基于网络拓属性和功能连接性的青少年抑郁症与睡眠障碍的诊断
Songhao Hu1,2,3, Xingyue Zuo1,2,3, Dairui Yu2,3
1School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230022, China.
BMC psychiatry
|September 27, 2025
概括
使用中间中心性 (BC) 和功能连接性 (FC) 的大脑网络分析可以帮助识别抑郁青少年的睡眠障碍. 这种神经成像方法对诊断有希望.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 医疗成像医学成像
背景情况:
- 睡眠障碍在患有抑郁症的青少年中很普遍.
- 目前神经成像诊断技术对于睡眠障碍在这个人群是有限的.
- 研究了大脑网络的特征,如中间中心性 (BC) 和功能连接性 (FC).
研究的目的:
- 使用大脑网络特征预测抑郁青少年的睡眠障碍.
- 为了识别抑郁青少年睡眠障碍的神经成像标志物.
- 评估将BC和FC分析与用于诊断的机器学习相结合的有效性.
主要方法:
- 分析了来自117名抑郁青少年的休息状态fMRI数据.
- 计算了全脑功能连接 (FC) 和中间中心性 (BC).
- 支持矢量机 (SVM) 分类器使用交叉验证和独立数据集进行训练和验证.
主要成果:
- 在患有睡眠障碍的青少年中,在右中旋中升高的BC和在左中 cingulate 和 paracingulate gyri/左尾状核中降低的BC被观察到.
- 在FC中发现了显著的变化,特别是在左中椎和右中椎之间.
- 集成BC和FC的SVM模型在内部实现了81.40%的准确性,在外部实现了74.19%的准确性.
结论:
- 患有睡眠障碍的抑郁青少年表现出显著的功能性大脑网络变化.
- 将脑网络分析 (BC和FC) 与机器学习相结合,为基于神经成像的诊断提供了一个有前途的途径.
- 这种方法可能会导致抑郁青少年睡眠障碍的新生物标志物.
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