J.N. Stroh ^1,2* Brandon Foreman ^3,4 Tellen D. Bennett ^1,5 Jennifer Briggs ² Soojin Park ^6,7 David Albers ^1,2,6

• ¹ Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
• ² Department of Bioengineering, College of Engineering, Design and Computing, University of Colorado Denver, Aurora, Colorado, United States
• ³ Gardner Neuroscience Institute, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States
• ⁴ Department of Neurology and Rehabilitation Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States
• ⁵ Children's Hospital Colorado, Aurora, Colorado, United States
• ⁶ Department of Biomedical Informatics,Graduate School of Arts and Sciences, Columbia University, New York, NY, United States
• ⁷ Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York City, New York, United States

Background: The protocols and therapeutic guidance established for treating traumatic brain injuries (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamics models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity.A hypothesis-and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset.Results: Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern-where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear.Conclusions: Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help to personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.