Regular articleComplexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis
Introduction
Analysis of spontaneous blood oxygen level-dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) has been implemented mainly in research on functional brain connectivity by examining interregional correlations in BOLD activity (Biswal et al., 1997, Friston et al., 2003), while the temporal properties of BOLD signals remain largely unexplored. The observation that spontaneous BOLD activity in the resting human brain is not random noise (Fox et al., 2007, Zarahn et al., 1997), but specifically organized, has shed new insight into neuroscientific research (Garrett et al., 2010).
Analysis of variability in BOLD signals has been proposed as a significant indicator of aging, in which BOLD activity in older brains is less variable than that in younger brains (Garrett et al., 2010, Garrett et al., 2011). A lack of variability in brain activity may be implicated in age-related neural processing deficits and a concomitant decline in cognitive function, warranting comprehensive investigation to identify the correlation between the dynamic processes associated with BOLD activity and cognitive function among the elderly.
The finding of a reduction in the variability of BOLD signals with aging is analogous to the generic notion that a loss of physiologic complexity is correlated with the aging process. It has long been observed that physiologic output (e.g., heart rate) under healthy conditions typically exhibits multiscale variability, long-range correlation, and nonlinearity (Buchman, 2002, Goldberger et al., 2002a). Increased complexity in physiologic output has been proposed to be correlated with healthy conditions whereas aging and pathological conditions often show a reduction in the complexity of physiologic output (Costa et al., 2002, Costa et al., 2005, Goldberger et al., 2002a, Goldberger et al., 2002b; Lipsitz and Goldberger, 1992). This complexity may arise from the interaction among structural or functional units and feedback loops operating over a wide range of temporal and spatial scales, enabling the organism to adapt to the events of everyday life (Costa et al., 2002, Costa et al., 2005, Goldberger et al., 2002b, Lipsitz and Goldberger, 1992, Peng et al., 2009).
In this study, we therefore hypothesized that (1) complexity of spontaneous BOLD activity is reduced in the older group, compared with the younger group, and (2) cognitive performance among the elderly is correlated positively to the complexity of spontaneous BOLD activity. In addition, because spontaneous BOLD activity was represented mostly in the default mode network (DMN) brain areas (Greicius et al., 2003), we also expected that the correlation between cognitive performance and complexity of spontaneous BOLD activity may exist mainly in DMN areas. We applied a well developed complexity measure—multiscale entropy (MSE) analysis (Costa et al., 2002), capable of quantifying the complexity of dynamic processes across multiple time scales, to analyze BOLD time series data.
Complexity is typically assessed using entropy-based methods by quantifying the regularity (orderliness) of a time series (Costa et al., 2002, Pincus, 1991, Richman and Moorman, 2000, Rosso et al., 2002). Entropy increases with the degree of irregularity, reaching its maximum in completely random systems. However, the conventional entropy-based approach could yield contradictory results in which a high degree of entropy is also observed in pathological conditions, such as heart rate rhythm in atrial fibrillation (Costa et al., 2003a).
MSE analysis was therefore developed as a biologically meaningful measure of complexity (Costa et al., 2002, Costa et al., 2005) by quantifying sample entropy (Richman and Moorman, 2000) over multiple time scales inherent in a time series. MSE has been applied to the analysis of heart rate time series (Norris et al., 2008a, Norris et al., 2008b, Yang et al., 2011), electromyogram (Istenic et al., 2010), human gait (Costa et al., 2003b), posture sway (Costa et al., 2007, Manor et al., 2010), and electroencephalogram (EEG) (Catarino et al., 2011, Escudero et al., 2006, Mizuno et al., 2010, Park et al., 2007, Protzner et al., 2010, Takahashi et al., 2010).
Because no prior study has applied MSE to quantify the complexity of BOLD time series, we also sought to determine appropriate parameters for calculating MSE in BOLD data. Our aims were therefore 3-fold: (1) to empirically investigate the appropriate parameters and time scale factors for MSE analysis of BOLD signals; (2) to compare MSE of BOLD signals between the younger and older group; and (3) to identify brain areas that could be potentially correlated with cognitive function in the older people based on analysis of the complexity of spontaneous brain activity. To this end, we conducted a resting fMRI experiment on a cohort of cognitively normal Han Chinese younger and older males.
Section snippets
Participants
This study included 99 elderly Han Chinese male subjects recruited from the community and a public veterans housing complex in northern Taiwan (aged 80.6 ± 5.4 years; education: 5.4 ± 5.1 years). For comparison, 56 normal younger male subjects (aged 27.5 ± 4.1 years; education: 18.6 ± 2.7 years) were recruited from the community. The study was conducted in accordance with the Declaration of Helsinki, receiving approval from the local Institutional Review Board. Informed consent was obtained
Determining appropriate parameters for MSE calculation in BOLD signals
Fig. 1 shows the number of AAL brain regions with a significant difference in sample entropy between groups of subjects with low and high cognitive score. The comparison was made by calculating MSE using a wide range of parameters. Generally, MSE calculation with pattern length m = 1 yielded higher number of significant AAL regions than that with m = 2. For results obtained with m = 1, the peak number of significant AAL regions occurred at similarity factor r = 0.25 for scale 2, r = 0.3 for
Discussion
There is a high degree of heterogeneity in the cognitive changes that occur with aging (Wilson et al., 2002). Delineating the correlations between cognitive function and structural and functional properties in brain imaging can reveal important insights into the neurobiology of aging. A great deal of previous work has been conducted on the correlation between the structure of the brain and cognitive performance among healthy elderly subjects (Kaup et al., 2011). Most structural studies have
Disclosure statement
The authors have no conflicts of interest.
The study was conducted in accordance with the Declaration of Helsinki, with approval from the local Institutional Review Board. Informed consent was obtained from all subjects before commencement of the study.
Acknowledgements
This work was supported by Taipei Veterans General Hospital, Taiwan (grants VGHUST100-G1-4-1, V99ER3-004, and V100C-013); the National Science Council (NSC) of Taiwan (grants NSC 95-2314-B-075-111; NSC 96-2314-B-075-075; NSC 97-2314-B-075-001-MY3); and NSC support for the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan (grant NSC 100-2911-I-008-001).
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