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

Abstract

Aims: Obesity 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 methods: We 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.

Results: A 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.

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

Keywords: eating behaviours; functional connectivity; intrinsic neural timescale; modular parameters; obesity; structural connectivity.

FIGURE 1

Associations of the INT with WC. (A) The INT was calculated by fitting the autocorrelation function to a nonlinear exponential function with an offset (left). Whole‐brain INT values are visualised on brain surfaces (right). (B) LASSO coefficients of the whole brain are displayed on the brain surface (top left), and the coefficients are summarised across seven intrinsic functional networks and subcortical regions (bottom left). The scatter plot displays the relationship between the mean INT values of the regions with nonzero LASSO coefficients and WC (right). INT intrinsic neural timescale; LASSO least absolute shrinkage and selection operator; WC waist circumference.

FIGURE 2

Topological underpinnings of the altered INT values. (A) Schematic representation of calculating the participation coefficient and within‐module degree from the structure–function coupling matrices. (B) The participation coefficient (top left) and within‐module degree (top right) of the regions with nonzero LASSO coefficients are visualised on brain surfaces. Scatter plots display the relationships of WC with the mean participation coefficient (left) and within‐module degree z‐score (right) across these regions. INT, intrinsic neural timescale; LASSO, least absolute shrinkage and selection operator; WC, waist circumference.

FIGURE 3

Receiver operating characteristic curves for classifying the participants into groups with and without obesity. The classification results based on WC, WHR and BMI are shown. AUC, area under the curve; BMI, body mass index; ROC, receiver operating characteristic; WC, waist circumference; WHR, waist‐to‐hip ratio.

FIGURE 4

Transcriptomic association analysis. (A) Between‐group differences in INT between individuals with obesity and those without obesity are visualised on brain surfaces (left). A circular plot shows mean INT values in individuals with and without obesity for brain regions that showed significant between‐group differences (right). (B) Cell‐type‐specific expression analysis. Hexagon size indicates the proportion of genes specifically expressed in a given cell type, with increasing enrichment stringency represented by progressively smaller hexagons (specificity index thresholds = 0.05, 0.01, 0.001 and 0.0001, from least specific [outer hexagons] to most specific [centre hexagons]). Colours represent the FDR‐corrected p‐values. (C) The bar plot shows the overlap ratio between genes associated with INT alterations and cell‐type‐specific gene sets. INT, intrinsic neural timescale; OB, obesity; w, with; w/o, without.