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The incidence of Alzheimer's disease (AD) and other forms of dementia isincreasing in most western countries. For a precise and early diagnosis,several examination modalities exist, among them single-photon emissioncomputed tomography (SPECT) and the electroencephalogram (EEG). The latter ishighly available, free of radiation hazards, and non-invasive. Thus, itsdiagnostic utility regarding different stages of dementia is of great interestin neurological research, along with the question of whether its utilitydepends on age or sex of the person being examined. However, SPECT or EEGmeasurements are intrinsically multivariate, and there has been a shortage ofsufficiently general inferential techniques for the analysis of multivariatedata in factorial designs when neither multivariate normality nor equality ofcovariance matrices across groups should be assumed. We adapt an asymptoticmodel based (parametric) bootstrap approach to this situation, demonstrate itsability, and use it for a truly multivariate analysis of the EEG and SPECTmeasurements, taking into account demographic factors such as age and sex.These multivariate results are supplemented by marginal effects bootstrapinference whose theoretical properties can be derived analogously to themultivariate methods. Both inference approaches can have advantages inparticular situations, as illustrated in the data analysis.

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