Rex Chin-Hao Chen1, Farid Atry2, Thomas Richner3, Sarah Brodnick2, Jane Pisaniello2, Jared Ness2, Aaron J Suminski2, Justin Williams2, Ramin Pashaie1. 1. Electrical Engineering, Computer Science Department, University of Wisconsin-Milwaukee, 3200N Cramer St., Milwaukee, WI, United States of America. 2. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States of America. 3. Biomedical Engineeirng Department, University of Minnesota, Minneapolis, MN, United States of America.
Abstract
OBJECTIVE: The main objective of this research was to study the coupling between neural circuits and the vascular network in the cortex of small rodents from system engineering point of view and generate a mathematical model for the dynamics of neurovascular coupling. The model was adopted to implement closed-loop blood flow control algorithms. APPROACH: We used a combination of advanced technologies including optogenetics, electrocorticography, and optical coherence tomography to stimulate selected populations of neurons and simultaneously record induced electrocorticography and hemodynamic signals. We adopted system identification methods to analyze the acquired data and investigate the relation between optogenetic neural activation and consequential electrophysiology and blood flow responses. MAIN RESULTS: We showed that the developed model, once trained by the acquired data, could successfully regenerate subtle spatio-temporal features of evoked electrocorticography and cerebral blood flow responses following an onset of optogenetic stimulation. SIGNIFICANCE: The long term goal of this research is to open a new line for computational analysis of neurovascular coupling particularly in pathologies where the normal process of blood flow regulation in the central nervous system is disrupted including Alzheimer's disease.
OBJECTIVE: The main objective of this research was to study the coupling between neural circuits and the vascular network in the cortex of small rodents from system engineering point of view and generate a mathematical model for the dynamics of neurovascular coupling. The model was adopted to implement closed-loop blood flow control algorithms. APPROACH: We used a combination of advanced technologies including optogenetics, electrocorticography, and optical coherence tomography to stimulate selected populations of neurons and simultaneously record induced electrocorticography and hemodynamic signals. We adopted system identification methods to analyze the acquired data and investigate the relation between optogenetic neural activation and consequential electrophysiology and blood flow responses. MAIN RESULTS: We showed that the developed model, once trained by the acquired data, could successfully regenerate subtle spatio-temporal features of evoked electrocorticography and cerebral blood flow responses following an onset of optogenetic stimulation. SIGNIFICANCE: The long term goal of this research is to open a new line for computational analysis of neurovascular coupling particularly in pathologies where the normal process of blood flow regulation in the central nervous system is disrupted including Alzheimer's disease.