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相关概念视频

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

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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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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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基于fMRI的多类DMDC模型有效解读ASD和ADHD之间的重叠.

Zahra Zolghadr1, Seyed Amir Hossein Batouli2, Hamid Alavi Majd1

  • 1Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Basic and clinical neuroscience
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概括
此摘要是机器生成的。

一个新的机器学习模型使用神经成像数据准确地区分注意力缺陷多动障碍 (ADHD),自闭症谱系障碍 (ASD) 和健康个体. 这种计算方法为神经发育障碍提供了更好的诊断能力.

关键词:
这是一个ADHD-200型的ADHD-200型.注意缺陷多动障碍 (ADHD) 是一种注意缺陷多动障碍.自闭症 自闭症 自闭症自闭症大脑成像数据交换 (ABIDE)分类 分类 分类 分类.功能连接性的功能连接性.高维的低样本大小的样本大小.数据最大分散分类器 (DMDC)功能磁力共振成像 (fMRI) 是一种

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科学领域:

  • 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.
  • 机器学习 机器学习

背景情况:

  • 神经发育障碍,如ADHD和ASD共享重叠的症状,使诊断和治疗复杂化.
  • 目前的诊断方法依赖于行为,由于症状重叠,行为可能不准确.
  • 神经成像为客观的诊断工具提供了潜力.

研究的目的:

  • 开发和评估一个计算框架,以使用功能神经成像数据来区分ADHD,ASD和健康对照.
  • 为了比较新型数据最大分散分类器 (DMDC) 与支持矢量机器 (SVM) 的性能.

主要方法:

  • 应用了一个双层多类数据最大分散分类器 (DMDC) 算法.
  • 利用来自ADHD-200和ABIDE的功能神经成像数据集.
  • 根据功能连接值将参与者分为ADHD,ASD或健康的对照组.

主要成果:

  • DMDC模型的整体准确率为62%,健康对照的具体准确率为51%,ASD的61%,ADHD的84%.
  • SVM模型对健康对照 (46%) 和ASD (46%) 的准确性较低,但与ADHD的准确性 (84%) 相匹配.
  • DMDC模型表现出优越的歧视能力,特别是对于ASD群体.

结论:

  • 新的DMDC方法在分类神经发育障碍和健康个体方面显示了可接受的表现,在某些方面表现优于SVM.
  • 功能连接模式,特别是涉及小脑的功能连接模式,是这些疾病的关键歧视因素.