Using gradient as a new metric for dynamic connectivity estimation from resting fMRI data

Ashkan Faghiri, Julia M. Stephen, Yu Ping Wang, Tony W. Wilson, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The study of time varying functional connectivity between different parts of the brain (the functional connectome) has emerged as an important aspect of brain imaging studies. The most widely used approach to estimate these time varying connectivities uses sliding window Pearson correlation to estimate connectivity between different parts of brain. The choice of the window length can impact the results and interesting information might go undetected. Here we propose a new approach that evaluates the gradient (both its magnitude and phase) defined in a new space as a metric for connectivity. Using a very small window, weighted average phase of these gradient values are calculated. Here using simulation, we show that our metric is capable of estimating even very short connectivity states and also provide additional information unavailable to a sliding-window approach. In addition the proposed method is utilized to analyze a real dataset.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1805-1808
Number of pages4
ISBN (Electronic)9781538636411
DOIs
StatePublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
CountryItaly
CityVenice
Period4/8/194/11/19

Fingerprint

Brain
Connectome
Magnetic Resonance Imaging
Neuroimaging
Imaging techniques
Datasets

Keywords

  • Connectivity
  • Dynamic
  • FMRI
  • Gradient

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Faghiri, A., Stephen, J. M., Wang, Y. P., Wilson, T. W., & Calhoun, V. D. (2019). Using gradient as a new metric for dynamic connectivity estimation from resting fMRI data. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (pp. 1805-1808). [8759523] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2019.8759523

Using gradient as a new metric for dynamic connectivity estimation from resting fMRI data. / Faghiri, Ashkan; Stephen, Julia M.; Wang, Yu Ping; Wilson, Tony W.; Calhoun, Vince D.

ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2019. p. 1805-1808 8759523 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Faghiri, A, Stephen, JM, Wang, YP, Wilson, TW & Calhoun, VD 2019, Using gradient as a new metric for dynamic connectivity estimation from resting fMRI data. in ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging., 8759523, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, IEEE Computer Society, pp. 1805-1808, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italy, 4/8/19. https://doi.org/10.1109/ISBI.2019.8759523
Faghiri A, Stephen JM, Wang YP, Wilson TW, Calhoun VD. Using gradient as a new metric for dynamic connectivity estimation from resting fMRI data. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society. 2019. p. 1805-1808. 8759523. (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2019.8759523
Faghiri, Ashkan ; Stephen, Julia M. ; Wang, Yu Ping ; Wilson, Tony W. ; Calhoun, Vince D. / Using gradient as a new metric for dynamic connectivity estimation from resting fMRI data. ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2019. pp. 1805-1808 (Proceedings - International Symposium on Biomedical Imaging).
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