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Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform

  • Haibin Yu
  • Feng Li
  • Tong Wu
  • Rui Li
  • Li Yao
  • Chuanyue Wang
  • Xia Wu
ORIGINAL RESEARCH

Abstract

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.

Keywords

Depression Resting-state functional magnetic resonance imaging Hilbert-Huang transform Hilbert-weighted mean frequency Multivariate receiver operating characteristic analysis 

Notes

Acknowledgements

The authors gratefully acknowledge Beijing Normal University Imaging Center for Brain Research for the contributions in MRI data acquisition.

Funding

This work was supported by the Funds for the general Program of the National Natural Science Foundation of China (61571047, 81471389), Beijing Science and Technology Commission (D121100005012002), Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201403) and CAS Key Laboratory of Mental Health, Institute of Psychology (KLMH2015G06).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Authors and Affiliations

  • Haibin Yu
    • 1
  • Feng Li
    • 2
  • Tong Wu
    • 1
  • Rui Li
    • 3
  • Li Yao
    • 1
    • 4
  • Chuanyue Wang
    • 2
  • Xia Wu
    • 1
    • 4
  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.Beijing Key Laboratory for Mental Disorders, Center of Schizophrenia, Beijing Institute for Brain DisordersBeijing Anding Hospital of Capital Medical UniversityBeijingChina
  3. 3.CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
  4. 4.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina

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