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Brain Variability

Group leader: PD Dr. Susanne Weis

AG Susanne

Just as humans differ from each other, so do their brains. To understand the origin of individual differences in character traits, cognitive performance or human behavior, a thorough understanding of the variability of brain organization is fundamental. Especially in the clinical context it is centrally important to understand the continuum spanning normal variance of brain organization as well as pathological variations. Patterns derived from classical group studies typically are of minor informative value when considering an individual patient. Rather, specific individual patterns must be considered for each patient in order to be able to predict the course of the disease and select treatment methods accordingly.

The group "Brain Variability" headed by Susanne Weis investigates the relationship between variability of structural and functional brain organization and individual differences in experience, information processing and behavior. The group’s research aims for a better understanding and ultimately prediction of brain changes with aging as well as in neurological and psychiatric diseases. For this purpose, both "systematic variability" such as gender differences and age effects as well as "individual variability" related to individual personality traits, differences in performance or cognitive impairments are investigated. Further factors influencing individual brain variability, like hormonal fluctuations, time-of-day rhythms, motivation changes, and other internal and external factors are also considered.

Brain Variability

The investigation of individual differences in brain organization requires methodological approaches and analysis strategies that are fundamentally different to those that have widely been used for classical group analyses of functional imaging data. Big data samples and machine learning approaches are used to predict individual differences on the basis of structural and functional neuroimaging data in this approach, an algorithm learns specific patterns based on a large number of examples and is then able predict or classify previously unseen data. These analysis methods require both very large data sets as well as high performance computational resources, both of which are available in INM-7. Research in the “Brain Variability” group is conducted in close collaboration with the team Data and Platforms and the group Applied Machine Learning.

This fundamental research does not only enable a more accurate prognosis of individual disease progression, but it also supports personalized medicine. A possible future goal is the inclusion of a wide range of multimodal data, which could further improve individualized predictions.