Skip to main content
Log in

Age-Related Task Sensitivity of Frontal EEG Entropy During Encoding Predicts Retrieval

  • Original Paper
  • Published:
Brain Topography Aims and scope Submit manuscript

Abstract

Age-related declines in memory may be due in part to changes in the complexity of neural activity in the aging brain. Electrophysiological entropy provides an accessible measure of the complexity of ongoing neural activity. In the current study, we calculated the permutation entropy of the electroencephalogram (EEG) during encoding of relevant (to be learned) and irrelevant (to be ignored) stimuli by younger adults, older adults, and older cognitively declined adults. EEG entropy was differentially sensitive to task requirements across groups, with younger and older controls exhibiting greater control of encoding-related activity than older declined participants. Task sensitivity of frontal EEG during encoding predicted later retrieval, in line with previous evidence that cognitive decline is associated with reduced ability to self-initiate encoding-related processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abásolo D, Hornero R, Espino P (2005) Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with Approximate Entropy. Clin Neurophysiol 11(16):1826–1834

    Article  Google Scholar 

  • Abásolo D, Hornero R, Espino P, Alvarez D, Poza J (2006) Entropy analysis of the EEG background activity in Alzheimer’s disease patients. Physiol Meas 27:241

    Article  PubMed  Google Scholar 

  • Bandt C, Pompe B (2002) Permutation Entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):1–4

    Article  Google Scholar 

  • Besthorn C, Sattel H, Geiger-Kabisch C, Zerfass R, Forstl H (1995) Parameters of EEG dimensional complexity in Alzheimer’s disease. Electroencephalogr Clin Neurophysiol 95:84–89

    Article  PubMed  CAS  Google Scholar 

  • Bhattacharya J (2000) Complexity analysis of spontaneous EEG. Acta Neurobiol Exp 60:495–501

    CAS  Google Scholar 

  • Brenner RP, Reynolds CF, Ulrich RF (1988) Diagnostic efficacy of computerised spectral versus visual EEG analysis in elderly normal, demented and depressed subjects. Electroencephalogr Clin Neurophysiol 69:110–117

    Article  PubMed  CAS  Google Scholar 

  • Bressler SL, Kelso JAS (2001) Cortical coordination dynamics and cognition. Trends Cogn Sci 5(1):26–36

    Article  PubMed  Google Scholar 

  • Brewer JB, Zhao Z, Desmond JE, Glover GH, Gabrieli JD (1998) Making memories: brain activity that predicts how well visual experience will be remembered. Science 281(5380):1185–1187

    Article  PubMed  CAS  Google Scholar 

  • Bruhn J, Röpcke H, Hoeft A (2000) Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology 92(3):715

    Article  PubMed  CAS  Google Scholar 

  • Buckner RL (2004) Memory and executive function review in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 44:195–208

    Article  PubMed  CAS  Google Scholar 

  • Buckner RL, Kelley WM, Petersen SE (1999) Frontal cortex contributes to human memory formation. Nat Neurosci 2(4):311–314

    Article  PubMed  CAS  Google Scholar 

  • Cao Y, Tung WW, Gao J, Protopopescu V, Hively L (2004) Detecting dynamical changes in time series using the permutation entropy. Phys Rev E 70(4):1–7

    Google Scholar 

  • Collins P, Hogan M, Kilmartin L, Keane M, Kaiser J, Fischer K (2010) Electroencephalographic coherence and learning: distinct patterns of change during word learning and figure learning tasks. Mind Brain Educ 4(4):208–218

    Article  Google Scholar 

  • Committee AESEPN (1990) Guidelines for standard electrode position nomenclature. American Electroencephalographic Society

  • Cover TM, Thomas JA (1991) Elements of information theory, vol 6. Wiley Online Library, New York

    Book  Google Scholar 

  • Craik FIM, Byrd M (1982) Aging and cognitive deficits: the role of attentional resources. In: Craik FIM, Trehub S (eds) Aging and cognitive processes: advances in the study of communication and affect, vol 8. Plenum Press, New York, pp 191–211

    Chapter  Google Scholar 

  • Craik FIM, Rose NS (2011) Memory encoding and aging: a neurocognitive perspective. Neurosci Biobehav Rev

  • Crossman ER, Szafran J (1956) Changes with age in the speed of information-intake and discrimination. Experientia Suppl 4:128–135

    Google Scholar 

  • Fabiani M, Karis D, Donchin E (1986) P300 and recall in an incidental memory paradigm. Psychophysiology 23(3):298–308

    Article  PubMed  CAS  Google Scholar 

  • Frank B, Pompe B, Schneider U, Hoyer D (2006) Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses. Med Biol Eng Comput 44:179–187

    Article  PubMed  CAS  Google Scholar 

  • Goldberger AL (1996) Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 347:1312–1314

    Article  PubMed  CAS  Google Scholar 

  • Goldberger AL, Peng CK, Lipsitz LA (2002) What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 23(1):23–26

