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相关概念视频

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

109
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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Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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相关实验视频

Updated: Jul 17, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
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注意缺陷多动障碍的亚型分类与分层二元假设测试框架.

Yuan Gao1, Huaqing Ni1, Ying Chen2

  • 1College of Information Science and Engineering, Hohai University, Nanjing, People's Republic of China.

Journal of neural engineering
|August 30, 2023
PubMed
概括

这项研究引入了一种新的框架,用于通过大脑连接来诊断注意力缺陷多动症 (ADHD) 亚型,达到97%以上的准确性. 这种方法可以识别关键的大脑连接,以改善ADHD的诊断和治疗.

关键词:
多动症和多动症的亚型测试二元假设测试二元假设测试大脑的功能连接性 功能连接性深度学习是一种深度学习.多类分类是多类分类的分类.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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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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科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 精确诊断注意力缺陷多动症 (ADHD) 亚型对于儿童的有效治疗至关重要.
  • 目前使用MRI数据的机器学习方法在ADHD亚型诊断中达到不到80%的准确性.

研究的目的:

  • 为了提高ADHD亚型诊断的准确性.
  • 为了确定ADHD亚型的可靠生物标志物.
  • 开发一个有效的框架来分类ADHD亚型.

主要方法:

  • 提出了一个分层二元假设测试 (H-BHT) 框架,利用大脑功能连接 (FC) 作为输入.
  • 采用二阶段决策树策略进行亚型分类.
  • 在两个阶段提取了典型的FC,以确定歧视性生物标志物.

主要成果:

  • 在ADHD-200休息状态fMRI数据集上获得了97.1%的平均准确率和0.947的卡帕得分.
  • 在ADHD亚型之间确定了歧视性的功能连接模式.
  • 证明了H-BHT框架在ADHD亚型识别中的有效性.

结论:

  • H-BHT框架为ADHD亚型分类提供了一个高度准确的方法.
  • 已识别的FC模式可以作为ADHD亚型的潜在生物标志物.
  • 这种方法为心理健康的多类分类提供了有价值的参考.