Electrophysiological entropy in younger adults, older controls and older cognitively declined adults

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

“…It should be noted that there is still no clear consensus regarding the direction of FD changes in relation to cognitive processing. Again, indirect suggestion emerges from studies that examined changes in complexity in pathologic conditions (Henderson et al 2006;Hogan et al 2012), reporting general loss of complexity with disease and aging. This may suggest that the loss of neurons, as in Alzheimer's disease (AD) (Henderson et al 2006) that shows an FD decrease, reflects neuronal loss, therefore making it suitable for identification of AD.…”

The association of physical activity to neural adaptability during visuo-spatial processing in healthy elderly adults: A multiscale entropy analysis

“…In addition, studies using calculation of sample entropy also support this argument (Garrett et al, 2013;Hogan et al, 2012). Sample entropy, in short, is a modification of Shannon's entropy (Shannon and Weaver, 1949) and Pincus' appropriate entropy (Pincus, 1991), which calculates the repetitions of similar sequences in a physiological time series signal (Hogan et al, 2012;Richman & Moorman, 2000). Thus, the more unpredictable the dynamic signals are, the higher the values of sample entropy would be, and vice versa.…”

“…One notable study by Garrett, Kovacevic, McIntosh, and Grady (2010), examining the standard deviations (SDs) of BOLD (blood oxygen level-dependent) signals in young and elderly adults during fixation blocks, reported that the brain signals in elderly adults were generally less varied relative to those in younger adults, possibly reflecting an age-related decrease in network complexity and integration. In addition, studies using calculation of sample entropy also support this argument (Garrett et al, 2013;Hogan et al, 2012). Sample entropy, in short, is a modification of Shannon's entropy (Shannon and Weaver, 1949) and Pincus' appropriate entropy (Pincus, 1991), which calculates the repetitions of similar sequences in a physiological time series signal (Hogan et al, 2012;Richman & Moorman, 2000).…”

“…Signal complexity reflects some aspects of changes in magnitude from moment to moment in neurophysiological time series (Costa, Goldberger, & Peng, 2002Garrett et al, 2013;Goldberger, Peng, & Lipsitz, 2002). This type of measure can provide information different from that acquired via mean signals (Garrett, Kovacevic, McIntosh, & Grady, 2012;Hogan et al, 2006) and is considered to reflect neural adaptability and efficiency during cognitive functioning (Garrett et al, 2013;Heisz, Shedden, & McIntosh, 2012;Hogan et al, 2012;Liang et al, 2014;Yang et al, 2013). Mounting evidence has demonstrated the utility of measuring brain signal complexity for the investigation of various physical and mental states, including developmental and aging processes (Garrett et al, 2012(Garrett et al, , 2013O'Hora et al, 2013), diseases (Catarino, Churches, Baron-Cohen, Andrade, & Ring, 2011;Hogan et al, 2006), and cognitive performance (Liang et al, 2014).…”

Permutation Entropy Analysis of EEG of Mild Cognitive Impairment Patients During Memory Activation Task

“…Entropy is one such measure which estimates the complexity or predictability of a system from real-world time series [1,4,6,17]. Results of the existing studies on EEG signals of AD patients using such measures have identified lowered entropy values indicating lowered complexity or flexibility for information processing and transmission [1,7,8,19]. Multi-scale entropy analysis has revealed scale discrepancies in MCI EEG as characteristic feature [15].…”