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Simas T, Chavez M, Rodriguez PR, et al. An algebraic topological method for multimodal brain networks comparisons. Front Psychol. 2015;6:904doi: 10.3389/fpsyg.2015.00904.
Simas, T., Chavez, M., Rodriguez, P. R., & Diaz-Guilera, A. (2015). An algebraic topological method for multimodal brain networks comparisons. Frontiers in psychology, 6904. https://doi.org/10.3389/fpsyg.2015.00904
Simas, Tiago, et al. "An algebraic topological method for multimodal brain networks comparisons." Frontiers in psychology vol. 6 (2015): 904. doi: https://doi.org/10.3389/fpsyg.2015.00904
Simas T, Chavez M, Rodriguez PR, Diaz-Guilera A. An algebraic topological method for multimodal brain networks comparisons. Front Psychol. 2015 Jul 06;6:904. doi: 10.3389/fpsyg.2015.00904. eCollection 2015. PMID: 26217258; PMCID: PMC4491601.
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Front Psychol. 2015 Jul 06;6:904. doi: 10.3389/fpsyg.2015.00904. eCollection 2015.

An algebraic topological method for multimodal brain networks comparisons.

Frontiers in psychology

Tiago Simas, Mario Chavez, Pablo R Rodriguez, Albert Diaz-Guilera

Affiliations

  1. Departament de Física Fonamental, Facultat de Física, Universitat de Barcelona Barcelona, Spain ; Department of Psychiatry, University of Cambridge Cambridge, UK ; Telefonica Research, Edificio Telefonica Barcelona, Spain.
  2. Centre National de la Recherche Scientifique-UMR-7225, Hôpital Pitié Salpêtrière, Bat ICM Paris, France.
  3. Telefonica Research, Edificio Telefonica Barcelona, Spain.
  4. Departament de Física Fonamental, Facultat de Física, Universitat de Barcelona Barcelona, Spain.

PMID: 26217258 PMCID: PMC4491601 DOI: 10.3389/fpsyg.2015.00904

Abstract

Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional). Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network.

Keywords: algebraic statistics; functional connectivity; multilayer; multiplex; network analysis; structure-activity relationship

References

  1. Nat Rev Neurosci. 2001 Apr;2(4):229-39 - PubMed
  2. Clin Neurophysiol. 2012 Jun;123(6):1067-87 - PubMed
  3. Nonlinear Biomed Phys. 2007 Jul 05;1(1):3 - PubMed
  4. Neuroscientist. 2015 Jun;21(3):290-305 - PubMed
  5. Phys Rev Lett. 2013 Apr 26;110(17):174102 - PubMed
  6. Nat Rev Neurosci. 2014 Oct;15(10):683-95 - PubMed
  7. Proc Natl Acad Sci U S A. 2004 Nov 16;101(46):16132-7 - PubMed
  8. Nat Rev Neurosci. 2009 Mar;10(3):186-98 - PubMed
  9. Neuroimage. 2008 Nov 15;43(3):554-61 - PubMed
  10. Neuroimage. 2008 Apr 15;40(3):1064-76 - PubMed
  11. Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Sep;88(3):032807 - PubMed
  12. Neuroimage. 2002 Jan;15(1):273-89 - PubMed
  13. Proc Natl Acad Sci U S A. 2009 Feb 10;106(6):2035-40 - PubMed
  14. Neuroimage. 2010 Sep;52(3):766-76 - PubMed
  15. Nat Rev Neurosci. 2011 Jan;12(1):43-56 - PubMed
  16. Proc Natl Acad Sci U S A. 2007 Jun 12;104(24):10240-5 - PubMed
  17. Chaos. 2009 Jun;19(2):023119 - PubMed

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