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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 24, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
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适应性时空神经网络用于使用功能性fMRI识别ADHD.

Bo Qiu1, Qianqian Wang2, Xizhi Li1

  • 1School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China.

Frontiers in neuroscience
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ASTNet,这是一种用于识别注意力缺陷多动障碍 (ADHD) 的新型神经网络,使用静止状态功能磁共振成像 (rs-fMRI). ASTNet有效地捕获全球大脑活动模式,以改善ADHD分类.

关键词:
适应性学习是一种适应性学习.动态功能连接的功能连接.功能磁力共振成像 (fMRI) 是一种当地和全球进化模式的发展模式.时间依赖性时间依赖性

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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
  • 人工智能的人工智能

背景情况:

  • 计算机辅助诊断对于注意力缺陷多动障碍 (ADHD) 的识别至关重要.
  • 静止状态功能磁共振成像 (rs-fMRI) 的动态功能连接 (dFC) 分析捕捉了大脑活动异常,但往往忽略了全球时间模式和自适应性学习.

研究的目的:

  • 利用rs-fMRI数据开发一种用于ADHD识别的先进方法.
  • 通过整合全球动态进化和适应性学习来解决现有的dFC方法的局限性.

主要方法:

  • 为ADHD分类提出了一个自适应的时空神经网络 (ASTNet).
  • 使用非重叠的滑动窗口来分割rs-fMRI时间序列.
  • 实现了自适应功能连接生成 (AFCG) 来建模空间关系和时间依赖挖掘 (TDM) 模块来捕捉全球时间动态.

主要成果:

  • 与现有方法相比,ASTNet在自动化ADHD分类中表现出优异的表现.
  • 提出的方法有效地捕捉了大脑活动中的空间关系和全球时间依赖.

结论:

  • 开发的ASTNet通过rs-fMRI分析在ADHD识别方面取得了重大进展.
  • 由于ASTNet能够建模适应性和全球动态大脑活动模式,提高了分类准确性.