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Functional connectome signature of general psychopathology in middle-aged and older adults: Evidence from multi-cohort, multi-ethnic analyses.

Code ↔ Paper

3 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 3 matches
  1. [1] § Results › General psychopathology brain network scores at baseline predict mortality risk ↔ scripts/mortality_prediction_cox.R, lines 1–62 · score 0.65 · Cox model, hazard ratio, education, sex, CI, brain score
  2. [2] § Methods › Brain connectivity patterns and clinical trajectories ↔ scripts/longitudinal_prediction.R, lines 108–186 · score 0.59 · post hoc, ANOVAs, FDR, Tukey, GAD, AUDIT
  3. [3] § Methods › Prediction of mortality risk ↔ scripts/mortality_prediction_cox.R, lines 1–62 · score 0.58 · hazard ratios, Cox, education, sex, mortality, brain score

Paper

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The authors' code

R · 97 lines · 4.3 KB · no license · 2 matches

  1. library(survival)
  2. library(tidyverse)
  3. library(broom)
  4. library(gridExtra)
  5. library(lmtest)
  7. #load data
  8. brain = read.csv('synthetic_data/synthetic_cox_data.csv')
  10. res.cox <- coxph(Surv(time, survival) ~ age + sex + Years_of_education + TDI + brain_score_lv1+brain_score_lv2, data = brain)
  11. summary(res.cox)
  13. res.cox_base <- coxph(Surv(time, survival) ~ age + sex+ Years_of_education + TDI , data = brain)
  14. summary(res.cox_base)
  16. lrtest(res.cox_base,res.cox)
  18. # Define the desired order of terms
  19. desired_order <- c("brain_score_lv2", "brain_score_lv1", "TDI", "Years_of_education", "sex", "age")
  21. plot1 = coxph(Surv(time, survival) ~ age + sex + Years_of_education + TDI + brain_score_lv1 + brain_score_lv2, data = brain) %>%
  22. tidy() %>%
  23. arrange(term) %>%
  24. mutate(term = factor(term, levels = desired_order)) %>%
  25. mutate(upper = estimate + 1.96 * std.error,
  26. lower = estimate - 1.96 * std.error,
  27. color = ifelse((upper > 0 & lower < 0), "black", ifelse(estimate > 1, "green4", "red3"))) %>%
  28. mutate(across(all_of(c("estimate", "lower", "upper")), exp)) %>%
  29. ggplot(aes(estimate, term, color = color)) +
  30. geom_vline(xintercept = 1, color = "red",linetype = "dashed", alpha = 0.5) +
  31. geom_linerange(aes(xmin = lower, xmax = upper), size = 4, alpha = 0.5) +
  32. geom_point(size = 5) +
  33. theme_minimal(base_size = 16) +
  34. scale_color_identity() +
  35. xlim(c(0, 2)) +
  36. labs(y = NULL,
  37. x = "Hazard ratio estimate") +
  38. theme(text = element_text(size = 40, family = "sans"),
  39. legend.position = 'none',
  40. panel.border = element_blank(),
  41. axis.line = element_line(),
  42. panel.grid.major = element_blank(),
  43. panel.grid.minor = element_blank(),
  44. axis.text.x = element_text(colour = "black", size = 40),
  45. axis.text.y = element_text(colour = "black", size = 40)) +
  46. scale_y_discrete(labels = c("Years_of_education" = "Education", "sex" = "Sex",
  47. "brain_score_lv1" = "LV1 Brain score",
  48. "brain_score_lv2" = "LV2 Brain score",
  49. "age" = "Age"))
  51. cox_model = res.cox
  52. # Get model summary
  53. ## OR point estimate table
  54. desired_order2 <- c("age", "sex", "Years_of_education", "TDI", "brain_score_lv1", "brain_score_lv2")
  56. cox_summary <- tidy(res.cox, exponentiate = TRUE, conf.int = TRUE) %>%
  57. mutate(term = factor(term, levels = desired_order2)) %>%
  58. arrange(term) %>%
  59. mutate(upper = conf.high,
  60. lower = conf.low,
  61. color = ifelse((upper > 0 & lower < 0), "black", ifelse(estimate > 1, "green4", "red3"))) %>%
  62. mutate(CI = paste0(round(lower, 2), " - ", round(upper, 2)))
  64. table_base <- ggplot(cox_summary) +
  65. ylab(NULL) + xlab(" ") +
  66. theme(plot.title = element_text(hjust = 0.5, size=30),
  67. axis.text.x = element_text(color="white", hjust = -0.1, size = 25), ## This is used to help with alignment
  68. axis.line = element_blank(),
  69. axis.text.y = element_blank(),
  70. axis.ticks = element_blank(),
  71. axis.title.y = element_blank(),
  72. legend.position = "none",
  73. panel.background = element_blank(),
  74. panel.border = element_blank(),
  75. panel.grid.major = element_blank(),
  76. panel.grid.minor = element_blank(),
  77. plot.background = element_blank())
  79. tab1 <- table_base +
  80. labs(title = "space") +
  81. geom_text(aes(y = factor(rev(term), levels = desired_order2), x = 1, label = sprintf("%0.2f", round(estimate, digits = 2))),
  82. size = 13, vjust = -1.5,
  83. color = ifelse((cox_summary$lower < 1 & cox_summary$upper > 1), "black", "red")) + ## Adjusted condition for coloring
  84. ggtitle("HR") +
  85. theme(plot.title = element_text(margin = margin(b = 20),face = "bold")) ## Increase margin between title and text
  88. tab2 <- table_base +
  89. geom_text(aes(y = factor(rev(term), levels = desired_order2), x = 1, label = CI),
  90. size = 13, vjust = -1.5,
  91. color = ifelse((cox_summary$lower < 1 & cox_summary$upper > 1), "black", "red")) + ## Adjusted condition for coloring
  92. ggtitle("95% CI") +
  93. theme(plot.title = element_text(margin = margin(b = 20),face = "bold")) ## Increase margin between title and text
  95. # Arrange the plots and tables
  96. lay <- matrix(c(1,1,1,1,1,1,1,1,1,1,2,3,3), nrow = 1)
  97. grid.arrange(plot1, tab1, tab2, layout_matrix = lay)

mortality_prediction_cox.R at commit baa4810, no license · at the source

Overview

Authors: Thuan Tinh Nguyen1,2, Kwun Kei Ng1,2, Voon Hao Lew1,2, Janice Jue Xin Koi1,2, Wen Liang Loh1,2, Woon-Puay Koh2,3, Juan Helen Zhou1,2,4
ORCID iDs: Thuan Tinh Nguyen, Wen Liang Loh, Juan Helen Zhou
  1. Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  2. Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  3. Institute for Human Development and Potential (IHDP), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
  4. Department of Electrical and Computer Engineering & Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore
Institutions: National University of Singapore (Singapore); Agency for Science, Technology and Research (Singapore)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1308
Dates: received 1 February 2026; accepted 24 June 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1308 · PMID 42483417 · PMCID PMC13386346 · OpenAlex W7167090296
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: functional connectivity, psychopathology, transdiagnostic, middle-aged and older adults, brain networks, UK Biobank
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National University of Singapore; Ministry of Education (MOE-T2EP40120-0007 & T2EP2-0223-0025, MOE-T2EP20220-0001); Ministry of Education, India; National University Health System; Medical Research Council; National Medical Research Council (CIRG21nov-0007, NMRC/OFLCG19May-0035, NMRC/CIRG/1485/2018, NMRC/CSA-SI/0007/2016, NMRC/MOH-00707-01, NMRC/CG/435 M009/2017-NUH/NUHS, CIRG21nov-0007, HLCA23Feb-0004, and OFIRG24Jul-0049, OFLCG19MAY-0035, NMRC/CSA-SI/0007/2016, NMRC/OFLCG19May‐0035, HLCA23Feb‐0004)
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Mental health disorders are increasingly prevalent in middle-aged and older adults, a population undergoing substantial brain network reorganization. We aimed to identify whole-brain connectivity patterns associated with transdiagnostic psychiatric dimensions and their links to mental health trajectories and mortality. We analyzed resting-state functional connectivity from the UK Biobank (N = 6529) using multivariate partial least squares analysis to identify latent variables linking brain networks with mental health symptoms. Associations with longitudinal mental health outcomes and mortality risk were examined. Validation of the psychopathology-linked connectome constructs was conducted in HCP-Aging (N = 697) and a Singapore-based community dwelling elderly cohort known as the SG70 Study (N = 943). Two robust latent variables emerged. The first represented a general psychopathology factor (p = 0.0006, 28.0% of the overall covariance), marked by altered connectivity in the somatomotor and default mode networks. The second (p < 0.0001, 17.2% of the overall covariance) distinguished affective disorders from alcohol use disorder via attentional and subcortical network patterns. Importantly, the general psychopathology brain scores differentiated groups with varying future depression trajectories (F(3) = 16.47, p < 0.0001) and were linked to elevated mortality risk (HR = 1.31, CI [1.06–1.62], p = 0.014). This same connectivity signature was also associated with general mental health outcomes in HCP-Aging (rho = 0.13, p = 0.015) and depression in SG70 (rho = 0.07, p = 0.031), demonstrating cross-country and multiethnic robustness. Our findings reveal a stable, interpretable brain connectome-based signature of general psychopathology in later life. This work provides insight into mechanisms of vulnerability and suggests that brain-based markers may help indicate risk and differentiate patterns of symptom persistence, transition, and remission across disorders in aging populations.

