Cel 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. Dziedzina nauki medical and health sciencesclinical medicineangiologyvascular diseasesnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningmedical and health sciencesbasic medicineneurologystrokeengineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging Program(-y) 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 Temat(-y) MSCA-IF-2016 - Individual Fellowships Zaproszenie do składania wniosków H2020-MSCA-IF-2016 Zobacz inne projekty w ramach tego zaproszenia System finansowania MSCA-IF-GF - Global Fellowships Koordynator DEUTSCHES ZENTRUM FUR NEURODEGENERATIVE ERKRANKUNGEN EV Wkład UE netto € 239 860,80 Adres VENUSBERG-CAMPUS 1/99 53127 Bonn Niemcy Zobacz na mapie Region Nordrhein-Westfalen Köln Bonn, Kreisfreie Stadt Rodzaj działalności Research Organisations Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Uczestnictwo w unijnych programach w zakresie badań i innowacji Opens in new window sieć współpracy HORIZON Opens in new window Koszt całkowity € 239 860,80 Partnerzy (1) Sortuj alfabetycznie Sortuj według wkładu UE netto Rozwiń wszystko Zwiń wszystko Partner Organizacje partnerskie biorą udział w realizacji działania, jednak nie podpisują umowy o grant. THE GENERAL HOSPITAL CORPORATION Stany Zjednoczone Wkład UE netto € 0,00 Adres FRUIT STREET 55 02114 Boston Ma Zobacz na mapie Rodzaj działalności Other Linki Kontakt z organizacją Opens in new window Uczestnictwo w unijnych programach w zakresie badań i innowacji Opens in new window sieć współpracy HORIZON Opens in new window Koszt całkowity € 160 130,40
