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

Cappozzo, Andrea; Ferraccioli, Federico; Stefanucci, Marco; Secchi, Piercesare

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].

MSC:

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

Keywords:

data objects; functional data analysis; principal components; multimodal imaging; neuroscience