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

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 conference › Paper › peer-review

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 language	English
Publication status	Published - 8 Dec 2017
Event	NIPS 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

Workshop	NIPS 2017 Workshop on Optimization
Abbreviated title	NIPS
Country/Territory	United States
City	Long Beach
Period	8/12/17 → …

Keywords

stat.ML
eess.SP
q-bio.NC

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https://arxiv.org/pdf/1711.08359.pdf

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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, California, United States. https://arxiv.org/pdf/1711.08359.pdf

@conference{afaad1ffcb5f42ee9333436677b93c5a,

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

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. ",

keywords = "stat.ML, eess.SP, q-bio.NC",

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

note = "Presented at NIPS 2017 Workshop on Machine Learning for Health; NIPS 2017 Workshop on Optimization : 10th NIPS Workshop on Optimization for Machine Learning, NIPS ; Conference date: 08-12-2017",

year = "2017",

month = dec,

day = "8",

language = "English",

}

Fruehwirt, W, Gerstgrasser, M, Zhang, P, Weydemann, L, Waser, M, Schmidt, R, Benke, T, Dal-Bianco, P, Ransmayr, G, Grossegger, D, Garn, H, Peters, GW, 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, 8/12/17. <https://arxiv.org/pdf/1711.08359.pdf>

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease. / Fruehwirt, Wolfgang; Gerstgrasser, Matthias; Zhang, Pengfei et al.
2017. Paper presented at NIPS 2017 Workshop on Optimization, Long Beach, California, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease

AU - Fruehwirt, Wolfgang

AU - Gerstgrasser, Matthias

AU - Zhang, Pengfei

AU - Weydemann, Leonard

AU - Waser, Markus

AU - Schmidt, Reinhold

AU - Benke, Thomas

AU - Dal-Bianco, Peter

AU - Ransmayr, Gerhard

AU - Grossegger, Dieter

AU - Garn, Heinrich

AU - Peters, Gareth W.

AU - Roberts, Stephen

AU - Dorffner, Georg

N1 - Conference code: 10

PY - 2017/12/8

Y1 - 2017/12/8

N2 - 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.

AB - 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.

KW - stat.ML

KW - eess.SP

KW - q-bio.NC

M3 - Paper

T2 - NIPS 2017 Workshop on Optimization

Y2 - 8 December 2017

ER -

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

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