Professor Kougioumtzoglou develops data-driven techniques to analyze dynamic cerebral autoregulation to enable personalized blood pressure management for patients after stroke

Professor Ioannis Kougioumtzoglou is working with Professor Eliza Miller and Professor Randolph Marshall (Department of Neurology, Vagelos College of Physicians and Surgeons) to develop data-driven techniques based on joint time-frequency analysis tools (e.g., [1]) and dimension reduction approaches (e.g., [2]) for quantitative analysis of dynamic cerebral autoregulation, i.e., the ability of the cerebral vasculature to regulate cerebral blood flow in response to rapid changes in blood pressure. Loss of dynamic cerebral autoregulation can lead to strokes through either hyperperfusion causing blood-brain barrier compromise and brain hemorrhage, or hypoperfusion causing ischemic strokes.  

The ultimate research objective is to propose a biomarker for indicating healthy versus impaired autoregulation function, and for predicting eventually life-threatening events based on relevant patient data. The long-term goal of this interdisciplinary team is to operationalize the above mathematical approaches to design, beta-test and validate a diagnostic instrument which can reliably measure dynamic cerebral autoregulation in real time at the bedside. This instrument will incorporate transcranial Doppler, a non-invasive monitoring device, and software capable of analyzing and processing multiple continuous signals in real time.    

This work was funded through the Columbia University SEAS Translational Acceleration Research (STAR) Fund, which promotes interdisciplinary research across the University.

[1] Miller E., Dos Santos K. R. M., Marshall R. S., Kougioumtzoglou I. A., 2020. Joint time-frequency analysis of dynamic cerebral autoregulation via generalized harmonic wavelets, Physiological Measurement, vol. 41: 024002: 1-11. 

[2] Dos Santos K. R. M., Katsidoniotaki M. I., Miller E., Petersen N. H., Marshall R. S., Kougioumtzoglou I. A., 2023. Reduced-order modeling and analysis of dynamic cerebral autoregulation via diffusion maps, Physiological Measurement, vol. 44, 044001: 1-13.

Proposed framework based on diffusion maps for parsimonious modeling and analysis of dynamic cerebral autoregulation