Analytic Signal-Based Causal Network Estimator for Hemodynamic Signal Analysis in the Brain

Abstract

The connectivity and the causality were estimated using functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (f-NIRS) signals to introduce an optimal networks analysis technique for hemodynamic signals. Instantaneous phase information was utilized to analyze the fMRI time series and the f-NIRS signals in order to estimate connectivity and causal networks in the brain. To identify an optimal estimator, the conducted computer-based Monte Carlo simulation using fMRI mimicking signals under various realistic conditions. The simulation results showed that the phase-information-based approach can be an optimal causal estimator for hemodynamic signals.