Simon Eickhoff

Panel Member

Jülich Contributions to HBP

The Forschungszentrum Jülich and its institutes contribute substantially to several areas of research within the Human Brain Project. The Institute of Neuroscience and Medicine (INM-1), for example, provides fundamental neurobiological information about the structure and function of the human brain and is responsible for developing the HBP Human Brain Atlas as part of the publicly accessible infrastructure. INM-1 director Katrin Amunts leads Subproject 2 (Human Brain Organization) and the Science and Infrastructure Board (SIB), and INM-1 group leader Timo Dickscheid is the co-lead of HBP’s neuroinformatics platform. INM-6 develops multi-scale models of the brain, develops and maintains the NEST simulation code for large spiking neuronal networks, and advances digital workflows for the analysis of electrophysiological data. The INM-7 creates workflows for the analysis of structural and functional MRI images of the living human brain that can be used as input into machine-learning models for the prediction of individual phenotypes in health and disease. The mission of the HPAC Platform, which is led by Thomas Lippert, Jülich Supercomputing Centre, is to build, integrate and operate the federated supercomputing and data infrastructure for the Human Brain Project enabling scientists to run large-scale, data-intensive, interactive simulations, to manage large amounts of data and to implement and manage complex workflows. The Simulation Laboratory Neuroscience has a bridging function, connecting neuroscience applications with the methods and resources of high performance computing.

In future, virtual models of the brain will make it easier to understand the structure and function of the healthy and diseased brain, and enable new drugs to be developed and tested. The human brain will also be used as a model for neuro-inspired/neuromorphic computing technologies.

These and other research activities, which are carried out within the framework of Jülich’s Supercomputing and Modeling for the Human Brain (SMHB), comprise the focus of this session.

Speakers: Thomas Lippert, Abigail Morrison, Anna Lührs, Timo Dickscheid, Simon Eickhoff, Forschungszentrum Jülich

Bridging the gap: From large-scale aggregation to individual prediction

Over the last two decades, neuroimaging has provided ever increasing knowledge about the macroscopic structure, function and connectivity of the human brain as well as the aberrations thereof in patients with neurological and psychiatric disorders. In this context, the long predominant paradigm has been to compare (mean) local volumes or levels of activity between groups, or to correlate these to behavioral phenotypes. Such approach, however, is intrinsically limited in terms of the possible insight into inter-individual differences and with respect to application in clinical practice. Recently, however, the increasing availability of large-scale cohort data and an emerging focus on statistical learning models that are tested for their ability to predict 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 related to data processing, workflow management and the need for high-performance computing. In this presentation, I will outline some of these perspectives and challenges from the perspective of neuroimaging studies on inter-individual variability and clinical investigations. In this context, one of the approaches that may hold a particularly relevant perspective is the integration of previous knowledge on brain organization. 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. This integrated knowledge then can provide key a priori information for dimensionality reduction and feature selection aiding the development of machine-learning models based on smaller but deeper characterized datasets.

This approach allows to leverage the very large amounts of data towards knowledge on human brain organization that can then be used to infer cognitive and social traits in previously unseen individual subjects or to objectively classify and subtype individual patients with, e.g., Parkinson's disease or Schizophrenia based on MRI scans. These developments will 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.

Short CV

Simon Eickhoff studied medicine and received his doctorate degree in neuroanatomy in 2006. After serving as an assistant professor and clinical resident for Psychiatry at the RWTH Aachen in 2009, from 2011 – 2016 he was professor for cognitive neuroscience at the Heinrich-Heine University in Düsseldorf and head of the Brain Network Modeling group at the Forschungszentrum Jülich. Since 2017 he is full professor and director of the Institute for Systems Neuroscience at the Heinrich-Heine University in Düsseldorf and sind 2018 also 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. Working 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.