使用具有结构约束的功能连接体的图形卷积网络来对精神病谱系障碍进行分类
Madison Lewis1, Wenlong Jiang2, Nicholas D Theis3
1Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15213, United States.
概括
这项研究使用机器学习来识别患有亚临床精神病的人的大脑网络差异. 深度学习模型实现了超过63%的准确性,突出了PGI地区作为一个关键的网络枢纽.
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.
- 机器学习 机器学习
背景情况:
- 分类精神病谱状况,包括下值精神病类似经历 (PLEs),由于微妙的神经生物学差异,提出了挑战.
- 跨诊断的方法与现代对精神病神经生物学的理解一致.
- 从健康的对照中区分患有PLEs的个体需要超越传统诊断类别的先进分析方法.
研究的目的:
- 探索机器学习的实用性,特别是支持矢量机 (SVM) 和图形卷积网络 (GCN),用于对患有精神病谱状况的个体进行分类.
- 通过使用大脑网络特征,识别神经生物学标记,区分患有PLEs的个体与健康对照.
- 研究深度学习的潜力,以提高诊断准确度,并使精神病的早期干预成为可能.
主要方法:
- 利用来自不同精神病谱状况和健康对照者的功能和结构性大脑网络数据.
- 使用支持向量机器 (SVM) 和图形卷积网络 (GCN) 用各种边缘选择技术进行分类.
- 应用了MultiVERSE算法来生成网络嵌入作为SVM的输入特征.
主要成果:
- 最好的分类模型 (SVM和GCN) 的准确度超过63%.
- 确定了右下巴皮层 (PGi) 中的一个区域,作为一个关键的网络枢纽,将PLEs患者与对照人群区分开来.
- 类激活映射突出了PLE组中不同的突出区域,包括背侧前额叶,轨道,极前额叶和横侧皮.
结论:
- 深度学习方法在区分患有亚临床精神病的个体和健康对照者方面表现有前途.
- 这些发现表明,有可能提高诊断准确性,建立诊断的神经生物学基础,并为早期干预策略提供信息.
- 识别的PGI区域和前额/叶皮层模式为精神病谱的神经生物学提供了洞察力.
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