score 0.53 · Cole Anticevic, functional connectivity, matrix, scan, fMRI, correlation
  7. [7] § Results › Age and graph-based brain functional connectivity measures ↔ fmri_network_analysis_negedges.py, lines 4–52 · score 0.52 · normalized strength, Clustering Coefficient, Participation Coefficient, global, nodal, shortest

Paper

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The authors' code

MATLAB · 81 lines · 5 KB · no license · 2 matches

  1. % fmri_stats_palm.m
  2. %
  3. % Purpose:
  4. % Runs FSL PALM (Permutation Analysis of Linear Models) permutation tests
  5. % on subject-level graph-theory network metrics derived from ABSOLUTE edge
  6. % weights (dissertation version; main text results). Final sample N = 512.
  7. %
  8. % PALM is called once per network metric. Each call runs 5000 permutations
  9. % with family-wise error correction across contrasts, two-tailed testing,
  10. % log p-values, and demeaning. Site is modeled as an exchangeability block.
  11. %
  12. % Model parameters:
  13. % Main predictors (in order): loneliness, age, loneliness x age interaction,
  14. % sex, household income, marital status
  15. % Covariates: total GMV, mean FD, MoCA, site (4 dummy variables) + intercept
  16. %
  17. % Inputs (all paths relative to working directory set by run_fmri_stats_palm.slurm):
  18. % Nodal metric CSVs (subjects x parcels, n = 512):
  19. % abs_nodal_<metric>_n512.csv
  20. % Global metric CSVs (subjects x 1, n = 512):
  21. % abs_global_<metric>_n512.csv
  22. % Design matrix: abs_design_matrix_n512.csv
  23. % T-contrast file: abs_Tcontrast.csv
  24. % F-contrast file: abs_Fcontrast.csv
  25. % Exchangeability blocks: abs_site_block_n512.csv
  26. %
  27. % Outputs (written to palm_output/<metric>/):
  28. % PALM output files: palm_abs_<metric>_* (t-stats, FWE-p, etc.)
  29. %
  30. % Note:
  31. % Output directories (palm_output/<metric>/) must exist before running.
  32. % They are created automatically by run_fmri_stats_palm.slurm.
  33. %
  34. % Usage:
  35. % Run via SLURM: sbatch run_fmri_stats_palm.slurm
  36. % Or directly in MATLAB (from the correct working directory):
  37. % run('fmri_stats_palm.m')
  40. % ------------------------------------------------------------------------------
  41. % PALM flags used in all calls:
  42. % -vg auto : automatically infer variance groups
  43. % -corrcon : FWE correction across contrasts
  44. % -twotail : two-tailed testing
  45. % -n 5000 : number of permutations
  46. % -logp : output log10(p) instead of raw p
  47. % -demean : demean the input data
  48. % -nouncorrected: suppress uncorrected p-value output
  49. % -eb : exchangeability blocks (site)
  50. % ------------------------------------------------------------------------------
  53. % Betweenness centrality (nodal)
  54. palm -i abs_nodal_betweencent_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/betweencent/palm_abs_betweencent
  56. % Closeness centrality (nodal)
  57. palm -i abs_nodal_closecent_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/closecent/palm_abs_closecent
  59. % Strength (nodal)
  60. palm -i abs_nodal_strength_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/strength/palm_abs_strength
  62. % Normalized strength (nodal)
  63. palm -i abs_nodal_normstrength_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/normstrength/palm_abs_normstrength
  65. % Eigenvector centrality (nodal)
  66. palm -i abs_nodal_eigencent_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/eigencent/palm_abs_eigencent
  68. % PageRank centrality (nodal)
  69. palm -i abs_nodal_pgcent_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/pgcent/palm_abs_pgcent
  71. % Clustering coefficient (nodal)
  72. palm -i abs_nodal_clustercoef_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/clustercoef/palm_abs_clustercoef
  74. % Participation coefficient — CA network partition (nodal)
  75. palm -i abs_nodal_partcoef_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/partcoef/palm_abs_partcoef
  77. % Modularity — CA network partition (global, 1 value per subject)
  78. palm -i abs_global_modularity_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/modularity/palm_abs_modularity
  80. % Average shortest path length (global, 1 value per subject)
  81. palm -i abs_global_average_shortest_path_n512.csv -d abs_design_matrix_n512.csv -t abs_Tcontrast.csv -f abs_Fcontrast.csv -eb abs_site_block_n512.csv -vg auto -corrcon -twotail -n 5000 -logp -demean -nouncorrected -o palm_output/average_shortest_path/palm_abs_average_shortest_path

