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Three Testing Perspectives on Connectome Data

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS,volume 257)

Abstract

Guided by an application in the analysis of Magnetic Resonance Imaging (MRI) scans from the neuroimaging realm, we provide some perspectives on statistical techniques that are able to address issues commonly encountered when dealing with Magnetic Resonance images. The first section of the chapter is devoted to a boostrap-based inferential tool to test for correlation between anatomy and functional activity. The second provides a Bayesian framework to improve estimation of fiber counts from Diffusion Tensor Imaging (DTI) scans. The third one introduces an object-oriented framework to explore and perform testing over network-valued data.

Keywords

  • Hypothesis testing
  • Permutation tests
  • Object oriented data analysis
  • Bootstrap inference
  • Bayesian statistics
  • Graphical lasso

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Acknowledgements

The authors are very grateful to Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who graciously pre-processed the raw DTI and R-fMRI imaging data available at http://fcon_1000.projects.nitrc.org/indi/CoRR/html/nki_1.html, using the pipelines ndmg and C-PAC. Moreover, the authors would like to thank the organizing committee of StartUp Research for the splendid management of such a beautiful event. Alessandra Cabassi and Matteo Fontana wish to thank Dr. Davide Pigoli and Prof. Piercesare Secchi for the fruitful discussions.

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Correspondence to Matteo Fontana .

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Cabassi, A., Casa, A., Fontana, M., Russo, M., Farcomeni, A. (2018). Three Testing Perspectives on Connectome Data. 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_3

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