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基于男性OSA患者个体全脑功能连接的机器学习分类

Haijun Li¹, Jin Hong², Yudong Zhang³

¹Department of Radiology, PET Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, People's Republic of China.

Nature and science of sleep

|May 21, 2025

概括

个性化功能连接 (FC) 分析改善了阻塞性睡眠呼吸暂停 (OSA) 患者的分类,这些患者的认知能力正常,而认知能力有轻微的损害. 这种神经成像方法可以作为预测OSA认知衰退的标志物.

科学领域:

神经成像是一种神经成像.
睡眠医学睡眠医学
认知神经科学认知神经科学

背景情况:

阻塞性睡眠呼吸暂停 (OSA) 与由于大脑功能连接 (FC) 改变而导致的认知障碍有关.
之前的研究显示了群体水平的FC差异,但个体特异性模式仍未得到充分研究.

研究的目的:

在OSA患者中调查独特的个人FC特征.
评估个人FC的分类效率,以区分具有正常认知 (OSA-NC) 的OSA与具有轻度认知障碍 (OSA-MCI) 的OSA.

主要方法:

分析了82名OSA患者 (41名OSA-NC,41名OSA-MCI) 和84名健康对照 (HC) 的休息状态fMRI数据.
用多任务学习和稀疏凸交替结构优化得出个人FC.
支持矢量机器分类器用于OSA与HC分类和OSA-NC与OSA-MCI分类.

主要成果:

个人FC的分类准确度高于传统的基于Pearson的FC (OSA与HC:91.8%与79.5%;OSA-NC与OSA-MCI:81.3%与63.8%).
十大个人特定的FC网络,主要是默认模式和注意网络,在连接强度方面显示出显著的跨组差异.

结论:

静态个性化FC特征在OSA患者中被确定,这些患者有不同的认知障碍.
个人FC显著提高了OSA-NC与OSA-MCI的分类准确性.
个人FC可以作为一个潜在的神经成像标志物来预测OSA-MCI,并帮助临床诊断和治疗评估.

关键词:

大脑网络大脑网络在个人层面的个人层面.机器学习是机器学习.轻度的认知障碍轻度的认知障碍阻塞性睡眠呼吸暂停 (SOP) 的情况.

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