在精神性勃起功能障碍中改变大脑活动和功能连接:结合LOOCV-SVM-RFE和rs-fMRI的发现
Xue Liu1, Peining Niu2, Jinchen He3
1Department of Andrology Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing China; Chengdu University of Traditional Chinese Medicine Chengdu China.
Neuroscience
|January 11, 2025
概括
这项研究开发了一种机器学习模型,使用脑成像来识别心理性勃起功能障碍 (pED). 该模型根据功能性大脑活动差异准确地区分了PED患者和健康对照者.
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:
- 心理性勃起功能障碍 (pED) 与异常的大脑活动有关.
- 缺乏对PED的客观诊断工具.
研究的目的:
- 开发一个自动分类器,以区分pED患者与健康对照 (HCs).
- 为了确定与pED相关的基于大脑的特征.
主要方法:
- 分析了45名pED患者和43名HCs的休息状态功能磁共振成像 (fMRI) 数据.
- 计算了区域同质性 (ReHo) 和功能连接性 (FC).
- 一个具有递归特征消除 (SVM-RFE) 模型的支向量机被训练用于分类.
主要成果:
- 在pED患者中,左侧中圈,下额头圈,前带带圈和中额头圈的ReHo降低.
- 在右骨裂中观察到ReHo的增加.
- SVM-RFE模型在区分pED和HC时达到90.80%的准确率.
结论:
- 变化的区域大脑活动和特定区域的功能连接是PED的特征.
- 机器学习可以有效地识别这些功能性大脑变化.
- 开发的诊断模型显示出对客观PED诊断的希望.
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