Since its invention more than 20 years ago, functional Magnetic Resonance Imaging (fMRI) has played a central role in cognitive neuroscience. In recent years, ultra-high field MRI scanners (≥ 7 Tesla) have become available, producing data at previously unobtainable quality.
At present however, the full complexity of fMRI data is poorly understood. Our own previous research, as well as that of several other research groups around the world, has shown that only a small percentage of the variance of fMRI time courses is explained by standard analysis techniques. Furthermore, task-related brain activation appears to be much more global and distributed than previously believed. Ultra-high field data show much more detail, making it even more difficult to understand and interpret the full information content.
The aim of our research is to develop new mathematical methods for the analysis of both standard fMRI data (3 Tesla) as well as ultrahigh field fMRI data (≥ 7 Tesla). We aim to harvest as much of the information content of fMRI as possible in order to advance our understanding of human brain function. Specifically, our work has focused on the development of novel techniques for statistical inference and for the analysis of functional brain networks.

A number of recent studies have investigated machine learning techniques for predicting individual behaviour from fMRI. Even though encouraging results have been obtained, excessive scan times - especially in resting state fMRI - are a limiting factor.
In this study, we propose a new machine learning algorithm for predicting individual behaviour of healthy human subjects using both resting state (rsfMRI) as well as task-based fMRI (tfMRI). more
Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). more
One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. In this study, we propose a non-parametric and threshold-free framework called LISA to address this demand. more
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