使用可解释的机器学习方法可视化功能网络连接差异.
Mohammad S E Sendi1,2,3,4, Vaibhavi S Itkyal4,5, Sabrina J Edwards-Swart4
1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia.
Physiological measurement
|April 17, 2025
概括
可解释的人工智能识别了关键的大脑网络差异. 这种方法可以准确地区分精神分裂症患者,并使用功能网络连接生物标志物区分年龄组.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 休息状态功能磁共振成像 (fMRI) 显示功能网络连接 (FNC) 对于理解大脑疾病至关重要.
- 对于FNC分析的传统统计方法在区分患者群体方面存在局限性.
- 机器学习模型提供了改进的分类,但往往缺乏可解释性,阻碍了对其决策过程的理解.
研究的目的:
- 引入一种使用可解释机器学习 (SHapley 增量解释 - SHAPs) 的新型框架,以识别关键的 FNC 特性.
- 提高神经成像研究中机器学习模型的可解释性.
- 发现FNC生物标志物,以区分临床和人口群体.
主要方法:
- 开发和验证一种使用SHapley添加式解释 (SHAPs) 来进行FNC分析的新型框架.
- 使用机器学习模型应用框架,包括随机森林,XGBoost和CATBoost.
- 使用合成数据进行验证,然后将其应用于现实数据集进行群组比较.
主要成果:
- 该框架在区分对照组和精神分裂症 (SZ) 患者方面实现了81.04%的准确性.
- 该框架在区分中年人和老年人方面取得了71.38%的准确性.
- 发现的关键网络包括认知控制网络 (CCN),皮质下网络 (SCN) 和体运动网络.
结论:
- 基于SHAP的框架有效地识别了FNC的关键生物标志物,以区分不同的人口类别.
- 认知控制网络 (CCN) 和皮下网络 (SCN) 在区分精神分裂症患者与对照者以及区分年龄组方面具有重要意义.
- 这种可解释的人工智能方法为潜在的脑疾病和衰老的神经机制提供了可解释的见解.
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