The CHS cerebrovascular model
The goal of the CHS model is to establish an analytical relationship between the temporal dynamics of quantities that are directly measured with NIRS (namely the tissue concentrations of oxy- and deoxyhemoglobin) and three key hemodynamic and metabolic parameters (namely blood volume, blood flow velocity, and oxygen consumption). In CHS, the concentrations of oxy-, deoxy-, and total hemoglobin are indicated with O, D, and T, respectively, whereas blood volume, flow velocity, and oxygen consumption are indicated with v, f, and ȯ, respectively. The CHS model is based on treating the tissue microvasculature as a linear time-invariant system and finding the dynamic responses of O, D, and T to step changes in v, f, and ȯ. This results in the determination of transfer functions and impulse response functions that specify the dynamic relationships between (O, D, T) and (v, f, ȯ) in the frequency domain (for oscillatory hemodynamics) and in the time domain (for temporal transients), respectively. Key physiological parameters in the CHS model include the blood capillary and venous transit times, and a cutoff frequency of cerebral autoregulation, which is a measure of the tissue ability to regulate blood flow in the presence of arterial blood pressure fluctuations.
Fig. 1. Schematic representation of the CHS hemodynamic model, which treats the tissue microvasculature as a linear time-invariant system for which the inputs are blood volume (in the arterial, capillary, and venous compartments), capillary flow velocity, and oxygen consumption, and the outputs are measured quantities with NIRS or fMRI.
See also:
- S. Fantini, “Dynamic model for the tissue concentration and oxygen saturation of hemoglobin in relation to blood volume, flow velocity, and oxygen consumption: Implications for functional neuroimaging and coherent hemodynamics spectroscopy (CHS),” NeuroImage 85, 202-221 (2014).
- S. Fantini, “A new hemodynamic model shows that temporal perturbations of cerebral blood flow and metabolic rate of oxygen cannot be measured individually using functional near-infrared spectroscopy,” Physiol. Meas. 35, N1-N9 (2014).
- J. M. Kainerstorfer, A. Sassaroli, B. Hallacoglu, M. L. Pierro, and S. Fantini, “Practical steps for applying a new dynamic model to near-infrared spectroscopy measurements of hemodynamic oscillations and transient changes: Implications for cerebrovascular and functional brain studies,” Acad. Radiol. 21, 185-196 (2014).
- A. Sassaroli, J. M. Kainerstorfer, and S. Fantini, “Nonlinear extension of a hemodynamic linear model for coherent hemodynamics spectroscopy,” J. Theor. Biol. 389, 132-145 (2016).