Major depressive disorder (MDD) is a highly prevalent mental disorder that is often characterized by the reduced ability to feel pleasure along with experiences of low mood. Millions suffer from depression and finding ways to better understand the neural mechanisms behind it is especially important in discovering avenues for improved treatment and diagnoses. One of the ways to understand depression on a neural level is through the use of electroencephalography (EEG). EEG measures the electrical activity of the brain via electrodes placed on the scalp. From these signals, researchers can look at electrophysiological responses at specific times through analyzing event-related potentials (ERPs). Individuals with depression often demonstrate differing ERPs to various stimuli compared to healthy controls, which serves as a way to study depression through the use of EEG.
A study done by Hill et al. (2019) investigated various theoretical models explaining depression through the use of EEG, specifically looking at a specific ERP known as the late positive potential (LPP). The LPP is sensitive to attention toward stimuli with emotional salience and serves as a useful ERP to study for depression because studies have demonstrated that individuals with MDD show a decreased LPP response to stimuli of both positive and negative emotional valence. Using this, Hill et al. presented adults with images of either stimuli with negative emotional valence or stimuli of positive emotional valence along with neutral stimuli while an EEG system monitored their scalp’s electrical activity. The results revealed that higher scores on a depression scale were connected to a reduced LPP to both kinds of emotional stimuli (positive and negative), though not for the neutral stimuli. The study helped provide support for a model of depression known as the Emotion-Context Insensitivity model which theorizes that depression is related to a blunted reactivity toward both positive and negative emotional stimuli. Along with supporting models of depression, the study offers a better understanding of depression through the use of EEG which could have the potential to aid in the diagnosis of the illness.
While Hill et al. demonstrated a method using EEG to study reactivity toward pictures varying in emotional valence, other methods of using EEG to study depression look at changes in reactivity toward reward. Researchers at the University of Illinois at Chicago utilized EEG to show that they could predict whether an individual would respond better to an antidepressant or talk therapy based on the electrical activity on their scalp when they were responding to reward. First, it has been established in other studies that people with depression express a reduced positive signal when receiving rewards compared to those without depression. Using this, the researchers studied individuals’ reward responses using a task in which they would win or lose a small amount of money. Their responses were taken before and after treatment of either antidepressant or talk therapy, and the results demonstrated that the more the reward signal increased from before and after treatment, the fewer symptoms of depression the individual reported experiencing. Additionally, individuals whose reward responses were more blunted prior to receiving treatment experienced less depressive symptoms after treatment if they were given the antidepressants, and this finding did not hold consistent for those in the talk therapy group. This finding provides a unique avenue to consider for deciding different treatments of mental illnesses through the use of EEG.
Both of these studies effectively used EEG methods in order to study depression, shining light on potential neural markers to be used in diagnoses and even helping in determining how an individual will respond to treatment for depression based on ERPs prior to treatment. Millions of people suffer from depression and finding ways to improve diagnosis and treatment are important factors to addressing the illness. EEG presents itself as a non-invasive technique to study depression, and hopefully, studies in the future can continue to advance our understanding of mental illnesses to progress treatment outcomes.
References
Hill, K. E., South, S. C., Egan, R. P., & Foti, D. (2019). Abnormal emotional reactivity in depression: Contrasting theoretical models using neurophysiological data. Biological Psychology, 141, 35–43. https://doi.org/10.1016/j.biopsycho.2018.12.011
University of Illinois at Chicago. (2018, June 14). EEG can determine if a depressed patient will do better on antidepressants or talk therapy. ScienceDaily. https://www.sciencedaily.com/releases/2018/06/180614213553.htm
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