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基于功能网络的测量,机器学习方法对患有严重抑郁症的患者进行分类,以实现机器学习方法的可解释性

Andrey V Andreev¹, Semen A Kurkin¹, Drozdstoy Stoyanov²

¹Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14, A. Nevskogo str., Kaliningrad 236016, Russia.

Chaos (Woodbury, N.Y.)

|June 15, 2023

概括

机器学习可以使用大脑功能网络来区分主要抑郁症 (MDD) 患者和健康个体. 一种新的特征选择方法提高了模型的解释性和准确性.

科学领域:

神经科学是一个神经科学.
机器学习机器学习
精神病学是一个精神病学.

背景情况:

重度抑郁症 (MDD) 诊断依赖于临床评估,缺乏客观生物标志物.
休息状态功能磁共振成像 (rs-fMRI) 揭示了MDD中大脑功能网络的改变.
机器学习模型的可解释性对于临床翻译至关重要.

研究的目的:

开发一种可解释的机器学习方法来区分MDD患者与健康对照.
使用 rs-fMRI 数据识别可预测MDD的关键功能网络特征.
提高机器学习在精神病诊断中的临床实用性.

主要方法:

应用线性差异分析 (LDA) 对来自35名MDD患者和50名健康对照者的rs-fMRI数据.
作为特征,利用了全球网络的措施 (平均节点强度,聚类系数,边缘数).
实施了使用统计方法和包装算法的综合特征选择方法.

主要成果:

在无变特征空间中,MDD和对照组是不可区分的,但在由关键特征定义的3D空间中是可分离的.
通过所有或最强的网络连接,LDA实现了最高的准确性.
对参数平面的分析揭示了对类分离性和分类准确度值的洞察力.

结论:

综合特征选择方法为使用功能连接的MDD歧视提供了一种有效和可解释的方法.
这一策略提高了神经成像中机器学习模型的可解释性.
这种方法有可能应用于其他需要高精度和可解释性的机器学习任务.

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