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Inicio Revista de Psiquiatría y Salud Mental (English Edition) Another Godot who is still not coming: More on biomarkers for depression
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Vol. 15. Núm. 2.
Páginas 153-154 (abril - junio 2022)
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Vol. 15. Núm. 2.
Páginas 153-154 (abril - junio 2022)
Letter to the Editor
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Another Godot who is still not coming: More on biomarkers for depression
Otro Godot que todavía no viene: más sobre los biomarcadores de depresión
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277
Milena Čukića,b,
Autor para correspondencia
micukic@ucm.es

Corresponding author.
, Danka Savićc
a Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, Spain
b 3EGA B.V., Amsterdam, The Netherlands
c Vinča Institute for Nuclear Physics, Laboratory of Theoretical and Condensed Matter Physics 020/2, University of Belgrade, Belgrade, Serbia
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Dear Editor,

The July 2021 Editorial inspired us to add another “Godot” to the list. As the Author pointed out1changing clinical guidelines in the traditionally biomarker-aversive field of psychiatry is not an easy step-indeed. That so well applies to depression research, too.

The knowledge from decades-long research in complex systems dynamics offers tools for extracting information from electrophysiological signals (ECG, EEG, etc.). These tools provide high accuracy of detection of irregularities by quantifying subtle changes in signal patterns, using nonlinear measures, like different forms of statistical entropy (Shannon entropy, approximate entropy, sample entropy, multiscale entropy, etc.) or fractal dimension measures (Higuchi fractal dimension, detrended fluctuation analysis-DFA). Nonlinear parameters, like different forms of statistical entropy or fractal measures, calculated from electrophysiological signals (e.g., EEG or ECG), are demonstrated to be predictive of many psychiatric disorders and their phases. Beside diagnostics, complex system analysis can be used for monitoring therapy results (or forecasting responders to medication or other modalities of therapy like repetitive transcranial magnetic stimulation). Based on this analytic approach it is possible not only to accurately confirm depression, but also delineate between phases of disease (episode vs remission, like in2), differentiate between subtypes (melancholic vs non-melancholic depression), comorbidities, or even detect existing suicide risk.3 Knowing those additional information early in the process can help in effectively choosing the therapy that increases the probability that the patient would recover and avoid relapses. Pincus4 stresses the importance of dynamics of the systems, which requires a quantifier that is sensitive to the order of events in time series, for example, approximate entropy (ApEn). There is a lot of research demonstrating that nonlinear measures are much more accurate and reliable than the conventional ones in analyzing history sensitive systems.5 Widely used Fourier transform that is embedded in any software in any operating machine made to record electrophysiological signals, is proven to be redundant to fractal analysis6 and it is known to be not sensitive to detect early changes in the signal unlike other fractal and nonlinear methods.7

Perhaps especially urgent is detecting cardiovascular diseases (CVD) in people suffering from depression. The connection between these two diseases that carries a high mortality risk8 has long been known9 and yet monitoring heart function in depressive patients is far from clinical routine. The data can be easily obtained by novel portable ECG monitoring devices that are approved as medical-grade signal quality equivalent to holter, but are much more practical and comfortable to use by the patient her-/himself, leading to early detection of risks and potentially to personalized medicine at its very best. The data can then be processed by a combination of nonlinear analytics and advanced statistical procedures (to control, for example, for comorbidities, subtypes and other confounding factors10). Even better, the analysis can be empowered with machine learning applications11 that are widely in use due to high power of computation and cloud computing.

This process is neither costly nor invasive, so, why wait to save lives?

References
[1]
E. Fernández-Egea.
Waiting for Godot or the use of biomarkers in clinical practice.
Revista de Psiquiatría y Salud Mental (Barcelona), 14 (2021), pp. 123-124
[2]
M. Čukić, M. Stokić, S. Radenković, M. Ljubisavljević, S. Simić, D. Savić.
Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression.
Int J Res Meth Psychiatry, (2019),
[3]
A.H. Khandoker, V. Luthra, Y. Abouallaban, S. Saha, K.I. Ahmed, R. Mostafa, et al.
Predicting depressed patients with suicidal ideation from ECG recordings.
Med Biol Eng Comput, 55 (2017), pp. 793-805
[4]
S.M. Pincus.
Quantitative assessment strategies and issues for mood and other psychiatric serial study data.
Bipolar Disord, 5 (2003), pp. 287-294
[5]
A.L. Goldberger, C.K. Peng, L.A. Lipsitz.
What is physiologic complexity and how does it change with aging and disease?.
Neurobiol Aging, 23 (2002), pp. 23-26
[6]
A. Kalauzi, T. Bojić, A. Vuckovic.
Modeling the relationship between Higuchi's fractal dimension and Fourier spectra of physiological signals.
Med Biol Eng Comput, 50 (2012), pp. 689-699
[7]
W. Klonowski.
From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine.
Nonlinear Biomed Phys, 1 (2007), pp. 5
[8]
A.K. Dhar, D.A. Barton.
Depression and the link with cardiovascular disease.
Front Psychiatry, (21 March 2016),
[9]
Rottenberg J. Cardiac vagal control in depression: a critical analysis. Biol Psychol, 74, 200–11. http://dx.doi.org/10.1016/j.biopsycho.2005.08.010. Epub 2006 Oct 12.
[10]
Kemp AH, Quintana DS, Quinn CR, Hopkinson P, Harris AW. Major depressive disorder with melancholia displays robust alterations in resting state heart rate and its variability: implications for future morbidity and mortality. Front Psychol, 5, 1387. http://dx.doi.org/10.3389/fpsyg.2014.01387.
[11]
M. Čukić, M. Stokić, S. Simić, D. Pokrajac.
The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.
Cogn Neurodyn, 14 (2020), pp. 443-455

Editorial in Psychiatry and Mental Health, Elsevier.

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