The concept of brain reserve has been around for a few decades. Its general principle is guided by the observation that effects of disease or insults may not necessarily lead to negative outcomes, and that a certain threshold of injury needs to be exceeded before symptoms manifest. Often, brain reserve relates to the maximum capacity which a human has to ‘compensate’ for such diseases and injuries. As part of my research, I proposed an extension of this concept, called effective reserve (eR). Simply speaking, effective reserve is the remaining brain reserve, after negative effects, such as hypertension and/or age, have been taken into account, resembling the effective amount of brain reserve that a patient has left, in order to deal with new insults, such as stroke. To this extend, I first investigated the concept of eR in a cohort of stroke patients, in which we demonstrated that higher effective reserve is associated to a better outcome and might be related to vascular health. As a next step, we are investigating additional phenotypic information which will help us get to the bottom of what eR represents physiologically and how we can use it in the clinic to help doctors make better assessment and prediction of long-term outcome. For more details, have a look at the Artemis project.
Interpreting the brain and its connectivity profile as a network has quickly become common practice in analyzing the brain’s complex architecture. While many see the benefit of using the mathematical framework of graph theory to analyze topological aspects of these brain networks, the field is still in its early stages, where researchers try to fully elucidate its potential and make useful inferences relating to brain development and diseases. Comparability of brain networks has been a key challenge in the field, allowing us to investigate group differences and general brain alterations. My research in connectomics not only focuses on helping alleviate this challenge by investigating methodology that allows us to compare groups and individuals across parcellation scales, as well as creating reliable and robust network measures for topological analyses, but also on brain connectomics usability in disease detection and outcome prediction.
Based on the previous two projects, my additional interest in outcome prediction is a natural continuation of my ultimate aim to make modern analysis techniques as transformative as possible with direct translation, helping clinicians making informed decisions supporting targeted treatments. This goes hand in hand with disease detection. Utilizing various techniques, my research interests include the complete human life-span, from premature babies, and autism in adolescents, to stroke in the advanced stages of life. In particular in stroke cohorts, I am currently investigating different imaging phenotypes, in particular brain volumetric, and their relation to late stroke outcome as measured by the modified Rankin Scale score over two months after the acute stroke.