History

The relative lack of objective test results remains one of the most significant disadvantages facing healthcare professionals providing treatment to individuals with mental illness. Unlike other medical fields, psychiatrists do not generally utilize psychiatrists do not generally utilize physiological tests for diagnosis (Schiller, 2019).

Both therapy and medication management are dependent on accurate diagnosis, which historically has been reliant on somewhat clinical judgment, as well as the patient’s own description of their symptoms.

Access to objective testing would serve three purposes

01

Would reduce stigma by further legitimizing mental health diagnoses in the mind of the public

02

Decrease discrepancies between quality of psychiatric treatment and other forms of medical care

03

Facilitate standardization of diagnosis and treatment

Interest in using electroencephalography (EEG) to identify neural correlates of depression surfaced as early as four decades ago (Schiller, 2019). By the late 1980s, researchers were calling for the creation of EEG profiles to catalogue effects of different medications used to treat psychiatric disorders (Knott & Lapierre, 1987). Differences in patterns of brain activity were identified between people with untreated depression and people without depression. Additionally, specific changes in brain waves were found that correlated with antidepressant treatment (Knott & Lapierre, 1987).

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In addition to confirming findings that EEGs can differentiate people with depression More recent research has focused on depression biomarkers and predicting risk for developing depression. One such review of the literature found that a lack of EEG symmetry between the left and

right frontal lobes is an indication of depression risk. Additionally, EEG asymmetry can predict whether a person is likely to experience future negative emotions and perhaps even what their response to treatment might be (Allen & Reznik, 2015).

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EEG Utility in Diagnosis and Monitoring Treatment Outcomes

In addition to predictive value, EEG technology is useful for identifying severity of depression, a range that may be difficult to accurately determine simply through interviewing a patient in a clinical setting. A study on severity of depressive symptoms examined six different EEG indices across four EEG bands, and found that a reduction of symptoms correlated with decreased slow waves and increased fast waves (Knott, Mahoney Kennedy, & Evans, 2000). Additionally, it is of note that scores on a screening measure of depressive symptoms correlated with EEG results, suggestive of concurrent validity.

Avantatges EEG technology

Useful for identifying severity of depression

Range that may be difficult to accurately determine simply through interviewing a patient in a clinical setting

Ability to potentially account for the heterogeneity of the disorder

Diagnosis is currently made based on fulfilling a symptom count

This process results in aggregating patients with opposite symptoms together, considering them as simply having the same disorder (Goldberg, 2011).

A recent review of the literature on depression biomarkers found that all biomarkers are not equally salient in symptom detection. Connecting biomarkers with specific symptoms may prove especially useful in identifying patients who are at a higher risk for suicidality, which is of importance in managing risk (de Aguiar Neto & Garcia Rosa, 2019).

A meta-analysis identified the gamma band, theta band, and signal complexity as most important for diagnosing depression (Bachmann et al., 2018; Fitzgerald & Watson, 2018; Hosseinifard et al., 2013; Mahato & Paul, 2019; Shen et al., 2017; as cited in de Aguiar Neto & Garcia Rosa, 2019).

The alpha band offers increased diagnostic specificity such as differentiating major depression from bipolar disorder and identifying suicidal ideation, as well as aiding in the development of a prognosis (Dolsen et al., 2017; Nusslock et al., 2018; van der Vinne et al., 2017; as cited in de Aguiar Neto & Garcia Rosa, 2019).

Use of one linear and nonlinear measure yielded classification accuracy of 81% and 77%, respectively

Accuracy increased to 92% when combining three linear and nonlinear measures (Bachman et al., 2018).

Four nonlinear measures demonstrated 90% accuracy (Hosseinifard et al., 2013).

Support vector machine (SVM) analysis of band power achieved over 98% classification accuracy (Mahato & Paul, 2020). Another study also using SVM to assess synchronization likelihood achieved classification accuracy (Mumtaz et al., 2017).

As it appears that both the method of analysis and the combination of measures impacts classification accuracy, future research should continue to focus on improving precision in selection.

Including EEGs in the screening process

May also help in creating personalized treatment plans that are tailored to the needs of individual patients.

A study on factors influencing physicians’ decision to prescribe antidepressants found that physicians are motivated to prescribe when symptoms are severe, but also by social factors such as therapy being inaccessible and the patient’s attitude towards medication (Hyde et al., 2005).

This kind of decision-making lacks precision and is not based on data regarding whether a particular patient would actually benefit from medication. In contrast, EEG demonstrated utility in predicting patient response to medication (SSRIs or an SNRI) with approximately 60% accuracy.

