In a study recently published in the journal Biological Psychiatry, John Gabrieli and colleagues (Massachusetts Institute of Technology, MA, USA) have successfully utilized functional magnetic resonance imaging to identify children at increased risk of developing depression. The findings could help to develop screening programs to identify vulnerable children before symptoms appear.
The research team scanned the brains of 43 children without depression aged between 8 and 14 years old. Of these children, 27 had a family history of the condition and so were at high risk for developing later-life depression, whilst 16 had no family history of the illness.
The study analyzed the children’s brain scans for signs of synchronized activity between different regions of the brain during a state of rest, allowing for identification of natural communication channels.
Interestingly, the team determined that high-risk children had brain activity similar to that of adults with depression. High-risk children exhibited much stronger synchronization between the subgenual anterior cingulate cortex and the default network mode, brain regions that are most active during a resting state.
Additionally, high-risk children illustrated an overactive connection between the region of the brain involved in emotion processing, the amygdala, and the area of the brain involved in language processing, the inferior frontal gyrus. Lower-than-normal connectivity was also displayed in the frontal and parietal cortexes, areas that are key to thinking and decision making.
Previous studies analyzing the brains of adults with depression have identified abnormal activity in both the sgACC and the amygdala compared with healthy control groups.
“We’d like to develop the tools to be able to identify people at true risk, independent of why they got there, with the ultimate goal of maybe intervening early and not waiting for depression to strike the person,” commented study coauthor John Gabrieli.
The research suggests that functional magnetic resonance imaging could be used to identify children who may be at high risk for depression, allowing for early intervention and prevention of this mental illness. The team are now looking to track the at-risk children to investigate whether early treatment following screening could prevent episodes of depression as well as to evaluate how at-risk children avoid depression development.