How EEG Technology Helps Patients with Depression

The High Cost of Depression

In addition to resulting in 800,000 yearly deaths, depression is one of the most significant global causes of disability (WHO, 2020).

Depression also creates a disease burden, negatively impacting quality of life. In a study on quality of life expectancy, 18-year-olds with depression had nearly 29 fewer quality years than healthy adults (Jia et al., 2015).

Lifetime prevalence of depression, or the percentage of people that will experience depression at some point in their lifespan, is about 21%, with most people receiving the diagnosis experiencing symptoms that are moderate to severe. Further, less than 70% of people with depression are treated at all (Hasin et al., 2018). Some barriers to accessing appropriate care include lack of qualified providers, stigma around mental illness, and diagnostic inaccuracy (WHO, 2020).

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EEGs Have the Potential to Improve Current Assessment Practices

As previously discussed, the diagnostic accuracy of healthcare providers who are not trained mental health professionals was found to be about as good as random chance in identifying a depressed patient (Mitchell et al., 2009).

As most patients receive treatment for depression through a primary care provider, poor diagnostic accuracy leaves patients with the odds stacked against them from the beginning (Frank et al., 2003).

To put this statistic into perspective, diagnostic accuracy of this level would be viewed as substandard and unacceptable for virtually any other medical condition.

Globally, mental health research is underfunded, receiving about 4% of the entirety of available research funding (Woelbert et al., 2021). Most research in mental health does not focus on novel means of diagnosing mental health disorders or even on improving diagnostic accuracy, and thus clinicians may lack awareness of the problematic nature of current diagnostic practices. Further, there is likely little incentive to participate in research of this nature. This limited motivation may result from an overreliance on pharmaceuticals for treatment of mental health disorders, which makes diagnostic accuracy somewhat unnecessary as well as unprofitable. Medication is intended to manage symptoms; however, it is not necessarily created with the intent of treating a specific disorder encompassing a constellation of symptoms. Thus, there is little profit to be realized from accurately identifying a diagnosis.

The reason for the lack of outrage about this potentially unethical practice is likely twofold.

Many mental health disorders share significant symptom overlap as well as heterogeneity within each type of diagnosis (Doherty & Owen, 2014).

Further, when research is conducted on specific mental health disorders, structured diagnostic interviews (SDIs) are frequently used to identify participants (Rettew et al., 2009). However, due to difficulty with implementation in everyday practice, mental health professionals use clinical evaluations instead of these lengthy formal interviews.

This limits applicability of existing research to practice, considering that SDIs and clinical evaluations were found to correlate at only low to moderate levels (Rettew et al., 2009).

An additional challenge to diagnostic accuracy lies in the nature of diagnoses as somewhat arbitrary constructs

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Incorporating EEGs into diagnostic assessment may mitigate diagnostic challenges in a variety of ways. First, EEGs could aid in standardizing depression assessment if used widely. Should screening for depression with EEGs become standard practice, this method of detection can be integrated into routine medical exams. Further, technology that increases accessibility also

reduces barriers. Patients are likely to be more agreeable to diagnostic testing that is perceived as a part of medical care, rather than an additional test specific to mental health they must first request. Screening with EEGs also has the potential to reduce cultural bias that is an unavoidable component of all human interactions.

Cultural Considerations

Cultural factors influence how people experience and express depression, and this can impact diagnosis and treatment.

For example, people who identify more strongly with collectivist norms are more likely to report physical symptoms and suppress emotional symptoms (Chang et al., 2016).

These differences alter clinical presentation and may influence a clinician’s assessment.

EEG screenings may potentially mitigate cultural and language issues presented by applying paper and pencil measures originally written in English or developed in Western cultures to patients of diverse cultural and language backgrounds.

However, some evidence suggests that patterns in EEG features may be influenced by cultural or genetic factors as well (e.g., Alahmadi et al., 2016). Additionally, these researchers noted that care must be taken when using databases to interpret EEGs, and that comparative data align not only with the patient’s demographic variables such as age, gender, and socioeconomic status, but also consider relevant cultural factors.

Referenses learn more

Alahmadi, N., Evdokimov, S. A., Kropotov, Y., Müller, A. M., & Jäncke, L. (2016). Different resting state EEG features in children from Switzerland and Saudi Arabia. Frontiers in Human Neuroscience, 10, 559. doi:10.3389/fnhum.2016.00559
Chang, M. X., Jetten, J., Cruwys, T., & Haslam, C. (2016). Cultural identity and the expression of depression: A social identity perspective. Journal of Community & Applied Social Psychology. doi: 10.1002/casp.2291
Frank, R. G., Huskamp, H. A., & Pincus, H. A. (2003). Aligning incentives in the treatment of depression in primary care with evidence-based practice. Psychiatric Services, 54(5), 682-687. doi:10.1176/
Doherty, J. L., & Owen, M. J. (2014). Genomic insights into the overlap between psychiatric disorders: Implications for research and clinical practice. Genome Medicine, 6(24), 29. doi:10.1186%2Fgm546
Hasin, D. S., Sarvet, A. L., & Meyers, J. L. (2018). Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry, 75(4), 336-346. doi:10.1001/jamapsychiatry.2017.4602
Jia, H., Zack, M. M., Thompson, W. W., Crosby, A. E., & Gottesman, I. I. (2015). Impact of depression on quality-adjusted life expectancy (QALE) directly as well as indirectly through suicide. Social Psychiatry and Psychiatric Epidemiology. 50(6), 939-949. doi:10.1007%2Fs00127-015-1019-0
Mitchell, A. J., Vaze, A., & Rao, S. (2009). Clinical diagnosis of depression in primary care: A meta-analysis. Lancet, 374(9690), 609-619. doi:10.1016/s0140-6736(09)60879-5
Rettew, D. C., Lynch, A. D., Achenbach, T. M., Dumenci, L., & Ivanova, M. Y. (2009). Meta-analyses of agreement between diagnoses made from clinical evaluations and standardized diagnostic interviews. International Journal of Methods in Psychiatric Research, 18(3), 169-184. doi:10.1002/mpr.289
Woelbert, E., Lunell-Smith, K., White, R., & Kemmer, D. (2021). Accounting for mental health research funding: Developing a quantitative baseline of global investments. The Lancet Psychiatry, 8(3), 250-258. doi:10.1016/S2215-0366(20)30469-7
World Health Organization. (2020, January 30). Depression. Retrieved from