An index combining EEG measurements at different points in time was slightly more accurate, at 70% (Iosifescu et al., 2009). A similar study with an SSRI found that EEG analysis yielded over 82% predictive accuracy (Zhdanov et al., 2020).

screening_process

These results are important in demonstrating the wide range of utility EEG technology contributes to detection and treatment of depression. In summary, in addition to providing biomarkers to identify someone who is currently depressed, EEGs may assist in predicting a person’s future emotional state and potential improvement in response to treatment, as well as monitoring change in symptoms.

Referenses learn more

Allen, J. J., & Reznik, S. J. Frontal EEG Asymmetry as a promising marker of depression vulnerability: Summary and methodological concerns. Current Opinions in Psychology, 4, 93-97. doi:10.1016/j.copsyc.2014.12.017
*Bachmann, M., Päeske, L., Kalev, K., Aarma, K., Lehtmets, A., Ööpik, P., Lass, J., & Hinrikus, H. (2018). Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer Methods and Programs in Biomedicine, 155, 11-17. doi:10.1016/j.cmpb.2017.11.023
de Aguiar Neto, F. S., & Garcia Rosa, J. L. (2019). Depression biomarkers using noninvasive EEG: A review. Neuroscience and Biobehavioral Reviews, 105, 83-93. doi:10.1016/j.neubiorev.2019.07.021
*Dolsen, M.R., Cheng, P., Arnedt, J.T., Swanson, L., Casement, M.D., Kim, H.S., Goldschmied, J.R., Hoffmann, R.F., Armitage, R., & Deldin, P.J. (2017). Neurophysiological correlates of suicidal ideation in major depressive disorder: Hyperarousal during sleep. Journal of Affective Disorders. 212, 160–166. doi:10.1016/j.jad.2017.01.025.
* Fitzgerald, P.J., Watson, B.O. (2018). Gamma oscillations as a biomarker for major depression: an emerging topic. Translational Psychiatry 8(177). doi:10.1038/s41398-018-0239-y
Goldberg, D. (2011). The heterogeneity of “major depression.” World Psychiatry, 10(3), 226-228. doi:10.1002%2Fj.2051-5545.2011.tb00061.x
*Hosseinifard, B., Moradi, M. H., & Rostami, R. (2013). Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Computer Methods and Programs in Biomedicine, 109(3), 339-345. doi: 10.1016/j.cmpb.2012.10.008.
Hyde, J., Calnan, M., Prior, L., Lewis, G., Kessler, D., & Sharp, B. (2005). A qualitiative study exploring how GPs decide to prescribe antidepressants. British Journal of General Practice, 55(519), 755-762.
Iosifescu, D. V., Greenwald, S., Devlin, P., Mischoulon, D., Denniger, J. W., Alpert, JE., & Maurizio, F. (2009). Frontal EEG predictors of treatment outcome in major depressive disorder. European Neuropsychopharmacology, 19(11), 772-777. doi:10.1016/j.euroneuro.2009.06.001
Knott, V. J., & Lapierre, Y. D. (1987). Computerized EEG correlates of depression and antidepressant treatment. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 11(2-3), 213–221. doi:10.1016/0278-5846(87)90063-7
Knott, V., Mahoney, C., Kennedy, S., & Evans, K. (2000). Pre-treatment EEG and its relationship to depression severity and treatment outcome. Pharmacopsychiatry, 33(6), 201-205. doi: 10.1055/s-2000-8356
*Mahato, S., & Paul, S. (2019). Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): A review. In Nath, V., & Mandal, J. K., (Eds.), Nanoelectronics, Circuits and Communication Systems (pp. 323-335). Springer.
Mumtaz, W., Ali, S. S., Yasin, M. A., & Malik, A. S. (2017). A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Medical & Biological Engineering & Computing, 56, 233-246. doi:10.1007/s11517-017-1685-z
*Nusslock, R., Shackman, A.J., McMenamin, B.W., Greischar, L.L., Davidson, R.J., & Kovacs, M. (2018). Comorbid anxiety moderates the relationship between depression history and prefrontal EEG asymmetry. Psychophysiology, 55(1), e12953. doi: 10.1111/psyp.12953.
Schiller, M. (2019). Quantitative electroencephalography in guiding treatment of major depression. Frontiers in Psychiatry. doi:10.3389/fpsyt.2018.00779
*Shen, J., Zhao, S., Yao, Y., Wang, Y., & Feng, L. (2017). A novel depression detection method based on pervasive EEG and EEG splitting criterion. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. doi:10.1109/BIBM.2017.8217946.
*van der Vinne, N., Vollebregt, M.A., van Putten, M.J., & Arns, M. (2017). Frontal alpha asymmetry as a diagnostic marker in depression: fact or fiction? A meta-analysis. NeuroImage: Clinical, 16, 79–87. doi:10.1016/j.nicl.2017.07.006.
Zhdanov, A., Atluri, S., & Wong, W. (2020). Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression. Journal of the American Medical Association Network Open, 3(1), e1918377. doi:10.1001/jamanetworkopen.2019.18377
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