利用二元假设检验方法揭示边缘系统和小脑中关键的多动症生物标志物。

IF 2.6 4区 工程技术 Q1 Mathematics
Ying Chen, Lele Wang, Zhixin Li, Yibin Tang, Zhan Huan
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引用次数: 0

摘要

注意缺陷多动障碍(ADHD)是一种常见的儿童发育障碍。近年来,越来越多的模式识别方法被应用于多动症的神经影像学研究。然而,这些方法往往存在准确性和可解释性有限的问题,阻碍了它们对多动症相关生物标志物的识别。为了解决这些局限性,我们将边缘系统和小脑网络的低频波动幅度(ALFF)结果作为输入数据,并采用二元假设检验框架进行多动症生物标记物的检测。我们对多部位 ADHD-200 数据集的研究结果表明,平均分类准确率为 93%,表明输入脑区在多动症组和对照组之间具有很强的区分能力。此外,我们的方法还确定了丘脑、海马回和小脑Crus 2等关键脑区为生物标记物。总之,这项研究通过使用 ALFF 发现了边缘系统和小脑网络中潜在的多动症生物标志物,实现了高度可信的结果,可为多动症的诊断和治疗提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling critical ADHD biomarkers in limbic system and cerebellum using a binary hypothesis testing approach.

Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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