SpecialityUniversità di Pisa, IT

The role of dynamical noise power in complex neurophysiological systemsBackground: Complex dynamics in nonlinear physiological systems can be driven by intrinsic dynamical noise, which is difficult to estimate without prior knowledge or assumptions about system dynamics. Aim: We propose a formal method for estimating the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. Methodology: Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic data that included logistic maps and Pomeau-Manneville systems under various conditions. We also estimated noise on 70 heart rate variability series from healthy and pathological subjects, and 36 EEG healthy series in resting and mental stress tasks to characterize noise in physiological systems. Results: Our model-free method was able to discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounted for about 11% of EEG dynamics and about 32% of heartbeat dynamics, in relation to the series' standard deviation. Cardiovascular noise increased in pathological conditions compared to healthy dynamics, while cortical brain noise increased during mental arithmetic computations over the prefrontal and occipital regions. Brain noise was distributed differently across cortical regions. Conclusion: Physiological noise is an integral part of neurobiological dynamics and can be measured using the proposed framework in any biomedical series.