SCH: Mechanism-based dose titration for modifying brain electrophysiology during antidepressant therapy

Treatment-resistant depression (TRD) is a debilitating mental health condition in which patients experience persistent symptoms despite medical intervention, leading to adverse outcomes and an overall diminished quality of life. This condition imposes a significant financial burden on both individuals and healthcare systems due to frequent medical consultations, ongoing therapies, and lost productivity. TRD is typically defined by the failure to respond to at least two different classes of oral antidepressant medications, underscoring the critical need for new therapeutic approaches. One promising avenue for treating TRD and other neuropsychiatric disorders is the use of pharmacological agents that are active in the brain, such as general anesthetics like propofol. Emerging theories suggest that these treatments may work by inducing or modulating electrophysiological biomarkers (e.g., neural synchronization and oscillations) that have been linked to disease phenotypes. However, the development of these alternative treatments faces several technical bottlenecks. First is the substantial variation in drug dosing requirements between and within individuals. More fundamentally, manipulating an electrophysiological biomarker is mechanistically ambiguous, since it is unclear what circuit dynamics are being engaged by the drug. To obviate these issues, this proposal aims to develop personalized medicine strategies that will enable tailored drug dose titrations based not only on patient demographics but also on data-driven inferences of their individual brain dynamics and neural circuitry. Our specific motivation arises from the study of slow oscillatory activity, particularly slow waves (SWs) in the EEG that can be elicited by anesthetic drugs. Empirical evidence indicates that there may be an ‘optimal’ dosing range that promotes these SWs, and that doing so may lead to therapeutic effects in patients with TRD. To realize this clinical potential, we seek several computational advances: (i) A data-driven dynamical systems model to identify the neural dynamics that underlie EEG SWs, thereby resolving mechanistic ambiguity in detecting these oscillations; (ii) Modeling general anesthetics as perturbations to these latent neural dynamics, allowing for dose titration to be formulated as a manipulation of neural mechanisms, thus enabling (iii) Dosing strategies that induce the desired neural dynamics by means of a bifurcation. Our approach uses brain electrophysiology to create personalized dosing models, introducing a new paradigm for pharmacodynamics based on dynamical systems theory. RELEVANCE (See instructions): Treatment-resistant depression (TRD) is a severe form of major depressive disorder that is associated with persistent symptoms and reduced quality of life. A promising new treatment strategy for TRD involves using anesthetic drugs like propofol to modify pathological brain activity. This study will enable and optimize these treatments for TRD by developing new computational techniques to systematically predict how anesthetic drugs modify brain activity at the level of individual patients. Project Number: 1R01MH143055-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of Mental Health (NIMH) | Principal Investigator: ShiNung Ching (+1 co-PI) | Institution: WASHINGTON UNIVERSITY, SAINT LOUIS, MO | Award Amount: $284,333 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 IVBH-N (50)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11305319