Brain Scan may predict response of antidepressant in depression patients
USA: The researchers in a new study, published in the journal Nature Biotechnology, have discovered a neural signature that can predict which depression patients are likely to respond to the commonly prescribed antidepressant medication sertraline.
The study suggests that new machine learning techniques can identify complex patterns in a person's brain activity that correlate with meaningful clinical outcomes.
Major depression is one of the most common mental disorders, affecting about 7% of adults in the U.S. in 2017, but the symptoms experienced can vary from person to person. While some may experience many of the characteristic features—including persistent sad mood, feelings of hopelessness, loss of pleasure, and decreased energy—others may experience only a few. There are several evidence-based options available for treating depression, but determining which treatment is likely to work best for a specific person can be a matter of trial and error.
Antidepressants are widely prescribed but their efficacy relative to placebo is modest. This may be because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. In this study by Amit Etkin, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA, and colleagues sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo.
For the purpose, they designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309).
"There is a great need in psychiatry today for objective tests that can inform treatment and go beyond some of the limitations of our diagnostic system. Our findings are exciting because they reflect progress made toward this clinical goal, and they also show the potential of bringing sophisticated data analytic methods to psychiatry," explained Dr Etkin.
The researchers used SELSER to analyze data from the NIMH-funded Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care (EMBARC) study, a large randomized clinical trial of the antidepressant medication sertraline, a widely available selective serotonin reuptake inhibitor (SSRI). As part of the study, participants with depression were randomly assigned to receive either sertraline or placebo for eight weeks. The researchers applied SELSER to participants' pre-treatment EEG data, examining whether the machine learning technique could produce a model that predicted participants' depressive symptoms after treatment.
SELSER was able to reliably predict individual patient response to sertraline based on a specific type of brain signal, known as alpha waves, recorded when participants had their eyes open. This EEG-based model outperformed conventional models that used either EEG data or other types of individual-level data, such as symptom severity and demographic characteristics. Analyses of independent data sets, using several complementary methods, suggested that the predictions made by SELSER may extend to broader clinical outcomes beyond sertraline response.
In one independent data set, the researchers found that the EEG-based SELSER model predicted greater improvement for participants who had shown partial response to at least one antidepressant medication compared with those who had not responded to two or more medications, in line with the patients' clinical outcomes. Another independent data set showed that participants who were predicted by SELSER to show little improvement with sertraline were more likely to respond to treatment involving a specific type of non-invasive brain stimulation called transcranial magnetic stimulation (in combination with psychotherapy).
Work is now underway to further replicate these findings in large, independent samples to determine the value of SELSER as a diagnostic tool. According to Etkin, Trivedi, Wu, and colleagues, the present research highlights the potential of machine learning for advancing a personalized approach to treatment in depression.
"While work remains before the findings in our study are ready for routine clinical use, the fact that EEG is a low-cost and accessible tool makes the translation from research to clinical practice more possible in the near term. I hope our findings are part of a tipping point in the field with respect to the impact of machine learning and objective testing," Etkin concluded.
The study, "An electroencephalographic signature predicts antidepressant response in major depression," is published in the journal Nature Biotechnology.