使用激发/抑制比来优化自闭症和精神分裂症的分类
Lavinia Carmen Uscătescu1, Christopher J Hyatt2, Jack Dunn3
1Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA. lavinia.carmen.u@gmail.com.
Translational psychiatry
|July 9, 2025
概括
该研究发现,激发/抑制 (E/I) 比率,通过赫斯特指数测量,可以帮助区分自闭症 (AT) 和精神分裂症 (SZ). 将E/I比率与表型数据相结合,提高了AT和SZ的诊断准确度.
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.
- 计算生物学 计算生物学
背景情况:
- 不平衡的兴奋/抑制 (E/I) 比率与自闭症 (AT) 和精神分裂症 (SZ) 有关.
- 有限的研究直接比较了AT和SZ之间的E/I比重重叠和诊断潜力.
- 静止状态功能磁共振成像 (fMRI) 提供了一种非侵入性方法来估计E/I比率.
研究的目的:
- 量化神经类型对照 (TD),AT和SZ之间的E/I比率 (通过赫斯特指数估计) 的群体差异.
- 评估E/I比率在AT和SZ之间进行差异诊断的潜力.
- 在独立数据集中验证发现,评估E/I比率与表型数据的附加值.
主要方法:
- 从TD,AT和SZ参与者的53个休息状态fMRI数据的独立组件中计算了E/I比率指标赫斯特指数 (H).
- 采用随机森林 (RF) 分类来确定在探索性数据集中区分AT和SZ的关键H特征 (N=1074).
- 使用单独的H,单独的表型数据 (PANSS,ADOS,BVAQ,EQ,IQ) 和复制数据集中的组合特征 (N=134) 评估了分类性能.
主要成果:
- 赫斯特指数单独在探索数据集中达到84%的AUC,但在复制数据集中降至72%的AUC.
- 仅仅通过表型数据 (PANSS,ADOS,BVAQ,EQ,IQ) 才能获得适度的分类性能.
- 在复制数据集中将赫斯特指数与表型数据 (PANSS,ADOS,BVAQ,EQ,IQ) 结合起来,实现了最高的分类性能 (83%AUC).
结论:
- 由赫斯特指数估计的E/I比率显示了区分自闭症和精神分裂症的潜力.
- 将E/I比率的测量与已建立的表型数据相结合,可以显著提高诊断分类的准确性.
- 这些发现突显了神经生理学标记的有用性,例如E/I比率,可以改善复杂精神疾病的差异诊断.
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