Article | Published:

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

Nature Neuroscience volume 18, pages 16641671 (2015) | Download Citation

Abstract

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

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Acknowledgements

Data were provided in part by the Human Connectome Project, WU-Minn Consortium (principal investigators, D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 US National Institutes of Health (NIH) institutes and centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. This work was also supported by NIH grant EB009666 (R.T.C.), T32 DA022975 (D.S.) and the US National Science Foundation Graduate Research Fellowship Program (E.S.F. and M.D.R.).

Author information

Author notes

    • Emily S Finn
    •  & Xilin Shen

    These authors contributed equally to this work.

Affiliations

  1. Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA.

    • Emily S Finn
    • , Marvin M Chun
    •  & R Todd Constable
  2. Department of Diagnostic Radiology, Yale School of Medicine, New Haven, Connecticut, USA.

    • Xilin Shen
    • , Dustin Scheinost
    • , Jessica Huang
    • , Xenophon Papademetris
    •  & R Todd Constable
  3. Department of Psychology, Yale University, New Haven, Connecticut, USA.

    • Monica D Rosenberg
    •  & Marvin M Chun
  4. Department of Neurobiology, Yale University, New Haven, Connecticut, USA.

    • Marvin M Chun
  5. Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA.

    • Xenophon Papademetris
  6. Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA.

    • R Todd Constable

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Contributions

E.S.F., X.S., D.S., X.P. and R.T.C. conceptualized the study. X.S. designed and performed the identification analyses with support from E.S.F. and D.S. E.S.F. designed and performed the behavioral analyses with support from M.D.R. and J.H. X.S. and X.P. contributed unpublished data analysis tools and visualization software. X.P., M.M.C. and R.T.C. provided support and guidance with data interpretation. E.S.F. wrote the manuscript, with contributions from X.S. and comments from all other authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Emily S Finn.

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DOI

https://doi.org/10.1038/nn.4135