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ARTEMIS SIGNED

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

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

ARTEMIS project word cloud

Explore the words cloud of the ARTEMIS project. It provides you a very rough idea of what is the project "ARTEMIS" about.

acquired outcomes healthy datasets lack data underlying necessitating prime outlining translational wmh neuroimaging extracted surrogate variations patients fact presentation outcome promise disability models predict subpar symptoms quality amounts life prediction enriching extracting thereby biological individuals poorly reduce fail mri aging society shown functional construct cohorts automated biology additional phenotypes burden acting pipeline protective tend phenotype distinguish settings newly mechanisms demographic manifest patient hyperintensities cognitive differences ways lies decline techniques stroke strategies economic contributors utilize despite group worldwide adverse prevention clinical disease white prevalence diseases pipelines latent candidate

Project "ARTEMIS" data sheet

The following table provides information about the project.

Coordinator	DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV Organization address address: SIGMUND FREUD STRASSE 27 city: BONN postcode: 53127 website: www.dzne.de 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	http://www.markus-schirmer.com/artemis.html
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

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV Organization address address: SIGMUND FREUD STRASSE 27 city: BONN postcode: 53127 website: www.dzne.de contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DE (BONN)	coordinator	239˙860.00
2	THE GENERAL HOSPITAL CORPORATION THE GENERAL HOSPITAL CORPORATION Organization address address: FRUIT STREET 55 city: BOSTON MA postcode: 2114 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	US (BOSTON MA)	partner	0.00

Map

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.

Publications

List of publications.
year	authors and title	journal	last update
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 JimÃ©nez-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 â€“ 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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The information about "ARTEMIS" are provided by the European Opendata Portal: CORDIS opendata.