Personalized brain modeling of anesthetic effects to predict antidepressant response
ShiNung Ching, Ben Julian Palanca, MD, PhD, look at brain dynamics, neural circuits as clues to alternative approaches
While mental health conditions are being treated more than in the past, about one-quarter of people with depression are unable to get relief. Many patients don’t respond to oral antidepressants, which burdens both the individual and the health care system and stresses the need for alternative therapies. This may be the result of differences in neurotransmitters and responses to different medications that may restore the balance across neural circuits.
Neuroscientists, clinicians and engineers at Washington University in St. Louis seek to develop personalized medicine strategies for refractory depression that would tailor drug dosage based on a patient’s age, genetics, health conditions, brain dynamics and neural circuits. ShiNung Ching, professor of electrical & systems engineering in the Preston M. Green Department of Electrical & Systems Engineering in the McKelvey School of Engineering, and Ben Julian Palanca, MD, PhD, associate professor of anesthesiology and of psychiatry at WashU Medicine, will carry out the research with a four-year, $1.2 million grant from the National Institutes of Health (NIH).
Ching and Palanca will pursue the therapeutic potential of pharmaceuticals active in the brain, such as general anesthetics like propofol. While some theories suggest these treatments may work by inducing or modulating electrophysiological biomarkers linked to disease, there are technical roadblocks to developing these alternative treatments, including specific dosage requirements, which can vary from person to person, and the challenge of manipulating an electrophysiological biomarker.
The team’s idea is based on manipulating slow waves in the electroencephalogram (EEG), which records electrical activity of the brain. Slow waves are a key marker of brain function and health, have been associated with depression and could be used as a biomarker for both depression and response to treatment. Other studies have used other drugs, such as ketamine, that have enhanced slow wave activity correlated with improvements in mood and cognition. However, the researchers say slow waves are difficult to detect and predict, creating a challenge.
Slow waves can be induced by anesthetics, yet too little or too much of the drug can create different effects.
“We think there is an optimal dosing regimen that achieves the ‘sweet spot’ of expressing slow waves in the EEG, which would maximize potential therapeutic benefits,” Ching said. “But this dosing plan is likely quite variable in different people, so we plan to develop a data-driven modeling method to identify how the brain responds to propofol, in a manner that is sensitive to person-to-person differences.”
In addition to modeling, they plan to create a dose that could manipulate the neural mechanisms and design dosing strategies that reach the targeted dynamics.
“This research may have broader, future impacts on how other psychoactive drugs are dosed by providing the tools needed to understand how the drugs impact brain electrophysiology,” Ching said.
Ultimately, they plan to evaluate their model in conjunction with the ongoing SWIPED clinical trials, funded by a $2.9 million NIH award, with the goal of targeting the enhancement of sleep slow wave activity as a treatment for patients with refractory depression. Palanca and Eric J. Lenze, MD, the Wallace and Lucille K. Renard Professor and head of the Department of Psychiatry at WashU Medicine, are leading that study.
