Explore the words cloud of the ARTEMIS project. It provides you a very rough idea of what is the project "ARTEMIS" about.
The following table provides information about the project.
DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV
|Coordinator Country||Germany [DE]|
|Total cost||239˙860 €|
|EC max contribution||239˙860 € (100%)|
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
|Duration (year-month-day)||from 2017-04-01 to 2020-05-31|
Take a look of project's partnership.
|1||DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV||DE (BONN)||coordinator||239˙860.00|
|2||THE GENERAL HOSPITAL CORPORATION||US (BOSTON MA)||partner||0.00|
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|
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
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|
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
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|
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|
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "ARTEMIS" project.
For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.
Send me an email (firstname.lastname@example.org) and I put them in your project's page as son as possible.
Thanks. And then put a link of this page into your project's website.
The information about "ARTEMIS" are provided by the European Opendata Portal: CORDIS opendata.
Mathematics AnalogiesRead More
Narrative, Writing, and the Teotihuacan Language: Exploring Language History Through Phylogenetics, Epigraphy and IconographyRead More
Positive and Negative Asymmetry in Intergroup Contact: Its Impact on Linguistic Forms of Communication and Physiological ResponsesRead More