An Object Oriented Approach to Multimodal Imaging Data in Neuroscience
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.
KeywordsData objects Functional data analysis Principal components Multimodal Imaging Neuroscience
We acknowledge Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who pre-processed the raw DTI and R-fMRI imaging data available at http://fcon_1000.projects.nitrc.org/indi/CoRR/html/nki_1.html. We would like to deeply thank the StartUp Research Scientific Committee for efficiently and flawlessly organizing such a motivating experience. We thank Professor Francesca Greselin and Doctor Mauro Ceroni for their support and help throughout the drafting of this manuscript.
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