PNAS, February 3, 2009, vol. 106 no. 5 : 1614-1619 (doi: 10.1073/pnas.0811699106)
Selected as ‘ARF recommends’ on Alzforum.org (see news article)
Teresa Montez, Simon-Shlomo Poil, Bethany F. Jones, Ilonka Manshanden, Jeroen P. A. Verbunt, Bob W. van Dijk, Arjen B. Brussaard, Arjen van Ooyen, Cornelis J. Stam, Philip Scheltens, Klaus Linkenkaer-Hansen
Abstract: Encoding and retention of information in memory are associated with a sustained increase in the amplitude of neuronal oscillations for up to several seconds. We reasoned that coordination of oscillatory activity over time might be important for memory and, therefore, that the amplitude modulation of oscillations may be abnormal in Alzheimer disease (AD). To test this hypothesis, we measured magnetoencephalography (MEG) during eyes-closed rest in 19 patients diagnosed with early-stage AD and 16 age-matched control subjects and characterized the autocorrelation structure of ongoing oscillations using detrended fluctuation analysis and an analysis of the life- and waiting-time statistics of oscillation bursts. We found that Alzheimer’s patients had a strongly reduced incidence of alpha-band oscillation bursts with long life- or waiting-times (< 1 s) over temporo-parietal regions and markedly weaker autocorrelations on long time scales (1-25 seconds). Interestingly, the life- and waiting-times of theta oscillations over medial prefrontal regions were greatly increased. Whereas both temporo-parietal alpha and medial prefrontal theta oscillations are associated with retrieval and retention of information, metabolic and structural deficits in early-stage AD are observed primarily in temporo-parietal areas, suggesting that the enhanced oscillations in medial prefrontal cortex reflect a compensatory mechanism. Together, our results suggest that amplitude modulation of neuronal oscillations is important for cognition and that indices of amplitude dynamics of oscillations may prove useful as neuroimaging biomarkers of early-stage AD. (copyright by the National Academy of Sciences)
Citations : 34 (Related articles) (in the media)
O’Gorman et al, Coupling Between Resting Cerebral Perfusion and EEG, Brain Topography, November;DOI:10.1007/s10548-012-0265-7 (2012)
Hardstone et al., Detrended fluctuation analysis: A scale-free view on neuronal oscillations, Front Physiol. 2012; 3: 450.
Laskaris et al, Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses, Journal of Neuroscience Methods Volume 212, Issue 2, 30 January 2013, Pages 344–354
Gonzalez et al., External Drive to Inhibitory Cells Induces Alternating Episodes of High- and Low-Amplitude Oscillations, PLoS Comput Biol 8(8): e1002666. doi:10.1371/journal.pcbi.1002666 (2012)
Poil et al, Critical-State dynamics of avalanches and oscillations jointly emerge from balanced excitation/inhibition in neuronal networks. J Neurosci. 32(29):9817-9823 (2012)
Berthouze et al, Adaptive time-varying detrended fluctuation analysis, Journal of Neuroscience Methods, Volume 209, Issue 1, 30 July 2012, Pages 178–188
Poza et al, Spectral and Non-Linear Analyses of Spontaneous Magnetoencephalographic Activity in Alzheimer’s Disease, J Healthcare Engineering, Volume 3, Number 2, June 2012
Bruña et al, Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer’s disease using spectral entropies and statistical complexity measures, J. Neural Eng., 9, 036007, 2012
Daskalakis et al, Combined transcranial magnetic stimulation and electroencephalography: Its past, present and future, Brain Research Volume 1463, 29 June 2012, Pages 93–107
Smit et al., The Brain Matures with Stronger Functional Connectivity and Decreased Randomness of Its Network , PLoS ONE 7(5): e36896, 2012
Hansen, Action Mechanisms of Transcranial Direct Current Stimulation in Alzheimer’s Disease and Memory Loss, Front Psychiatry. 2012; 3: 48.
