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Neuroscience study finds the brain’s response to emotional conflict predicts antidepressant treatment outcomes

by Eric W. Dolan
January 22, 2020
Reading Time: 3 mins read
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Neuroimaging data from a large randomized controlled trial indicates that how people respond to antidepressant medication is predicted by how their brain processes conflicting emotional information. The findings have been published in Nature Human Behaviour.

“This study addressed two questions central to the ability to meaningfully use biology to understand and guide psychiatric treatment and drug development,” said study author Amit Etkin, the founder and CEO of Alto Neuroscience as well as a professor at Stanford University.

“One was whether there are identifiable biological differences between patients with depression that determine who responds to an antidepressant compared to a placebo. The second was what role emotion regulation plays in defining those biological attributes.”

“Prior to this study, it has been unclear whether the apparent small difference in treatment outcome between antidepressants and placebo historically have been due to problems with the medications (i.e. they are not particularly effective) or problems with the diagnosis (i.e. definition of the disorder in broad clinical terms lumps together people with very different biology.)”

The researchers examined functional magnetic resonance imaging (fMRI) data from the EMBARC trial. “EMBARC is by far the largest placebo-controlled neuroimaging study of antidepressants,” Etkin said.

The trial randomly assigned 309 depressed outpatients to receive either the selective serotonin reuptake inhibitor sertraline or placebo for 8 weeks.

The participants also underwent brain imaging prior to treatment, during which they were shown photographs in quick succession that offered sometimes conflicting messages such as an fearful face with the word “happy” or a smiling face with the word “fear.” The participants were instructed to identify the facial emotion with a key press, while trying to ignore the word.

The researchers then used machine learning analyses to identify specific brain regions that predicted whether participants would benefit from the SSRI treatment. The results showed that participants who had abnormal neural responses during emotional conflict were less likely to improve within eight weeks of starting the medication.

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“We found two very interesting things. First, it was very clear from our results that there are strong biological reasons for why a depressed patient responds to an antidepressant versus to a placebo,” Etkin told PsyPost.

“In other words, it seems that it is the catch-all way we make the clinical diagnosis of depression that is imprecise, and there are people for whom antidepressants work much better than placebo but others for whom there is no such difference. We were able to define these brain signatures in people using both conventional and machine learning analyses.”

“Second, we found that the reason people respond better to an antidepressant is that they seem to be better able to regulate emotion processing in an automatic manner. The better their brains did this, the greater the difference between the antidepressant and placebo,” Etkin explained.

Another analysis of EMBARC data, published in the American Journal of Psychiatry, found that patterns of functional connectivity in the brain “appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.”

But there is still a need for more research.

“As with any study, even one of a large population of patients studied over the course of a very detailed and exhaustive study, replication is needed to confirm the results. Such replications are unlikely to take the same form as EMBARC, which was a costly and effort-intensive study, and thus care must be taken to make sure that we progressively learn through each attempt at extension and generalization of these findings,” Etkin said.

“It would be nice in future work to see whether this kind of signal can be found with more clinic-ready brain imaging tools, such as EEG. Likewise, we have only started to scratch the surface of what it is that makes medication responders different from those who do not respond to medication, and thus a lot more work is needed at multiple levels (genetics, behavior, sleep, etc.)”

“Nonetheless, the general message from this paper is that it does seem that the imprecision inherent in our diagnoses is in large part to blame for the poor outcomes of the trial-and-error approach we currently rely on in psychiatric treatment,” Etkin concluded.

The study, “Brain regulation of emotional conflict predicts antidepressant treatment response for depression“, was authored by Gregory A. Fonzo, Amit Etkin, Yu Zhang, Wei Wu, Crystal Cooper, Cherise Chin-Fatt, Manish K. Jha, Joseph Trombello, Thilo Deckersbach, Phil Adams, Melvin McInnis, Patrick J. McGrath, Myrna M. Weissman, Maurizio Fava, and Madhukar H. Trivedi.

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