A study suggests that brain scans may PREDICT teenagers' depression weeks before their mood changes
- About half of all people who eventually experience depression have their first episode in adolescence.
- Depression in young people and adolescents can be difficult to diagnose through subjective tests
- Scientists at the University of Colorado discovered that brain-care systems work differently in adolescents who are or will become depressed
- They claim that testing could be a quicker and more accurate way to detect depression earlier.
By the Dailymail.com reporter
Published: 10:43 EDT, May 30, 2019 | Updated: 10:51 EDT, May 30, 2019
Clinical depression in adolescents can be diagnosed faster and more accurately through the use of brain scans, a new study suggests.
The researchers discovered that an imbalance in the functioning of brain systems related to attention can help predict the course of depression in adolescents.
They explained that proper coordination of networks within the brain helps us regulate our attention between external goals and self-centered or emotional thinking.
But scientists at the University of Colorado, Boulder, found that abnormalities in coordination between networks were not only evident in adolescents with more severe depression, but predicted an increase in depressive symptoms two weeks later.
Through the use of brain scans, researchers at the University of Colorado found that the lack of coordination between two brain networks marked and even predicted depression in adolescents (file)
"The teenage years are a time of remarkable growth and opportunity, as young people forge new relationships, learn to navigate intense emotions and make the transition to independence," said the study's first author, Dr. Roselinde Kaiser. .
– However, it is also during adolescence that a large and growing number of adolescents experience clinical depression and mood-related problems for the first time.
"Our challenge as doctors, scientists and parents is: how can we predict which adolescents will experience mood problems in the near future?"
Dr. Kaiser, an badistant professor in the department of psychology and neuroscience at the University of Colorado at Boulder, and her colleagues tested the idea of using MRI scanning to predict mood health in the future.
They measured the activity of the fronto-insular networks while the adolescents played a difficult computer game that involved emotional images.
He said that current predictive tools mainly use self-report, which can be unreliable in adolescents.
"Our results showed that adolescents who showed an unbalanced coordination between brain systems," said Dr. Kaiser.
That is, [their scans showed] "Two weeks later, less coordination between the areas involved in goal-directed care, and greater coordination between the areas involved in self-centered thinking, continued to report greater increases in depression, greater mood swings, and greater intensity. of negative humor in daily life ".
He said that the functioning of the network provided a better prediction of mood health in the future beyond the current symptoms, a critical distinction, since it suggests that the functioning of the frontal network could predict who could develop a more depressed serious between two adolescents with the same current symptoms.
The findings were published in the journal Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.
"This very interesting study highlights the important role that the frontoinsular circuits, measured with NMR during the processing of emotional stimuli, can play in the regulation of our mood, and how the deterioration in the function of this network can underlie the states of current and current negative moods, "said Dr Cameron Carter, editor of the magazine.
Although the study evaluated mood health only two weeks later, he said the findings indicate that the functioning of the fronto-insular network may be useful in predicting the health of future moods in adolescents.
If confirmed in longer clinical studies, the research team said their findings suggest that the measure could provide a predictor of neurobiological risk to help guide interventions to prevent serious depression.
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