药物使用和反社会人格对神经成像基于机器学习预测精神分裂症的影响
Matias Taipale1, Jari Tiihonen1,2,3, Juuso Korhonen4
1Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland.
Schizophrenia bulletin
|July 14, 2023
概括
用于诊断精神分裂症的机器学习模型受到伴随性物质使用障碍 (SUD) 和反社会人格障碍 (ASPD) 的影响. 这些并发症,特别是当它们一起出现时,会降低诊断的准确性,这表明它们有共同的脑部异常.
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.
- 人工智能的人工智能
背景情况:
- 基于神经成像的机器学习 (ML) 对精神分裂症 (SCH) 诊断具有前景.
- 像物质使用障碍 (SUD) 和反社会人格障碍 (ASPD) 等常见并发症对这些诊断模型的影响在很大程度上是未知的.
研究的目的:
- 调查SUD和/或ASPD的存在如何影响从神经成像数据中识别精神分裂症的ML模型的表现.
- 评估ML模型在区分精神分裂症与相关疾病中的敏感性和特异性.
主要方法:
- 在结构性MRI数据上训练了一个ML模型,以区分SCH与对照.
- 模型的性能在独立的法医精神病学样本上进行了评估,包括患有SCH,ASPD和SUD的个体,分析灵敏度和特异性.
- 来自公共数据集的结构MRI数据被用于模型培训和验证.
主要成果:
- 在共病性SUD和ASPD的存在下,ML模型在分类精神分裂症的准确性降低了.
- 模型错误地将大约50%的ASPD和SUD患者分类为精神分裂症患者.
- 分类的准确性各不相同,SCH+ASPD+SUD患者被分类为SCH94.4%,而无并发性SCH被分类为60.0%.
结论:
- ASPD和SUD显著影响基于神经成像的ML模型用于精神分裂症诊断的性能.
- 在SCH,ASPD和SUD之间与认知相关的大脑区域的融合异常可能解释了模型性能的降低.
- 需要进一步的研究来完善ML模型,以解释精神病学神经成像中的并发症.
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