Abstract
We present a novel method for global diffeomorphic phase alignment of time-series data from resting-state functional magnetic resonance imaging (rsfMRI) signals. Additionally, we propose a multidimensional, continuous, invariant functional representation of brain time-series data and solve a general global cost function that brings both the temporal rotations and phase reparameterizations in alignment. We define a family of cost functions for spatiotemporal warping and compare time-series warps across them. This method achieves direct alignment of time-series, allows population analysis by aligning time-series activity across subjects and shows improved global correlation maps, as well as z-scores from independent component analysis (ICA), while showing new information exploited by phase alignment that was not previously recoverable.
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Acknowledgments
This research was partially supported by the NIH/NIAAA awards K25AA024192 and R01-AA026834. Data acquisition and processing was also supported by NIH/NIMH award U01MH11000.
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Lee, D.S., Sahib, A., Narr, K., Nunez, E., Joshi, S. (2020). Global Diffeomorphic Phase Alignment of Time-Series from Resting-State fMRI Data. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_51
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DOI: https://doi.org/10.1007/978-3-030-59728-3_51
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