fmri_stats_palm.m, no license · at the source

Overview

Authors: Yen-Wen Chen1, Turhan Canli1,2
ORCID iDs: Yen-Wen Chen
  1. Department of Psychology, Stony Brook University, 100 Nicolls Road, West Campus, Stony Brook, NY 11794-2500, United States
  2. Department of Psychiatry, Stony Brook University, 101 Nicolls Road, East Campus, Stony Brook, NY 11794-8101, United States
Institutions: Stony Brook University (United States)
Journal: Oxford open neuroscience, volume 5, article kvag006
Dates: received 18 May 2026; accepted 20 June 2026; published online 17 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/oons/kvag006 · PMID 42582864 · PMCID PMC13458596 · OpenAlex W7169524193
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), computational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Graphs
Keywords: loneliness, aging, functional magnetic resonance imaging, resting-state functional connectivity, graph theory
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 114 references in the paper

Abstract

Loneliness, conceptualized as a multi-dimensional construct of unmet social needs, has been linked to adverse health outcomes across the lifespan, prompting significant interest in its underlying neural processes. Our study aimed to address the limitations of prior neuroimaging studies of loneliness by leveraging the Lifespan Human Connectome Project Aging dataset and applying graph theory to characterize its relationship with age and resting-state brain network organization. Socio-demographic measures confirmed prior work that higher loneliness was associated with younger age, being male, unmarried, and living alone. While loneliness showed no main effects on neural graph measures, a significant interaction between loneliness and age emerged for the local interconnectivity of the Default Model and Frontoparietal networks after adjusting for key socio-demographic factors. Conversely, older age was associated with lower functional connectivity, reduced global efficiency, and less modular brain network organization. Different graph measures showed distinct age-related associations, highlighting the heterogeneous nature of brain aging. The absence of a main effect of loneliness, while unexpected, underscores the complex, subjective nature of loneliness and suggests that its neural correlates may manifest differently across ages.

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 7 matches between paragraphs and lines of code.

OSF p6srv

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (3), Python (3), MATLAB (1)
Size: 10 files, 7 scripts
Software Heritage: not checked
Found in: the text, “Resting-state fMRI functional connectivity matri”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NetworkX (3 files), NumPy (3 files), pandas (3 files), tidyverse (3 files), broom (1 file), car (1 file), reshape2 (1 file), rstatix (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/p6srv/

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;
  • 7 scripts, each with its path and the digest of its content;
  • 7 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 availability

The data that support the findings of this study are openly available in the Lifespan Human Connectome Project. Data collection and sharing for the Lifespan Human Connectome Project Aging was supported by the National Institute On Aging of the National Institutes of Health under Award Number U01AG052564 and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The HCP-Aging 2.0 Release data used in this report came from DOI: 10.15154/1520707 (https://doi.org/10.15154/1520707). Data and/or research tools used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in mental health. Dataset identifier(s): 10.15154/g2tv-f889 (https://doi.org/10.15154/g2tv-f889). This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDA.

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, pages, dates, 2 authors, 5 keywords, 110 references.

Cite

This paper

Chen, Y.-W., & Canli, T. (2026). Social disconnection in the brain: loneliness and age across networks using graph theory. Oxford open neuroscience, 5, kvag006. https://doi.org/10.1093/oons/kvag006

BibTeX

@article{chen2026social,
author = {Chen, Yen-Wen and Canli, Turhan},
title = {{Social disconnection in the brain: loneliness and age across networks using graph theory}},
journal = {Oxford open neuroscience},
year = {2026},
month = jul,
volume = {5},
pages = {kvag006},
publisher = {Oxford University Press},
issn = {2753-149X},
doi = {10.1093/oons/kvag006},
url = {https://doi.org/10.1093/oons/kvag006},
pmid = {42582864},
pmcid = {PMC13458596}
}

RIS

TY - JOUR
AU - Chen, Yen-Wen
AU - Canli, Turhan
TI - Social disconnection in the brain: loneliness and age across networks using graph theory
T2 - Oxford open neuroscience
J2 - Oxf Open Neurosci
PY - 2026
DA - 2026/07/17
VL - 5
SP - kvag006
SN - 2753-149X
PB - Oxford University Press
DO - 10.1093/oons/kvag006
UR - https://doi.org/10.1093/oons/kvag006
LA - en
ER -

CSL-JSON

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