Here you find all information regarding the workshop program.
KEYNOTE 1: Neda Jahanshad
TITLE: AI/ML for continuously larger scale international neuroimaging research
Large-scale neuroimaging collaborations have been established to help tackle the reproducibility crisis in brain imaging research. These collaborations often involved dozens of research groups pooling together neuroimaging, clinical and computational resources together to amass study sample sizes on the order of tens of thousands of participants. However, data sharing and data heterogeneity remain necessary and important hurdles on the path towards efficient data analysis. Here, we will discuss common clinical and technical challenges faced when working with distributed and diverse data as well as the collaborative and neuroinformatic backbone of these collaborations. The discussion will emphasize the growing role that machine learning and artificial intelligence are playing, and will play, in multiple aspects of collaborative neurosciences. In this talk, the methods and applications discussed will be in the context of the ENIGMA consortium, but these may be applicable across many multi-site collaborative studies.
KEYNOTE 1: Catie Chang
TITLE: Computational approaches for investigating the dynamic brain
Advances in non-invasive imaging technology have enabled measuring human brain activity with increasing spatial and temporal detail. Furthermore, this data may be integrated with exciting developments in machine learning to investigate the brain’s functional organization and dynamics, and to develop clinical biomarkers. Here, we will discuss directions and challenges in leveraging computational and signal processing techniques to investigate human brain dynamics. In this context, we will also share our recent studies on decoding internal states, such as levels of alertness, from functional magnetic resonance imaging (fMRI) data.