Institute for Systems Neuroscience
Heinrich-Heine University Düsseldorf
From areas and networks to individual predictions
The long predominant paradigm in neuroimaging has been to compare (mean) local volume or activity between groups, or to correlate these to behavioral phenotypes. Such approach, however, is intrinsically limited in terms of possible insight into inter-individual differences and application in clinical practice. Recently, the increasing availability of large cohort data and tools for multivariate statistical learning, allowing the prediction of individual cognitive or clinical phenotypes in new subjects, have started a revolution in imaging neuroscience.
The transformation of systems neuroscience into a big data discipline poses a lot of new challenges, yet the most critical aspects is the still sub-optimal relationship between the extremely wide feature-space from neuroimaging and the comparably low number of subjects. This, however, is only true when approaching neuroimaging machine-learning in a naïve fashion, i.e., when ignoring the large body of existing work on human brain mapping. The regional segregation of the brain into distinct modules as well as the large-scale, distributed networks provide the fundamental organizational principles of the human brain and hence the basis for cognitive information processing. Importantly, both can now be mapped in a highly robust fashion by integrating information on hundreds or even thousands of individual subjects to provide a priori information.
This talk will outline the fundamental principles of topographic organization in the human brain as well as the possibilities but also challenges in defining brain regions in a multi-modal setting. One important consideration in this context is the distinction between finding the “true” regional organization of the brain and the use of brain atlases as data compression. I will then outline, how knowledge on human brain organization can be leveraged for inference on cognitive and social traits in previously unseen individual subjects or the prediction of diagnoses and subtype in individual patients with, e.g., Parkinson’s disease or Schizophrenia. Providing a bidirectional translation, such application will in turn provide information on the respective brain regions and networks. These developments will thus open up the possibility for a deeper understanding of inter-individual variability and the development of individualized healthcare while at the same time contributing to a better understanding of the human brain.
A pizza lunch will be served at 11:45am. The seminar will run from 12:00pm – 1:30pm.