Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks

Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.

[1]  M. Lawton,et al.  Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.

[2]  M. Lawton,et al.  Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living , 1969 .

[3]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[4]  V. Leirer,et al.  Development and validation of a geriatric depression screening scale: a preliminary report. , 1982, Journal of psychiatric research.

[5]  C. P. Hughes,et al.  A New Clinical Scale for the Staging of Dementia , 1982, British Journal of Psychiatry.

[6]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[7]  R J Sclabassi,et al.  Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. , 1986, Electroencephalography and clinical neurophysiology.

[8]  C F Reynolds,et al.  Diagnostic efficacy of computerized spectral versus visual EEG analysis in elderly normal, demented and depressed subjects. , 1988, Electroencephalography and clinical neurophysiology.

[9]  R. Llinás,et al.  The functional states of the thalamus and the associated neuronal interplay. , 1988, Physiological reviews.

[10]  S. L. Visser,et al.  Reliability, validity and follow-up of the EEG in senile dementia: sequelae of sequential measurement. , 1990, Electroencephalography and clinical neurophysiology.

[11]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[12]  P. M. Rossini,et al.  Brain excitability and electroencephalographic activation: non-invasive evaluation in healthy humans via transcranial magnetic stimulation , 1991, Brain Research.

[13]  Thomas Dierks,et al.  Dementia of the alzheimer type: Effects on the spontaneous EEG described by dipole sources , 1993, Psychiatry Research: Neuroimaging.

[14]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[15]  G. C. Román,et al.  Vascular dementia , 1993, Neurology.

[16]  Clinical and neuropathological criteria for frontotemporal dementia. The Lund and Manchester Groups. , 1994, Journal of neurology, neurosurgery, and psychiatry.

[17]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[18]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[19]  H. Braak,et al.  Staging of alzheimer's disease-related neurofibrillary changes , 1995, Neurobiology of Aging.

[20]  Aljoscha C. Neubauer,et al.  Ultradian rhythms in cognitive performance: no evidence for a 1.5-h rhythm , 1995, Biological Psychology.

[21]  W. Klimesch,et al.  Alpha frequency, reaction time, and the speed of processing information. , 1996, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[22]  O Almkvist,et al.  Quantitative electroencephalography power and coherence in Alzheimer's disease and mild cognitive impairment. , 1996, Dementia.

[23]  G C Muscas,et al.  Correlations of topographical EEG features with clinical severity in mild and moderate dementia of Alzheimer type. , 1997, Neuropsychobiology.

[24]  M. Kaminski,et al.  Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness. , 1997, Electroencephalography and clinical neurophysiology.

[25]  P. Scheltens,et al.  Diagnosing Alzheimer's disease in community-dwelling elderly: a comparison of EEG and MRI. , 1997, Dementia and geriatric cognitive disorders.

[26]  H Sattel,et al.  Discrimination of Alzheimer's disease and normal aging by EEG data. , 1997, Electroencephalography and clinical neurophysiology.

[27]  M. Kaminski,et al.  Information flow between hippocampus and related structures during various types of rat's behavior , 1997, Journal of Neuroscience Methods.

[28]  E. Jonkman,et al.  The role of the electroencephalogram in the diagnosis of dementia of the Alzheimer type: an attempt at technology assessment , 1997, Neurophysiologie Clinique/Clinical Neurophysiology.

[29]  C J Stam,et al.  Usefulness of non‐linear EEG analysis , 1998, Acta neurologica Scandinavica.

[30]  Hans Förstl,et al.  Clinical features of Alzheimer’s disease , 1999, European Archives of Psychiatry and Clinical Neuroscience.

[31]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[32]  C Jonker,et al.  The diagnostic value of electroencephalography in mild senile Alzheimer's disease , 1999, Clinical Neurophysiology.

[33]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[34]  T Dierks,et al.  Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study , 2000, Clinical Neurophysiology.

[35]  Thomas Dierks,et al.  Spatial pattern of cerebral glucose metabolism (PET) correlates with localization of intracerebral EEG-generators in Alzheimer's disease , 2000, Clinical Neurophysiology.

[36]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

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

[38]  M. Fuchs,et al.  A standardized boundary element method volume conductor model , 2002, Clinical Neurophysiology.

[39]  D. Lehmann,et al.  Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. , 2002, Methods and findings in experimental and clinical pharmacology.

[40]  N. Ponomareva,et al.  EEG Alterations in Subjects at High Familial Risk for Alzheimer’s Disease , 2003, Neuropsychobiology.

[41]  Cerebello-cerebral interactions during states of vigilance. , 2003, Cerebellum.

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

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

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

[45]  Nicholas Ayache,et al.  Automatic Classification of SPECT Images of Alzheimer's Disease Patients and Control Subjects , 2004, MICCAI.

[46]  Claudio Del Percio,et al.  Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study , 2004, NeuroImage.

[47]  P. Scheltens,et al.  Use of laboratory and imaging investigations in dementia , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[48]  S Minoshima,et al.  Diagnosis and management of dementia with Lewy bodies , 2005, Neurology.

[49]  Mohammad J Taherzadeh,et al.  Ethanol production from glucose and dilute-acid hydrolyzates by encapsulated S. cerevisiae. , 2005, Biotechnology and bioengineering.

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

[51]  Lars Arendt-Nielsen,et al.  Slow cortical potential shifts preceding sensorimotor interactions , 2005, Brain Research Bulletin.

