Network Models in Neuroimaging: A Survey of Multimodal Applications

Matteo Mancini, Mara Cercignani

Research output: Contribution to journalArticle

Abstract

Mapping the brain structure and function is one of the hardest problems in science. Different image modalities, in particular the ones based on magnetic resonance imaging (MRI) can shed more light on how it is organised and how its functions unfold, but a theoretical framework is needed. In the last years, using network models and graph theory to represent the brain structure and function has become a major trend in neuroscience. In this review, we outline how network modelling has been used in neuroimaging, clarifying what are the underlying mathematical concepts and the consequent methodological choices. The major findings are then presented for structural, functional and multimodal applications. We conclude outlining what are still the current issues and the perspective for the immediate future.

Original languageEnglish
Pages (from-to)63-91
Number of pages29
JournalFundamenta Informaticae
Volume163
Issue number1
DOIs
Publication statusPublished - Jan 1 2018

Fingerprint

Neuroimaging
Network Model
Brain
Neuroscience
Network Modeling
Graph theory
Magnetic Resonance Imaging
Model Theory
Magnetic resonance
Modality
Imaging techniques

Keywords

  • brain connectivity
  • connectome
  • graph thoery
  • Network models
  • neuroimaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Algebra and Number Theory
  • Information Systems
  • Computational Theory and Mathematics

Cite this

Network Models in Neuroimaging : A Survey of Multimodal Applications. / Mancini, Matteo; Cercignani, Mara.

In: Fundamenta Informaticae, Vol. 163, No. 1, 01.01.2018, p. 63-91.

Research output: Contribution to journalArticle

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