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Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder

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: Jul 7, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

动态功能连接分析与时间卷积网络用于注意力缺陷/多动症障碍识别.

Mingliang Wang^1,2,3, Lingyao Zhu¹, Xizhi Li¹

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

Frontiers in neuroscience

|December 27, 2023

概括

这项研究引入了一种新的深度学习模型,TDNet,用于分析静止状态fMRI数据中的动态功能连接 (dFC),以改进注意力缺陷/多动症障碍 (ADHD) 的识别. TDNet有效地捕捉了长距离的时间模式,优于现有的方法.

关键词:

注意力缺陷/多动症障碍.动态的特征动态的特征功能连接性的功能连接性时间卷积网络时间依赖,时间依赖.

更多相关视频

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

相关实验视频

Last Updated: Jul 7, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

科学领域:

神经成像是一种神经成像.
机器学习机器学习
精神病学是一个精神病学.

背景情况:

静止状态fMRI (rs-fMRI) 中的动态功能连接 (dFC) 提供了对随着时间的推移大脑活动异常的见解.
在注意力缺陷/多动症 (ADHD) 中用于dFC分析的现有的深度学习方法往往忽略了长距离的时间依赖.

研究的目的:

提出一种新的时间依赖神经网络 (TDNet),用于在rs-fMRI数据中增强FC表示学习和时间依赖跟踪.
通过分析动态大脑活动模式,实现ADHD的自动和准确识别.

主要方法:

rs-fMRI时间序列被细分,一个FC生成模块为每个细分段创建有区别的动态FC.
使用具有扩展卷积的时间卷积网络 (TCN) 来捕捉长距离的时间依赖.
完全连接的层被用于最终的疾病预测.

主要成果:

纳入 rs-fMRI 数据的动态特征显著提高了诊断性能.
数据驱动的动态FC网络比传统的基于Pearson相关性的方法更具信息性.
提议的TDNet模型在ADHD-200数据库上的最先进的方法相比,在ADHD识别方面表现优越.

结论:

使用TDNet的动态功能连接分析为ADHD识别提供了更有效的方法.
TDNet模型捕捉长距离时间模式的能力对于理解与ADHD相关的大脑活动异常至关重要.
这种数据驱动的方法为ADHD的自动诊断提供了一个有希望的工具.