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相关概念视频

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

46
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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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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通过深度学习和动态静止状态fMRI分析增强了ADHD的分类.

MohammadHadi Firouzi1, Kamran Kazemi2, Maliheh Ahmadi3

  • 1Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.

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概括

跳过投票网有效地使用来自脑部扫描的动态功能连接来分类注意力缺陷多动障碍 (ADHD). 这种深度学习模型在识别ADHD,其子类型和区分它与典型发育的儿童方面取得了很高的准确性.

关键词:
注意力缺陷/多动症 (ADHD) 障碍大脑动态功能连接 功能连接深度学习是一种深度学习.大多数投票方式.rs-fMRI 是一个

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Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
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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:

  • 注意缺陷多动性障碍 (ADHD) 在注意力,多动性和冲动性方面提出了挑战.
  • 休息状态功能磁共振成像 (rs-fMRI) 和功能连接性分析显示ADHD分类的希望,但准确性仍然有限.
  • 在ADHD儿童中,功能连接模式的动态变化越来越多.

研究的目的:

  • 引入Skip-Vote-Net,这是一个新的深度学习网络,用于使用动态连接分析对ADHD进行分类.
  • 评估Skip-Vote-Net在将ADHD与典型发育儿童 (TDC) 分类以及ADHD亚型 (ADHDI和ADHDC) 区分方面的表现.
  • 利用来自ADHD-200数据库 (纽约大学数据集) 的rs-fMRI数据进行可靠的分类.

主要方法:

  • 功能连接矩阵是使用Pearson的相关性从重叠的rs-fMRI数据段创建的.
  • 自动化解剖标记 (AAL) 116图谱定义了116个分析感兴趣的区域.
  • 跳过投票网 (Skip-Vote-Net) 是一个具有多数投票机制的深度学习网络,用于分类任务.

主要成果:

  • 跳过投票网实现了高分类准确率:97% (平衡) 和97.7% (不平衡) 的ADHD与TDC.
  • 区分ADHD亚型的准确性非常高:ADHDI与ADHDC的区别为99.4%.
  • 三类分类 (ADHDI,ADHDC,TDC) 的平均准确率为98.86%.

结论:

  • 与现有的ADHD分类方法相比,Skip-Vote-Net表现出更高的性能.
  • 提出的深度学习方法显示出作为ADHD及其亚型的有效诊断工具的巨大潜力.
  • 使用Skip-Vote-Net的动态连接分析为在儿童中识别ADHD提供了一个有希望的途径.