A new study provides evidence that certain patterns of electrical brain activity, particularly the phase resetting rate, is associated with changes in depressed mood. The findings, published in Scientific Reports, could help to facilitate the early detection of major depression and the development of novel treatments in the future.
Depression is a widespread and often debilitating mental health condition, affecting millions of people worldwide. While experts have long recognized its impact, the precise mechanisms underlying depression have remained elusive. Traditionally, diagnosis has relied on interviews and clinical observations, making it challenging to identify depression in its early stages or monitor its progression effectively.
Scientists have explored the potential of electroencephalogram (EEG) data, which measures electrical activity in the brain, as a tool for diagnosing and understanding depression. While previous research has shown promise in using EEG data to classify individuals as either depressed or healthy, the connection between specific EEG patterns and the nuances of daily mood fluctuations had yet to be fully explored.
The researchers embarked on this study with a twofold goal in mind. Firstly, they aimed to investigate whether changes in brain activity, as captured by EEG, could be indicative of shifts in a person’s daily mood. Secondly, they sought to develop practical biomarkers for early detection of depression. To achieve this, they revisited and reanalyzed EEG data from a previous study that examined the EEG patterns of healthy individuals with varying degrees of depressive tendencies.
“Many students at my university suffered from depression, and several of the students I supervised had also experienced depression,” explained study author Masahiko Morita, a professor at the University of Tsukuba. “My collaborator, Dr. Kawasaki, was studying the relationship between depression and EEG, so I applied the shallow neural network I had developed (which is more transparent than deep neural networks) to EEG analysis.”
The study involved ten volunteers, aged 18 to 34, recruited from a university and online sources. The researchers focused on comparing changes in EEG patterns within individuals, avoiding the potential biases associated with inter-individual comparisons.
Each measurement session consisted of two components. First, participants completed a self-report questionnaire called the Profile of Mood States 2nd edition (POMS-2). This questionnaire assessed various mood states, with a particular focus on the Depression-Dejection (DD) scores. These scores provided insights into the participants’ levels of depressed mood at the time of the measurement.
Participants were instructed to take an EEG device and a computer to their homes, where they performed daily self-measurements for two to four weeks. They were encouraged to maintain consistency in measurement times and were provided instructions on wearing the EEG device correctly. After completing the measurements, participants returned the equipment.
This EEG device recorded brain activity while participants were in a resting state, with both open and closed eyes. However, the study primarily concentrated on the data collected during the eyes-closed condition, as it showed more clarity and relevance to the investigation.
The study’s most compelling findings emerged when analyzing the EEG data in conjunction with mood scores. Notably, the mean relative angular speed (MRAS), mean normalized amplitude (MNA), and phase resetting rate (PRR) stood out as key features in understanding the relationship between brain activity and depressed mood.
- Mean Relative Angular Speed: This refers to the average speed at which certain brain waves move in a circular motion. It helps us understand how fast or slow these brain waves are spinning.
- Mean Normalized Amplitude: This measures the size or strength of brain waves and then averages it out. It gives us an idea of how big or small these waves are on average.
- Phase Resetting Rate: This indicates how often brain waves reset or synchronize with each other. It’s like looking at how often certain groups of brain waves start over or align with each other.
While these EEG features demonstrated systematic changes in their correlation with mood scores across different EEG frequencies, the PRR exhibited the strongest connection with changes in depressed mood. MRAS and MNA followed in significance.
Importantly, the researchers discovered that for some participants, the PRR’s correlation with mood scores exhibited significant swings. In simpler terms, the PRR varied in response to changes in their mood. For instance, when an individual’s mood deteriorated, the PRR at specific frequencies increased, and vice versa.
“As this study develops, it may be possible to routinely monitor the level of depressed mood in a few minutes, like taking a fever with a thermometer, and prevent or treat depression early,” Morita told PsyPost.
One of the most intriguing aspects of this research was the identification of what the researchers referred to as “characteristic frequencies.” These were specific EEG frequencies where the PRR correlated most significantly with changes in depressed mood.
What made these characteristic frequencies even more intriguing was their systematic arrangement. These frequencies appeared in a geometric progression, forming a distinct pattern across the EEG spectrum. These frequencies were unique to each participant, underlining the individuality of brain activity in relation to mood.
“I was surprised that a slight change in EEG frequency reversed the correlation with depressed mood, whereas most analyses to date have focused on frequency bands such as alpha and beta,” Morita said.
“Even more surprising was the discovery of brainwave activity reflecting depressed mood itself, because it was doubtful that such activity existed, as the researchers at Alphabet’s X who led Project Amber to detect signs of depression using EEG said, ‘We didn’t succeed in our original goal of finding a single biomarker for depression and anxiety. It is unlikely that one exists, given the complexity of mental health.'”
While this study offers promising insights into the potential link between brain waves and depression, it’s essential to acknowledge its limitations. The study featured a relatively small sample size, primarily comprised of healthy individuals. The research did not delve into the effects of diagnosed depression or the long-term impact of medication. Moreover, the study used a portable EEG device with fewer electrodes and a lower sampling rate than traditional lab-based setups, which may have influenced the results.
To further our understanding and practical applications, more extensive studies involving larger and more diverse participant groups, including individuals with diagnosed depression, are necessary.
“Scientifically, it is not at all clear why the phenomena we found occur, and further research is essential,” Morita explained. “A major problem for practical application is the large individual differences in EEG frequency (this is also a major reason why this finding has not been discovered before), and it is necessary to develop a method to compensate for this.”
The study, “Brainwave activities reflecting depressed mood: a pilot study“, was authored by Masahiko Morita, Ryusei Otsu, and Masahiro Kawasaki.
