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Altered Time-Frequency Feature in Default Mode Network of Autism Based on Improved Hilbert-Huang Transform
IEEE Journal of Biomedical and Health Informatics ( IF 6.7 ) Pub Date : 2021-02-01 , DOI: 10.1109/jbhi.2020.2993109
Han Zhang , Rui Li , Xiaotong Wen , Qing Li , Xia Wu

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency feature of the DMN has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective way to explore the time-frequency feature of the brain mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert weight frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN and identifies a potential neuroimaging biomarker of diagnostic reference for ASD.

中文翻译:

基于改进 Hilbert-Huang 变换的自闭症默认模式网络中时频特征的改变

自闭症谱系障碍(ASD)是一种普遍的神经发育障碍,其特征是兴趣受限和重复行为。使用功能性磁共振成像 (fMRI) 对大脑活动的无创测量表明,默认模式网络 (DMN) 中的异常是 ASD 的关键神经基础,但尚未揭示 DMN 的时频特征. Hilbert-Huang 变换 (HHT) 有利于生物医学信号的特征提取,最近被认为是探索大脑机制时频特征的有效方法。在这项研究中,包括 59 名 ASD 参与者和 46 名健康对照 (HC) 参与者在内的 105 名受试者的静息态 fMRI 数据集参与了基于改进的 HHT 和带有标签替换的改进的 k 均值聚类的时频聚类分析。与 HC 相比,ASD 在 DMN 的关键区域,包括内侧前额叶皮层 (MPFC)、后扣带皮层 (PCC) 和前扣带皮层 (ACC) 的高频段中选择性地显示出增强的希尔伯特权重频率 (HWF)。时频聚类分析揭示了 ASD 中 DMN 组织的改变。在后部 DMN 中,PCC 和双侧楔前叶在 HC 中分离,但在 ASD 中聚集;在前部 DMN 中,ASD 的 ACC、背侧 MPFC 和腹侧 MPFC 的簇相对分散。
更新日期:2021-02-01
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