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Attention-Deficit/Hyperactivity Disorder01:30

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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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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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编码和解码大脑动态功能连接,用于ADHD诊断.

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    概括
    此摘要是机器生成的。

    BRAINMAP是一种新的方法,增强了大脑动态功能连接 (FC) 分析,以改善注意力缺陷多动症 (ADHD) 检测. 它解决了FC建模中的关键挑战,导致更准确的诊断生物标志物.

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    科学领域:

    • 神经成像是一种神经成像.
    • 计算神经科学是一种神经科学.
    • 机器学习 机器学习

    背景情况:

    • 动态功能连接 (FC) 变化与认知功能相关.
    • 对于动态FC,传统的滑窗技术面临着诸如分布式转移和高维度等挑战.
    • 精确建模动态FC对于理解大脑功能和诊断神经系统疾病至关重要.

    研究的目的:

    • 介绍BRAINMAP (使用注意力进行INterpretability与Mamba辅助预测的双层表示),一种用于建模动态大脑功能连接的新方法.
    • 解决滑动窗技术的局限性,特别是分布式转移和高维度.
    • 为了提高注意力缺陷多动症 (ADHD) 检测的准确性,使用动态FC.

    主要方法:

    • BRAINMAP使用最佳运输来纠正在滑动窗口上的分布式转移.
    • 它使用图形神经网络 (GNN) 与注意力机制和Mamba块相结合,从功能性MR图像中捕获时空特征.
    • 引入了Top-K滑动窗特征选择算法,以诱导动态FC中的稀疏性.

    主要成果:

    • 与现有的最先进的动态FC模型相比,BRAINMAP在ADHD检测方面表现优越,在三个数据集 (ADHD-200,UCLA,CNI-TLC) 中,准确度提高了3%至12%.
    • 该模型确定了强大的生物标志物,特别是背部注意网络与视觉网络之间的连接.
    • 一项关联研究证实了已识别的生物标志物的临床相关性.

    结论:

    • BRAINMAP在为脑成像分析建模动态功能连接方面取得了重大进展.
    • 拟议的方法有效地解决了动态FC分析中的关键挑战,从而提高了ADHD的诊断准确性.
    • 已识别的生物标志物,如背部注意力视觉网络连接,对于ADHD诊断和理解其潜在的神经机制具有临床意义.