Abstract
BACKGROUND: Obesity, particularly in high-risk groups for food addiction, adversely impacts the brain's functional characteristics. However, its underlying neurobiological and molecular mechanisms remain elusive. The current study adopted a data-driven approach to investigate obesity-associated intrinsic functional architecture and neurotransmitter receptor patterns. METHODS: Resting-state fMRI data were acquired from 198 obese and 291 healthy weight individuals from the Human Connectome Project. Intrinsic connectivity contrast (ICC) and fractional amplitude of low-frequency fluctuations (fALFF) analyses were performed to identify the common altered brain regions and then seeds to whole brain functional connectivity (FC) analyses were conducted to determine obesity-related FC features. Additionally, the relationship between intrinsic functional characteristics and molecular imaging features was assessed to examine neurotransmitter-receptor distribution patterns underlying obesity. RESULTS: Obese individuals, compared to healthy weight individuals, showed aberrant ICC and fALFF in both the right dorsolateral prefrontal cortex (DLPFC) and left insula. For the FC results, the obese group displayed increased FC between the right DLPFC and precuneus, left insula and left inferior parietal lobule, right DLPFC as well as decreased FC between right DLPFC and left precentral, left postcentral gyrus, and bilateral paracentral lobule. Additionally, the fALFF alterations in insula/temploral pole and also the rDLPFC-PCL FC partially mediated the relationship between body mass index and the executive function. Furthermore, cross-modal correlation analyses indicated that ICC and fALFF alterations were related to noradrenaline transporter and dopamine receptor distributions, respectively. DISCUSSION: Together our findings suggested that obesity is associated with atypical neurotransmitter systems and dysfunctional architecture especially in the prefrontal cortex, insula, sensorimotor cortex, and default mode circuits. These may deepen our understanding the neurobiological basis of obesity and provide novel insights into neuroimaging-based treatment and intervention.