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. Fields of science medical and health sciencesclinical medicineangiologyvascular diseasesnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningmedical and health sciencesbasic medicineneurologystrokeengineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2016 - Individual Fellowships Call for proposal H2020-MSCA-IF-2016 See other projects for this call Funding Scheme MSCA-IF-GF - Global Fellowships Coordinator DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV Net EU contribution € 239 860,80 Address Venusberg-campus 1/99 53127 Bonn Germany See on map Region Nordrhein-Westfalen Köln Bonn, Kreisfreie Stadt Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Partners (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all Partner Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement. THE GENERAL HOSPITAL CORPORATION United States Net EU contribution € 0,00 Address Fruit street 55 02114 Boston ma See on map Links Contact the organisation Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 160 130,40
