通过图形神经网络描述精神疾病:抑郁症和精神分裂症的功能连接分析
Ji-Won Lee1, Ye-Eun Kim1, Mikhail Votinov2,3
1School of Electronic and Information Engineering, Kunsan National University, Gunsan, Republic of Korea.
图表神经网络揭示了主要抑郁症 (MDD) 和精神分裂症 (SZ) 中明显的脑网络障碍. 该研究确定了与临床症状相关的特定网络变化,有助于理解这些精神疾病.
科学领域:
- 神经科学
- 精神病学
- 医学的人工智能
背景情况:
- 大型抑郁症 (MDD) 和精神分裂症 (SZ) 是严重的精神疾病,具有广泛的脑网络障碍.
- 了解大规模网络变化的共同点和差异对于阐明共享和疾病特异的神经机制至关重要.
- 先进的深度学习,特别是图形神经网络 (GNN),提供了分析复杂大脑连接的新方法.
研究的目的:
- 采用深度学习技术,特别是GNN,以识别MDD和SZ中的共同和独特的大规模脑网络模式.
- 了解这些精神疾病的共同和疾病特征的神经基础.
- 探索GNN在精神疾病中的诊断分类和生物标志物发现的潜力.
主要方法:
- 应用最先进的GNN架构,包括基于注意力的层次聚合GNN (SAGPool),用于多站点静止状态的fMRI数据集.
- 大型抑郁症 (MDD) 和精神分裂症 (SZ) 患者与健康对照者的分类.
- 使用基于扰乱的可解释性方法来识别影响模型预测的关键功能连接.
主要成果:
- SAGPool GNN模型实现了高分类精度:MDD为71.50%,SZ为75.65%.
- 确定了大规模网络中断的明显模式:默认模式网络 (DMN) 在MDD和腹部注意网络 (VAN) 在SZ的改变.
- 特定的功能联系与SZ患者的临床症状有显著的相关性,包括积极和一般症状 (PANSS).
结论:
- 图形神经网络是揭示精神疾病中复杂的大脑连接模式的有效工具.
- 这项研究为MDD和SZ背后的不同神经机制提供了新的见解.
- 这些发现突显了基于图形的模型在精神病学研究中的诊断分类和生物标志物发现的潜力.
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