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Assessment of Reserve: Translational Evaluation of Medical Images and Statistics-Prediction models for outcomes of brain health

Total Cost €


EC-Contrib. €






Project "ARTEMIS" data sheet

The following table provides information about the project.


Organization address
city: BONN
postcode: 53127

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website
 Total cost 239˙860 €
 EC max contribution 239˙860 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-GF
 Starting year 2017
 Duration (year-month-day) from 2017-04-01   to  2020-05-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Stroke and cognitive decline are among the leading contributors to disease burden and long-term disability worldwide. Despite their prevalence, the contributing disease processes are not fully understood. This is in part due to the lack of (early) prediction models and ways to characterize protective mechanisms, which can help to distinguish between patients and healthy individuals before symptoms manifest. Such prediction models can facilitate prevention strategies for adverse cognitive and functional outcomes, thereby enriching patients’ life quality and reduce the economic burden on society. Advanced neuroimaging techniques, such as MRI, have provided additional insight into the underlying disease biology. One major challenge when using neuroimaging techniques lies in the fact that large amounts of data are required to account for variations in clinical presentation and assessment, necessitating the use of dedicated pipelines for extracting phenotypes. However, most pipelines are developed in research settings and tend to fail when applied to real-life clinical cohorts, leading to a subpar use of rich, available patient datasets.

Here, a fully-automated, translational pipeline for extracting MRI phenotypes from data acquired in clinical and research settings is developed with a particular focus on outlining white matter hyperintensities (WMH). WMH are a common phenotype in aging and across diseases; however, group differences are poorly understood. This makes WMH a prime candidate for extracting additional information, which can be used for outcome prediction. The proposed prediction models utilize newly extracted characteristics, clinical/demographic information and a latent variable construct to predict general cognitive decline and outcome after stroke. In particular, the proposed latent variable has shown promise in acting as a surrogate measure for protective mechanisms in stroke patients, where its biological meaning is assessed as part of this project.


year authors and title journal last update
List of publications.
2019 Ona Wu, Stefan Winzeck, Anne-Katrin Giese, Brandon L. Hancock, Mark R. Etherton, Mark J.R.J. Bouts, Kathleen Donahue, Markus D. Schirmer, Robert E. Irie, Steven J.T. Mocking, Elissa C. McIntosh, Raquel Bezerra, Konstantinos Kamnitsas, Petrea Frid, Johan Wasselius, John W. Cole, Huichun Xu, Lukas Holmegaard, Jordi Jiménez-Conde, Robin Lemmens, Eric Lorentzen, Patrick F. McArdle, James F. Meschia,
Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
published pages: 1734-1741, ISSN: 0039-2499, DOI: 10.1161/strokeaha.119.025373
Stroke 50/7 2019-07-22
2019 Markus D. Schirmer, Anne-Katrin Giese, Panagiotis Fotiadis, Mark R. Etherton, Lisa Cloonan, Anand Viswanathan, Steven M. Greenberg, Ona Wu, Natalia S. Rost
Spatial Signature of White Matter Hyperintensities in Stroke Patients
published pages: , ISSN: 1664-2295, DOI: 10.3389/fneur.2019.00208
Frontiers in Neurology 10 2019-09-02
2019 Markus D. Schirmer, Ai Wern Chung, P. Ellen Grant, Natalia S. Rost
Network structural dependency in the human connectome across the life span
published pages: 1-15, ISSN: 2472-1751, DOI: 10.1162/netn_a_00081
Network Neuroscience 2019-09-02
2019 Markus D. Schirmer, Mark R. Etherton, MD, PhD, Adrian V. Dalca, PhD, Anne-Katrin Giese, MD, Lisa Cloonan, MSc, Ona Wu, PhD, Polina Golland, PhD, Natalia S. Rost, MD, MPH, FAAN
Effective Reserve: A Latent Variable to Improve Outcome Prediction in Stroke
published pages: 63-69, ISSN: 1052-3057, DOI: 10.1016/j.jstrokecerebrovasdis.2018.09.003
Journal of Stroke and Cerebrovascular Diseases 28/1 2019-09-02
2019 Markus D. Schirmer, Adrian V. Dalca, Ramesh Sridharan, Anne-Katrin Giese, Kathleen L. Donahue, Marco J. Nardin, Steven J.T. Mocking, Elissa C. McIntosh, Petrea Frid, Johan Wasselius, John W. Cole, Lukas Holmegaard, Christina Jern, Jordi Jimenez-Conde, Robin Lemmens, Arne G. Lindgren, James F. Meschia, Jaume Roquer, Tatjana Rundek, Ralph L. Sacco, Reinhold Schmidt, Pankaj Sharma, Agnieszka Slowik,
White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts – The MRI-GENIE study
published pages: 101884, ISSN: 2213-1582, DOI: 10.1016/j.nicl.2019.101884
NeuroImage: Clinical 23 2019-09-02

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