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Metabolic syndrome severity and the energetic cost of brain network transitions: A normative modeling study of accelerated brain aging.

Code ↔ Paper

10 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 10 matches
  1. [1] § Results › MetS-sensitive cognitive states based on network control alterations ↔ cognitive_map_generation.py, lines 9–66 · score 0.78 · mental imagery, memory retrieval, working memory, verbal, navigation, inference
  2. [2] § Methods › Normative modeling of control energy across canonical brain networks: Estimating brain age gap (BAG) ↔ normative_modeling.py, lines 15–33 · score 0.73 · PCNtoolkit, normative modeling, BLR models, cubic, splines, age
  3. [3] § Methods › Normative modeling of control energy across canonical brain networks: Estimating brain age gap (BAG) ↔ normative_modeling.py, lines 124–151 · score 0.72 · household income, normative modeling, quintile, Townsend, alcohol, smoking
  4. [4] § Methods › Spatial colocalization of network controllability deviations with PET and cell-type gene expression ↔ colocalization.py, lines 58–81 · score 0.69 · Allen Human Brain, randomization, gene, cells, colocalization, permutation
  5. [5] § Results › Relationship between the activation energies of eight canonical brain networks and metabolic indicators ↔ PLSC.py, lines 15–33 · score 0.69 · latent variables, HbA1c, waist circumference, variance, PLSC, triglycerides
  6. [6] § Methods › Normative modeling of control energy across canonical brain networks: Estimating brain age gap (BAG) ↔ brain_age_gap.py, lines 33–45 · score 0.69 · predicted age, age corrected, fit, gap, splines, BAGs
  7. [7] § Methods › Spatial colocalization of network controllability deviations with PET and cell-type gene expression ↔ colocalization.py, lines 58–81 · score 0.68 · Allen Human Brain, colocalization, NiSpace, MetS, gene, cell
  8. [8] § Methods › Cardiometabolic and cognitive assessment ↔ tmap_generation.py, lines 3–28 · score 0.64 · HbA1c, waist circumference, diastolic, systolic, triglycerides, HDL
  9. [9] § Methods › Cardiometabolic and cognitive assessment ↔ multiple_regression.py, lines 92–109 · score 0.59 · HbA1c, waist circumference, DBP, SBP, triglycerides, HDL
  10. [10] § Results › Relationship between the activation energies of eight canonical brain networks and metabolic indicators ↔ PLSC.py, lines 15–33 · score 0.56 · latent variable, Bootstrap, BP, Variance, PLSC, permutation

