基于功能和结构MRI的强迫症诊断使用机器学习方法
Fang-Fang Huang1,2, Xiang-Yun Yang1, Jia Luo1
1Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
BMC psychiatry
|October 31, 2023
概括
使用功能性MRI (fMRI) 指数的机器学习模型有效地将强迫症患者与健康人区分开来. 结合的fMRI数据达到85%的准确性,突出了神经成像在强迫症诊断中的潜力.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 精神病学是一个精神病学.
背景情况:
- 神经成像,特别是磁共振成像 (MRI),已经确定了强迫症 (OCD) 的神经相关性.
- 这引起了人们对使用MRI指数来区分强迫症患者和健康人群的兴趣.
研究的目的:
- 通过机器学习方法探索基于MRI的指数用于诊断强迫症的潜力.
- 评估各种功能性MRI (fMRI) 和结构性MRI (sMRI) 索引的诊断性能.
主要方法:
- 50名强迫症患者和50名健康受试者被分为训练 (80%) 和测试 (20%) 组.
- 提取的fMRI指数 (ALFF,fALFF,ReHo,DC) 和sMRI指数 (灰质体积,皮质厚度,深) 作为特征.
- 用于最小绝对收缩和选择操作员 (LASSO) 回归来减少特征,以及支持矢量机 (SVM),后勤回归和随机森林用于模型开发.
主要成果:
- 使用组合fMRI指数 (ALFF,fALFF,ReHo,DC) 的SVM模型实现了最高的性能:94%的交叉验证精度和0.90的ROC曲线下的区域在测试组 (85%的精度).
- 选择的特征位于皮质-状-甲状腺-皮质 (CSTC) 电路内外.
- 基于单个MRI指数或fMRI和sMRI指数的模型显示出较低的诊断准确性,ALFF的SVM模型的最多75%的验证准确性.
结论:
- 综合fMRI指数显示,强迫症患者与健康个体的区别有很大的潜力.
- 各种fMRI指数的互补作用提高了分类准确性.
- 在CSTC电路内外的大脑区域的参与强调了强迫症诊断中全面特征选择方法的重要性.
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