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Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....

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Updated: Jun 26, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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ADHD分类与交叉数据集特征选择用于生物标志物一致性检测.

Xiaojing Meng^1,2, Ying Chen³, Yuan Gao⁴

¹The Affiliated Hospital of Xuzhou Medical University, Xuzhou, People's Republic of China.

Journal of neural engineering

|May 8, 2024

概括

这项研究引入了一种新方法,在不同数据集中一致识别注意力缺陷多动症 (ADHD) 的大脑生物标志物,提高诊断可靠性.

关键词:

在ADHD的分类,ADHD的分类.生物标志物检测生物标志物检测跨数据集的特征选择.分组为SVM-RFE的SVM-RFE.

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科学领域:

神经科学是一个神经科学.
计算精神病学是一种计算精神病学.
生物标志物发现发现

背景情况:

注意缺陷多动性障碍 (ADHD) 是一个常见的儿童神经发育障碍.
目前的数据驱动的ADHD诊断方法缺乏跨数据集的生物标志物一致性,阻碍了可靠性和可解释性.
在ADHD分类中变化的学习特征破坏了已识别的生物标志物的可靠性.

研究的目的:

开发一个跨数据集特征选择模块,以提高ADHD生物标志物的一致性.
提高ADHD诊断方法的可靠性和可解释性.
使用连接组梯度数据识别稳定的ADHD生物标志物.

主要方法:

提出了一个跨数据集特征选择 (FS) 模块,使用基于SVM的集成递归特征消除 (G-SVM-RFE).
将G-SVM-RFE模块集成到二元假设测试 (BHT) 框架中.
利用连接组梯度数据用于ADHD分类和生物标志物识别.

主要成果:

在不同数据集的ADHD分类中,平均准确率为96.7%.
识别了主要来自全球大脑区域的歧视性梯度组件.
识别了具有高出现频率的特定大脑区域作为一致的ADHD生物标志物.

结论:

拟议的方法提高了ADHD的生物标志物一致性和诊断准确性.
已识别的生物标志物与现有的关于ADHD脑功能障碍的研究一致.
该方法为ADHD机制提供了增强的生物学解释.