Abstract
Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores.
Highlights
More than two billion adults worldwide are overweight and have obesity-related health problems such as diabetes, hypertension, and stroke[1,2]
Networks of obtained independent components (ICs) were defined by comparing ICs with known fMRI resting state networks (RSNs)
The main objective of this study was to find brain networks related to eating behaviors and obesity based on neuroimaging analysis
Summary
More than two billion adults worldwide are overweight and have obesity-related health problems such as diabetes, hypertension, and stroke[1,2]. Eating behavior is believed to be linked with brain networks besides factors such as hormone modulation, impulsivity and inhibitory control[1]. The objective of our study was to find the relationship between eating behavior and brain networks in obesity. Nodes were computed with spatial maps derived from group independent component analysis (ICA), a data-driven method with better sensitivity to quantify functional connectivity than conventional region-based methods[27,28]. We aimed to (1) find spatial maps derived from a group ICA approach and weighted edge values, (2) identify group-wise brain network differences in degree centrality, a graph theoretical measure, between HW and non-HW groups, and (3) quantify the relationship between brain networks and eating behavior using identified networks, associated degree values and TFEQ scores. We hypothesized that cognition network might show connectivity differences between HW and non-HW group, and might correlate significantly with eating behavior
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