Quantitative electroencephalographic biomarkers behind major depressive disorder. (July 2021)

• Record Type: Journal Article
• Authors: Knociková, Juliana A.; Petrásek, Tomáš
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: EEG -- Spectral analysis -- Wavelet transformation -- Nonlinear dynamics -- Entropy -- Major depressive disorder; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102596
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Major depressive disorder (MDD) is a severe psychiatric condition with increasing incidence. Diagnostics and development of novel therapeutic approaches are, however, hampered by the lack of reliable quantitative biomarkers enabling prediction of clinical outcomes. EEG is considered as an optimal source of such data due to its broad availability, but traditional power spectral analysis was not designed for complex non-stationary EEG recordings with nonlinear nature, and therefore often fails as a diagnostic and prognostic tool for MDD. As brain activity is a highly complex, nonlinear and mostly irregular system, it can best be explained using the measures of multiple time-frequency resolution, especially the wavelet analysis, chaos theory and methods of nonlinear dynamics, such as fractal dimension or entropy. This non-conventional approach has proven to be highly sensitive to specific alterations of brain dynamics related to MDD. In this review, we consider the neurophysiological correlates of MDD, describe the different analytical approaches, ranging from the traditional ones to the highly innovative, and discuss their diagnostic relevance and practical utility. Our aim is to provide a current view of the complex determinants related to brain activity under MDD, and emphasize the importance of interdisciplinary approaches to neurophysiological signal processing.