The effects of age on multiscale entropy measurement of neurophysiological signal complexity (1125.6)

Abstract

Complex physiological signals are neither deterministic nor random, showing a mixture of long‐range dependencies and irregularities (i.e., information) over multiple time scales. In humans, aging and pathology are associated with diminished CNS neurophysiological complexity and impaired cognition. In this study, we computed multiscale entropy (MSE) to measure the time scale dependent complexity (i.e., information) of the resting state BOLD fMRI signal in humans over a large age span (N=138; ages 8‐83; 58 male). Physiological noise (heart rate, respiration, head motion and local white matter [WM] signals) was removed from fMRI time courses using linear regression. Data were then bandpass filtered (.008‐.1 Hz) to isolate low frequency fluctuations. MSE was computed with parameters m=2 and r=.2 for temporal scales 1‐18. BOLD signal in gray matter (GM) and WM gained information with increasing temporal scale and exceeded the information gain from bandpass filtered white and 1/f noise. GM sustained the information over coarse scales better than WM. Age groups stratified MSE profiles showing an age‐related loss of signal complexity in GM that was maximal at scale 18. In contrast, MSE from WM did not dissociate age groups at any scale. These results show that BOLD fMRI is a complex physiological signal characterized by a tissue‐specific age‐related reduction in complexity.