Decoding depression: How changes in brain waves reveal treatment responses
19 décembre 2024
By Narem Karakoyun
What if we could predict the right depression treatment for each person, faster and more effectively? A new study from the Canadian Biomarker Integration Network in Depression (CAN-BIND), one of OBI’s Integrated Discovery Programs, is working to make that future possible.
In their research, a team of scientists explored how changes in brain activity can reveal how well people will respond to treatments for depression—offering hope for faster recoveries and more personalized care.
What was the study about?
Depression is a complex condition that does not respond to a one-size-fits-all approach. This study focused on understanding how early and late changes in brain activity relate to symptom improvements in people treated with escitalopram, a common antidepressant.
Over 100 participants with major depressive disorder (MDD) received an 8-week treatment with escitalopram. Using an electroencephalogram (EEG), a cap with electrodes is placed on the head to measure brain activity at rest, tracking how brain waves—like delta, theta, and gamma waves—changed over time. The findings were compared to people receiving cognitive-behavioral therapy (CBT) to understand how different treatments affect the brain.
What did the scientists uncover?
In the paper, which was published in Translational Psychiatry in October 2024, the research team pointed to three key discoveries.
- Changes in brain waves are associated with treatment success
- After 8 weeks, delta and theta waves – both of which are associated with cognitive and emotional controls – increased in people whose symptoms improved with escitalopram. These changes were unique to escitalopram and not seen in those treated with CBT, suggesting potential specific biological targets that could aid in the development of more tailored treatments.
- Early brain changes matter
- Theta waves increased after just two weeks – linking to initial symptom improvement. These early signals could help predict if a treatment will work, saving time and effort.
- Shared mechanisms exist across treatments
- Reduction in alpha waves was a common marker of success for both escitalopram and CBT, signally a possible shift from internal rumination to a more engaged, outward focus, which can be important in overcoming depression.
Steps towards predicting treatment success and personalized care
These findings open new pathways to study how the brain responds to depression treatments.
Dr. Faranak Farzan of CAN-BIND, the study’s principal investigator, and first author Benjamin Schwartzmann, a PhD Candidate at Simon Fraser University, explained why these results matter.
EEG changes could guide doctors in selecting the right treatment, they noted, reducing the trial-and-error process and helping people feel better sooner, resulting in faster, personalized care. Additionally, EEG is widely available and easy to use, making it a practical tool for improving mental health care across diverse communities. “This is an objective way to see how and if a drug is working for a person,” Dr. Farzan said. “Understanding how different therapies affect brain waves may lead to innovative approaches, such as combining treatments or developing new neurotechnology-based diagnostics or treatments, which offers hope for quicker, more effective treatments, tailored to each person’s needs.”
Given that it takes about two weeks for an antidepressant to begin to show effects on mood, and these changes in brain waves might be part of the explanation of what treatment might be helping someone. And if a drug is failing a person, this novel approach can let the patient and clinician switch to a different treatment faster. CAN-BIND's study is a step toward smarter, more effective care for depression. By identifying patterns in brain activity, researchers are creating a future where mental health treatments are faster, more accurate, and more hopeful. The CAN-BIND team is currently conducting a clinical trial to evaluate the utility of EEG as a biomarker using baseline EEG to allocate participants to treatments.