Measuring brain connectivity via shape analysis of fMRI time courses and spectra

David S. Lee, Amber M. Leaver, Katherine L. Narr, Roger P. Woods, Shantanu H. Joshi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We present a shape matching approach for functional magnetic resonance imaging (fMRI) time course and spectral alignment. We use ideas from differential geometry and functional data analysis to define a functional representation for fMRI signals. The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their power spectral densities. Experimental results show significant increases in pairwise node to node correlations and coherences following alignment. We apply this method for finding group differences in connectivity between patients with major depression and healthy controls.

Original languageEnglish (US)
Title of host publicationConnectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings
EditorsLeonardo Bonilha, Guorong Wu, Paul Laurienti, Brent C. Munsell
PublisherSpringer Verlag
Pages125-133
Number of pages9
ISBN (Print)9783319671581
DOIs
StatePublished - Jan 1 2017
Event1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 14 2017Sep 14 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10511 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/14/179/14/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, D. S., Leaver, A. M., Narr, K. L., Woods, R. P., & Joshi, S. H. (2017). Measuring brain connectivity via shape analysis of fMRI time courses and spectra. In L. Bonilha, G. Wu, P. Laurienti, & B. C. Munsell (Eds.), Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 125-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67159-8_15