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International Workshop on Connectomics in Neuroimaging

CNI 2019: Connectomics in NeuroImaging pp 95–105Cite as

Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11848))

Abstract

Brain parcellations play a key role in functional connectomics. A set of standard neuro-anatomical brain atlases are in common use in most studies. In addition, data-driven parcellations computed from fMRI data using a variety of clustering algorithms have also been used. Recent studies set out to determine the best parcellation in terms of quality and reliability have remained inconclusive without a clear winner. In this work, we investigated the utility of test-retest reliability of functional connectivity as an evaluation metric for comparing parcellations. Specifically, using data from the human connectome project, we compared a data-driven parcellation and a geometric parcellation using Intraclass Correlation Coefficient (ICC). We also investigated the impact of parcellation granularity on the test-retest reliability. We observed that the ICCs for geometric parcellation are better than those of a data-driven parcellation, suggesting that the FCs computed using regular parcels in the geometric atlases are more reliable than those computed using a data-driven parcellation.

J. Zeng and A. T. Dang—Contributed equally to this paper.

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References

  1. HCP 1200 subjects data release. https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release

  2. HCP S1200 release reference manual. https://www.humanconnectome.org/storage/app/media/documentation/s1200/HCP_S1200_Release_Reference_Manual.pdf

  3. Julia clustering package. https://github.com/JuliaStats/Clustering.jl

  4. Andellini, M., Cannatà, V., Gazzellini, S., Bernardi, B., Napolitano, A.: Test-retest reliability of graph metrics of resting state MRI functional brain networks: a review. J. Neurosci. Methods 253, 183–192 (2015)

    Article  Google Scholar 

  5. Arslan, S., Ktena, S.I., Makropoulos, A., Robinson, E.C., Rueckert, D., Parisot, S.: Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 170, 5–30 (2018)

    Article  Google Scholar 

  6. Atluri, G., MacDonald III, A., Lim, K.O., Kumar, V.: The brain-network paradigm: using functional imaging data to study how the brain works. Computer 49(10), 65–71 (2016)

    Article  Google Scholar 

  7. Bearden, C.E., Thompson, P.M.: Emerging global initiatives in neurogenetics: the enhancing neuroimaging genetics through meta-analysis (ENIGMA) consortium. Neuron 94(2), 232–236 (2017)

    Article  Google Scholar 

  8. Beckmann, C.F., Mackay, C.E., Filippini, N., Smith, S.M.: Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage 47(Suppl 1), S148 (2009)

    Article  Google Scholar 

  9. Blumensath, T., et al.: Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. Neuroimage 76, 313–324 (2013)

    Article  Google Scholar 

  10. Braun, U., et al.: Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage 59(2), 1404–1412 (2012)

    Article  Google Scholar 

  11. Cao, H., et al.: Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. Neuroimage 84, 888–900 (2014)

    Article  Google Scholar 

  12. Craddock, R.C., James, G.A., Holtzheimer III, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914–1928 (2012)

    Article  Google Scholar 

  13. Dubois, J., Adolphs, R.: Building a science of individual differences from fMRI. Trends Cogn. Sci. 20(6), 425–443 (2016)

    Article  Google Scholar 

  14. Eickhoff, S.B., Yeo, B.T., Genon, S.: Imaging-based parcellations of the human brain. Nat. Rev. Neurosci. 19, 672–686 (2018)

    Article  Google Scholar 

  15. Gordon, E.M., Laumann, T.O., Adeyemo, B., Huckins, J.F., Kelley, W.M., Petersen, S.E.: Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26(1), 288–303 (2014)

    Article  Google Scholar 

  16. Jack Jr., C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging: Off. J. Int. Soc. Magn. Reson. Med. 27(4), 685–691 (2008)

    Article  Google Scholar 

  17. Jbabdi, S., Woolrich, M.W., Behrens, T.E.J.: Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. NeuroImage 44(2), 373–384 (2009)

    Article  Google Scholar 

  18. Kelly, C., Biswal, B.B., Craddock, R.C., Castellanos, F.X., Milham, M.P.: Characterizing variation in the functional connectome: promise and pitfalls. Trends Cogn. Sci. 16(3), 181–188 (2012)

    Article  Google Scholar 

  19. Koo, T.K., Li, M.Y.: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163 (2016)

    Article  Google Scholar 

  20. Meindl, T., et al.: Test-retest reproducibility of the default-mode network in healthy individuals. Hum. Brain Mapp. 31(2), 237–246 (2010)

    Google Scholar 

  21. Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A.: Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86(2), 420–428 (1979)

    Article  Google Scholar 

  22. Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A.: The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 44(3), 893–905 (2009)

    Article  Google Scholar 

  23. Potkin, S.G., Ford, J.M.: Widespread cortical dysfunction in schizophrenia: the FBIRN imaging consortium. Schizophr. Bull. 35(1), 15–18 (2008)

    Article  Google Scholar 

  24. Preti, M.G., Bolton, T.A., Van De Ville, D.: The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160, 41–54 (2017)

    Article  Google Scholar 

  25. Rosen, B.R., Savoy, R.L.: fMRI at 20: has it changed the world? Neuroimage 62(2), 1316–1324 (2012)

    Article  Google Scholar 

  26. Thirion, B., Varoquaux, G., Dohmatob, E., Poline, J.B.: Which fMRI clustering gives good brain parcellations? Front. Neurosci. 8, 167 (2014)

    Article  Google Scholar 

  27. Thomas Yeo, B., et al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011)

    Article  Google Scholar 

  28. Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)

    Article  Google Scholar 

  29. Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)

    Article  Google Scholar 

  30. Zilles, K., Amunts, K.: Centenary of Brodmann’s map-conception and fate. Nat. Rev. Neurosci. 11(2), 139 (2010)

    Article  Google Scholar 

  31. Zuo, X.N., Kelly, C., Adelstein, J.S., Klein, D.F., Castellanos, F.X., Milham, M.P.: Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. Neuroimage 49(3), 2163–2177 (2010)

    Article  Google Scholar 

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Acknowledgements

This work was supported by NSF Grant IIS-1850204. The computational work is performed using the Data Analytics Cluster acquired through the Ohio Dept. of Higher Education’s RAPIDS grant in 2018.

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Authors and Affiliations

  1. Department of EECS, University of Cincinnati, Cincinnati, OH, 45221, USA

    Jianfeng Zeng, Anh The Dang & Gowtham Atluri

Authors
  1. Jianfeng Zeng
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  2. Anh The Dang
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  3. Gowtham Atluri
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Corresponding author

Correspondence to Gowtham Atluri .

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Editors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Markus D. Schirmer

  2. Johns Hopkins University, Baltimore, MD, USA

    Archana Venkataraman

  3. Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

  4. University of North Carolina, Greensboro, NC, USA

    Minjeong Kim

  5. Harvard Medical School, Boston, MA, USA

    Ai Wern Chung

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Zeng, J., Dang, A.T., Atluri, G. (2019). Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation. In: Schirmer, M., Venkataraman, A., Rekik, I., Kim, M., Chung, A. (eds) Connectomics in NeuroImaging. CNI 2019. Lecture Notes in Computer Science(), vol 11848. Springer, Cham. https://doi.org/10.1007/978-3-030-32391-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-32391-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32390-5

  • Online ISBN: 978-3-030-32391-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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