UT Southwestern Medical Center’s Peter O’Donnell Jr. Brain Institute continues leading at the forefront of neurological breakthroughs. The Center for Depression Research and Clinical Care (CDRC), which is part of the O’Donnell Brain Institute, continues to drive transformative discoveries that are revolutionizing how we diagnose, treat, and prevent mental health and mood disorders. New research from the CDRC will bring more effective depression treatment to patients using artificial intelligence (AI) and brain scans.
EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) began in 2011 as a national trial led by Madhukar Trivedi, M.D., Director of the CDRC. The multi-site study found that an EEG (electroencephalogram) combined with an artificial intelligence system has the power to eliminate the guesswork in selecting effective depression treatment.
This discovery will help doctors accurately predict if an antidepressant will work solely based on a patient’s brain activity. This breakthrough will have a revolutionary impact on the field of psychiatry and the quality of patient care for patients with mental health disorders.
As we celebrate the incredible progress that has been made by Dr. Trivedi and his team, we are grateful for the philanthropic support that makes research like this possible. Through the Hersh Foundation, Julie and Ken Hersh gave a $5 million lead gift to establish the CDRC. This gift has propelled innovation forward, with an emphasis on early detection and treatment of depression and mood disorders. With leadership from community members who invest in the brilliant minds behind unprecedented medical advances, we continue paving the way for a future without brain illness.
Article courtesy of UT Southwestern Medical Center Newsroom
DALLAS – Feb. 10, 2020 – Artificial intelligence may soon play a critical role in choosing which depression therapy is best for patients.
A national trial initiated by UT Southwestern in 2011 to better understand mood disorders has produced what scientists are calling the project’s flagship finding: a computer that can accurately predict whether an antidepressant will work based on a patient’s brain activity.
The new research is the latest among several studies from the trial that cumulatively show how high-tech strategies can help doctors objectively diagnose and prescribe depression treatments. Although implementing these approaches will take time, researchers predict tools such as AI, brain imaging, and blood tests will revolutionize the field of psychiatry in the coming years.
“These studies have been a bigger success than anyone on our team could have imagined. We provided abundant data to show we can move past the guessing game of choosing depression treatments and alter the mindset of how the disease should be diagnosed and treated.”
Madhukar Trivedi, M.D., a UT Southwestern psychiatrist who oversaw the multi-site trial involving Stanford, Harvard and other institutions
The study published in Nature Biotechnology included more than 300 participants with depression who were randomly chosen to receive either a placebo or an SSRI (selective serotonin reuptake inhibitor), the most common class of antidepressant. Researchers used an electroencephalogram, or EEG, to measure electrical activity in the participants’ cortex before they began treatment. The team then developed a machine-learning algorithm to analyze and use the EEG data to predict which patients would benefit from the medication within two months.
Not only did the AI accurately predict outcomes, further research suggested that patients who were doubtful to respond to an antidepressant were likely to improve with other interventions such as psychotherapy or brain stimulation.
The findings were validated in three additional patient groups.
“This study takes previous research, showing that we can predict who benefits from an antidepressant, and actually brings it to the point of practical utility.”
Amit Etkin, M.D., Ph.D., a Stanford University psychiatry professor who worked with Trivedi to develop the algorithm.
Among the next steps, researchers say, is developing an AI interface that can be widely integrated with EEGs across the country, as well as seeking approval from the U.S. Food and Drug Administration.