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Although mental health disorders are being addressed more frequently nowadays, approximately one-quarter of individuals experiencing depression struggle to find relief. A considerable number of patients do not react positively to oral antidepressants, placing a strain on both the individual and the healthcare system and highlighting the necessity for alternative treatments. This could stem from variations in neurotransmitter levels and responses to diverse medications that aim to restore equilibrium across neural networks.
Researchers, medical professionals, and engineers at Washington University in St. Louis are working to create personalized medicinal approaches for treatment-resistant depression, adjusting drug dosages according to a patient’s age, genetics, health status, brain activity, and neural circuits. ShiNung Ching, a professor in electrical and systems engineering at the McKelvey School of Engineering, along with Ben Julian Palanca, MD, PhD, an associate professor of anesthesiology and psychiatry at WashU Medicine, will conduct this research thanks to a four-year, $1.2 million grant from the National Institutes of Health (NIH).
Ching and Palanca will explore the therapeutic possibilities of substances that influence the brain, like general anesthetics such as propofol. Some hypotheses suggest that these treatments may operate by inducing or adjusting electrophysiological biomarkers associated with the disorder, yet there are technical challenges to creating these alternative therapies, such as individual-specific dosage needs and the difficulty of manipulating an electrophysiological biomarker.
The team’s concept involves manipulating slow waves in the electroencephalogram (EEG), which captures the brain’s electrical activity. Slow waves are significant indicators of brain functionality and wellness, have been linked to depression, and could serve as a biomarker for both the disorder and treatment efficacy. Other investigations have examined different medications, like ketamine, which have increased slow-wave activity associated with enhancements in mood and cognitive function. Nonetheless, the researchers note that slow waves are challenging to identify and predict, posing a considerable obstacle.
Slow waves can be triggered by anesthetics, but incorrect dosages—either too low or too high—can lead to varying results.
“We believe there exists an optimal dosing strategy that achieves the ‘sweet spot’ for generating slow waves in the EEG, maximizing potential therapeutic outcomes,” Ching remarked. “However, this dosing plan is likely to differ significantly among individuals, so we intend to develop a data-driven modeling approach to determine how the brain reacts to propofol, being mindful of person-to-person variations.”
In addition to modeling, they aim to formulate a dosage that can influence neural mechanisms and create dosing strategies that achieve the desired dynamics.
“This study could have wider, future implications for how other psychoactive drugs are administered by providing the necessary tools to comprehend how these substances affect brain electrophysiology,” Ching added.
Ultimately, they aim to assess their model in conjunction with the ongoing SWIPED clinical trials, which are funded by a $2.9 million NIH grant, intending to enhance sleep slow-wave activity as a treatment option for patients with treatment-resistant 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 spearheading that study.
Initially published on the McKelvey Engineering website
The article Personalized brain modeling of anesthetic effects to predict antidepressant response first appeared on The Source.
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