Paper

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The authors' code

Python · 180 lines · 6.6 KB · no license · 2 matches

  1. import pandas as pd
  2. import numpy as np
  3. import os
  4. from patsy import dmatrix
  5. from pcntoolkit.normative import estimate
  7. cohort = pd.read_csv('cohort.csv')
  8. activation = pd.read_csv('activation.csv') ## controllability.csv, metabolic_feature.csv
  9. data = pd.merge(cohort, activation, how='inner', on='eid')
  10. data.to_csv('whole_data.csv', index=False)
  12. ability = [col for col in activation.columns if col != 'eid']
  13. # ability = ['BMI', 'Weight', 'Waist circumference', 'Hip circumference', 'HbA1c', 'HDL', 'LDL', 'Cholesterol', 'Triglycerides', 'Systolic BP', 'Diastolic BP']
  15. """
  16. ==========================================================================
  17. UKB Normative Modeling Pipeline
  18. ---------------------------------------------------------------------------
  19. ✓ preprocessing: sex, income, alcohol, education, ethnity(White vs Non-White),
  20. Townsend quintile(1–5), smoking/center one-hot
  21. ✓ spline(age) : cubic B-spline, df=5, no intercept
  22. ✓ covariates : covariates.txt
  23. ✓ site (dummy) : site.txt
  24. ✓ response : <network>_response.txt
  25. ✓ BLR modeling : PCNtoolkit
  26. ==========================================================================
  27. """
  28. # === [0] Basic setting & module =====================================================
  29. import os
  30. import numpy as np
  31. import pandas as pd
  32. from patsy import dmatrix
  33. from pcntoolkit.normative import estimate
  35. # -----------------------------------------------------------------
  36. DATA_FILE = "whole_data.csv"
  37. OUT_DIR = "blr_spline_pcntoolkit_outputs"
  38. NETWORKS = ability
  40. os.makedirs(OUT_DIR, exist_ok=True)
  42. # === [1] Import data ======================================================
  43. df = pd.read_csv(DATA_FILE)
  45. # === [2] NaN processing ------------------------------------------------------
  46. def _to_nan(x):
  47. if isinstance(x, str) and x.strip() in ["Do not know", "Prefer not to answer"]:
  48. return np.nan
  49. return x
  51. cols_str = ['sex', 'household_income', 'alcohol',
  52. 'education', 'smoking', 'center', 'ethnity']
  53. df[cols_str] = df[cols_str].applymap(_to_nan)
  55. # === [3] Individual covariate encoding ---------------------------------------------------
  56. # 3-1) sex (0 = Male, 1 = Female) --------------------------------------------
  57. df['sex'] = df['sex'].map({'Male': 0, 'Female': 1})
  59. # 3-2) household_income (ordinal 0–4) ----------------------------------------
  60. income_order = {
  61. 'Less than 18,000': 0, '18,000 to 30,999': 1,
  62. '31,000 to 51,999': 2, '52,000 to 100,000': 3,
  63. 'Greater than 100,000': 4
  64. }
  65. df['household_income'] = df['household_income'].map(income_order)
  66. df['household_income'].fillna(int(df['household_income'].median()), inplace=True)
  68. # 3-3) alcohol (ordinal 0–5) --------------------------------------------------
  69. alcohol_order = {
  70. 'Never': 0, 'Special occasions only': 1, 'One to three times a month': 2,
  71. 'Once or twice a week': 3, 'Three or four times a week': 4,
  72. 'Daily or almost daily': 5
  73. }
  74. df['alcohol'] = df['alcohol'].map(alcohol_order)
  75. df['alcohol'].fillna(int(df['alcohol'].median()), inplace=True)
  77. # 3-4) education (0–3) --------------------------------------------------------
  78. def simplify_education(text):
  79. if pd.isna(text): return np.nan
  80. if 'College or University degree' in text: return 3
  81. if 'A levels/AS levels' in text: return 2
  82. if 'O levels/GCSEs' in text: return 1
  83. if 'None of the above' in text: return 0
  84. return 1
  85. df['education'] = df['education'].apply(simplify_education)
  86. df['education'].fillna(int(df['education'].median()), inplace=True)
  88. # 3-5) ethnity → White vs Non-White (binary) ----------------------------------
  89. white_cats = ['British', 'Irish', 'Any other white background', 'White']
  90. df['is_nonwhite'] = df['ethnity'].apply(
  91. lambda x: 1.0 if pd.notna(x) and x not in white_cats
  92. else 0.0 if pd.notna(x) and x in white_cats
  93. else np.nan
  94. )
  95. df['is_nonwhite'].fillna(df['is_nonwhite'].median(), inplace=True)
  96. df.drop(columns=['ethnity'], inplace=True)
  98. # 3-6) Townsend index → 5-quantile (1–5) --------------------------------------
  99. df['townsend_quintile'] = pd.qcut(
  100. df['townsend'], 5, labels=[1, 2, 3, 4, 5]
  101. ).astype(float)
  102. df['townsend_quintile'].fillna(3, inplace=True)
  104. # 3-7) smoking & center one-hot -----------
  105. for col in ['smoking', 'center']:
  106. df[col].fillna('Unknown', inplace=True)
  108. df = pd.get_dummies(df, columns=['smoking', 'center'], drop_first=False)
  110. # ✅ one-hot bool type → float
  111. df = df.astype(float)
  113. # === [4] Type change ==================================
  114. if len(df.select_dtypes('object').columns):
  115. raise ValueError(f"Still remain string: {df.select_dtypes('object').columns.tolist()}")
  117. # === [5] Spline(age) generation =====================================================
  118. spline_df = dmatrix(
  119. "bs(age, df=5, degree=3, include_intercept=False)",
  120. {"age": df["age"]}, return_type='dataframe'
  121. )
  122. spline_df.columns = [f"spline_{i}" for i in range(spline_df.shape[1])]
  124. # === [6] covariate dataframe & save ======================================
  125. # smoking_/center_ dummy column
  126. smoking_dummies = [c for c in df.columns if c.startswith('smoking_')]
  127. center_dummies = [c for c in df.columns if c.startswith('center_')]
  129. '''
  130. non_spline_vars = [
  131. 'sex', 'household_income', 'alcohol', 'education',
  132. 'townsend_quintile', 'is_nonwhite'
  133. ] + smoking_dummies + center_dummies
  134. '''
  136. non_spline_vars = ['sex']
  138. cov_df = pd.concat(
  139. [spline_df.reset_index(drop=True), df[non_spline_vars].reset_index(drop=True)],
  140. axis=1
  141. )
  143. cov_path = os.path.join(OUT_DIR, "covariates.txt")
  144. site_path = os.path.join(OUT_DIR, "site.txt")
  146. cov_df.to_csv(cov_path, index=False, header=False, sep=' ')
  147. pd.DataFrame({'site': [0] * len(cov_df)}).to_csv(
  148. site_path, index=False, header=False, sep=' '
  149. )
  151. print(f"✅ covariates.txt saved → {cov_df.shape[0]} subjects × {cov_df.shape[1]} vars")
  153. # === [7] BLR model training ========================================================
  154. for net in NETWORKS:
  156. print(f"\n▶ BLR model training: {net}")
  158. resp_path = os.path.join(OUT_DIR, f"{net}_response.txt")
  159. df[[net]].to_csv(resp_path, index=False, header=False, sep=' ')
  161. # result directory
  162. net_outdir = os.path.join(OUT_DIR, net)
  163. os.makedirs(net_outdir, exist_ok=True)
  165. # PCNtoolkit BLR
  166. estimate(
  167. covfile=cov_path,
  168. respfile=resp_path,
  169. outdir=net_outdir,
  170. alg="blr",
  171. savemodel=True,
  172. standardize=True,
  173. variables=list(cov_df.columns),
  174. cvfolds=10,
  175. outputsuffix=net
  176. )
  178. print(f"✅ complete → result save: {net_outdir}")
  180. print("\n🎉 All network BLR pipeline completed!")

