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.

Cappozzo, A., Ferraccioli, F., Stefanucci, M., Secchi, P. (2018). An object oriented approach to multimodal imaging data in neuroscience. In D.D. Antonio Canale (a cura di), Studies in Neural Data Science : Startup Research 2017, Siena, Italy, June 25-27 (pp. 57-73). Springer New York LLC [10.1007/978-3-030-00039-4_4].

An object oriented approach to multimodal imaging data in neuroscience

Cappozzo, Andrea;
2018

Abstract

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.
Capitolo o saggio
Data objects; Functional data analysis; Multimodal Imaging; Neuroscience; Principal components;
Data objects, Functional data analysis, Principal components, Multimodal Imaging, Neuroscience
English
Studies in Neural Data Science : Startup Research 2017, Siena, Italy, June 25-27
9783030000387
Cappozzo, A., Ferraccioli, F., Stefanucci, M., Secchi, P. (2018). An object oriented approach to multimodal imaging data in neuroscience. In D.D. Antonio Canale (a cura di), Studies in Neural Data Science : Startup Research 2017, Siena, Italy, June 25-27 (pp. 57-73). Springer New York LLC [10.1007/978-3-030-00039-4_4].
Cappozzo, A; Ferraccioli, F; Stefanucci, M; Secchi, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/246978
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