An object oriented approach to multimodal imaging data in neuroscience. (English) Zbl 1414.92160

Canale, Antonio (ed.) et al., Studies in neural data science. StartUp research 2017, Siena, Italy, June 25–27, 2017. Cham: Springer. Springer Proc. Math. Stat. 257, 57-73 (2018).
Summary: We propose a methodological framework for exploring complex multimodal imaging data from a neuroscience study with the aim of identifying a data-driven group structure in the patients sample, possibly connected with the presence/absence of lifetime mental disorder. The functional covariances of fMRI signals are first considered as data objects. Appropriate clustering procedures and low dimensional representations are proposed. For inference, a Frechet estimator of both the covariance operator itself and the average covariance operator is used. A permutation procedure to test the equality of the covariance operators between two groups is also considered. We finally propose a method to incorporate spatial dependencies between different brain regions, merging the information from both the structural networks and the dynamic functional activity.
For the entire collection see [Zbl 1415.92006].


92C55 Biomedical imaging and signal processing
92B20 Neural networks for/in biological studies, artificial life and related topics
92C20 Neural biology
Full Text: DOI Link