隐性疾病因子在与精神病理相关的个体中的定量表达 尺寸和治疗反应反应
Shaoling Zhao1,2, Qian Lv3, Ge Zhang4
1Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.
这项研究使用机器学习从自闭症谱系障碍 (ASD) 和注意力/缺陷过度活动障碍 (ADHD) 患者的大脑连接数据中识别出四种潜在疾病因素. 这些因素与不同的症状领域相关,并预测强迫症障碍 (OCD) 的结果.
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:
- 在自闭症谱系障碍 (ASD),注意力缺陷过度活动障碍 (ADHD) 和强迫症障碍 (OCD) 中,精神病并发症很普遍.
- 在个体层面上对这些共发生的症状背后的共享和独特的神经机制进行分析仍然具有挑战性.
研究的目的:
- 使用无监督机器学习从静止状态功能连接 (rsFC) 数据中推导潜在疾病因素.
- 将这些因素与ASD,ADHD和强迫症患者的维度症状和治疗结果联系起来.
主要方法:
- 利用从混合ASD/ADHD队列的rsFC数据上使用层次化的贝叶斯框架和无监督的机器学习.
- 应用了正统的相关性分析,将潜伏因素与维度症状联系起来.
- 在独立的亚临床和强迫症队列中验证的模型,包括那些接受神经外科手术的人.
主要成果:
- 确定了四个与行为调节 (因素-1),沟通 (因素-2),焦虑 (因素-3) 和适应性行为 (因素-4) 显著相关的潜在因素.
- 因子-1和因子-3分别显示了强迫症和焦虑症队伍中症状评分的显著预测能力.
- 对强迫症患者的因子-1的介入期间变化与治疗结果相关.
结论:
- 数据衍生潜伏疾病因素可以量化个体表达,以告知跨精神疾病的维度症状和治疗反应.
- 这种方法可以通过揭示潜在的神经机制来促进定量精神病学诊断和个性化干预.
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