@inproceedings{91f1f6f9ddc543d39ccbaf2f98108def,
title = "Measuring brain connectivity via shape analysis of fMRI time courses and spectra",
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.",
author = "Lee, {David S.} and Leaver, {Amber M.} and Narr, {Katherine L.} and Woods, {Roger P.} and Joshi, {Shantanu H.}",
note = "Funding Information: Acknowledgments. This research was supported by the NIH/NIAAA award K25AA024192, and the NIH/NIMH awards K24MH102743 and U01MH110008. Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 1st 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 ; Conference date: 14-09-2017 Through 14-09-2017",
year = "2017",
doi = "10.1007/978-3-319-67159-8_15",
language = "English (US)",
isbn = "9783319671581",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "125--133",
editor = "Leonardo Bonilha and Guorong Wu and Paul Laurienti and Munsell, {Brent C.}",
booktitle = "Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings",
}