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Speaker

Simon Eickhoff, Heinrich-Heine University in Düsseldorf, Germany

Abstract

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 and the robust mapping of functional networks. I will then illustrate, how this knowledge on human brain organization can be leveraged for inference on socio-affective or cognitive traits in previously unseen individual subjects or psychopathology in mental disorders. Providing a bidirectional translation, such application will in turn provide information on the respective brain regions and networks.

Speaker Bio

Simon Eickhoff is a full professor and chair of the Institute for Systems Neuroscience at the Heinrich-Heine University in Düsseldorf and the director of the Institute of Neuroscience and Medicine (INM-7, Brain and Behavior) at the Forschungszentrum Jülich. He is furthermore a visiting professor at the Chinese Academy of Science Institute of Automation. Workig at the interface between neuroanatomy, data-science and brain medicine, the he aims to obtain a more detailed characterization of the organization of the human brain and its inter-individual variability in order to better understand its changes in advanced age as well as neurological and psychiatric disorders. This goal is pursued by the development and application of novel analysis tools and approaches for large-scale, multi-modal analysis of brain structure, function and connectivity as well as by machine-learning for single subject prediction of cognitive and socio-affective traits and ultimately precision medicine.