Imaging Language

Research interests and projects

Over the past few decades, real-time tracking of cortical current flow and accurate localisation of blood oxygenation changes have offered complementary windows to the functional architecture of language in the human brain (MEG, fMRI; local activation, connectivity patterns). 

The neuroimaging domain has reached its first level of maturity: we now know how to reliably measure and quantify different types of neuroimaging signals, we know that those signals show good intraindividual reproducibility but notable inter-individual variability in language tasks and, phenomenologically, we know what type of group-level functional effects to expect in a large variety of experimental conditions. To reach the next phase in the neuroscience of language, it is essential 

• to uncover the neural mechanisms and representations reflected in the neuroimaging signals, 
• to advance from group-level descriptions to quantitative model-based individual-level predictions, and 
• to bring together the complementary information provided by different neuroimaging metrics in a principled way. 

To reach these goals, we combine careful experimental design and data collection and analysis with computationally explicit models and machine learning approaches.