Depression is a well-known disease in the sense that it is a really common psychiatric condition. Most people know someone who went through it or experienced the disease themselves; it affects around one in 15 adults (6.7%) in any given year, and one in six people (16.6%) will experience depression at some point in their lifetime. Although depression can occur at any time, it usually first appears during the late teens to mid-20s (Torres, 2020).
This article will go into two emerging technologies that are being applied in different ways to fight the disease.
AI
Fortunately, there is a huge range of available medications for depression treatment. However, many patients don’t respond to the first or even second medications they are prescribed. As a result, psychiatrists usually need to take a trial-and-error approach, leading to the delay of finding an effective medication for a certain patient of months and sometimes even years (Hampson, 2022).
While searching for a better solution, some researchers are exploring the application of machine learning to predict to which antidepressant will a specific patient respond to. In a study published in September 2022, researchers describe a new machine learning algorithm that predicts the patient’s response to the antidepressant Sertraline while analyzing its brains electrical activity, with an accuracy of 83.7%.
Maryam Ravan is an associate professor at the New York Institute of Technology (Electrical and Computer Engineering) and took part in the study. She notes that the current method for prescribing treatment for people going through depression is considerably inefficient.
“Frustration with these inefficiencies led our group to determine if quantitative methods based on machine-learning analysis of brain electrical-activity patterns could offer more accurate clinical guidance,” she says. “Our data and that of others suggest that this is indeed the case.”
In the study, researches used electroencephalogram (EEG) data from patients with depression prior to treatment. EEG is a relatively simple test that measures electrical activity in the brain while attaching small, metal discs (electrodes) to the scalp. Data was collected from 228 participants with major depressive disorder, who were then randomly assigned to a placebo or actual treatment with Sertraline.
Maryam Ravan, one of the researches that took part in the study stated that that machine-learning approaches require massive data sets to ensure that results are reliable, and this study was based on a relatively small sample size. “[But], if our algorithms are truly as accurate as we think they are, application in the real world would lead to a great improvement in the efficiency and effectiveness of psychiatric treatment,” she says, specially since portable EEG devices are currently widely available and this usage could easily be implemented without major efforts.
VR
Researchers are also exploring CBT (Cognitive Behavior Therapy) through the use of VR. CBT is characterized by a collection of therapeutic techniques that supports patients in identifying and changing disturbing emotional patterns that have a negative influence on them (Lindner et al., 2019).
This field of study it’s still in it’s early stages, and more than 95% of the papers available on the subject have been published after 2017 (Baghaei et al., 2021). However, most of the recent studies demonstrated that VR can be an effective treatment for supporting the patients in their journey against depression and anxiety and recommended VR as a viable tool to be used in a clinical environment (Baghaei et al., 2021).
It’s clear that new technologies can be applied in a variety of fields and on each wave of improvement can have new applications that were not envisioned before. Using them to address critical issues that affect the quality of life of a large number of the population is a really good way to continue advancing with them, pursuing more and more solutions.
References
Oakley, T., Coskuner, J., Cadwallader, A., Ravan, M. and Hasey, G. (2022). EEG biomarkers to predict response to sertraline and placebo treatment in major depressive disorder. IEEE Transactions on Biomedical Engineering, pp.1–11. doi:10.1109/TBME.2022.3204861.
Torres, F. (2020). What Is Depression? [online] Psychiatry. Available at: https://www.psychiatry.org/patients-families/depression/what-is-depression.
Hampson, M. (2022). AI Can Offer Insight into Who Responds to Anti-Depressants. [online] IEEE Spectrum. Available at: https://spectrum.ieee.org/at-last-insight-into-who-responds-to-anti-depressants [Accessed 15 Oct. 2022].
Baghaei, N., Chitale, V., Hlasnik, A., Stemmet, L., Liang, H.-N. and Porter, R. (2021). Virtual Reality for Supporting the Treatment of Depression and Anxiety: Scoping Review. JMIR Mental Health, [online] 8(9), p.e29681. doi:10.2196/29681.
Lindner, P., Hamilton, W., Miloff, A. and Carlbring, P. (2019). How to treat depression with low-intensity virtual reality interventions: Perspectives on translating cognitive behavioral techniques into the virtual reality modality and how to make anti-depressive use of virtual Reality–Unique experiences. Frontiers in Psychiatry, [online] 10. doi:10.3389/fpsyt.2019.00792.
I really like that you chose this topic, very interesting point of discussion these days, especially among scientists who are trying to find ways to treat neuronal problems. I read a similar article discussing about the progress made with an application which not only analyzes brain activity, and that is during the patient’s sleep, but it also interacts with its electrical activity. I believe the evolution of such applications is promising as it gives experts more insights into the science of the brain. You can find the idea pitched here: https://fb.watch/gd4umTd9D5/