Nikulin et al, Attenuation of long-range temporal correlations in the amplitude dynamics of alpha and beta neuronal oscillations in patients with schizophrenia, NeuroImage, Volume 61, Issue 1, 15 May 2012, Pages 162–169
Morley et al., The SAMP8 Mouse: A Model to Develop Therapeutic Interventions for Alzheimer’s Disease , Current Pharmaceutical Design, Volume 18, Number 8, March 2012 , pp. 1123-1130
Sekihara et al., Removal of Spurious Coherence in MEG Source-Space Coherence Analysis, IEEE Transactions on Biomedical Engineering, 58(11), 3121, 2011
Ally, B. A. (2011) Using EEG and MEG to Understand Brain Physiology in Alzheimer’s Disease and Related Dementias, in The Handbook of Alzheimer’s Disease and Other Dementias (eds A. E. Budson and N. W. Kowall), Wiley-Blackwell, Oxford, UK. doi: 10.1002/9781444344110.ch20
Bhattacharya et al., Alpha and Theta Rhythm Abnormality in Alzheimer’s Disease: A Study Using a Computational Model, Advances in Experimental Medicine and Biology, 2011, Volume 718, 57-73
Smit et al, Scale-Free Modulation of Resting-State Neuronal Oscillations Reflects Prolonged Brain Maturation in Humans, J. Neurosci, 31(37): 13128-13136, 2011
Hindriks et al, Dynamics underlying spontaneous human alpha oscillations: A data-driven approach, Neuroimage, 57 (2): 440-451, 2011
Jin et al, How reliable are the functional connectivity networks of MEG in resting states?, AJP – JN Physiol December 1, 2011 vol. 106 no. 6 2888-2895
Poil et al, Fast network oscillations in vitro exhibit a slow decay of temporal auto-correlations, Eur J Neurosci, 2011
Leiser et al, Aligning strategies for using EEG as a surrogate biomarker: A review of preclinical and clinical research, Biochemical Pharmacology, Volume 81, Issue 12, 1408-1421, 2011
Alonso et al, MEG Connectivity Analysis in Patients with Alzheimer’s Disease Using Cross Mutual Information and Spectral Coherence, Ann. Biomed Eng, 39(1):524-536, 2011
Orekhova et al, Unraveling superimposed EEG rhythms with multi-dimensional decomposition, J. Neurosci Methods, 195(1):47-60, 2011
Protznet et al, Hippocampal signal complexity in mesial temporal lobe epilepsy: a noisy brain is a healthy brain, Archives Italiennes de Biologie, 148: 289-297, 2010.
Zaehle et al, Transcranial Alternating Current Stimulation Enhances Individual Alpha Activity in Human EEG, PLoS ONE 5(11), 2010
Dragomir et al, Modeling carbachol-induced hippocampal network synchronization using hidden Markov models, Journal of Neural Engineering,7(5), 2010
Liu et al, Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography, Neuroimage, 51(1), 2010
Kello et al, Scaling laws in cognitive sciences, Trends in Cognitive Sciences, 14(5), 2010
Berthouze et al, Human EEG shows long-range temporal correlations of oscillation amplitude in Theta, Alpha and Beta bands across a wide age range, Clinical Neurophysiology, 2010
Williams and Sachdev, Magnetoencephalography in neuropsychiatry: ready for application?, Curr Opin Psychiatry. 2010 Mar 4.
Shin J, Theta rhythm heterogeneity in humans, Clinical Neurophysiology, Vol 121, 2010, DOI: 10.1016/j.clinph.2009.12.008
Nikulin VV et al, Non-zero mean and asymmetry of neuronal oscillations have different implications for evoked responses, Clinical Neurophysiology,Vol 121, 2010, DOI: 10.1016/j.clinph.2009.09.028
McCaffrey P et al, Alzheimer Research Series on the Default Network, J. Alzheimer’s disease, Vol 19, 2010, DOI: 10.3233/JAD-2010-1277
van Aerde KI et al, Flexible spike timing of layer 5 neurons during dynamic beta oscillation shifts in rat prefrontal cortex, Journal of Physiology, 2009, Volume 587 Issue 21, Pages 5177 – 5196, doi:10.1113/jphysiol.2009.178384.
Linkenkaer-Hansen K., Complex fluctuations in neuronal oscillations: From a qualitative hallmark to disease-sensitive quantitative traits. Frontiers in Neuroinformatics. 2009. Conference Abstract: 2nd INCF Congress of Neuroinformatics. doi: 10.3389/conf.neuro.11.2009.08.130