Statistical evaluation of complexity tests for fMRI timeseries data

Complexity measures of rs-fMRI signals based on non-linear timeseries analyses have been proposed for quantifying the predictability of fMRI signals. There are multiple mathematical methods for evaluating complexity in timeseries signals. This study evaluates the optimal parameter settings in seven complexity tests by analyzing mean complexity measures of grey matter (GM) and CSF across multiple complexity tests with different parameter settings and different fMRI acquisition protocols. Furthermore, complexity evaluation was performed on surrogate timeseries data. Overall, Multiscale Sample Entropy, Fuzzy Entropy, and Hurst Exponent consistently showed an increase in mean complexity of GM compared to CSF in original data.