Cel Functional magnetic resonance imaging (fMRI) is the dominating approach to research in the mapping of neural activity in the human brain. State of the art data analysis techniques employ a statistical parametric mapping (SPM) strategy to convert raw signal into interpretable images by processing data in a pipeline of task-specific modules. This approach, despite its simplicity and reliability, presents a set of inconveniences, including low interconnectivity among modules, resulting in suboptimal solutions. In this project we aim at making a major contribution to the field by replacing the step-by-step data processing pipeline by a deep neural network. We hypothesise that this will achieve better solutions by propagating the effects of module-based decisions through the network, jointly optimizing the whole processing pipeline. Moreover, fMRI low temporal resolution will be alleviated by means of a post-processing treatment, where advanced interpolation techniques will be used. We will release a freely accessible software tool that integrates with SPM, supplying an easy-to-use framework including advanced techniques for an automatic multivariate non-linear data analysis. The generated deep network solution will be applied in a multidisciplinary study in neurofeedback, where subjects will learn relaxation strategies guided by fMRI technology. At the end of the project, we expect our tool to become a useful standard practise in the field. Dziedzina nauki natural sciencescomputer and information sciencessoftwarenatural sciencescomputer and information sciencesartificial intelligencecomputer visionengineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imagingnatural sciencescomputer and information sciencesdata sciencedata processingnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence 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-2014-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF) Zaproszenie do składania wniosków H2020-MSCA-IF-2014 Zobacz inne projekty w ramach tego zaproszenia System finansowania MSCA-IF-EF-ST - Standard EF Koordynator DANMARKS TEKNISKE UNIVERSITET Wkład UE netto € 212 194,80 Adres ANKER ENGELUNDS VEJ 101 2800 Kongens Lyngby Dania Zobacz na mapie Region Danmark Hovedstaden Københavns omegn Rodzaj działalności Higher or Secondary Education Establishments 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 € 212 194,80
