Evidence map›Paper›PMID 40566750›Full record

ArticleDiabetes, obesity & metabolism2025

Alterations in the functional MRI-based temporal brain organisation in individuals with obesity.

Seoah Lee, Jong Young Namgung, Jae-Ho Han, Bo-Yong Park

Abstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Seoah LeeDepartment of Mathematics, Inha University, Incheon, Republic of Korea.
Jong Young NamgungDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Jae-Ho HanDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Bo-Yong ParkDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.ORCID 0000-0001-7096-337X

Funding

Institute for Information and Communications Technology Promotion 2022-0-00448/RS-2022-II220448Institute for Information and Communications Technology Promotion IITP-2025-RS-2022-00156225Institute for Information and Communications Technology Promotion RS-2021-II212068National Research Foundation of Korea RS-2024-00415812
6 · The paper itself

Abstract

aimsObesity is associated with functional alterations in the brain. Although spatial organisation changes in the brains of individuals with obesity have been widely studied, the temporal dynamics in their brains remain poorly understood. Therefore, in this study, we investigated variations in the intrinsic neural timescale (INT) across different degrees of obesity using resting-state functional and diffusion magnetic resonance imaging data from the enhanced Nathan Kline Institute Rockland Sample database. MATERIALS AND

methodsWe examined the relationship between the INT and obesity phenotypes using supervised machine learning, controlling for age and sex. To further explore the structure-function characteristics of these regions, we assessed the modular network properties by analysing the participation coefficients and within-module degree derived from the structure-function coupling matrices. Finally, the INT values of the identified regions were used to predict eating behaviour traits.

resultsA significant negative correlation was observed, particularly in the default mode, limbic and reward networks. We found a negative association with the participation coefficients, suggesting that shorter INT values in higher-order association areas are related to reduced network integration. Moreover, the INT values of these identified regions moderately predicted eating behaviours, underscoring the potential of the INT as a candidate marker for obesity and eating behaviours.

conclusionsThese findings provide insight into the temporal organisation of neural activity in obesity, highlighting the role of specific brain networks in shaping behavioural outcomes.

Indexed as

BrainMagnetic Resonance ImagingObesityAdultFeeding BehaviorFemaleHumansMachine LearningMaleMiddle Agedeating behavioursfunctional connectivityintrinsic neural timescalemodular parametersobesitystructural connectivity

Identifiers

PMID40566750
PMCPMC12326918

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.