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使用结构磁共振成像特征进行自闭症谱系障碍的整体分类

Yanli Zhang-James¹, Jan K Buitelaar^2,3,4,

¹Department of Psychiatry and Behavioral Sciences SUNY Upstate Medical University Syracuse New York USA.

|July 11, 2023

概括

仅仅结构性MRI数据就显示出诊断自闭症谱系障碍 (ASD) 的潜力有限. 将成像与其他数据类型相结合,可能有助于开发有效的ASD诊断生物标志物.

科学领域:

神经成像是一种神经成像.
机器学习机器学习
发育神经科学的发展神经科学.

背景情况:

自闭症谱系障碍 (ASD) 呈现出社会沟通缺陷和重复性行为.
自闭症的神经发育起源与大脑的结构和功能变化有关.
目前缺乏临床上可行的ASD诊断成像生物标志物.

研究的目的:

用机器学习评估结构磁共振成像 (sMRI) 数据用于ASD的诊断潜力.
评估ML模型在基于脑成像特征的ASD分类中的性能.

主要方法:

将机器学习模型应用于来自ENIGMA联盟的sMRI体积和皮质厚度数据的大数据集.
利用了1833名患有自闭症的受试者和1838名对照者,年龄范围为1.5-64岁.
在分类任务中使用了堆叠的额外树分类器.

主要成果:

性能最好的模型实现了接收器运行特征曲线 (AUC) 下的面积为0.62.
精度回忆曲线下的面积为0.58,表明分类性能中等.
学习曲线分析表明,额外的培训数据不会显著改善模型性能.

结论:

单独的sMRI体积和皮质厚度数据可能不足以用于临床上有用的ASD诊断.
未来的ASD成像分类器的开发可以从整合多式联络数据 (例如fMRI) 和先进的ML技术 (例如CNN) 中获益.
结合不同的数据模式对于提高ASD诊断工具的准确性和临床实用性至关重要.

关键词:

这就是为什么MRI是MRI.自闭症谱系障碍自闭症谱系障碍生物标志物生物标志物这是分类分类的分类.机器学习是机器学习.

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