# Godot is not coming: when we will let innovations enter psychiatry?

**Authors:** Milena B. Čukić  
**arXiv:** [2110.12873](https://arxiv.org/abs/2110.12873)  
**Date:** 2021-10-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2110.12873v1/

## Key points

- Current diagnostic practices in psychiatry do not rely on objective biophysical evidence
- The recent pandemic has highlighted the need to address the rising number of mood disorders, particularly depression, in a more efficient way
- Detection based on electrophysiological signals such as electroencephalogram and electrocardiogram-based methods can improve the diagnostic process
- Electromagnetic stimulation such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have been proven to ameliorate symptoms of depression
- Physiological complexity studies and advanced statistical methods like machine learning and Bayesian inference approach can be used to connect these practices with explanations
- Fractal and nonlinear measures can adequately quantify previously undetected changes in intrinsic dynamics of physiological systems providing the basis for early detection of depression
- Early screening of cardiovascular risks in depression is advocated for
- Innovations should enter psychiatry sooner rather than later since many other neuroimaging techniques have already been accepted into clinical practice over time while some still await approval even though they were discovered more than 50 years ago.

## Summary

In the field of psychiatry, current diagnostic practices do not rely on objective biophysical evidence. However, the recent pandemic has highlighted the need to address the rising number of mood disorders, particularly depression, in a more efficient way. To improve the diagnostic process, several already developed practices can be utilized. These include detection based on electrophysiological signals such as electroencephalogram and electrocardiogram-based methods that have been shown to be accurate for clinical practice. Additionally, various modalities of electromagnetic stimulation such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have been proven to ameliorate symptoms of depression. In this work by Milena B. Čukić from the Institute for Technology of Knowledge at Complutense University in Madrid, Spain; General Physiology with Biophysics Dpt., School of Biology at Belgrade University in Belgrade, Republic of Serbia; and EGA B.V., Amsterdam, the Netherlands; these practices are connected with explanations coming from physiological complexity studies and advanced statistical methods like machine learning and Bayesian inference approach. Fractal and nonlinear measures can adequately quantify previously undetected changes in intrinsic dynamics of physiological systems providing the basis for early detection of depression. The author also advocates for early screening of cardiovascular risks in depression which is connected to previously described decomplexification of the autonomous nervous system resulting in clinically recognized symptoms. Additional information about the level of complexity can help clinicians make better decisions in the therapeutic process thereby increasing overall effectiveness treatment leading to an increase in quality life for patients. The author argues that innovations should enter psychiatry sooner rather than later since many other neuroimaging techniques have already been accepted into clinical practice over time while some still await approval even though they were discovered more than 50 years ago.