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Hierarchical Graphical Model for Learning Functional Network Determinants

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Studies in Neural Data Science (START UP RESEARCH 2017)

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

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Abstract

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.

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of Statistical Sciences, University of Padova, Padua, Italy

    Emanuele Aliverti & Sally Paganin

  2. Department of Statistics, Informatics and Applications, University of Florence, Florence, Italy

    Laura Forastiere

  3. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Tullia Padellini

  4. Faculty of Informatics, University of Italian Switzerland, Lugano, Switzerland

    Ernst Wit

Authors
  1. Emanuele Aliverti
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  2. Laura Forastiere
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  3. Tullia Padellini
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  4. Sally Paganin
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  5. Ernst Wit
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Corresponding author

Correspondence to Tullia Padellini .

Editor information

Editors and Affiliations

  1. Department of Statistical Sciences, University of Padova, Padua, Italy

    Antonio Canale

  2. Department of Decision Sciences, Bocconi University, Milan, Italy

    Daniele Durante

  3. Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy

    Lucia Paci

  4. Department of Statistical Sciences, University of Padova, Padua, Italy

    Bruno Scarpa

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Aliverti, E., Forastiere, L., Padellini, T., Paganin, S., Wit, E. (2018). Hierarchical Graphical Model for Learning Functional Network Determinants. 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_2

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