Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
This work addresses the need for objective, affordable diagnostic tools for Alzheimer's disease in clinical practice, though it appears incremental as it combines existing methods (Riemannian tangent space mapping and elastic net regression) on new data.
The researchers tackled the problem of diagnosing Alzheimer's disease by developing a cost-effective EEG-based framework to predict brain atrophy markers, achieving results using data from one of the largest prospective EEG trials with MRI biomarkers.
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.