Artificial intelligence (AI) has identified specific brain activity patterns in depressed individuals who are less responsive to certain antidepressants, according to two new studies led by researchers from the University of Texas (UT) Southwestern Medical Center in Dallas.
The studies are part of a large national trial (EMBARC) intended to establish biology-based, objective strategies to help treat mood disorders and minimize the trial and error of prescribing treatments. If successful, scientists envision using a battery of tests such as brain imaging and blood analyses to increase the odds of finding the right treatment.
“We need to end the guessing game and find objective measures for prescribing interventions that will work,” said Dr. Madhukar Trivedi, who oversees EMBARC and is founding Director of UT Southwestern’s Center for Depression Research and Clinical Care.
“People with depression already suffer from hopelessness, and the problem can become worse if they take a medication that is ineffective.”
The studies, involving more than 300 participants, used imaging to analyze brain activity in both a resting state and during the processing of emotions. Both studies involved a healthy control group and people with depression who either received antidepressants or placebo.
Of those who received medication, researchers found associations between how the brain is wired and whether a participant was likely to improve within two months of taking an antidepressant.
Trivedi said imaging the brain’s activity in various states was important to get a more accurate picture of how depression manifests in a particular patient. For some people, he said, the more relevant data will come from their brains’ resting state, while in others the emotional processing will be a critical component and a better predictor for whether an antidepressant will work.
“Depression is a complex disease that affects people in different ways,” he said. “Much like technology can identify us through fingerprints and facial scans, these studies show we can use imaging to identify specific signatures of depression in people.”
The researchers analyzed data from the 16-week EMBARC trial, which evaluated patients with major depressive disorder through brain imaging and various DNA, blood, and other tests. The goal was to address a troubling finding from a previous study led by Trivedi that had revealed that up to two-thirds of patients do not adequately respond to their first antidepressant.
EMBARC’s first study, published in 2018, focused on how electrical activity in the brain can indicate whether a patient is likely to benefit from an SSRI (selective serotonin reuptake inhibitor), the most common class of antidepressant.
The finding has been followed by related research that identifies other predictive tests for SSRIs, most recently the resting-state brain imaging study published in the American Journal of Psychiatry and the second imaging study published in Nature Human Behaviour.
In the second imaging study, the researchers used artificial intelligence to determine associations between the effectiveness of an antidepressant and how a patient’s brain processes emotional conflict.
Participants undergoing brain imaging were shown photographs in quick succession that offered sometimes conflicting messages such as an angry face with the word “happy,” or vice versa. Each participant was asked to read the word on the photograph before clicking to the next image.
However, rather than observe only neural regions believed to be relevant to predicting antidepressant benefits, the researchers used machine learning to analyze activity in the entire brain.
“Our hypotheses for where to look have not panned out, so we wanted to try something different,” said Trivedi.
AI identified specific brain regions, including the lateral prefrontal cortices, which were most important in predicting whether participants would benefit from an SSRI. The findings revealed that participants who had abnormal neural responses during emotional conflict were less likely to improve within eight weeks of starting the medication.
Source: UT Southwestern Medical Center