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使用sMRI和XGBoost的形态连接性诊断自闭症谱系障碍的框架

Vaibhavi Gupta¹, Gokul Manoj¹, Aditi Bhattacharya²

¹School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.

Studies in health technology and informatics

|October 23, 2023

概括

这项研究开发了一种自动化方法,用于使用脑成像和机器学习来诊断自闭症谱系障碍 (ASD). 该方法实现了94.16%的准确性,识别了参与ASD分类的关键大脑区域.

科学领域:

神经成像是一种神经成像.
机器学习机器学习
神经学神经学

背景情况:

自闭症谱系障碍 (ASD) 诊断依赖于行为观察,缺乏客观生物标志物.
结构磁共振成像 (sMRI) 揭示了ASD大脑中的解剖学差异.
自动诊断工具可以提高ASD评估的效率和客观性.

研究的目的:

开发和验证一个自动化的机器学习模型,用于使用sMRI数据诊断ASD.
识别特定的大脑区域和特征,表明ASD.
探索开发的方法用于选其他神经系统疾病的潜力.

主要方法:

使用FreeSurfer进行sMRI数据的预处理.
根据Destrieux地图集将大脑区域划分为148个感兴趣的区域.
提取形态特征 (体积,厚度,表面积,平均曲率) 并通过皮尔森相关计算形态连接性.
应用XGBoost用于特征选择和诊断模型构建.

主要成果:

该诊断模型使用前18个特征实现了94.16%的平均准确性.
额头和边缘大脑区域被确定为ASD分类的重要贡献者.
该方法在区分自闭症病例与对照病例方面表现出高效.

结论:

拟议的自动化方法有效地使用sMRI和机器学习对自闭症谱系障碍进行分类.
鉴定的特征和大脑区域为ASD的神经解剖学基础提供了洞察力.
这种方法对ASD和潜在的其他神经疾病的早期查和诊断充满希望.

关键词:

自闭症谱系障碍自闭症谱系障碍形态连接性形态连接性皮尔森相关性皮尔森相关性结构磁共振成像结构磁共振成像在XGBoost中使用.

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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.