Abstract
We present a method for multimodal brain data registration that aligns shapes of nodal network configurations in an invertible manner. We use ideas from shape analysis to represent an individual subject data configuration as an element on a hypersphere, where geodesics have closed form solutions. The method not only performs inter-subject data registration, but also allows for the construction of a population data template to which all subject data configurations can be registered. Results show compression of data measures and significant reduction in variance after registration. We also observe increased predictive power of regions of interest (ROI) node identification, significant increases in pairwise network connectivity measures, as well as significant increases in canonical correlations with age after registration.
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This research was supported by the NIH/NIAAA award K25AA024192.
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Lee, D.S. et al. (2019). Multimodal Data Registration for Brain Structural Association Networks. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_42
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DOI: https://doi.org/10.1007/978-3-030-32245-8_42
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