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Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains

  • Keri L. Anderson
  • Jeffrey S. Anderson
  • Sourabh Palande
  • Bei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)

Abstract

The functional architecture of the brain can be described as a dynamical system where components interact in flexible ways, constrained by physical connections between regions. Using correlation, either in time or in space, as an abstraction of functional connectivity, we analyzed resting state fMRI data from 1003 subjects. We compared several data preprocessing strategies and found that independent component-based nuisance regression outperformed other strategies, with the poorest reproducibility in strategies that include global signal regression. We also found that temporal vs. spatial functional connectivity can encode different aspects of cognition and personality. Topological analyses using persistent homology show that persistence barcodes are significantly correlated to individual differences in cognition and personality, with high reproducibility. Topological data analyses, including approaches to model connectivity in the time domain, are promising tools for representing high-level aspects of cognition, development, and neuropathology.

Notes

Acknowledgements

This work was supported by NIH grants R01EB022876, R01MH080826, and NSF grant IIS-1513616.

Supplementary material

473761_1_En_8_MOESM1_ESM.pdf (17.6 mb)
Supplementary material 1 (pdf 17995 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Keri L. Anderson
    • 1
  • Jeffrey S. Anderson
    • 1
  • Sourabh Palande
    • 1
  • Bei Wang
    • 1
  1. 1.University of UtahSalt Lake CityUSA

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