Large-Scale Brain Network Connectivity Mediates the Association Between Metabolic Risk Factors and Cognition: An fMRI Study of the Human Connectome Project.

Overview

Authors: Yunus Soleymani¹, Seyed Amirhossein Batouli¹, Kazem Khalagi^2,3, Ata Pourabbasi^1,4

ORCID iDs: Yunus Soleymani, Seyed Amirhossein Batouli, Kazem Khalagi, Ata Pourabbasi

Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Obesity and Eating Habits Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

Institutions: Tehran University of Medical Sciences (Iran); Endocrinology and Metabolism Clinical Sciences Institute; Osteoporosis Research Center; Obesity and Eating Habits Research Center

Journal: International journal of endocrinology and metabolism, volume 24, issue 4, article e168867

Dates: received 7 December 2025; accepted 18 July 2026; published online 11 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.5812/ijem-168867 · PMID 42763643 · PMCID PMC13589462 · OpenAlex W7212905344

Open access: diamond, a free copy (OpenAlex)

Status: data only

Categories: fMRI (modality), human (organism), cognitive (subfield)

Methods: Connectivity, Smoothing, state filtering, decompositions, Statistics, Preprocessing, Machine learning

Keywords: Metabolic Factors, Resting-state fMRI, Independent Component Analysis, Brain Networks, Cognition, Mediation Analysis

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Background: Subclinical metabolic disturbances have been associated with functional brain changes; however, the neural pathways mediating their cognitive effects remain unclear.

Objectives: This study investigated whether large-scale brain network connectivity mediates associations between metabolic risk factors and cognition in healthy young adults.

Methods: Data were obtained from 676 participants (22 - 37 years) in the Human Connectome Project. Metabolic indices (body mass index (BMI), glycated hemoglobin, thyroid-stimulating hormone (TSH), systolic/diastolic blood pressure, and hematocrit) were assessed alongside the National Institutes of Health (NIH) Toolbox Cognition Battery scores, the Mini-Mental State Examination, and fluid intelligence measures. Resting-state fMRI data underwent group independent component analysis to identify 14 intrinsic connectivity networks. Partial correlations were computed between network time series, and multiple regression models were adjusted for age, sex, race, and education. Mediation analyses with bootstrapped confidence intervals were conducted to test whether network connectivity explained the metabolic–cognition relationships.

Results: We found that large-scale resting-state networks significantly mediated the associations between specific metabolic risk factors and cognitive performance. Specifically, BMI, hematocrit, and TSH showed significant associations with both cognition and network connectivity (all P < 0.05). BMI-related reductions in vocabulary performance were fully mediated by altered connectivity in the frontoparietal–language, sensorimotor–frontoparietal, primary visual–auditory, and default mode–executive attention networks and partially mediated (compensatory effect) by salience–sensorimotor connectivity (indirect effects: –0.0238, -0.0115, -0.0110, -0.0096, and 0.0068, respectively; 95% CIs: [–0.0421, –0.0085], [-0.0239, -0.0030], [-0.0237, -0.00016], [-0.0206, -0.0014], and [0.0001, 0.0165], respectively). Hematocrit’s positive association with vocabulary performance was partially mediated by the primary visual–auditory, salience–auditory, and language–dorsal attention networks, with primary visual–ventral attention connectivity exerting a suppressive effect (indirect effects: 0.0094, 0.0149, 0.0074, and -0.0113, respectively; 95% CIs: [0.0007, 0.0215], [0.0032, 0.0319], [0.0001, 0.0197], and [-0.0249, -0.0012], respectively). TSH was positively associated with fluid intelligence via a direct pathway (P = 0.010), without mediation by resting-state connectivity.

Conclusions: These findings highlight large-scale brain networks as potential intermediate phenotypes that link metabolic health to cognition, suggesting that targeted neuromodulation of vulnerable circuits, combined with metabolic interventions, may offer novel strategies for preserving cognition in at-risk populations.

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

Code

The paper links to its data, not to its authors' code: see the Data section.

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Data

Datasets cited

humanconnectome.org/study/hcp-young-adult, at Human Connectome Project; found in “Data Availability:”

Data Availability

Data of this study were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) and was funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The data are publicly available at https://www.humanconnectome.org/study/hcp-young-adult/data-releases.

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, issue, pages, dates, 4 authors, 6 keywords, 43 references.

Cite

This paper

Soleymani, Y., Batouli, S. A., Khalagi, K., & Pourabbasi, A. (2026). Large-Scale Brain Network Connectivity Mediates the Association Between Metabolic Risk Factors and Cognition: An fMRI Study of the Human Connectome Project. International journal of endocrinology and metabolism, 24(4), e168867. https://doi.org/10.5812/ijem-168867

BibTeX

@article{soleymani2026large, author = {Soleymani, Yunus and Batouli, Seyed Amirhossein and Khalagi, Kazem and Pourabbasi, Ata}, title = {{Large-Scale Brain Network Connectivity Mediates the Association Between Metabolic Risk Factors and Cognition: An fMRI Study of the Human Connectome Project}}, journal = {International journal of endocrinology and metabolism}, year = {2026}, month = aug, volume = {24}, number = {4}, pages = {e168867}, publisher = {Brieflands}, issn = {1726-913X}, doi = {10.5812/ijem-168867}, url = {https://doi.org/10.5812/ijem-168867}, pmid = {42763643}, pmcid = {PMC13589462} }

RIS

TY - JOUR AU - Soleymani, Yunus AU - Batouli, Seyed Amirhossein AU - Khalagi, Kazem AU - Pourabbasi, Ata TI - Large-Scale Brain Network Connectivity Mediates the Association Between Metabolic Risk Factors and Cognition: An fMRI Study of the Human Connectome Project T2 - International journal of endocrinology and metabolism J2 - Int J Endocrinol Metab PY - 2026 DA - 2026/08/11 VL - 24 IS - 4 SP - e168867 SN - 1726-913X PB - Brieflands DO - 10.5812/ijem-168867 UR - https://doi.org/10.5812/ijem-168867 LA - en ER -

CSL-JSON

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