Hierarchical Graphical Model for Learning Functional Network Determinants

  • Emanuele Aliverti
  • Laura Forastiere
  • Tullia PadelliniEmail author
  • Sally Paganin
  • Ernst Wit
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 257)


Analysis of brain functionality is a stimulating research topic from both a neuroscientific and statistical perspective. Although several works have improved our comprehension of the relationship between subject-specific information and brain architecture, many questions remain open. The aim of this paper is to relate functional connectivity patterns with subject-specific features and brain constraints, such as age and mental illness of the subject and lobes membership for brain regions, and illustrate whether these phenotypes affect the neurophysiological dynamics. To address such goal we consider a modular approach that allows to remove noise from the fMRI data, estimate the functional dependency structure and relate functional architecture with structural and phenotypical information.


Functional connectivity Gaussian graphical models Hierarchical models Modular estimation 



The authors are grateful to the organizing committee of StartUp Research Lucia Paci, Antonio Canale, Daniele Durante and Bruno Scarpa for giving them the opportunity to take on such an inspiring challenge in a stimulating environment. The authors also wish to thank Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, for pre-processing and providing the raw DTI and R-fMRI, and the the other participants to Start-Up Research for prolific discussions, both during and after the meeting.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Emanuele Aliverti
    • 1
  • Laura Forastiere
    • 2
  • Tullia Padellini
    • 3
    Email author
  • Sally Paganin
    • 1
  • Ernst Wit
    • 4
  1. 1.Department of Statistical SciencesUniversity of PadovaPaduaItaly
  2. 2.Department of Statistics, Informatics and ApplicationsUniversity of FlorenceFlorenceItaly
  3. 3.Department of Statistical SciencesSapienza University of RomeRomeItaly
  4. 4.Faculty of InformaticsUniversity of Italian SwitzerlandLuganoSwitzerland

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