Why the study?
Linear analysis of fNIRS signals may underrepresent complex nonlinear metabolic activity, and no study had evaluated the utility of complex characterizations compared with standard methods such as mean hemoglobin.
Does entropy analysis of fNIRS signals improve the identification of cortical activation areas compared to standard mean hemoglobin analysis in subjects performing cognitive and motor tasks?
Does entropy analysis of fNIRS signals improve the identification of cortical activation areas compared to standard mean hemoglobin analysis in subjects performing cognitive and motor tasks?
Entropy analysis of fNIRS signals can uncover complementary cortical activation areas that are missed by traditional linear mean hemoglobin analysis.
May extend fNIRS mapping of cognitive activation; leaves open clinical utility pending validation.
Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis.
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Ghouse et al. (2020) studied this question.
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