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The diagnosis of Alzheimer's disease (AD) in routine clinical practice ismost commonly based on subjective clinical interpretations. Quantitativeelectroencephalography (QEEG) measures have been shown to reflectneurodegenerative processes in AD and might qualify as affordable and therebywidely available markers to facilitate the objectivization of AD assessment.Here, we present a novel framework combining Riemannian tangent space mappingand elastic net regression for the development of brain atrophy markers. Whilemost AD QEEG studies are based on small sample sizes and psychological testscores as outcome measures, here we train and test our models using data of oneof the largest prospective EEG AD trials ever conducted, including MRIbiomarkers of brain atrophy.

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease