Event segmentation and neural states
How does our brain enable us to perceive and remember the complex real-world experiences that we have every day? How do we segment our experience into discrete events that we can understand and remember? At which timescales do different brain regions organize our experiences? To be able to answer these questions, we have developed new analysis methods that can identify transitions between neural activity patterns in fMRI or M/EEG data (neural state boundaries). Neural state boundaries are associated with the perceptions of event boundaries that we experience subjectively. In this research, we use naturalistic stimuli like movies that are more similar to our real life experiences than the stimuli that are traditionally used in cognitive neuroscience.
The aging brain
Aging is a complex process of change and a good demonstration of the brain’s resilience. Even though cells in all regions of the nervous system are affected by aging, older adults are typically able to function very well in daily life. The key to understanding this resilience is in the study of brain function. Our research is focused on using and developing innovative measures to increase our understanding of brain function and of how it gives rise to our experiences and behavior over the lifespan. This approach moves beyond simply focusing on brain areas that are active in a particular task condition, by looking at how different brain regions are communicating to achieve a particular goal and how this communication is modulated by changes in the internal or external environment.
Robust analysis methods
Valid and reliable methods are the cornerstone of progress in cognitive neuroscience, which is why our lab spends a lot of time on verifying methods with simulations and developing new methods when needed. Examples of previous projects related to this topic are: the development of a new method for identifying state transitions in fMRI data with naturalistic stimuli; work on an alternative method for cluster-based multiple comparison corrections in fMRI; identifying optimal pipelines for functional connectivity analyses that are aimed at studying inter-individual variability and introducing a multivariate connectivity measure that is more reliable and robust than the traditional univariate Pearson correlation measure.