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

Wolfgang Fruehwirt, Matthias Gerstgrasser, Pengfei Zhang, Leonard Weydemann, Markus Waser, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Dieter Grossegger, Heinrich Garn, Gareth W. Peters, Stephen Roberts, Georg Dorffner

Research output: Contribution to conferencePaper

Abstract

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.
Original languageEnglish
Publication statusPublished - 8 Dec 2017
EventNIPS 2017 Workshop on Optimization: 10th NIPS Workshop on Optimization for Machine Learning - Long Beach, Long Beach, United States
Duration: 8 Dec 2017 → …
Conference number: 10

Workshop

WorkshopNIPS 2017 Workshop on Optimization
Abbreviated titleNIPS
CountryUnited States
CityLong Beach
Period8/12/17 → …

Keywords

  • stat.ML
  • eess.SP
  • q-bio.NC

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    Fruehwirt, W., Gerstgrasser, M., Zhang, P., Weydemann, L., Waser, M., Schmidt, R., Benke, T., Dal-Bianco, P., Ransmayr, G., Grossegger, D., Garn, H., Peters, G. W., Roberts, S., & Dorffner, G. (2017). Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease. Paper presented at NIPS 2017 Workshop on Optimization, Long Beach, United States. https://arxiv.org/pdf/1711.08359.pdf