Multimodal Data Analysis

This lecture aims at covering the statistical background required to perform association analysis in typical imaging-genetics studies. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group.

• (The hitchhiker‘s guide to) Imaging-Genetics </button>
• Colab notebook </button>
• Presentation

References

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• L. Antelmi, N. Ayache, P. Robert, M. Lorenzi. Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. 36th International Conference on Machine Learning (ICML), 2019.
• L. Shen and P.M. Thompson. Brain Imaging Genomics: Integrated Analysis and Machine Learning. Proceedings of the IEEE, 2019.