A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as mild cognitive impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

[1]  Jiang Zheng-yan,et al.  Abnormal cortical functional connections in Alzheimer's disease: analysis of inter- and intra-hemispheric EEG coherence. , 2005 .

[2]  Selin Aviyente.  Information-theoretic signal processing on the time-frequency plane and applications , 2005, 2005 13th European Signal Processing Conference.

[3]  Z. Jiang,et al.  Abnormal cortical functional connections in Alzheimer's disease: analysis of inter- and intra-hemispheric EEG coherence. , 2005, Journal of Zhejiang University. Science. B.

[4]  Hualou Liang,et al.  Causal influence: advances in neurosignal analysis. , 2005, Critical reviews in biomedical engineering.

[5]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[6]  Andrzej Cichocki,et al.  Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals , 2009, BMC Neuroscience.

[7]  N Yamaguchi,et al.  Reduced Interhemispheric EEG Coherence in Alzheimer Disease: Analysis During Rest and Photic Stimulation , 1998, Alzheimer disease and associated disorders.

[8]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[9]  A. Grossmann,et al.  Cycle-octave and related transforms in seismic signal analysis , 1984 .

[10]  David G. Stork,et al.  Pattern Classification , 1973 .

[11]  Francesco Rundo,et al.  Fronto-parietal coupling of brain rhythms in mild cognitive impairment: A multicentric EEG study , 2006, Brain Research Bulletin.

[12]  T. Koenig,et al.  Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naı̈ve patients with schizophrenia: preliminary results , 2001, Schizophrenia Research.

[13]  C. Stam,et al.  Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets , 2002 .

[14]  Michael Eichler,et al.  On the Evaluation of Information Flow in Multivariate Systems by the Directed Transfer Function , 2006, Biological Cybernetics.

[15]  C. J. Stam,et al.  EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task , 2004, Clinical Neurophysiology.

[16]  C. J. Stam,et al.  Global dynamical analysis of the EEG in Alzheimer’s disease: Frequency-specific changes of functional interactions , 2008, Clinical Neurophysiology.

[17]  D O Walter,et al.  Changes in brain functional connectivity in Alzheimer-type and multi-infarct dementia. , 1992, Brain : a journal of neurology.

[18]  W. Shankle,et al.  A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage Alzheimer's disease , 2002, Clinical Neurophysiology.

[19]  Nikolaos G. Bourbakis,et al.  Neural Network Approach for Image chromatic Adaptation for Skin Color Detection , 2007, Int. J. Neural Syst..

[20]  Leilei Zheng,et al.  Inter-and intra-hemispheric EEG coherence in patients with mild cognitive impairment at rest and during working memory task , 2006, Journal of Zhejiang University SCIENCE B.

[21]  D. Loiselle,et al.  Event-Related Potentials: A Methods Handbook , 2006, Neurology.

[22]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[23]  G. Buzsáki.  Rhythms of the brain , 2006 .

[24]  Michael Eichler,et al.  Abstract Journal of Neuroscience Methods xxx (2005) xxx–xxx Testing for directed influences among neural signals using partial directed coherence , 2005 .

[25]  M. Browne,et al.  Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising , 2002, Clinical Neurophysiology.

[26]  Mario Parra,et al.  The role of the cerebral coherence in the evolution of the patient with Alzheimer's disease , 1997 .

[27]  R Quian Quiroga,et al.  Performance of different synchronization measures in real data: a case study on electroencephalographic signals. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Vladimir Krajca,et al.  Objective Assessment of the Degree of Dementia by Means of EEG , 2003, Neuropsychobiology.

[29]  M. Kaminski,et al.  Granger causality and information flow in multivariate processes. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[31]  Régine Le Bouquin-Jeannès,et al.  Linear and nonlinear causality between signals: methods, examples and neurophysiological applications , 2006, Biological Cybernetics.

[32]  Martin D. Buhmann,et al.  Radial basis function , 2010, Scholarpedia.

[33]  S. Bressler,et al.  Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data , 2006, Journal of Neuroscience Methods.

[34]  D Liberati,et al.  EEG coherence in Alzheimer's disease. , 1998, Electroencephalography and clinical neurophysiology.

[35]  Y. Hochberg.  A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[36]  Osvaldo A. Rosso,et al.  Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations , 2001, Biological Cybernetics.

