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使用fMRI时间序列和功能连接用于自闭症分类:将Mamba和KAN集成到域对抗神经网络中.

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    本研究引入了一种新方法,使用Domain Adversarial Neural Network (DANN) 与Mamba和Kolmogorov-Arnold Network (KAN) 模型,通过减少域偏差来改善fMRI数据的自闭症分类.

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    科学领域:

    • 神经科学是一个神经科学.
    • 机器学习 机器学习
    • 生物医学工程 生物医学工程

    背景情况:

    • 功能性磁共振成像 (fMRI) 分析用于自闭症分类通常受到域偏差的阻碍.
    • 这些偏见会对诊断模型的准确性和可靠性产生负面影响.
    • 现有的方法可能无法充分解决神经成像数据领域转移的挑战.

    研究的目的:

    • 开发一种新的自闭症分类管道,使用fMRI数据来缓解域诱导的偏见.
    • 为了提取域不变特征,这些特征仍然对分类具有信息意义.
    • 提高自闭症诊断模型的稳定性和通用性.

    主要方法:

    • 采用了域对抗神经网络 (DANN) 架构,将Mamba集成为fMRI时间序列和Kolmogorov-Arnold网络 (KAN) 进行功能连接.
    • DANN框架包括一个提取器,一个域名分类器和一个标签分类器,经过对抗训练.
    • 提取器内的并行处理路径使用了Mamba和KAN,其特征用于分类.

    主要成果:

    • 拟议的方法实现了72.56%的精度和72.46%的曲线下面积 (AUC).
    • 实验结果表明与不使用表型信息的最先进方法可比.
    • 反对训练成功地确保了提取的特征的域不变性.

    结论:

    • 这种新型管道有效地减少了基于fMRI的自闭症分类中的域诱导偏见.
    • 在DANN框架内整合Mamba和KAN提供了一个强大的解决方案.
    • 这种方法显着有望提高使用神经成像数据诊断自闭症的临床相关性和准确性.