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Principal Investigator  
Principal Investigator's Name: Svenja Caspers
Institution: University Düsseldorf; Research Centre Jülich
Department: Institute for Anatomy I; INM-1
Country:
Proposed Analysis: We would like to use the ADNI data for evaluation and analysis of generalizability of our results from a large population-based cohort on normal variability in brain aging, particularly focusing on the changes of brain structure as well as functional and structural connectivity in older age in relation to genetic, environmental and lifestyle influencing factors. The delineation of the normal variability in the general population as compared to neurodegenerative processes is of particular interest, particularly amongst our efforts to predict brain cognitive outcome from brain phenotypes in the normally aging brain. Here, training the models based on subjects already experiencing disease would be very helpful. The aim furthermore is to include different influencing factors to facilitate characterization of individual aging trajectories across different cohorts.
Additional Investigators  
Investigator's Name: Christiane Jockwitz
Proposed Analysis: Within the overall framework described, Christiane Jockwitz will mainly focus on the characterization of the individual phenotype characterizations and generalizability across cohorts with particular focus on the cognitive outcomes using mainly brain structural analyses.
Investigator's Name: Nora Bittner
Proposed Analysis: Within the overall framework described, Nora Bittner will focus on connectivity analyses as well as structural brain analyses, including BrainAGE calculations, in relation to lifestyle factors e.g. using moderator and mediator analyses for explanation of the effects on the brain and cognition.
Investigator's Name: Camilla Krämer
Proposed Analysis: Within the overall framework described, Camilla Krämer as a PhD student to finalize her PhD these months will be focusing on the prediction of cognitive outcome based on multimodal imaging data and using a combination of different machine-learning algorithms for the evaluation of the algorithms working best in normal aging-related cognitive phenomena.