[52]  Vitoantonio Bevilacqua,et al.  A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast Cancer Classification Problem , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[53]  Andrew C. N. Chen,et al.  Anticipation of somatosensory and motor events increases centro-parietal functional coupling: An EEG coherence study , 2006, Clinical Neurophysiology.

[54]  Roberto D. Pascual-Marqui,et al.  Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization , 2007, 0710.3341.

[55]  Valer Jurcak,et al.  10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems , 2007, NeuroImage.

[56]  Christoph Lehmann,et al.  Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.

[57]  P. Scheltens,et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria , 2007, The Lancet Neurology.

[58]  Roberto D. Pascual-Marqui,et al.  Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: frequency decomposition , 2007, 0711.1455.

[59]  Juan Manuel Górriz,et al.  Automatic Classification System for the Diagnosis of Alzheimer Disease Using Component-Based SVM Aggregations , 2008, ICONIP.

[60]  Claudio Babiloni,et al.  White matter vascular lesions are related to parietal‐to‐frontal coupling of EEG rhythms in mild cognitive impairment , 2008, Human brain mapping.

[61]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[62]  Claudio Babiloni,et al.  Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease , 2009, NeuroImage.

[63]  J. Kowalski,et al.  Evidence-Based Evaluation of Diagnostic Accuracy of Resting EEG in Dementia and Mild Cognitive Impairment , 2009, Clinical EEG and neuroscience.

[64]  H. Vankova.  Mini Mental State , 2010 .

[65]  Claudio Babiloni,et al.  Global functional coupling of resting EEG rhythms is related to white-matter lesions along the cholinergic tracts in subjects with amnesic mild cognitive impairment. , 2010, Journal of Alzheimer's disease : JAD.

[66]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[67]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease: Report of the NINCDS—ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease , 2011, Neurology.

[68]  P. Rossini,et al.  Resting state cortical electroencephalographic rhythms and white matter vascular lesions in subjects with Alzheimer's disease: an Italian multicenter study. , 2011, Journal of Alzheimer's disease : JAD.

[69]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[70]  Ana Carolina Lorena,et al.  Improving Alzheimer's Disease Diagnosis with Machine Learning Techniques , 2011, Clinical EEG and neuroscience.

[71]  R. Pascual-Marqui,et al.  Resting-State EEG Source Localization and Functional Connectivity in Schizophrenia-Like Psychosis of Epilepsy , 2011, PloS one.

[72]  Rolando J. Biscay-Lirio,et al.  Assessing interactions in the brain with exact low-resolution electromagnetic tomography , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[73]  Clifford R. Jack,et al.  Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: Recommendations from the Alzheimer’s Association Research Roundtable Workgroup , 2011, Alzheimer's & Dementia.

[74]  G. B. Frisoni,et al.  MCI patients’ EEGs show group differences between those who progress and those who do not progress to AD , 2011, Neurobiology of Aging.

[75]  Nick C Fox,et al.  The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[76]  Katarzyna J. Blinowska,et al.  Review of the methods of determination of directed connectivity from multichannel data , 2011, Medical & Biological Engineering & Computing.

[77]  Ki-Young Jung,et al.  Region and frequency specific changes of spectral power in Alzheimer’s disease and mild cognitive impairment , 2011, Clinical Neurophysiology.

[78]  P. Rossini,et al.  Stability of clinical condition in mild cognitive impairment is related to cortical sources of alpha rhythms: An electroencephalographic study , 2011, Human brain mapping.

[79]  Nick C Fox,et al.  The Diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease , 2011 .

[80]  P. Rossini,et al.  Effects of acetylcholinesterase inhibitors and memantine on resting-state electroencephalographic rhythms in Alzheimer’s disease patients , 2013, Clinical Neurophysiology.

[81]  C. Babiloni,et al.  Resting state EEG rhythms as network disease markers for drug discovery in alzheimer's disease , 2013 .

[82]  P. Rossini,et al.  Effects of pharmacological agents, sleep deprivation, hypoxia and transcranial magnetic stimulation on electroencephalographic rhythms in rodents: Towards translational challenge models for drug discovery in Alzheimer’s disease , 2013, Clinical Neurophysiology.

[83]  Nick C Fox,et al.  Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria , 2014, The Lancet Neurology.

[84]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[85]  Vitoantonio Bevilacqua,et al.  Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[86]  Loreto Gesualdo,et al.  Occipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer's disease , 2015, Neurobiology of Aging.

[87]  Knut Engedal,et al.  Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup , 2015, Dementia and Geriatric Cognitive Disorders.

[88]  Vitoantonio Bevilacqua,et al.  An Optimized Feed-forward Artificial Neural Network Topology to Support Radiologists in Breast Lesions Classification , 2016, GECCO.

[89]  C. Babiloni,et al.  Brain neural synchronization and functional coupling in Alzheimer's disease as revealed by resting state EEG rhythms. , 2016, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[90]  P. Rossini,et al.  Neurophysiological assessment of Alzheimer's disease individuals by a single electroencephalographic marker. , 2015, Journal of Alzheimer's disease : JAD.

[91]  G. Frisoni,et al.  Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms , 2016, Front. Neurosci..

[92]  Dennis G. Zill,et al.  Advanced Engineering Mathematics , 2021, Technometrics.