Using machine learning techniques, scientists were able to draw an electrical map of depression.
Depression is more common than we may think. In fact, the National Institutes of Health NIH estimate that more than 16 million adults in the United States have experienced at least one major depressive episode in their lives.
Deemed the leading cause of disability among U.S. individuals aged 15 to 44, depression is a debilitating psychiatric disorder whose neurological underpinnings are being slowly unraveled by more and more studies.
A new study has now investigated the electrical brain patterns of mice subjected to stressful circumstances. The findings helped to create a map of depression that enabled the scientists to differentiate between rodents that were prone to the condition and those that were not.
The research was supervised by Dr. Kafui Dzirasa, who is an associate professor of psychiatry and behavioral sciences at the Duke University School of Medicine in Durham, NC, and the findings were published in the journal Cell.
Studying the brain s symphony
The new research used machine learning techniques that had been developed by the same Dr. Dzirasa and his colleagues a few years ago.
The aim of such techniques is to enable scientists to examine the electrical activity of not only individual parts of the brain, but that of several brain areas at once.
As Dr. Dzirasa explains, You can think of different brain regions as individual instruments in an orchestra.
We are interested in not just what each instrument is doing, he adds, but how the instruments coordinate themselves to generate music.
So, to examine this symphony in our fellow mammals, the researchers studied the brains of mice that were forced to share a cage with another threatening, aggressive rodent for 10 days.
Before and after this experiment, Dr. Dzirasa and team took measurements of the electrical activity in several brain regions commonly associated with depression.
As a result of the stressful living situation, some of the mice developed symptoms akin to those of depression in humans: trouble sleeping, dysregulation in their circadian rhythms, anhedonia — or the inability to take pleasure in daily activities — and social avoidance.
A predictive signature of depression
Using machine learning, the researchers uncovered a dynamic brain network able to [predict] the emergence of major depressive disorder-related behavioral dysfunction in mice.
They describe the trajectory of the brain s electrical activity in this network, saying that the patterns start in the brain s prefrontal cortex and ventral striatum, relay through amygdala and ventral tegmental area, and converge in ventral hippocampus.
The activity in this network, the scientists say, is intensified by acute threat. [T]hese findings reveal a convergent mechanism through which [major depressive disorder] vulnerability is mediated in the brain.
Dr. Dzirasa explains the significance of the findings, saying, What we are essentially creating is an electrical map of depression in the brain.
We hope this could be used as a predictive signature of depression, in the same way that blood pressure is a predictive signature of who will ultimately have a heart attack or stroke, he adds.
To date, the most effective treatment for depression remains electroconvulsive therapy, but it comes along with a lot of side effects [ ] It might be possible to target electricity to the right place in the right way to create a treatment that doesn t have the same side effects as putting electricity everywhere.
Dr. Kafui Dzirasa