    Article  PubMed  Google Scholar 

  • Gordon EB, Sim M (1967) The E.E.G. in presenile dementia. J Neurol Neurosurg Psychiatry 30(3):285–291

    Article  PubMed  CAS  Google Scholar 

  • Groth A (2005) Visualization of coupling in time series by order recurrence plots. Phys Rev E 72(4):46220

    Article  Google Scholar 

  • Hasher L, Zacks RT (1988) Working memory, comprehension, and aging: a review and a new view, vol 22. Academic Press, San Diego

    Google Scholar 

  • Hedden T, Gabrieli JDE (2004) Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci 5(2):87–96

    Article  PubMed  CAS  Google Scholar 

  • Hegger R, Kantz H, Matassini L, Schreiber T (2000) Coping with nonstationarity by overembedding. Phys Rev Lett 84(18):4092–4095

    Article  PubMed  CAS  Google Scholar 

  • Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29:306–310

    Article  PubMed  CAS  Google Scholar 

  • Hogan MJ (2004) The cerebellum in thought and action: a fronto-cerebellar aging hypothesis. New Ideas Psychol 22(2):97–125

    Article  Google Scholar 

  • Hogan MJ, Carolan L, Roche RAP, Dockree PM, Kaiser J, Bunting BP, Robertson IH, Lawlor BA (2006) Electrophysiological and information processing variability predicts memory decrements associated with normal age-related cognitive decline and Alzheimer’s disease (AD). Brain Res 1119(1):215–226

    Article  PubMed  CAS  Google Scholar 

  • Hogan M, Kilmartin L, Keane M, Collins P (2012) Electrophysiological entropy in younger adults, older controls and older cognitively declined adults. Brain Res 1445:1–10

    Article  PubMed  CAS  Google Scholar 

  • Inouye T, Shinosaki K, Sakamoto H, Toi S, Ukai S, Iyama A, Katsuda Y, Hirano M (1991) Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr Clin Neurophysiol 79:204–210

    Article  PubMed  CAS  Google Scholar 

  • Izhikevich E (2007) Dynamical systems in neuroscience: the geometry of excitability and bursting. The MIT Press, Cambridge

    Google Scholar 

  • Jeong J (2004) EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 115(7):1490–1505

    Article  PubMed  Google Scholar 

  • Jeong J, Kim SY, Han SH (1998) Non-linear dynamical analysis of the EEG in Alzheimer’s disease with optimal embedding dimension. Electroencephalogr Clin Neurophysiol 106(3):220–228

    Article  PubMed  CAS  Google Scholar 

  • Kail R (1997) The neural noise hypothesis: evidence from processing speed in adults with multiple sclerosis. Aging Neuropsychol Cogn 4(3):157–165

    Article  Google Scholar 

  • Kantz H, Schreiber T, Mackay RS (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Kaplan DT, Furman MI, Pincus SM, Ryan SM, Lipsitz LA, Goldberger AL (1991) Aging and the complexity of cardiovascular dynamics. Biophys J 59(4):945–949

    Article  PubMed  CAS  Google Scholar 

  • Kennel M, Brown R, Abarbanel H (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45(6):3403

    Article  PubMed  Google Scholar 

  • Kirchhoff BA, Anderson BA, Barch DM, Jacoby LL (2012) Cognitive and neural effects of semantic encoding strategy training in older adults. Cereb Cortex 22(4):788–799

    Article  PubMed  CAS  Google Scholar 

  • Liddell DW (1958) Investigations of E.E.G. findings in presenile dementia. J Neurol Neurosurg Psychiatry 21:173–176

    Article  PubMed  CAS  Google Scholar 

  • Logan JM, Sanders AL, Snyder AZ, Morris JC, Buckner RL (2002) Under-recruitment and nonselective recruitment: dissociable neural mechanisms associated with aging. Neuron 33(5):827–840

    Article  PubMed  CAS  Google Scholar 

  • Mandelbrot BB (1982) The fractal geometry of nature. W. H. Freeman and Company, New York

    Google Scholar 

  • Naveh-Benjamin M, Brav TK, Levy O (2007) The associative memory deficit of older adults: the role of strategy utilization. Psychol Aging 22(1):202–208

    Article  PubMed  Google Scholar 

  • Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45(9):712–716

    Article  Google Scholar 

  • Paller KA, Wagner AD (2002) Observing the transformation of experience into memory. Trends Cogn Sci 6(2):93–102

    Article  PubMed  Google Scholar 

  • Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88(6):2297–2301

    Article  PubMed  CAS  Google Scholar 

  • Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2012) nlme: linear and nonlinear mixed effects models. R package version 3.1-104

  • R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0

  • Richman J, Moorman J (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049

    PubMed  CAS  Google Scholar 

  • Rowe EJ, Schnore MM (1971) Item concreteness and reported strategies in paired-associate learning as a function of age. J Gerontol 26(4):470–475

    Article  PubMed  CAS  Google Scholar 

  • Salthouse Ta (2011) Effects of age on time-dependent cognitive change. Psychol Sci 22(5):682–688

