College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, Beijing, 100875, China.
Beijing Key Laboratory for Mental Disorders, Center of Schizophrenia, Beijing Institute for Brain Disorders, Beijing Anding Hospital of Capital Medical University, Beijing, 10088, China.
Brain Imaging Behav. 2018 Dec;12(6):1556-1568. doi: 10.1007/s11682-017-9816-6.
Major depressive disorder is a common disease worldwide, which is characterized by significant and persistent depression. Non-invasive accessory diagnosis of depression can be performed by resting-state functional magnetic resonance imaging (rs-fMRI). However, the fMRI signal may not satisfy linearity and stationarity. The Hilbert-Huang transform (HHT) is an adaptive time-frequency localization analysis method suitable for nonlinear and non-stationary signals. The objective of this study was to apply the HHT to rs-fMRI to find the abnormal brain areas of patients with depression. A total of 35 patients with depression and 37 healthy controls were subjected to rs-fMRI. The HHT was performed to extract the Hilbert-weighted mean frequency of the rs-fMRI signals, and multivariate receiver operating characteristic analysis was applied to find the abnormal brain regions with high sensitivity and specificity. We observed differences in Hilbert-weighted mean frequency between the patients and healthy controls mainly in the right hippocampus, right parahippocampal gyrus, left amygdala, and left and right caudate nucleus. Subsequently, the above-mentioned regions were included in the results obtained from the compared region homogeneity and the fractional amplitude of low frequency fluctuation method. We found brain regions with differences in the Hilbert-weighted mean frequency, and examined their sensitivity and specificity, which suggested a potential neuroimaging biomarker to distinguish between patients with depression and healthy controls. We further clarified the pathophysiological abnormality of these regions for the population with major depressive disorder.
重度抑郁症是一种全球性的常见疾病，其特征是显著且持续的抑郁。可以通过静息态功能磁共振成像(rs-fMRI)对抑郁症进行非侵入性辅助诊断。然而，fMRI 信号可能不满足线性和稳定性。希尔伯特-黄变换(HHT)是一种适用于非线性和非平稳信号的自适应时频局部化分析方法。本研究旨在将 HHT 应用于 rs-fMRI 以找到抑郁症患者的异常脑区。共纳入 35 例抑郁症患者和 37 例健康对照者进行 rs-fMRI 检查。对 rs-fMRI 信号进行 HHT 以提取 Hilbert 加权平均频率，并应用多元接收器工作特征分析找到具有高灵敏度和特异性的异常脑区。我们观察到患者和健康对照组之间 Hilbert 加权平均频率的差异主要出现在右侧海马体、右侧海马旁回、左侧杏仁核以及左右尾状核。随后，将上述区域纳入比较区域同质性和低频波动分数幅度方法的结果中。我们发现了 Hilbert 加权平均频率存在差异的脑区，并检验了其灵敏度和特异性，这提示了一种潜在的神经影像学生物标志物，可以区分抑郁症患者和健康对照者。我们进一步阐明了这些区域的病理生理学异常，为重度抑郁症患者提供了更深入的了解。