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 3 matches between paragraphs and lines of code.

hzlab/2026_Nguyen_ImagingNeuroscience_Connectome_Psychopathology

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: baa4810611f54a1777d0fae89a2e47f260949d39, 23 April 2026
Languages: MATLAB (2), R (2)
Size: 24 files, 4 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), broom (1 file), ggplot2 (1 file), Statistics and Machine Learning Toolbox (1 file), survival (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/hzlab/2026_Nguyen_ImagingNeuroscience_Connectome_Psychopathology

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;
  • 4 scripts, each with its path and the digest of its content;
  • 3 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 Code Availability

The data used in this study were obtained from the UK Biobank and the Human Connectome Project in Aging (HCP-Aging). These data are available to qualified researchers through application to the respective data access procedures. All code used in these analyses is publicly available on GitHub at https://github.com/hzlab/2026_Nguyen_ImagingNeuroscience_Connectome_Psychopathology

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 2, 28 September 2026

  • Funding: added National University of Singapore; Ministry of Education - Singapore: MOE-T2EP40120-0007 & T2EP2-0223-0025, MOE-T2EP20220-0001; Ministry of Education, India; National University Health System; Medical Research Council; National Medical Research Council: CIRG21nov-0007, NMRC/OFLCG19May-0035, NMRC/CIRG/1485/2018, NMRC/CSA-SI/0007/2016, NMRC/MOH-00707-01, NMRC/CG/435 M009/2017-NUH/NUHS, CIRG21nov-0007, HLCA23Feb-0004, and OFIRG24Jul-0049, OFLCG19MAY-0035, NMRC/CSA-SI/0007/2016, NMRC/OFLCG19May‐0035, HLCA23Feb‐0004

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 6 keywords, 92 references.

Cite

This paper

Nguyen, T. T., Ng, K. K., Lew, V. H., Koi, J. J. X., Loh, W. L., Koh, W.-P., & Zhou, J. H. (2026). Functional connectome signature of general psychopathology in middle-aged and older adults: Evidence from multi-cohort, multi-ethnic analyses. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1308. https://doi.org/10.1162/imag.a.1308

BibTeX

@article{nguyen2026functional,
author = {Nguyen, Thuan Tinh and Ng, Kwun Kei and Lew, Voon Hao and Koi, Janice Jue Xin and Loh, Wen Liang and Koh, Woon-Puay and Zhou, Juan Helen},
title = {{Functional connectome signature of general psychopathology in middle-aged and older adults: Evidence from multi-cohort, multi-ethnic analyses}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1308},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1308},
url = {https://doi.org/10.1162/imag.a.1308},
pmid = {42483417},
pmcid = {PMC13386346}
}

RIS

TY - JOUR
AU - Nguyen, Thuan Tinh
AU - Ng, Kwun Kei
AU - Lew, Voon Hao
AU - Koi, Janice Jue Xin
AU - Loh, Wen Liang
AU - Koh, Woon-Puay
AU - Zhou, Juan Helen
TI - Functional connectome signature of general psychopathology in middle-aged and older adults: Evidence from multi-cohort, multi-ethnic analyses
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/20
VL - 4
SP - IMAG.a.1308
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1308
UR - https://doi.org/10.1162/imag.a.1308
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Functional connectome signature of general psychopathology in middle-aged and older adults: Evidence from multi-cohort, multi-ethnic analyses",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Nguyen",
"given": "Thuan Tinh"
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"container-title-short": "Imaging Neurosci (Camb)",
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"date-parts": [
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20
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}
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