    Article  PubMed  Google Scholar 

  • Salthouse TA, Lichty W (1985) Tests of the neural noise hypothesis of age-related cognitive change. J Gerontol 40(4):443–450

    Article  PubMed  CAS  Google Scholar 

  • Sarkar D (2008) Lattice: multivariate data visualization with R. Springer, New York. ISBN 978-0-387-75968-5

  • Schaie KW (1996) Intellectual development in adulthood: the Seattle longitudinal study. Cambridge University Press, Cambridge

    Google Scholar 

  • Schinkel S, Marwan N, Kurths J (2009) Brain signal analysis based on recurrences. J Physiol Paris 103(6):315–323

    Article  PubMed  Google Scholar 

  • Schinkel S, Zamora-López G, Dimigen O, Sommer W, Kurths J (2011) Functional network analysis reveals differences in the semantic priming task. J Neurosci Methods 197(2):333–339

    Article  PubMed  Google Scholar 

  • Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Urbana

    Google Scholar 

  • Sleigh JW, Steyn-Ross DA, Steyn-Ross ML, Grant C, Ludbrook G (2004) Cortical entropy changes with general anaesthesia: theory and experiment. Physiol Meas 25(4):921–934

    Article  PubMed  CAS  Google Scholar 

  • Sosnoff JJ, Newell KM (2011) Aging and motor variability: a test of the Neural Noise Hypothesis. Exp Aging Res 37(4):377–397

    Article  PubMed  Google Scholar 

  • Sporns O (2002) Network analysis, complexity, and brain function. Complexity 8(1):56–60

    Article  Google Scholar 

  • Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol B 11(3):288–295

    Article  Google Scholar 

  • Sun X, Zou Y, Nikiforova V, Kurths J, Walther D (2010) The complexity of gene expression dynamics revealed by permutation entropy. BMC Bioinformatics 11(1):607

    Article  PubMed  Google Scholar 

  • Takens F (1981) Detecting strange attractors in turbulence. In: Rand DA, Young LS (eds) Dynamical systems and turbulence, Springer lecture notes in mathematics, vol 898. Springer, Berlin

  • Terman D (2005) An introduction to dynamical systems and neuronal dynamics. Lect Notes Math 1860:21–68

  • Thomasson N, Hoeppner TJ, Webber CL, Zbilut JP (2001) Recurrence quantification in epileptic EEGs. Phys Lett A 279(January):94–101

    Article  CAS  Google Scholar 

  • Tognoli E, Kelso JAS (2009) Brain coordination dynamics: true and false faces of phase synchrony and metastability. Prog Neurobiol 87(1):31–40

    Article  PubMed  Google Scholar 

  • Tononi G, Edelman GM, Sporns O (1998) Complexity and coherency: integrating information in the brain. Trends Cogn Sci 6613(December):474–484

    Article  Google Scholar 

  • Vaillancourt DE, Newell KM (2002) Changing complexity in human behavior and physiology through aging and disease. Neurobiol Aging 23(1):1–11

    Article  PubMed  Google Scholar 

  • Verhaeghen P, Marcoen A (1994) Production deficiency hypothesis revisited: adult age differences in strategy use as a function of processing resources. Aging Neuropsychol Cogn 1(4):323–338

    Article  Google Scholar 

  • Wagner AD, Koutstaal W, Schacter DL (1999) When encoding yields remembering: insights from event-related neuroimaging. Philos Trans R Soc Lond B 354(1387):1307–1324

    Article  CAS  Google Scholar 

  • Welford AT (1981) Signal, noise, performance, and age. Hum Factors 23(1):97–109

    PubMed  CAS  Google Scholar 

  • West RL (1996) An application of prefrontal cortex function theory to cognitive aging. Psychol Bull 120(2):272–292

    Article  PubMed  CAS  Google Scholar 

  • Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer, New York

    Google Scholar 

  • Woyshville MJ, Calabrese JR (1994) Quantification of occipital EEG changes in Alzheimer’s disease utilizing a new metric: the fractal dimension. Biol Psychiatry 35(6):381–387

    Article  PubMed  CAS  Google Scholar 

  • Zanin M, Zunino L, Rosso OA, Papo D (2012) Permutation entropy and its main biomedical and econophysics applications: a review. Entropy 14(8):1553–1577

    Article  Google Scholar 

Download references

Acknowledgements

DOH was supported by a Grant from ERASMUS and by sabbatical leave from the School of Psychology at NUI Galway. SS was supported by the German Research Foundation (DFG) in the Research Group FOR 868Computational Modeling of Behavioral, Cognitive, and Neural Dynamics. Supplementary material is available in the online version of this article. The software used for this study will be provided at: http://people.physik.hu-berlin.de/schinkel.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Denis O’Hora.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material (ZIP 1,167 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

O’Hora, D., Schinkel, S., Hogan, M.J. et al. Age-Related Task Sensitivity of Frontal EEG Entropy During Encoding Predicts Retrieval. Brain Topogr 26, 547–557 (2013). https://doi.org/10.1007/s10548-013-0278-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10548-013-0278-x

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy