Researchers have discovered a neural signature that predicts whether individuals with depression are likely to benefit from sertraline, a commonly prescribed antidepressant medication. The findings, published in Nature Biotechnology, suggest that new machine-learning techniques can identify complex patterns in a person’s brain activity that correlate with meaningful clinical outcomes. The research was funded by the National Institute of Mental Health (NIMH), part of the National Institutes of Health.
Previous research has suggested that specific components of brain activity, as measured by resting-state electroencephalography (EEG), could yield insight into how people will respond to certain treatments. However, researchers have yet to develop predictive models that can differentiate between response to antidepressant medication and response to placebo and that can also predict outcomes for individual patients. Both features are essential for the neural signature to have clinical relevance.
Researchers drew on insights from neuroscience, clinical science, and bioengineering to build an advanced predictive model. They developed a new machine-learning algorithm specialized for analyzing EEG data called SELSER (Sparse EEG Latent SpacE Regression). They hypothesized that this algorithm might be able to identify robust and reliable neural signatures of antidepressant treatment response.
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. The present research highlights the potential of machine learning for advancing a personalized approach to treatment in depression.
