McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.
Hum Brain Mapp. 2020 Dec;41(17):4912-4924. doi: 10.1002/hbm.25167. Epub 2020 Aug 17.
Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole-brain functional connectivity remains poorly understood. Here, we identified a neuroimaging-based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large-scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole-brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole-brain functional connectivity and have important implications for the obesity neuroimaging community.
奖赏和躯体感觉回路中的神经调节机制紊乱导致对进食的欲望增加和抑制控制减少,从而导致肥胖。尽管有许多研究探讨肥胖的大脑机制,但宏观全脑功能连接的作用仍知之甚少。在这里,我们通过在一个跨越四个独立队列的大规模(n~2400)数据集上使用功能连接分析结合机器学习,确定了与肥胖表型相关的基于神经影像学的功能连接模式。我们发现,包含奖赏回路的大脑区域与肥胖表型呈正相关,而用于感觉处理的大脑区域则呈负相关。我们的研究为理解全脑功能连接如何与肥胖表型相关提供了一个新的视角。此外,我们通过在包含多个年龄段和种族的受试者的三个独立数据集上,将功能连接模式与肥胖表型相关联,证明了我们发现的普遍性。我们的研究结果表明,肥胖表型可以从宏观全脑功能连接的角度来理解,这对肥胖神经影像学领域具有重要意义。