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相关概念视频

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

131
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....
131

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相关实验视频

Updated: Jul 20, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

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使用结构性大脑MRI和个人特征数据与机器学习框架的ADHD诊断.

Dhruv Chandra Lohani1, Bharti Rana1

  • 1Department of Computer Science, University of Delhi, Delhi, India.

Psychiatry research. Neuroimaging
|August 3, 2023
PubMed
概括

通过使用结构性MRI和个人特征来自动诊断注意力缺陷/多动障碍 (ADHD),达到75%的准确性. 这种方法通过分析大脑结构和个人数据,有助于客观地对ADHD进行分类.

科学领域:

  • 神经成像是一种神经成像.
  • 计算精神病学是一种计算精神病学.
  • 机器学习 机器学习

背景情况:

  • 注意缺陷/多动障碍 (ADHD) 的自动诊断仍然是一个重大挑战,需要客观和非侵入性的方法.
  • 结构磁共振成像 (sMRI) 提供了对大脑形态的洞察,可能有助于ADHD的分类.
  • 将个人特征 (PC) 与神经成像数据相结合可能会提高诊断准确度.

研究的目的:

  • 利用结构性MRI和PC数据开发和评估一个用于ADHD分类的自动诊断系统.
  • 确定突出的神经成像和个人特征特征,以区分ADHD与典型发育儿童 (TDC).
  • 为了比较各种机器学习分类器对ADHD诊断的性能.

主要方法:

  • 使用了316名ADHD和316名TDC个体的年龄平衡数据集.
  • 从sMRI扫描中提取了体积灰质 (GM) 特征 (AAL3图谱) 和皮质厚度 (CT) 特征 (Destrieux图谱).
  • 使用最小冗余最大相关性 (mRMR) 和整体特征选择 (EFS) 进行了特征选择.
  • 五个分类器 (k-NN,逻辑回归,线性SVM,RBSVM,随机森林) 经过训练并使用十倍交叉验证方案进行评估.

主要成果:

  • 75%的最高分类准确度是使用CT和PC功能与辐射式SVM (RBSVM) 和线性SVM分类器结合EFS.实现的.
关键词:
基于阿特拉斯的特征提取.选择功能选择功能选择.K-最近的邻居.后勤回归的逻辑回归随机的森林随机的森林支持矢量机器的支持矢量机器.

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  • 分析显示,与TDC相比,ADHD个体在15个大脑区域增加了GM体积,在27个大脑区域减少了皮质厚度.
  • 在七个实验设置中探索了不同的特征组合.

    • 结论:

    • 使用结构性MRI和个人特征数据,可以实现自动化ADHD分类.
    • 用SVM变体和EFS分析皮层厚度和个人特征,显示出ADHD诊断的前景.
    • 观察到的大脑结构差异 (GM体积增加,CT体积减少) 提供了ADHD的潜在生物标志物.