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LINUX Cluster Project

Individualized Functional Connectome Analyses: Validation and Application


  • Name: Klinik und Poliklinik für Radiologie
  • Address: Marchioninistraße 15, 81377 München
  • Project Proposal Date: 2019-08-19 16:49:10


Abstract The capacity to identify the unique functional architecture of an individual’s brain is a critical step towards personalized medicine and understanding the neural basis of variations in human cognition and behavior. Recently, we developed a novel cortical parcellation approach to accurately map the functional organization at the individual level using resting-state fMRI (Wang et al., 2015). Although the algorithm was tested across different subject populations and data types including task fMRI data, a validation by improved correspondence to neurocognitive parameters remains to be performed. Our aim is to test if individualized functional connectivity measures can predict neurocognitive performance more accurately than classic atlas-based connectivity measures. Furthermore, we aim to supply the individualized connectivity measures back to the NAKO database, in order to enable other researchers to investigate association of individualized connectivity measures with other parameters obtained within the NAKO framework. Wissenschaftlicher Hintergrund / Fragestellungen / Ziele (Scientific background, aims) We developed a novel cortical parcellation approach to accurately map individualized functional brain networks (Wang et al., 2015). A population-based functional atlas and a map of inter-individual variability (Mueller et al., 2013) were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and was validated by invasive cortical stimulation mapping in surgical patients, suggesting great potential for use in clinical applications. To date, most fMRI studies rely on group designs that allow for the detection of pathological mechanisms by statistically comparing groups of patients to groups of healthy controls. Although this approach has taught us a lot about pathophysiologic processes, it cannot capture brain changes in individuals. In order to make fMRI applicable to the individual patient level, either as a diagnostic, prognostic, or monitoring marker, it is essential to elaborate algorithms that can robustly capture inter- as well as intra-subject differences. Defining functional brain network on the individual level, as opposed to applying population-based atlases, is an important step towards the exploration of the individual “brain connectome”. The aims of the proposed project are 1. to test if functional connectivity parameters based on individualized networks and connectomes can better predict neurocognitive performance than atlas-based functional connectivity (validation) 2. to provide these individualized functional connectivity parameters to the German National Cohort, so they can be applied for specific hypothesis testing (application). Hypothesen To validate our algorithms, we plan to apply it to 2 domains of neurocognition and hypothesize that 1. Individualized default network connectivity predicts memory performance more accurately than atlas-based default network connectivity