Optimizing the measurement of sample entropy in resting-state fMRI data - Roediger, Donovan J.; Butts, Jessica; Falke, Chloe; Fiecas, Mark B.; Klimes-Dougan, Bonnie; Mueller, Bryon A.; Cullen, Kathryn R. (Frontiers in Neurology, 2024)
The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g. scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field.