Affiliation: Laboratory for Mental Health Mapping, Carl Zeiss Professor for Artificial Intelligence in Neural Systems Imaging, Friedrich Schiller University Jena, Jena, Germany

Abstract: Complex brain disorders are defined by heterogeneity, comorbidity, and unknown etiology, properties that limit the clinical value of group-mean comparisons. Normative modelling offers a unifying alternative: analogous to pediatric growth charts, it estimates the expected distribution of a brain measure across the lifespan and expresses each individual measurement as a standardized deviation, enabling person-specific inference from population-scale data. I will present three complementary lines of work that demonstrate this framework across modalities. A lifespan normative reference for spectral EEG oscillations consolidates fifty years of electrophysiological research into a transferable model of disease-related deviations. Fine-grained hippocampal geometry from heterogeneous structural MRI yields lifespan trajectories that generalize across cohorts and reveal interpretable disease-related shape alterations. Functional brain network dynamics estimated with state-space models on resting-state fMRI provide reproducible signatures, normative lifespan trajectories, fingerprint-level identifiability, out-of-distribution robustness, and longitudinal tracking at N = 1. Together, these studies establish normative modelling as a robust, interpretable, and clinically actionable foundation for precision brain phenotyping, turning individual differences into readable signals for early detection, risk stratification, and individualized intervention across the lifespan.

Webex-Link: https://fu-berlin.webex.com/fu-berlin-en/j.php?MTID=mc8ddd71ebbb976a8e378be8d5f75e937