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Based on the widespread but well-ordered brain tissue loss that occurs with healthy aging into senescence, this work presents a novel magnetic resonance imaging (MRI)-based biomarker, which identifies normal and abnormal aging-related brain atrophy. The novel BrainAGE approach is based on a database of structural MRI data, aggregating the complex, multidimensional aging patterns across the whole brain to one single value, i.e. the estimated brain age. Consequently, subtle deviations in normal brain atrophy can be directly quantified in terms of years by analyzing one standard MRI scan per subject. Various neuro-degenerative diseases especially Alzheimer s disease (AD) are widely linked to advanced brain aging. The BrainAGE approach is applied to identify advanced brain aging in subjects with mild cognitive impairment and AD, to predict conversion to AD, to relate individual BrainAGE scores with disease severity and prospective worsening of cognitive functions. Furthermore, BrainAGE identifies various risk factors of pathological brain aging that may precede the onset of clinical symptoms (e.g., diabetes mellitus type 2, metabolic syndrome).

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2001 MA "Psychology of Excellence", LMU Munich; 2004 Diploma in Psychology, MLU Halle; 2004 2007 Research assistant, ZNL, University Ulm; 2007 2013 Research assistant, Structural Brain Mapping Group, FSU Jena; 2013 PhD "Cognitive Neuro-psychology", UZH Zurich; since 2013 Post-Doc studies, Structural Brain Mapping Group, FSU Jena

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