31. Simulations of fMRI data with usage of estimated physiological artifacts

Abstract

Introduction Simulation of fMRI data is a useful tool to verify or to compare different processing methods. Typically simulated data are created using combination of modeled signals and regions of interest with addition of some noise (Gaussian, sometimes low-frequency noise, autocorrelation or polynomial drift). Our previous experiences indicated that such simplification is not enough to simulate 4D data suitable for independent component analysis (ICA). In this work, we introduced new approach which uses estimates of physiological artifacts from real data to create more realistic structure of simulated fMRI data. Methods Synthetic fMRI datasets were created using in-house fMRI simulator. We combined three regions of interest sharing the same signal fluctuation according to block stimulation, three other regions with low-frequency fluctuations and random events uncorrelated to stimulation, EPI template, Gaussian and low-frequency noise, and polynomial drift. RETROICOR algorithm was used to estimate spatial maps and temporal characteristics of cardiac and breathing processes from resting-state fMRI data. These estimates of physiological artifacts were combined with synthetic data in multiplicative manner to form the final 4D datasets. ICA was used to process both (1) pure synthetic datasets, and (2) datasets with physiological noise to compare the suitability of our process. Results Typically 4 components were estimated using ICA on pure synthetic data showing all modeled regions. Using more realistic datasets, typically 7 additional components related to physiological noise were estimated. Conclusion Using more realistic simulated data will enable us, for instance, to test classification algorithm and to make ICA calculation more realistic because using ICA with resting-state data results typically in 20 components (and more than half are probably related to artifacts). We proved that using spatiotemporal characteristics of physiological processes estimated from real data, it is possible to create more realistic simulations of fMRI data.