Elsevier

Neurobiology of Aging

Volume 34, Issue 2, February 2013, Pages 428-438
Neurobiology of Aging

Regular article
Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis

https://doi.org/10.1016/j.neurobiolaging.2012.05.004Get rights and content

Abstract

The nonlinear properties of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals remain unexplored. We test the hypothesis that complexity of BOLD activity is reduced with aging and is correlated with cognitive performance in the elderly. A total of 99 normal older and 56 younger male subjects were included. Cognitive function was assessed using Cognitive Abilities Screening Instrument and Wechsler Digit Span Task. We employed a complexity measure, multiscale entropy (MSE) analysis, and investigated appropriate parameters for MSE calculation from relatively short BOLD signals. We then compared the complexity of BOLD signals between the younger and older groups, and examined the correlation between cognitive test scores and complexity of BOLD signals in various brain regions. Compared with the younger group, older subjects had the most significant reductions in MSE of BOLD signals in posterior cingulate gyrus and hippocampal cortex. For older subjects, MSE of BOLD signals from default mode network areas, including hippocampal cortex, cingulate cortex, superior and middle frontal gyrus, and middle temporal gyrus, were found to be positively correlated with major cognitive functions, such as attention, orientation, short-term memory, mental manipulation, and language. MSE from subcortical regions, such as amygdala and putamen, were found to be positively correlated with abstract thinking and list-generating fluency, respectively. Our findings confirmed the hypothesis that complexity of BOLD activity was correlated with aging and cognitive performance based on MSE analysis, and may provide insights on how dynamics of spontaneous brain activity relates to aging and cognitive function in specific brain regions.

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).

References (77)

  • M.J. Lowe et al.

    Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations

    Neuroimage

    (1998)
  • F.A. Middleton et al.

    Basal ganglia output and cognition: evidence from anatomical, behavioral, and clinical studies

    Brain Cogn

    (2000)
  • B. Milner

    The medial temporal-lobe amnesic syndrome

    Psychiatr. Clin. North Am

    (2005)
  • T. Mizuno et al.

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

    Clin. Neurophysiol

    (2010)
  • P.R. Norris et al.

    Reduced heart rate multiscale entropy predicts death in critical illness: a study of physiologic complexity in 285 trauma patients

    J. Crit. Care

    (2008)
  • M.E. Raichle et al.

    A default mode of brain function: a brief history of an evolving idea

    Neuroimage

    (2007)
  • M.L. Ries et al.

    Task-dependent posterior cingulate activation in mild cognitive impairment

    Neuroimage

    (2006)
  • O.A. Rosso et al.

    Brain electrical activity analysis using wavelet-based informational tools

    Phys. A

    (2002)
  • J.D. Schmahmann et al.

    Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems

    Cortex

    (2008)
  • F. Schneider et al.

    The resting brain and our self: self-relatedness modulates resting state neural activity in cortical midline structures

    Neuroscience

    (2008)
  • Y.I. Sheline et al.

    Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly

    Biol. Psychiatry

    (2010)
  • T. Takahashi et al.

    Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis

    Neuroimage

    (2010)
  • T. Takahashi et al.

    Age-related variation in EEG complexity to photic stimulation: a multiscale entropy analysis

    Clin. Neurophysiol

    (2009)
  • S.J. Tsai et al.

    Interleukin-1 beta (C-511T) genetic polymorphism is associated with cognitive performance in elderly males without dementia

    Neurobiol. Aging

    (2010)
  • N. Tzourio-Mazoyer et al.

    Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain

    Neuroimage

    (2002)
  • D.E. Vaillancourt et al.

    Changing complexity in human behavior and physiology through aging and disease

    Neurobiol. Aging

    (2002)
  • L. Wang et al.

    Changes in hippocampal connectivity in the early stages of Alzheimer's disease: evidence from resting state fMRI

    Neuroimage

    (2006)
  • A.C. Yang et al.

    Reduced physiologic complexity is associated with poor sleep in patients with major depression and primary insomnia

    J. Affect. Disord

    (2011)
  • E. Zarahn et al.

    Empirical analyses of BOLD fMRI statisticsI. Spatially unsmoothed data collected under null-hypothesis conditions

    Neuroimage

    (1997)
  • S. Achard et al.

    Efficiency and cost of economical brain functional networks

    PLoS Comput. Biol

    (2007)
  • G.E. Alexander et al.

    Parallel organization of functionally segregated circuits linking basal ganglia and cortex

    Annu. Rev. Neurosci

    (1986)
  • D.M. Amodio et al.

    Meeting of minds: the medial frontal cortex and social cognition

    Nat. Rev. Neurosci

    (2006)
  • B.B. Biswal et al.

    Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps

    NMR Biomed

    (1997)
  • T.G. Buchman

    The community of the self

    Nature

    (2002)
  • R.L. Buckner et al.

    Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory

    J. Neurosci

    (2005)
  • W.K. Caird

    Aging and short-term memory

    J. Gerontol

    (1966)
  • D. Cheng et al.

    Reduced physiological complexity in robust elderly adults with the APOE epsilon4 allele

    PLoS One

    (2009)
  • M. Costa et al.

    Multiscale entropy analysis of complex physiologic time series

    Phys. Rev. Lett

    (2002)
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