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体重指数的神经解剖学特征意味着冲动延迟折扣和一般认知能力
Hui Xu1, Max M Owens1, James MacKillop1,2
1Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton/McMaster University, Hamilton, Ontario, Canada.
Obesity (Silver Spring, Md.)
|October 19, 2023
概括
机器学习确定了与肥胖相关的大脑结构概况 (身体质量指数). 这种特征涉及动机大脑网络,与自我控制和认知能力有关.
科学领域:
- 神经成像是一种神经成像.
- 肥胖问题研究研究
- 机器学习 机器学习
背景情况:
- 肥胖,以过度脂肪为特征,并以身体质量指数 (BMI) 衡量,与大脑结构差异有关,尽管研究结果不一致.
- 以前的研究已经探索了BMI和大脑解剖学之间的关系,结果各不相同.
研究的目的:
- 使用机器学习识别与BMI相关的神经解剖形态学概况.
- 在一大群人中,描述与肥胖相关的大脑结构模式.
主要方法:
- 弹性净回归模型与交叉验证被应用于1100名参与者的神经成像数据的人类连接组项目年轻成年人研究.
- 一个多区域的神经解剖学概况被导出并使用持有测试集进行验证.
主要成果:
- 神经解剖学概况显著预测了BMI (训练中的R 2 = 10.05%,测试中的8.87%).
- 发现的关键大脑区域是默认模式,执行控制和突出网络的一部分.
- 大脑形状特征和BMI之间的关联部分是由冲动延迟折扣和一般认知能力介导的.
结论:
- 确定了BMI的强大,机器学习衍生的神经解剖学概况.
- 这种概况涉及动机网络中的大脑区域.
- 这些发现表明,自我调节能力和认知功能是与肥胖的功能联系.
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