In the Journals

EEG-based analyses can pinpoint efficacy of antidepressant vs. placebo

Amit Etkin

Network functional connectivity analyses based on electroencephalography, or EEG, effectively differentiate between responses to an antidepressant vs. placebo, according to results of a secondary analysis of a randomized clinical trial published in JAMA Psychiatry. Researchers noted that this ability to differentiate may help establish a placebo signature in clinical trials through an alternative direction.

“Our findings indicate that, while as an average across the population antidepressants seem only slightly better than placebo, this is in fact due to major neurobiological differences between patients rather than the drugs not being effective,” Amit Etkin, MD, PhD, of the department of psychiatry and behavioral sciences at Stanford University, told Healio Psychiatry. “Antidepressants are effective, but only for a subpopulation of depressed patients who our research suggests can be identified using sophisticated EEG analyses.”

Etkin and colleagues analyzed data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care (EMBARC) study to determine whether neural moderators of antidepressant treatment could be revealed using EEG connectivity. The trial included a random sample outpatients with depression aged 18 to 65 years who were not taking medication for depression. These patients were assigned to take either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks and their resting-state EEG data were collected. The researchers for the present study used intent-to-treat prediction linear mixed models to determine pretreatment connectivity patterns associated with response to sertraline vs. placebo.

A total of 221 participants underwent EEG recordings and provided high-quality pretreatment EEG data. Etkin and colleagues corrected for multiple comparisons and found moderation by connections within and between widespread cortical regions for both the placebo and antidepressant groups. This moderation was most prominent in the parietal region, they noted. Further, lower gamma-band and greater alpha-band connectivity predicted worse antidepressant outcomes and better placebo outcomes. Moderating connections with lower connectivity levels were associated with higher levels of anhedonia. Connectivity features that change from baseline to 1 week of treatment were distinct from those that moderate treatment response differentially by treatment group. The researchers reported a group mean score of 18.35 at baseline and 26.14 across all time points on the Hamilton Rating Scale for Depression.

In a related editorial, Adrienne Grzenda, PhD, of the department of psychiatry at the University of California, Los Angeles, and Alik S. Widge, MD, PhD, of the department of psychiatry and behavioral sciences at the University of Minnesota in Minneapolis, wrote that Etkin and colleagues’ study is “a large step forward for EEG biomarker research.”

“It is one of the first reports in this field to apply modern statistical practice, and through doing so demonstrates how much we still have to learn,” they wrote. “It introduces a broader community to the potential of the EMBARC data set, which is now readily available for further data mining.”

They also noted that the power envelope connectivity method of analysis may provide value in study areas ranging from biomarkers to basic science.

“Our field has spent the past decade arguing about whether antidepressants are effective medications, but our findings suggest that perhaps the better question is whether the diagnosis of depression lacks the precision to identify those patients for whom these drugs are highly impactful and distinguish these patients from those needing other therapeutic approaches,” Etkin said. “Identifying with EEG those patients who are most likely to benefit from a drug compared with placebo would increase the likelihood of drug trials working out, and thus new drugs reaching patients. Likewise, if our findings are replicated and extended, patients in the clinic can undergo EEG to determine who is a particularly good candidate for antidepressant medication, and who might need a different treatment (such as psychotherapy, novel antidepressants, neurostimulation, etc).” – by Joe Gramigna

Disclosures: Etkin reports salary and equity from Alto Neuroscience Inc., holding equity in three companies, receiving research funding from several institutions and serving as a consultant for several pharmaceutical companies. Grzenda reports grants from the American Psychiatric Association Foundation, as well as personal fees from The Carlat Report. Widge reports patents pending with applications in the area of deep brain stimulation and neural oscillations. Please see the study for all other authors’ relevant financial disclosures.

Amit Etkin

Network functional connectivity analyses based on electroencephalography, or EEG, effectively differentiate between responses to an antidepressant vs. placebo, according to results of a secondary analysis of a randomized clinical trial published in JAMA Psychiatry. Researchers noted that this ability to differentiate may help establish a placebo signature in clinical trials through an alternative direction.

“Our findings indicate that, while as an average across the population antidepressants seem only slightly better than placebo, this is in fact due to major neurobiological differences between patients rather than the drugs not being effective,” Amit Etkin, MD, PhD, of the department of psychiatry and behavioral sciences at Stanford University, told Healio Psychiatry. “Antidepressants are effective, but only for a subpopulation of depressed patients who our research suggests can be identified using sophisticated EEG analyses.”

Etkin and colleagues analyzed data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care (EMBARC) study to determine whether neural moderators of antidepressant treatment could be revealed using EEG connectivity. The trial included a random sample outpatients with depression aged 18 to 65 years who were not taking medication for depression. These patients were assigned to take either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks and their resting-state EEG data were collected. The researchers for the present study used intent-to-treat prediction linear mixed models to determine pretreatment connectivity patterns associated with response to sertraline vs. placebo.

A total of 221 participants underwent EEG recordings and provided high-quality pretreatment EEG data. Etkin and colleagues corrected for multiple comparisons and found moderation by connections within and between widespread cortical regions for both the placebo and antidepressant groups. This moderation was most prominent in the parietal region, they noted. Further, lower gamma-band and greater alpha-band connectivity predicted worse antidepressant outcomes and better placebo outcomes. Moderating connections with lower connectivity levels were associated with higher levels of anhedonia. Connectivity features that change from baseline to 1 week of treatment were distinct from those that moderate treatment response differentially by treatment group. The researchers reported a group mean score of 18.35 at baseline and 26.14 across all time points on the Hamilton Rating Scale for Depression.

In a related editorial, Adrienne Grzenda, PhD, of the department of psychiatry at the University of California, Los Angeles, and Alik S. Widge, MD, PhD, of the department of psychiatry and behavioral sciences at the University of Minnesota in Minneapolis, wrote that Etkin and colleagues’ study is “a large step forward for EEG biomarker research.”

“It is one of the first reports in this field to apply modern statistical practice, and through doing so demonstrates how much we still have to learn,” they wrote. “It introduces a broader community to the potential of the EMBARC data set, which is now readily available for further data mining.”

They also noted that the power envelope connectivity method of analysis may provide value in study areas ranging from biomarkers to basic science.

“Our field has spent the past decade arguing about whether antidepressants are effective medications, but our findings suggest that perhaps the better question is whether the diagnosis of depression lacks the precision to identify those patients for whom these drugs are highly impactful and distinguish these patients from those needing other therapeutic approaches,” Etkin said. “Identifying with EEG those patients who are most likely to benefit from a drug compared with placebo would increase the likelihood of drug trials working out, and thus new drugs reaching patients. Likewise, if our findings are replicated and extended, patients in the clinic can undergo EEG to determine who is a particularly good candidate for antidepressant medication, and who might need a different treatment (such as psychotherapy, novel antidepressants, neurostimulation, etc).” – by Joe Gramigna

Disclosures: Etkin reports salary and equity from Alto Neuroscience Inc., holding equity in three companies, receiving research funding from several institutions and serving as a consultant for several pharmaceutical companies. Grzenda reports grants from the American Psychiatric Association Foundation, as well as personal fees from The Carlat Report. Widge reports patents pending with applications in the area of deep brain stimulation and neural oscillations. Please see the study for all other authors’ relevant financial disclosures.