Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer's Disease

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.

[1]  Edward Challis,et al.  Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI , 2015, NeuroImage.

[2]  Shih-Jen Tsai,et al.  The Influence of Apolipoprotein E Epsilon4 Polymorphism on qEEG Profiles in Healthy Young Females: A Resting EEG Study , 2012, Brain Topography.

[3]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[4]  A. Howe,et al.  The clinical utility of the auditory P300 latency subcomponent event-related potential in preclinical diagnosis of patients with mild cognitive impairment and Alzheimer’s disease , 2014, Brain and Cognition.

[5]  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.

[6]  Rudolph E. Tanzi,et al.  Alzheimer's disease: the cholesterol connection , 2003, Nature Neuroscience.

[7]  R. Adler,et al.  Random Fields and Geometry , 2007 .

[8]  Alexandre Barachant,et al.  A Plug&Play P300 BCI Using Information Geometry , 2014, ArXiv.

[9]  Joachim M. Buhmann,et al.  The Balanced Accuracy and Its Posterior Distribution , 2010, 2010 20th International Conference on Pattern Recognition.

[10]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[11]  Alexandre Barachant,et al.  A New Generation of Brain-Computer Interface Based on Riemannian Geometry , 2013, ArXiv.

[12]  A. Howe,et al.  Meta-analysis of the endogenous N200 latency event-related potential subcomponent in patients with Alzheimer’s disease and mild cognitive impairment , 2014, Clinical Neurophysiology.

[13]  Mark H. Johnson,et al.  Novel Machine Learning Methods for ERP Analysis: A Validation From Research on Infants at Risk for Autism , 2012, Developmental neuropsychology.

[14]  D A Bennett,et al.  Rate of cognitive decline and mortality in Alzheimer’s disease , 2003, Neurology.

[15]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[16]  Carl E. Rasmussen,et al.  Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..

[17]  A. Levey,et al.  Event-related potential changes in groups at increased risk for Alzheimer disease. , 1999, Archives of neurology.

[18]  Marta Kutas,et al.  Cognitive event-related potentials: Biomarkers of synaptic dysfunction across the stages of Alzheimer's disease , 2011 .

[19]  Manfred Kaps,et al.  Effect of ApoE ε4 Allele on Visual Evoked Potentials and Resultant Flow Coupling in Patients With Alzheimer , 2010, Journal of geriatric psychiatry and neurology.

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

[21]  Huaxi Xu,et al.  Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy , 2013, Nature Reviews Neurology.

[22]  L. Canuet,et al.  Resting-State Network Disruption and APOE Genotype in Alzheimer's Disease: A lagged Functional Connectivity Study , 2012, PloS one.

[23]  Jean-François Dartigues,et al.  Fast Cognitive Decline at the Time of Dementia Diagnosis: A Major Prognostic Factor for Survival in the Community , 2007, Dementia and Geriatric Cognitive Disorders.

[24]  D. Hu,et al.  Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. , 2012, Brain : a journal of neurology.

[25]  Bernd Saletu,et al.  Artifact processing in topographic mapping of electroencephalographie activity in neuropsychopharmacology , 1992, Psychiatry Research: Neuroimaging.