Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity

Abstract

This paper presents a state-space hemodynamic model by which any event-related hemodynamic prediction function (i.e., the basis function of the design matrix in the general linear model) is obtained as an output of the model. To model the actual event-related behavior during a task period (intra-activity dynamics) besides the contrasting behavior among the different task periods and against the rest periods (inter-activity dynamics), the modular system is investigated by parametric subspace-based state-space modeling of actual hemodynamic response to an impulse stimulus. This model provides a simple and computationally efficient way to generate the event-related basis function for an experiment by just convolving the developed hemodynamic model with the impulse approximation of the experimental stimuli. The demonstration of the stated findings is carried out by conducting finger-related experiments with slow- and fast-sampling near-infrared spectroscopy instruments to model and validate the cortical hemodynamic responses. The generated basis functions of the finger-related experiments are adapted from real data to validate the incorporation of non-delayed and real-time event-related features and to effectively demonstrate a dynamic-modeling-based online framework. The proposed method demonstrates potential in estimating event-related intra- and inter-activation dynamics and thereby outperforms the classical Gaussian approximation method.