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Mapping the Spatio-Temporal Functional Coherence in the Resting Brain

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Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy (MBIA 2019, MFCA 2019)

Abstract

Human brain functions are underlined by spatially and temporally coherent activity. Characterizing the spatio-temporal coherence (STC) of brain activity is then important to understand brain function, which however is still elusive in the literature. In this study, we proposed a new method to measure the spatio-temporal incoherence (STIC) by segmenting the fMRI time series of neighboring voxels into a series of continuous 4-dimensional elements and recording the similarity of every element to all others. STIC was then calculated as the log differential of the similarity sum of all elements and that when the time window is increased by 1 – a process similar to and directly extended from the time-embedding based approximate entropy calculation. Experiment results showed that STIC revealed the correct irregularity difference between random noise and spatio-temporally coherent signal. STIC was less sensitive to noise than a multi-variate entropy measure. When applied to 917 young health subjects’ resting-state fMRI, we identified highly replicable STIC maps with very fine cortical structures. Females and more matured brain (older here) had higher STIC. Higher STIC in putamen showed a trend of correlations with better mental examination outcome. These data showed STIC as a potential functional brain marker.

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Acknowledgement

HCP data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

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Wang, Z. (2019). Mapping the Spatio-Temporal Functional Coherence in the Resting Brain. In: Zhu, D., et al. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy. MBIA MFCA 2019 2019. Lecture Notes in Computer Science(), vol 11846. Springer, Cham. https://doi.org/10.1007/978-3-030-33226-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-33226-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33225-9

  • Online ISBN: 978-3-030-33226-6

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