When I was trained to be a physical therapist, I realised I missed the theoretical challenge and noticed that only practical work just wasn’t it for me. I therefore continued my education and started the research master of Human Movement Sciences at the VU. There, I focused on neurodegenerative diseases and did a one-year internship in neural dynamics. In this internship I applied a mathematical model to MEG data of Parkinson patients. I then realised that I wanted to continue a career in science, more specifically by doing research in the field of neurodegenerative diseases. In 2016 I started working at the department of Anatomy and Neurosciences within VUmc. My PhD project entails the analysis of longitudinal MEG data of neuro-oncological and MS patients. Thereby, I will apply a mathematical model, aiming to predict (and eventually prevent) cognitive decline in both patient groups. What I like most about this project is the combination of studying clinical datasets and modeling. Eventually I hope to contribute to more knowledge in this field of research and to better care for people with a neurodegenerative disease.
In 2014 I started my PhD project after finishing a master in Neurosciences. During my bachelor and master I enjoyed working in the lab as well as spending time behind the computer analysing MRI images. In this PhD project I have the opportunity to combine both modalities, which is challenging, both also very exciting. In this project I focus on the network alterations in the brain in glioma (brain tumor) patients with fMRI, DTI and MEG. In the lab, I study how cellular and molecular features of the tumor relate to these network alterations by investigating tumor samples of patients that underwent tumor surgery.
I’m a neuroscientist, trying to make sense of our brain. I like the brain for all its complexity, and try to understand more of it by using graph theory. This theory-governed but still data-driven approach allows for investigation of how the brain works on multiple scales and with several imaging modalities. I’m particularly interested in using these methods to improve diagnosis and treatment in neuro-oncology and epilepsy, and always try to relate the abstract topology of the brain network back to behavior. In this video I explain my research in 180s (in Dutch).
Most of the time, you can find me at the department of Anatomy and Neurosciences of the VU University Medical Center in Amsterdam (The Netherlands), where I hold an assistant professor faculty position. I also spend quite some time at the Martinos Center for Biomedical Imaging in Boston (MA, USA) as a visiting scholar.
In our latest paper we show that in glioma patients connections between different brain areas are affected on a broad scale. Specifically connections linked to brain regions that play a central role in information processing (hubs) are altered.
By analysing 71 functional MRI scans of glioma (brain tumor) patients and a cohort of healthy controls we first made a ‘connectomic profile’ of all the functional connections in the brain (connections are based on brain regions that show similar activation patterns). The connectomic profile represents the distribution of the strength of all the connections between all brain regions. This analysis showed us that the profile of glioma patients was different from healthy controls, such that patients had less variance in the distribution of connection strengths than their matched healthy controls. By taking a closer look at the type of connections that might be altered in brain tumor patients, we found that specifically connections linked to brain areas that play a central role in the brain, the so-called hubs, were impacted, but in a differential manner. Connections between hub areas were decreased in brain tumor patients and connections between hub areas and other regions showed an increase in connectivity compared to healthy controls. Next, we investigated what the clinical relevance was of these different features. We found that the connectomic profile differed between types of glioma malignancy. Also, in the case of the most malignant tumor type, the connectomic profile was a predictor for progression of the tumor.
These new findings show us the impact of a tumor on global brain functioning and that hubs have a special and complex role in this process. From a clinical point of view, the results underscore the promise of using connectomics as a future biomarker in brain tumor patients.