SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

Abstract

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3–10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

Shared and Specific Independent Components Analysis for Between-Group Comparison

“…To investigate its performance in a more complex and realistic situation, we used fMRI Simulation Toolbox (SimTB) to generate a two-dimensional fMRI-like data set under a model of spatiotemporal separability Dea, Anderson, Allen, Calhoun, & Adali, 2011;Erhardt, Allen, Wei, Eichele, & Calhoun, 2012) . In the context of fMRI functional connectivity analysis (Vahdat, Darainy, Milner, & Ostry, 2011), the simulated data can be expressed as the product of time histories (mixing matrix) and spatial maps (sources).…”

A two-step super-Gaussian independent component analysis approach for fMRI data

“…The simulated datasets with different contrast-to-noise ratio (CNR) levels were generated using the simulation toolbox SimTB (http:// mialab.mrn.org/software/simtb/index.html) (Erhardt et al, 2012). We generated 20 spatial maps, each of which had 270 × 270 voxels with a baseline intensity of 800 (see Fig.…”

“…Moreover, the other 19 spatial components had unique events that occurred with a probability of 0.2 at each TR and unique event modulation coefficients that were equal to 1. The unique events refer to unexplained deviations that are unique to each source and subject (Erhardt et al, 2012). The simulated fMRI responses that were added to the other 19 spatial maps were derived from the convolution of the unique events with HRF.…”

A Novel Brain Networks Enhancement Model (BNEM) for BOLD fMRI Data Analysis With Highly Spatial Reproducibility

“…The SimTB toolbox [48] (http://mialab.mrn.org/software) was used to generate a simulation dataset to evaluate the performance of BNEM. SimTB could provide the ground truths of the simulated spatial maps and the corresponding time courses (TC), which could be applied to assess the pre/postfiltering and reproduced signals comparably.…”