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相关概念视频

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

55
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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相关实验视频

Updated: Jun 25, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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基于机器学习的特征选择,以使用全脑白质微观结构识别注意力缺陷多动症:一项纵向研究.

Huey-Ling Chiang1, Chi-Shin Wu2, Chang-Le Chen3

  • 1Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei City, Taiwan; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.

Asian journal of psychiatry
|May 31, 2024
PubMed
概括

机器学习确定了区分注意力缺陷/多动障碍 (ADHD) 的关键白质微观结构特征. 特定脑道的发育变化对于ADHD的识别至关重要,并且可能与认知能力的改善相关.

关键词:
更多关于 ADHD ADHD 的文章扩散光谱成像成像技术纵向研究是一项纵向研究.机器学习就是机器学习.白物质微观结构 白物质微观结构

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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相关实验视频

Last Updated: Jun 25, 2025

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12:21

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科学领域:

  • 神经成像是一种神经成像.
  • 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.

背景情况:

  • 注意缺陷/多动症 (ADHD) 诊断依赖于行为症状,缺乏客观生物标志物.
  • 白质微观结构的改变与ADHD病理生理学有关.

研究的目的:

  • 使用机器学习识别独特的白质微观结构特征,使ADHD与典型的发展控制 (TDC) 有区别.
  • 评估在ADHD中基线和纵向白质变化的诊断价值.

主要方法:

  • 扩散光谱成像 (DSI) 在两个时间点对51名ADHD患者和60名TDC患者进行.
  • 评估了三种机器学习模型:基线特征,组合时间点和包括变化速率在内的特征.
  • 采用随机森林算法进行分类.

主要成果:

  • 结合纵向变化和基线特征的模型实现了最高的分类性能 (AUC = 0.73).
  • 关键的区别特征包括每年变化的相对速率,如上纵和前端插入管道.
  • 某些区域更快的变化率与视觉注意力和记忆功能的改善有关.

结论:

  • 白质微观结构及其发育轨迹为ADHD提供了重要的诊断价值.
  • 这些发现突出了神经成像生物标志物的潜力,用于客观地识别ADHD并了解发育偏差.