Pediatric Neuroscientist

Focus of research

Marsh Königs (1987) is assistant professor in developmental neuroscience at Emma Children’s Hospital of Amsterdam UMC. He is educated in Biomedical Sciences (BSc), Cognitive Neuropsychology (MScRes, cum laude) and Clinical Neuropsychology (MSc, cum laude). Marsh obtained his PhD degree (2016, cum laude) for his research on the impact of pediatric traumatic brain injury on brain structure, neurocogition and behavior.


Current research focuses on the impact of disease and treatment on brain structure and function of children, in the context of daily life problems. As such, dr. Königs works closely together with prof. dr. Jaap Oosterlaan as co-lead of the Emma Neuroscience Group (currently supervising 8 PhD students) and is part of the Follow Me program for structured follow-up. Dr. Königs also acts as director of science at the Daan Theeuwes Center for Intensive Neurorehabilitation, is member of the Dutch Work Group on Brain Injury in Children and Young Adults (HEJ!) and collaborates with professional sports organization on concussion management at amateur and elite sport level (Royal Dutch Football Association [KNVB], AFC Ajax). Dr. Königs specializes in (i) neuroscientific outcome measurements, (ii) clinical outcome prediction for precision medicine; (iii) and data-driven heath care innovation (Health Intelligence).


Neuroscientific outcome measurement

This research line is focused on the development and application of neuroscientific outcome measurements. Dr. Konigs developed the Emma Toolbox for Neurocognitive Functioning, a standardized test battery for continuous assessment of neurocognitive functioning in children and adults. Corresponding analysis methodology has been developed to apply network theory to neurocognitive data at the individual patient level, allowing to reconstruct the neurocognitive network and explore the value neurocognitive network organization for our understanding of healthy development and the impact of disease on neurocognitive development. For neurocognitive assessment in babies and infants, eye-tracking paradigms have been developed allowing to measure oculomotor control, information processing, attention, learning and memory in babies as from 6 months of age. Lastly, this research line involves the use of advanced MRI methodology to determine brain integrity in terms of white matter integrity, structural and functional connectivity and metabolite concentrations.


Clinical outcome prediction for precision medicine

This research line aims to contribute to the transition towards precision medicine by improving clinical outcome prediction. Together with the Quantitative Data Analytics Group (prof. Mark Hoogendoorn & dr. Frank Bennis) we investigate the added value of machine learning models for clinical outcome prediction. Moreover, we are developing a machine learning pipeline that is optimized for clinical prediction problems. Moreover, we aim to innovate machine learning models to better handle frequent challenges present in clinical data, such as smaller sample sizes, combinations of static data (e.g. patient characteristics) and dynamic data (e.g. vital parameter timeseries), data sparsity (e.g. laboratory assessments) and irregularly sampled data (e.g. time ries of variable length). The ultimate objective of this research line is to implement valuable clinical prediction models into clinical practice.


Data-driven health care innovation (Health Intelligence)

This research line is focused on the development and management of structured clinical (follow-up) programs. Such programs are designed to improve clinical care by integrating multiple  monodisciplinary care paths into one standardized multidisciplinary car path that integrates the care delivered by the relevant medical specialists. The programs are standardized by consensus among medical specialists, involves structured registration of clinical assessment in the electronic patient file and integrates with other clinical data sources (e.g. medical devices, patient reported outcome measurements). Consequently, the structured clinical data flow into rich and ever accumulating databases that are re-used for care evaluation and scientific research aimed at data-driven care innovation.



  • Member of the National Workgroup for Acquired Brain Injury in Youth (HEJ!)
  • Member of the ENIGMA Consortium, task force on pediatric brain injury