Department of Biomedical Engineering
Danilo Bzdok has studied medicine between 2006 and 2012 at RWTH Aachen University, Université de Lausanne, and Harvard Medical School. From 2013 to 2015 he then pursued a PhD in computer science on machine learning working at INRIA Saclay & Neurospin near Paris and Heinrich-Heine University Düsseldorf. From 2015 to 2019 Dr. Bzdok headed the section for “Social and Affective Neuroscience” at the Department of Psychiatry, RWTH Aachen University, as an Assistant Professor.
There is now increasing momentum in data sharing, open access, and data collection consortia that build richly annotated "big data" repositories for brain and behavior. This unprecedented data setting creates a rapidly growing potential to provide principled answers to human brain organization and its disturbances in brain disease. Dr. Bzdok will take the opportunity to explore, formalize, and predict brain phenotypes of hidden population variation by capitalising on heterogeneous data sources to tackle open questions in systems neuroscience in a way that also paves new ways for precision medicine in brain health.
One of the least expected discoveries that emerged from imaging neuroscience is the "default network". This macroscopical brain network in the recently evolved association cortex probably has a highest metabolic consumption and features the perhaps highest neuronal baseline activity. Functional processing in this network is associated with a diversity of human-defining psychological processes: complex social cognition, such as perspective-taking, language and moral judgment, as well as the imagination of events and places in future and past. At the same time, the default network has been linked to a range of neurodegenerative and psychiatric disorders, including dementia and schizophrenia. Despite its significance for human intelligence, the physiological purpose of this network remains essentially unknown.
His research group is dedicated to such interdisciplinary challenges in a domain-agnostic approach (especially high- but also low-level cognitive processes) leveraging several recently emerged population datasets (such as UK Biobank, HCP, CamCAN, ABCD) across levels of observation (brain structure and function, consequences from brain lesion, or common-variant genetics) using a broad toolkit of bioinformatic methods (machine-learning, high-dimensional statistics, and probabilistic Bayesian hierarchical modeling).
The key ambition is to bring closer neuroscience and learning predictive patterns from data. Due to the complexity of the patterns that need to be detected in life experience, neural processing, and genomics, human intuition alone may not be sufficient to provide explicit, fine-detailed brain mechanisms. My combination of backgrounds allows identifying pressing questions in medical imaging and health, reframing them as machine learning problems, and translating new insight into biomedicine. His research team is focused on data-guided analysis techniques for large datasets from a systems neuroscience perspective. He believes that strong interdisciplinarity, with an equal footing in research object and research method, is a prerequisite for forward progress in quantitative neuroscience and personalized medicine.
Brain Functional Modeling
Bayesian Hierarchical Modeling