Over the past decade, brain connectivity has become a central theme in the neuroimaging community. At the same time, causal inference has recently emerged as a major research topic in machine learning. Even though the two research questions are closely related, interactions between the neuroimaging and machine-learning communities have been limited.

The aim of this workshop is to initiate productive interactions between neuroimaging and machine learning by introducing the workshop audience to the different concepts of connectivity/causal inference employed in each of the communities. Special emphasis is placed on discussing commonalities as well as distinctions between various approaches in the context of neuroimaging. Due to the increasing relevance of brain connectivity for analyzing mental states, we also highly welcome contributions discussing applications of brain connectivity measures to real-world problems such as brain-computer interfacing or mental state monitoring.


We solicit contributions on new approaches to connectivity and/or causal inference for neuroimaging data as well as on applications of connectivity inference to real-world problems. Contributions might address, but are not limited to, the following topics:

  • Effective connectivity & causal inference
    • Dynamic causal modelling
    • Granger causality
    • Structural equation models
    • Causal Bayesian networks
    • Non-Gaussian linear causal models
    • Causal additive noise models
  • Functional connectivity
    • Canonical correlation analysis
    • Phase-locking
    • Imaginary coherence
    • Independent component analysis
  • Applications of brain connectivity to real-world problems
    • Brain-computer interfaces
    • Mental state monitoring

Organization committee

Program committee