Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform
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.
KeywordsDepression Resting-state functional magnetic resonance imaging Hilbert-Huang transform Hilbert-weighted mean frequency Multivariate receiver operating characteristic analysis
The authors gratefully acknowledge Beijing Normal University Imaging Center for Brain Research for the contributions in MRI data acquisition.
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.
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 was obtained from all individual participants included in the study.
- Anand, A., Li, Y., Wang, Y., Gardner, K., & Lowe, M. J. (2007). Reciprocal effects of antidepressant treatment on activity and connectivity of the mood regulating circuit: an FMRI study. The Journal of Neuropsychiatry and Clinical Neurosciences, 19(3), 274–282.CrossRefPubMedPubMedCentralGoogle Scholar
- Bluhm, R., Williamson, P., Lanius, R., Théberge, J., Densmore, M., Bartha, R., … & Osuch, E. (2009). Resting state default-mode network connectivity in early depression using a seed region-of-interest analysis: decreased connectivity with caudate nucleus. Psychiatry and Clinical Neurosciences, 63(6), 754–761.CrossRefPubMedGoogle Scholar
- Donnelly, D. (2006). The fast Fourier and Hilbert-Huang transforms: a comparison. Computational Engineering in Systems Applications, IMACS Multiconference on (Vol. 1, pp. 84–88). IEEE.Google Scholar
- Dunn, R. T., Kimbrell, T. A., Ketter, T. A., Frye, M. A., Willis, M. W., Luckenbaugh, D. A., & Post, R. M. (2002). Principal components of the Beck Depression Inventory and regional cerebral metabolism in unipolar and bipolar depression. Biological Psychiatry, 51(5), 387–399.CrossRefPubMedGoogle Scholar
- El Khouli, R. H., Macura, K. J., Barker, P. B., Habba, M. R., Jacobs, M. A., & Bluemke, D. A. (2009). Relationship of temporal resolution to diagnostic performance for dynamic contrast enhanced MRI of the breast. Journal of Magnetic Resonance Imaging, 30(5), 999–1004.CrossRefPubMedPubMedCentralGoogle Scholar
- Ferenci, P., Lockwood, A., Mullen, K., Tarter, R., Weissenborn, K., & Blei, A. T. (2002). Hepatic encephalopathy—definition, nomenclature, diagnosis, and quantification: final report of the working party at the 11th World Congresses of Gastroenterology, Vienna, 1998. Hepatology, 35(3), 716–721.CrossRefPubMedGoogle Scholar
- Guo, W. B., Liu, F., Chen, J. D., Gao, K., Xue, Z. M., Xu, X. J., … & Chen, H. F. (2012). Abnormal neural activity of brain regions in treatment-resistant and treatment-sensitive major depressive disorder: a resting-state fMRI study. Journal of Psychiatric Research, 46(10), 1366–1373.CrossRefPubMedGoogle Scholar
- Huang, M., Wu, P., Liu, Y., Bi, L., & Chen, H. (2008). Application and contrast in brain-computer interface Between hilbert-huang transform and wavelet transform. In Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for (pp. 1706–1710). IEEE.Google Scholar
- Huang, N. E. (2014). Hilbert-Huang transform and its applications (Vol. 16). World Scientific.Google Scholar
- Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. The Royal Society.CrossRefGoogle Scholar
- Huang, N. E., & Wu, Z. (2008). A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics, 46(2).Google Scholar
- Kempton, M. J., Salvador, Z., Munafò, M. R., Geddes, J. R., Simmons, A., Frangou, S., & Williams, S. C. (2011). Structural neuroimaging studies in major depressive disorder: meta-analysis and comparison with bipolar disorder. Archives of General Psychiatry, 68(7), 675–690.CrossRefPubMedGoogle Scholar
- Kenny, E. R., O’Brien, J. T., Cousins, D. A., Richardson, J., Thomas, A. J., Firbank, M. J., & Blamire, A. M. (2010). Functional connectivity in late-life depression using resting-state functional magnetic resonance imaging. The American Journal of Geriatric Psychiatry, 18(7), 643–651.CrossRefPubMedGoogle Scholar
- Liu, Z., Xu, C., Xu, Y., Wang, Y., Zhao, B., Lv, Y., … & Du, C. (2010). Decreased regional homogeneity in insula and cerebellum: a resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Research: Neuroimaging, 182(3), 211–215.CrossRefPubMedGoogle Scholar
- McIntyre, R. S., Harrison, J., Loft, H., Jacobson, W., & Olsen, C. K. (2016). The effects of vortioxetine on cognitive function in patients with major depressive disorder: a meta-analysis of three randomized controlled trials. International Journal of Neuropsychopharmacology, 19(10).Google Scholar
- Otte, C., Gold, S. M., Penninx, B. W., et al. (2016). Major Depressive Disorder. Nature Reviews Disease Primers, 2,(16065).Google Scholar
- Pizzagalli, D. A., Holmes, A. J., Dillon, D. G., Goetz, E. L., Birk, J. L., Bogdan, R., … & Fava, M. (2009). Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. American Journal of Psychiatry, 166(6), 702–710.CrossRefPubMedPubMedCentralGoogle Scholar
- Song, X., Zhang, Y., & Liu, Y. (2014). Frequency specificity of regional homogeneity in the resting-state human brain. PloS One, 9(1), e86818.Google Scholar
- Song, X., Zhou, S., Zhang, Y., Liu, Y., Zhu, H., & Gao, J. H. (2015). Frequency-dependent modulation of regional synchrony in the human brain by eyes open and eyes closed resting-states. PloS One, 10(11), e0141507.Google Scholar
- Surhone, L. M., Tennoe, M. T., Henssonow, S. F., & Cauchy, A. L. (2013). Cauchy Principal Value. Betascript Publishing.Google Scholar
- Tahmasian, M., Knight, D. C., Manoliu, A., Schwerthöffer, D., Scherr, M., Meng, C., … & Drzezga, A. (2013). Aberrant intrinsic connectivity of hippocampus and amygdala overlap in the fronto-insular and dorsomedial-prefrontal cortex in major depressive disorder. Frontiers in Human Neuroscience, 7.Google Scholar
- Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289.CrossRefPubMedGoogle Scholar
- Van Someren, E. J. (2011). Slow brain oscillations of sleep, resting state and vigilance (Vol. 193). Elsevier.Google Scholar
- Zou, Q. H., Zhu, C. Z., Yang, Y., Zuo, X. N., Long, X. Y., Cao, Q. J., … & Zang, Y. F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141.CrossRefPubMedPubMedCentralGoogle Scholar