Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy

Abstract

Objective

Multiscale entropy (MSE) is a recently proposed entropy-based index of physiological complexity, evaluating signals at multiple temporal scales. To test this method as an aid to elucidating the pathophysiology of Alzheimer’s disease (AD), we examined MSE in resting state EEG activity in comparison with traditional EEG analysis.

Methods

We recorded EEG in medication-free 15 presenile AD patients and 18 age- and sex-matched healthy control (HC) subjects. MSE was calculated for continuous 60-s epochs for each group, concurrently with power analysis.

Results

The MSE results from smaller and larger scales were associated with higher and lower frequencies of relative power, respectively. Group analysis demonstrated that the AD group had less complexity at smaller scales in more frontal areas, consistent with previous findings. In contrast, higher complexity at larger scales was observed across brain areas in AD group and this higher complexity was significantly correlated with cognitive decline.

Conclusions

MSE measures identified an abnormal complexity profile across different temporal scales and their relation to the severity of AD.

Significance

These findings indicate that entropy-based analytic methods with applied at temporal scales may serve as a complementary approach for characterizing and understanding abnormal cortical dynamics in AD.

Introduction

Electroencephalography (EEG) has been widely employed as a non-invasive clinical research tool for examining normal and pathologic neurophysiological temporal dynamics at a fine temporal resolution. Since brain activity is regulated by multiple couplings and feedback loops among multiple neuronal populations, its output signals exhibit complex temporal fluctuations, which are not simply attributable to noise but rather reflect nonlinear dynamical processes (Sporns et al., 2000, Tononi et al., 1998). As such, nonlinear EEG complexity analyses have been successfully applied and provide a novel understanding of physiological processes in both healthy and pathological conditions (Stam, 2005).

The neuropathology of Alzheimer’s disease (AD) is characterized by generalized neuronal cell loss, neurofibrillary tangles, and senile plaques in different widespread brain regions, and these changes leads to cognitive and behavioral disturbance. How these neurobiological changes translate into functional disturbances can be understood in terms of a neocortical disconnection syndrome (Delbeuck et al., 2003) that results from local neuronal death and deficiency of neurotransmitters (Dringenberg, 2000) or altered long cortico-cortical association fibers (Kavcic et al., 2008), rather than by a specific regional alterations. This cortical disconnection is supported by a large number of electrophysiological studies demonstrating a loss of functional interactions using well-established linear (Jeong, 2004) and nonlinear coherence measures (Jeong et al., 2001, Stam et al., 2003). Although this cortical disconnection theoretically could give rise to increased complexity in electrophysiological signals, the nonlinear complexity measurements have been consistently found as complexity loss in EEG signals in AD (Jeong, 2004, Stam, 2005).

Over the past decade, novel nonlinear approaches have addressed many of the methodological limitations of traditional nonlinear approaches, including vulnerability to noise (Grassberger and Procaccia, 1983a) or data set size requirements (Eckmann and Ruelle, 1992). Among others, approximate entropy (ApEn) (Pincus, 1991), and its refined version, sample entropy (SampEn) (Richman and Moorman, 2000) were developed as practically tractable entropy-based measures which can be applied to stochastic, deterministic, and composite physiologic processes (Pincus, 1991, Richman and Moorman, 2000). These measures determine the probability of finding specific patterns or resemblance in a time series, thereby examining the irregularity or predictability in a time series. Several studies have demonstrated the utility of ApEn (Abásolo et al., 2005, Abásolo et al., 2007) and SampEn (Abásolo et al., 2006) in AD with high sensitivity and specificity. Although these two entropy measures assess temporal dynamics, they are limited in their scope to only short-range temporal dynamics (Costa et al., 2005, Goldberger et al., 2002). In order to investigate the variability in physiological signals across multiple temporal scales, Costa et al. (2002) introduced multiscale entropy (MSE), an extension of SampEn method using temporal coarse-graining procedures, in recognition of the likelihood that dynamical complexity of biological signals may operate across a range of temporal scales. This extension to larger temporal scales may allow the detection of history effects in long-range temporal dynamics i.e., neural dynamics that relate current activity to past activation states that have been stored dynamically through feedbacks loops at multiple, hierarchic levels of cortical processing (Fell et al., 2000). If functional disconnection in AD is originated at multiple, hierarchic levels, complex fluctuation of their neurophysiological output signal need to be characterized by multiple scales. In this context, MSE profiles provide useful insights into the network controlling mechanisms underlying physiological dynamics (Costa et al., 2005) which might be altered in AD due to disconnection syndrome. A few studies have demonstrated the utility of MSE in exploring EEG changes with aging (Takahashi et al., 2009), schizophrenia (Takahashi et al., 2010), and AD (Escudero et al., 2006, Park et al., 2007). Escudero et al. (2006) and Park et al. (2007) examined resting state EEG activity of AD using MSE and found that AD patients had less complexity, consistent with previous nonlinear EEG analysis findings in AD. Although the advantage of MSE analysis is based on the utility of examining long-range temporal dynamics, their explorations were limited their scope to examining relatively short-range temporal dynamics using short length epochs.

Our aim was to investigate possible disturbances in the long-range temporal dynamics in EEG signals in AD and examine their relevance to cognitive function. As such, building on previous AD studies using MSE analysis, we widen the scope to examining in larger temporal scales with longer interest epochs in AD patients and their relation to cognitive decline, and explored their relevancy to that of traditional power analysis. Further, we examined patients that were unmedicated and with relatively young presenile onset, thus avoiding the potential confounds of medication effects and other factors associated with aging such as vascular changes. To this end, we examined resting state EEG activity using MSE in medication-free AD subjects.