Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

[1]  T. Sejnowski,et al.  Dynamic Brain Sources of Visual Evoked Responses , 2002, Science.

[2]  B. Jansen,et al.  Phase synchronization of the ongoing EEG and auditory EP generation , 2003, Clinical Neurophysiology.

[3]  Andrzej Cichocki,et al.  Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin? , 2011, International journal of Alzheimer's disease.

[4]  M. Tsolaki,et al.  Cognitive event-related potentials: Longitudinal changes in mild cognitive impairment , 2011, Clinical Neurophysiology.

[5]  Gérard Rondouin,et al.  Diagnostic Value of Event-Related Evoked Potentials N200 and P300 Subcomponents in Early Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment , 2007, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[6]  J. Morris,et al.  Predictors of preclinical Alzheimer disease and dementia: a clinicopathologic study. , 2005, Archives of neurology.

[7]  J. Polich.  Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[8]  P. Good,et al.  Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .

[9]  Claudio Babiloni,et al.  Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mild cognitive impairment and Alzheimer's disease , 2013, Human brain mapping.

[10]  A. A. Ioannides,et al.  Single-trial variability in early visual neuromagnetic responses: an explorative study based on the regional activation contributing to the N70m peak , 2003, NeuroImage.

[11]  G. Caravaglios,et al.  Auditory event-related potentials in subcortical vascular cognitive impairment and in Alzheimer’s disease , 2006, Journal of Neural Transmission.

[12]  S. Fotopoulos,et al.  Mining information from event-related recordings , 2004, IEEE Signal Processing Magazine.

[13]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[14]  Andrzej Cichocki,et al.  Multiway array decomposition analysis of EEGs in Alzheimer's disease , 2012, Journal of Neuroscience Methods.

[15]  Arjen van Ooyen,et al.  Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease , 2009, Proceedings of the National Academy of Sciences.

[16]  R. VanRullen,et al.  Ongoing EEG Phase as a Trial-by-Trial Predictor of Perceptual and Attentional Variability , 2011, Front. Psychology.

[17]  Yong He,et al.  Mapping the Alzheimer’s Brain with Connectomics , 2012, Front. Psychiatry.

[18]  Roberto Hornero,et al.  A Study on the Possible Usefulness of Detrended Fluctuation Analysis of the Electroencephalogram Background Activity in Alzheimer's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[19]  Thomas Grunwald,et al.  Neural Bases of Cognitive ERPs: More than Phase Reset , 2004, Journal of Cognitive Neuroscience.

[20]  E. Tangalos,et al.  Mild Cognitive Impairment Clinical Characterization and Outcome , 1999 .

[21]  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.

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

[23]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[24]  J. Polich,et al.  Alzheimer's disease and P300: review and evaluation of task and modality. , 2005, Current Alzheimer research.

[25]  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.

[26]  G. Rousselet,et al.  Single-Trial Analyses: Why Bother? , 2011, Front. Psychology.

[27]  E. Anderson.  Hudson et al. , 1977 .

[28]  E. Golob,et al.  Auditory cortical activity in amnestic mild cognitive impairment: relationship to subtype and conversion to dementia. , 2007, Brain : a journal of neurology.

[29]  R. Homma,et al.  Understanding and characterizing Olfactory responses [A manifold learning approach based on optical recordings] , 2008, IEEE Engineering in Medicine and Biology Magazine.

[30]  Claudio Del Percio,et al.  Development and assessment of methods for detecting dementia using the human electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[31]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[32]  Dejan Vucinic,et al.  A Manifold Learning Approachcto Understanding and Characterizing Olfactory Responses from Optical-recordings , 2008 .

[33]  Frank Padberg,et al.  Value of event-related P300 subcomponents in the clinical diagnosis of mild cognitive impairment and Alzheimer's Disease. , 2002, Psychophysiology.

[34]  A. Cichocki,et al.  Diagnosis of Alzheimer's disease from EEG signals: where are we standing? , 2010 .

[35]  Salil H. Patel,et al.  Characterization of N200 and P300: Selected Studies of the Event-Related Potential , 2005, International journal of medical sciences.

[36]  A Bezerianos,et al.  Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals. , 1997, Electroencephalography and clinical neurophysiology.

[37]  Claudio Del Percio,et al.  Donepezil effects on sources of cortical rhythms in mild Alzheimer's disease: Responders vs. Non-Responders , 2006, NeuroImage.

[38]  N. Laskaris,et al.  Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses , 2002, Clinical Neurophysiology.

[39]  Magda Tsolaki,et al.  Usefulness of event-related potentials in the assessment of mild cognitive impairment , 2008, BMC Neuroscience.

[40]  Andreas A. Ioannides,et al.  Exploratory data analysis of evoked response single trials based on minimal spanning tree , 2001, Clinical Neurophysiology.

[41]  C. Jack,et al.  Mild cognitive impairment – beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment , 2004, Journal of internal medicine.

[42]  J. Morris,et al.  Mild cognitive impairment as a clinical entity and treatment target. , 2005, Archives of neurology.

[43]  Chiou-Lian Lai,et al.  The role of event-related potentials in cognitive decline in Alzheimer’s disease , 2010, Clinical Neurophysiology.

[44]  O. Jensen,et al.  Rhythmic Pulsing: Linking Ongoing Brain Activity with Evoked Responses , 2010, Front. Hum. Neurosci..

[45]  Edward J Golob,et al.  Auditory event-related potentials during target detection are abnormal in mild cognitive impairment , 2002, Clinical Neurophysiology.

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

[47]  G. Caravaglios,et al.  Decreased amplitude of auditory event-related delta responses in Alzheimer's disease. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.