Welcome! My name is Jelmer Borst, and I am currently working as an independent researcher in the lab of Niels Taatgen at the University of Groningen. I supervise three PhD candidates: Christina Jin, Hagit Shaposhnik, and Trudy Buwalda.
My main research interest is the development and improvement of analysis methods that connect computational (cognitive) models to neuroimaging data. Better methods will enable a more fine-grained analysis of the astounding amount of neuroimaging data that is collected worldwide. Ultimately, this should lead to a better understanding of the human mind.
As a first step, we have used a symbolic process model for model-based fMRI analysis, showing that it outperforms traditional and parametric fMRI analysis methods. Subsequently, we applied this method to five previously published datasets, and used these analyses to locate working memory updates and declarative retrievals within the fronto-parietal network. More recently, together with John Anderson, I have used Hidden Markov Models in combination with multi-variate pattern analysis to automatically discover cognitive processing stages in EEG data. I'm currently applying both the model-based analysis and MVPA-HMM methods to fMRI, EEG, and MEG in my NWO Veni project.
Until 2011, I was a PhD student in the department of Artificial Intelligence of the University of Groningen. Together with my advisors, Niels Taatgen and Hedderik van Rijn, I investigated a cognitive bottleneck in human multitasking: processing intermediate representations. In addition, we developed a technique to map components of symbolic process models directly on the brain using model-based fMRI analysis.