[37]  Daniele Marinazzo,et al.  Radial basis function approach to nonlinear Granger causality of time series. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Donna L. Hudson,et al.  Synchronization Measures of the Scalp Electroencephalogram Can Discriminate Healthy from Alzheimer's Subjects , 2007, Int. J. Neural Syst..

[39]  L. Williams,et al.  Contents , 2020, Ophthalmology (Rochester, Minn.).

[40]  Li Ping,et al.  The Factor Graph Approach to Model-Based Signal Processing , 2007, Proceedings of the IEEE.

[41]  E. John,et al.  Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment , 2005, Neurobiology of Aging.

[42]  Maria G. Knyazeva,et al.  Assessment of EEG synchronization based on state-space analysis , 2005, NeuroImage.

[43]  Andrzej Cichocki,et al.  Quantifying Statistical Interdependence PART III: n > 2 Multi-Dimensional Point Processes , 2009 .

[44]  W. Singer,et al.  Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology , 2006, Neuron.

[45]  Zhou Yubin,et al.  A Wireless EEG Sensors System for Computer Assisted Detection of Alpha Wave in Sleep , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[46]  G. Adler,et al.  EEG coherence in Alzheimer’s dementia , 2003, Journal of Neural Transmission.

[47]  Andrzej Cichocki,et al.  On the synchrony of steady state visual evoked potentials and oscillatory burst events , 2009, Cognitive Neurodynamics.

[48]  J Wackermann,et al.  Global dimensional complexity of multichannel EEG in mild Alzheimer's disease and age-matched cohorts. , 1997, Dementia and geriatric cognitive disorders.

[49]  J. Pernier,et al.  Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human , 1996, The Journal of Neuroscience.

[50]  M. Rosenblum,et al.  Identification of coupling direction: application to cardiorespiratory interaction. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  T. Fog.  ACTA NEUROLOGICA SCANDINAVICA , 1976 .

[52]  R Anghinah,et al.  [Alpha band coherence analysis of EEG in healthy adult's and Alzheimer's type dementia patients]. , 2000, Arquivos de neuro-psiquiatria.

[53]  José Carlos Príncipe,et al.  Correntropy as a Novel Measure for Nonlinearity Tests , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[54]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[55]  Don H. Johnson,et al.  Symmetrizing the Kullback-Leibler Distance , 2001 .

[56]  P L Calderón,et al.  [The role of the cerebral coherence in the progress of the patient with Alzheimer's disease]. , 1997, Revista de neurologia.

[57]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[58]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[59]  Dong Ming,et al.  Linear and Nonlinear Quantitative EEG Analysis , 2008, IEEE Engineering in Medicine and Biology Magazine.

[60]  Tilo Kircher,et al.  Dynamic regulation of EEG power and coherence is lost early and globally in probable DAT , 2001, European Archives of Psychiatry and Clinical Neuroscience.

[61]  G. Rangarajan,et al.  Multiple Nonlinear Time Series with Extended Granger Causality , 2004 .

[62]  A. Cichocki,et al.  EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease , 2005, Clinical Neurophysiology.

[63]  Andrzej Cichocki,et al.  Quantifying Statistical Interdependence by Message Passing on Graphs—Part I: One-Dimensional Point Processes , 2009, Neural Computation.

[64]  Maren Grigutsch,et al.  EEG oscillations and wavelet analysis , 2005 .

[65]  S. Rombouts,et al.  Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease , 2005, Clinical Neurophysiology.

[66]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[67]  C. Babiloni,et al.  Conversion from mild cognitive impairment to Alzheimer’s disease is predicted by sources and coherence of brain electroencephalography rhythms , 2006, Neuroscience.

[68]  Vassilis Tsiaras,et al.  Assessment of linear and non-linear EEG synchronization measures for evaluating mild epileptic signal patterns , 2006 .

[69]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[70]  F. Vialatte.  Modélisation en bosses pour l'analyse de motifs oscillatoires reproductibles dans l'activité de populations neuronales: applications à l'apprentissage olfactif chez l'animal et à la détection précoce de la maladie d'Alzheimer , 2005 .

[71]  R Quian Quiroga,et al.  Wavelet entropy: a measure of order in evoked potentials. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[72]  Renato Anghinah,et al.  Estudo da coerência do eletrencefalograma para a banda de frequência alfa em indivíduos adultos normais e com provável demência do tipo Alzheimer , 2000 .

