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Understanding Dependency Patterns in Structural and Functional Brain Connectivity Through fMRI and DTI Data

  • Marta Crispino
  • Silvia D’Angelo
  • Saverio RanciatiEmail author
  • Antonietta Mira
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 257)

Abstract

Neuroscience and neuroimaging have been providing new challenges for statisticians and quantitative researchers in general. As datasets of increasing complexity and dimension become available, the need for statistical techniques to analyze brain related phenomena becomes prominent. In this paper, we delve into data coming from functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI). The aim is to combine information from both sources in order to learn possible patterns of dependencies among regions of interest (ROIs) of the brain. First, we infer positions of these regions in a latent space, using the observed structural connectivity provided by the DTI data, to understand if physical spatial coordinates suitably reflect how ROIs are effectively interconnected. Secondly, we inspect Granger causality in the fMRI data in order to capture patterns of activations between ROIs. Then, we compare results from the analysis on these datasets, to find a link between functional and structural connectivity. Preliminary findings show that latent space positions well reflect hemisphere separation of the brain but are not perfectly connected to all the other structural partitions (that is, lobe, cortex, etc.); furthermore, activations of ROIs inferred from fMRI data are tied to observed structural connections derived from DTI scans.

Keywords

Network analysis Resting state fMRI DTI Latent space models Penalized weighted regression 

Notes

Acknowledgements

Data were provided by Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who graciously preprocessed 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 thank Ritabrata Dutta for initial discussions during ‘StartUp Research’ and for comments to the final version of the manuscript. Also, we would like to thank the organizers of ‘StartUp Research’ event, www.congressi.unisi.it/startupresearch/, for creating the opportunity for this research contribution and the other working groups present at the event for fruitful discussions.

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marta Crispino
    • 1
  • Silvia D’Angelo
    • 2
  • Saverio Ranciati
    • 3
    Email author
  • Antonietta Mira
    • 4
    • 5
  1. 1.Univ. Grenoble Alpes, Inria, CNRS, LJKGrenobleFrance
  2. 2.Department of Statistical SciencesSapienza University of RomeRomeItaly
  3. 3.Department of Statistical SciencesUniversity of BolognaBolognaItaly
  4. 4.Institute of Computational ScienceUniversità della Svizzera italianaLuganoSwitzerland
  5. 5.Department of Science and High TechnologyUniversità dell’InsubriaComoItaly

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