Using EEG, SPECT, and Multivariate Resampling Methods to Differentiate Between Alzheimer's and other Cognitive Impairments

Abstract

The incidence of Alzheimer's disease (AD) and other forms of dementia is increasing in most western countries. For a precise and early diagnosis, several examination modalities exist, among them single-photon emission computed tomography (SPECT) and the electroencephalogram (EEG). The latter is highly available, free of radiation hazards, and non-invasive. Thus, its diagnostic utility regarding different stages of dementia is of great interest in neurological research, along with the question of whether its utility depends on age or sex of the person being examined. However, SPECT or EEG measurements are intrinsically multivariate, and there has been a shortage of sufficiently general inferential techniques for the analysis of multivariate data in factorial designs when neither multivariate normality nor equality of covariance matrices across groups should be assumed. We adapt an asymptotic model

Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM

“…The validity of this procedure (also named parametric bootstrap WTS) is proven in Konietschke et al (2015). This procedure is not only applicable for MANOVA but also for RM designs (Bathke et al (2016)). However, Friedrich et al (2017a) proposed a more favourable technique for RM designs.…”

“…We consider the data set EEG from the MANOVA.RM package: At the Department of Neurology, University Clinic of Salzburg, 160 patients were diagnosed with either AD, MCI, or SCC, based on neuropsychological diagnostics (Bathke et al, 2016) We find significant effects at level α = 0.01 of the whole-plot factors sex and diagnosis, while none of the sub-plot factors or interactions become significant.…”

“…• the Wald-type statistic (WTS) using the asymptotic χ 2 -distribution (Brunner, 2001), the permutation technique proposed in Friedrich et al (2017a) as well as a parametric (Bathke et al, 2016) and a wild bootstrap approach for p-value estimation.…”