[73]  Z. Jiang,et al.  Study on EEG power and coherence in patients with mild cognitive impairment during working memory task , 2005, Journal of Zhejiang University. Science. B.

[74]  Blanco,et al.  Time-frequency analysis of electroencephalogram series. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[75]  F. Babiloni,et al.  Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data , 2005, Clinical Neurophysiology.

[76]  Görsev Yener,et al.  Decrease of evoked delta, theta and alpha coherences in Alzheimer patients during a visual oddball paradigm , 2008, Brain Research.

[77]  T. Gasser,et al.  EEG coherence in Alzheimer disease. , 1994, Electroencephalography and clinical neurophysiology.

[78]  M. Breakspear.  "Dynamic" connectivity in neural systems: theoretical and empirical considerations. , 2004, Neuroinformatics.

[79]  Selin Aviyente,et al.  A measure of mutual information on the time-frequency plane , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[80]  Z. Šidák.  Rectangular Confidence Regions for the Means of Multivariate Normal Distributions , 1967 .

[81]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[82]  D. Selkoe.  Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[83]  Laura Astolfi,et al.  Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG. , 2004, Magnetic resonance imaging.

[84]  Richard Kronland-Martinet,et al.  Asymptotic wavelet and Gabor analysis: Extraction of instantaneous frequencies , 1992, IEEE Trans. Inf. Theory.

[85]  Toshihiko Kinoshita,et al.  Global Approach to Multichannel Electroencephalogram Analysis for Diagnosis and Clinical Evaluation in Mild Alzheimer’s Disease , 2004, Neuropsychobiology.

[86]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[87]  S. Nakaaki,et al.  Relationship between EEG dimensional complexity and neuropsychological findings in Alzheimer's disease , 2000, Psychiatry and clinical neurosciences.

[88]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[89]  R. Chapman,et al.  Brain event-related potentials: Diagnosing early-stage Alzheimer's disease , 2007, Neurobiology of Aging.

[90]  Andrzej Cichocki,et al.  Split-test Bonferroni correction for QEEG statistical maps , 2008, Biological Cybernetics.

[91]  C. Granger.  Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[92]  M. Rowan,et al.  Memory-related EEG power and coherence reductions in mild Alzheimer's disease. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[93]  Carlos Couto,et al.  Low-power 2.4-GHz RF transceiver for wireless EEG module plug-and-play , 2006, 2006 13th IEEE International Conference on Electronics, Circuits and Systems.

[94]  T. Demiralp,et al.  Human EEG gamma oscillations in neuropsychiatric disorders , 2005, Clinical Neurophysiology.

[95]  Rémi Gervais,et al.  A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics , 2007, Neural Networks.

[96]  R. Gervais,et al.  Blind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer's Disease , 2022 .

[97]  C. Stam,et al.  EEG synchronization in mild cognitive impairment and Alzheimer's disease , 2003, Acta neurologica Scandinavica.

[98]  Steffen Moritz,et al.  Late-Onset Depression with Mild Cognitive Deficits: Electrophysiological Evidences for a Preclinical Dementia Syndrome , 2004, Dementia and Geriatric Cognitive Disorders.

[99]  Claudio Babiloni,et al.  Abnormal fronto‐parietal coupling of brain rhythms in mild Alzheimer's disease: a multicentric EEG study , 2004, The European journal of neuroscience.

[100]  Z. Hidasi,et al.  Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer's disease , 2009, Journal of the Neurological Sciences.

[101]  Dietrich Lehmann,et al.  Global, Regional, and Local Measures of Complexity of Multichannel Electroencephalography in Acute, Neuroleptic-Naive, First-Break Schizophrenics , 1998, Biological Psychiatry.

[102]  Patrick Celka,et al.  Carmeli's S index assesses motion and muscle artefact reduction in rowers' electrocardiograms , 2006, Physiological measurement.

[103]  Andrzej Cichocki,et al.  Measuring Neural Synchrony by Message Passing , 2007, NIPS.

[104]  M. Kaminski,et al.  Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method , 2003, Journal of Neuroscience Methods.

[105]  Jaeseung Jeong.  EEG dynamics in patients with Alzheimer's disease , 2004, Clinical Neurophysiology.

[106]  J. Gore,et al.  Mutual information analysis of the EEG in patients with Alzheimer's disease , 2001, Clinical Neurophysiology.

[107]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[108]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[109]  John D. Storey.  A direct approach to false discovery rates , 2002 .