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.
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Acknowledgements
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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Cappozzo, A., Ferraccioli, F., Stefanucci, M., Secchi, P. (2018). An Object Oriented Approach to Multimodal Imaging Data in Neuroscience. In: Canale, A., Durante, D., Paci, L., Scarpa, B. (eds) Studies in Neural Data Science. START UP RESEARCH 2017. Springer Proceedings in Mathematics & Statistics, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-00039-4_4
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