Protocol | Published:

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

Nature Protocols volume 12, pages 506518 (2017) | Download Citation

Abstract

Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain–behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: (i) feature selection, (ii) feature summarization, (iii) model building, and (iv) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a considerable amount of the variance in these measures. It has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain–behavior prediction. As CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no experience in machine learning or optimization will find it easy to implement these protocols. Depending on the volume of data to be processed, the protocol can take 10–100 min for model building, 1–48 h for permutation testing, and 10–20 min for visualization of results.

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Acknowledgements

M.D.R. and E.S.F. are supported by US National Science Foundation Graduate Research Fellowships. This work was also supported by US National Institutes of Health grant EB009666 to R.T.C.

Author information

Affiliations

  1. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.

    • Xilin Shen
    • , Dustin Scheinost
    • , Xenophon Papademetris
    •  & R Todd Constable
  2. Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut, USA.

    • Emily S Finn
    • , Marvin M Chun
    •  & R Todd Constable
  3. Department of Psychology, Yale University, New Haven, Connecticut, USA.

    • Monica D Rosenberg
    •  & Marvin M Chun
  4. Department of Neuroscience, Yale School of Medicine, 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

X.S., E.S.F., D.S., X.P., and R.T.C. conceptualized the study. X.S. developed this protocol with help from E.S.F. and D.S. E.S.F. developed the prediction framework with help from X.S. and M.D.R. E.S.F., X.P., and X.S. contributed previously unpublished tools. X.P. developed the online visualization tools with help from X.S. and D.S. X.P., M.M.C., and R.T.C. provided support and guidance with data interpretation. All authors made valuable comments on the manuscript.

Competing interests

X.P. is a consultant for Electrical Geodesics Inc.

Corresponding author

Correspondence to R Todd Constable.

Supplementary information

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    Supplementary Note

    Performance comparison between CPM- and SVR-based methods.

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DOI

https://doi.org/10.1038/nprot.2016.178

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