联合分类网络分析显示,阻断性睡眠呼吸暂停-睡眠暂停综合征的节点功能障碍和尺寸特异性变化
Zhiyuan Yang1, Maoyuan Jiang2, Jialiu Jiang3
1Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
阻塞性睡眠呼吸暂停-呼吸暂停综合征 (OSAHS) 导致大脑网络发生变化. 同类网络分析和机器学习为OSAHS查提供了一个有希望的,准确的方法.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 睡眠医学 睡眠医学
背景情况:
- 阻塞性睡眠呼吸暂停-呼吸暂停综合征 (OSAHS) 是常见的,与大脑变化有关.
- 目前对OSAHS大脑网络模式和查方法的理解有限.
研究的目的:
- 在OSAHS中使用共分类网络分析分析功能性大脑网络.
- 评估这些网络模式在OSAHS查中的诊断实用性.
主要方法:
- 174名参与者按呼吸暂停-呼吸暂停指数 (AHI) 和氧和度 (SpO2 nadir) 分组.
- 方法包括睡眠测试,神经心理测试,MRI,共同分类网络分析,传统的图形理论和随机森林建模.
- 共同分类网络使用共识模块化进行分析,而传统网络使用图形理论.
主要成果:
- 中度至重度的OSAHS和低氧化群体表现出较差的呼吸系统参数.
- 同级别分析显示,视觉,背部注意力,突出/腹部注意力和执行控制网络的显著变化.
- 使用共同分类指标的随机森林模型显示了OSAHS的高诊断准确性.
结论:
- OSAHS导致大脑网络的广泛功能障碍,受低氧化和呼吸暂停频率的影响.
- 同级网络分析优于传统方法来检测与OSAHS相关的网络异常.
- 基于网络参数的机器学习模型显示了有效的OSAHS查的潜力.
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