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从脑网络特征中自动识别精神分裂症,使用图形卷积神经网络
Guimei Yin1, Ying Chang2, Yanli Zhao3
1College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China.
Asian journal of psychiatry
|July 7, 2023
概括
这项研究引入了一种新的图形卷积神经网络 (GCN) 模型,用于使用电脑电图 (EEG) 数据诊断精神分裂症. 该GCN模型实现了90.01%的准确性,识别了用于诊断的重要大脑区域.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 精神病学是一个精神病学.
背景情况:
- 精神分裂症的诊断依赖于主观的临床经验,缺乏客观的方法.
- 图形卷积神经网络 (GCN) 显示出在精神疾病中分析复杂的空间关联信息的前景.
研究的目的:
- 开发和验证使用GCN和静止电脑电图 (EEG) 数据的自动精神分裂症识别模型.
- 确定关键的大脑区域及其网络特征对于精神分裂症诊断至关重要.
主要方法:
- 利用了来自103名精神分裂症第一发作患者和92名正常对照者的静止状态EEG数据.
- 使用来自时间域,频域和大脑网络分析的节点特征训练了一个GCN模型.
- 采用十倍交叉验证来评估模型性能,重点关注theta频段和相锁值.
主要成果:
- 对于精神分裂症,GCN模型实现了高识别准确率90.01%.
- 额叶被确定为区分精神分裂症患者与对照者的最重要的区域.
- 在显著区域和临床指标中探索节点拓特征之间的相关性.
结论:
- 提出的基于GCN的方法显示出高适用性和准确性,用于对象识别和诊断精神分裂症.
- 这种方法提供了一个新的,数据驱动的工具来补充精神分裂症的现有诊断实践.
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