Advanced Spatiotemporal Statistical • Frameworks for brain connectivity (AI4BC)


In recent years, modeling the functioning of the brain as a network has brought new perspectives for understanding the evolution of pathologies such as neurodegenerative diseases or consciousness disorders, especially at the subject level. These networks consist of a set of brain regions that interact by exchanging packets of information, and which can be classified by their role in the networks, for example as hubs or communities. In the case of neurodegenerative or neuropsychiatric diseases, the identification of key regions as hubs has clarified the diagnosis and evolution of the diseases. Neuroimaging facilities are now available in most hospitals, making it possible to observe brain activity during task or resting states. However, this creates a massive amount of data that can be difficult to process and analyse adequately to obtain reproducible results. Robust statistical analyses are needed for achieving such goals where heterogeneity of groups of patients are frequent, brain structure is well documented and acquisition of data has specific physical properties. Most of the recent studies are based on group analysis and usually, statistical methods do not take into account the spatiotemporal dependence within the data. The objective of this project is to develop methods for robust and statistically consistent estimation of networks using functional data analysis for multivariate data sets such as those observed in neuroimaging. The complementarity of the five partners of this project will allow to design statistical methods adapted to the context of multivariate time series and robust to the specific noises observed in brain neuroimaging.


2 PhD students and one Post-doc are arriving within the team.

Published on  January 9, 2024
Updated on January 9, 2024