normative_modeling.py at commit 20f728e, no license · at the source

Overview

Authors: Hyun Soo Park1, Bumseok Jeong1
ORCID iDs: Hyun Soo Park, Bumseok Jeong
  1. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Republic of Korea
Institutions: Korea Advanced Institute of Science and Technology (South Korea)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1282
Dates: received 19 November 2025; accepted 29 May 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1282 · PMID 42344102 · PMCID PMC13288504 · OpenAlex W7163340319
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), computational (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Smoothing, state filtering, decompositions
Keywords: metabolic syndrome, network control theory, normative modeling, structural connectome, cognitive function
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (RS-2025-00562004); Medical Scientist Training Program from the Ministry of Science & ICT of Korea; Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (RS-2024–00440131); Korea Health Industry Development Institute (KHIDI)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Although metabolic syndrome is a modifiable risk factor for functional decline in various organs, the brain network mechanisms linking metabolic cost to functional vulnerability remain poorly understood. We applied network control theory to diffusion magnetic resonance imaging-derived connectomes in 25,697 adults aged 40–70 years to quantify the energetic cost of engaging 8 large-scale brain networks. Metabolic syndrome was defined according to established criteria, with a composite score (0–5) based on the presence of central obesity, elevated triglycerides, reduced high-density lipoprotein cholesterol, hypertension, and impaired glycemic control. Normative modeling provided age-adjusted network deviations and brain age gap estimates. Associations with metabolic indicators and behavioral cognitive performance (7 tasks in UK Biobank) were tested, and meta-analytic mapping (123 cognitive states in Neurosynth) was used to identify at-risk domains. Severity of metabolic syndrome was associated with distinct, network-specific energetic alterations, including progressively increased subcortical control energy cost, decreased visual, and threshold changes in attentional and executive networks. The control energy profiles of the subcortical and visual networks exhibited accelerated brain age gap trajectories, reflecting significant departures from normative age-matched expectations. Waist circumference was the strongest metabolic predictor, with effects amplified in older age. Partial least squares correlation analysis revealed associations of metabolic profile (central obesity and dyslipidemia) with higher subcortical/dorsal attention/control and lower visual network energy. Vulnerability mapping indicated that episodic memory, spatial navigation, and processing speed were the domains at most at risk in relation to higher metabolic syndrome severity scores. Behavioral testing confirmed lower memory and processing speed scores in participants with metabolic syndrome. Overall, metabolic syndrome reshapes the brain’s energetic landscape, accelerates functional brain aging, and selectively impairs memory-related networks. Network control theory-derived metrics offer a mechanistic and sensitive biomarker linking metabolic health to cognitive function. Central adiposity was the primary modifiable driver, underscoring the importance of early, targeted metabolic interventions to preserve network efficiency and cognitive resilience.

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 10 matches between paragraphs and lines of code.

phs9416/UKB_MetS_NCT

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 20f728ebf5ff5630f33f1ef594836ebafc1f5c77, 1 October 2025
Languages: Python (11), Shell (1)
Size: 14 files, 12 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: pandas (10 files), NumPy (9 files), Matplotlib (6 files), seaborn (5 files), statsmodels (5 files), SciPy (3 files), Nilearn (2 files), scikit-learn (2 files), NiBabel (1 file), NiMARE (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/phs9416/UKB_MetS_NCT/

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

Data originate from the UK Biobank and will be accessible contingent upon authorization from the UK Biobank (https://www.ukbiobank.ac.uk/). Code used for analysis is available on our GitHub repository (https://github.com/phs9416/UKB_MetS_NCT/).

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 4 funders, 70 references.

Cite

This paper

Park, H. S., & Jeong, B. (2026). Metabolic syndrome severity and the energetic cost of brain network transitions: A normative modeling study of accelerated brain aging. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1282. https://doi.org/10.1162/imag.a.1282

BibTeX

@article{park2026metabolic,
author = {Park, Hyun Soo and Jeong, Bumseok},
title = {{Metabolic syndrome severity and the energetic cost of brain network transitions: A normative modeling study of accelerated brain aging}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1282},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1282},
url = {https://doi.org/10.1162/imag.a.1282},
pmid = {42344102},
pmcid = {PMC13288504}
}

RIS

TY - JOUR
AU - Park, Hyun Soo
AU - Jeong, Bumseok
TI - Metabolic syndrome severity and the energetic cost of brain network transitions: A normative modeling study of accelerated brain aging
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/22
VL - 4
SP - IMAG.a.1282
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1282
UR - https://doi.org/10.1162/imag.a.1282
LA - en
ER -

CSL-JSON

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