Multimodal EEG, MRI and PET data fusion for Alzheimer's disease diagnosis

Abstract

Alarmingly increasing prevalence of Alzheimer's disease (AD) due to the aging population in developing countries, combined with lack of standardized and conclusive diagnostic procedures, make early diagnosis of Alzheimer's disease a major public health concern. While no current medical treatment exists to stop or reverse this disease, recent dementia specific pharmacological advances can slow its progression, making early diagnosis all the more important. Several noninvasive biomarkers have been proposed, including P300 based EEG analysis, MRI volumetric analysis, PET based metabolic activity analysis, as alternatives to neuropsychological evaluation, the current gold standard of diagnosis. Each of these approaches, have shown some promising outcomes, however, a comprehensive data fusion analysis has not yet been conducted to investigate whether these different modalities carry complementary information, and if so, whether they can be combined to provide a more accurate analysis. In this effort, we provide a first look at such an analysis in combining EEG, MRI and PET data using an ensemble of classifiers based decision fusion approach, to determine whether a strategic combination of these different modalities can improve the diagnostic accuracy over any of the individual data sources when used with an automated classifier. Results show an improvement of up to 10%-20% using this approach compared to the classification performance obtained when using each individual data source.