Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

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

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Schematic of CPM.
Figure 2: Visualizing selected connectivity features.
Figure 3: Example CPM code for Steps 1–3.
Figure 4: Example CPM code for Steps 4–8 (a).
Figure 5: Example permutation test code for Steps 8–10.
Figure 6: Online visualization tool for making circle plots and glass brain plots described in Steps 11–14.

Similar content being viewed by others

References

  1. Kriegeskorte, N., Simmons, W.K., Bellgowan, P.S. & Baker, C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).

    Article  CAS  Google Scholar 

  2. Vul, E., Harris, C., Winkielman, P. & Pashler, H. Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4, 274–290 (2009).

    Article  Google Scholar 

  3. Gabrieli, J.D., Ghosh, S.S. & Whitfield-Gabrieli, S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).

    Article  CAS  Google Scholar 

  4. Finn, E.S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).

    Article  CAS  Google Scholar 

  5. Rosenberg, M.D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  7. Nooner, K.B. et al. The NKI-Rockland Sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 6, 152 (2012).

    Article  Google Scholar 

  8. Milham, M.P., Fair, D., Mennes, M. & Mostofsky, S.H. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012).

    Google Scholar 

  9. Satterthwaite, T.D. et al. The Philadelphia Neurodevelopmental Cohort: a publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage 124, 1115–1119 (2016).

    Article  Google Scholar 

  10. Van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J. & Coalson, T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22, 2241–2262 (2012).

    Article  Google Scholar 

  11. Shen, X., Tokoglu, F., Papademetris, X. & Constable, R.T. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).

    Article  CAS  Google Scholar 

  12. Power, J.D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  14. Whelan, R. & Garavan, H. When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol. Psychiatry 75, 746–748 (2014).

    Article  Google Scholar 

  15. Smith, S. et al. HCP beta-release of the Functional Connectivity MegaTrawl. https://www.humanconnectome.org/documentation/S500/HCP500_MegaTrawl_April2015.pdf (2015).

  16. Smith, S.M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    Article  Google Scholar 

  17. Smola, A. & Vapnik, V. Support vector regression machines. Adv. Neural Inf. Process. Syst. 9, 155–161 (1997).

    Google Scholar 

  18. Dosenbach, N.U. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010).

    Article  CAS  Google Scholar 

  19. Brown, M.R. et al. ADHD-200 global competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Front. Syst. Neurosci. 6, 69 (2012).

    Article  Google Scholar 

  20. Plitt, M., Barnes, K.A. & Martin, A. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage Clin. 7, 359–366 (2015).

    Article  Google Scholar 

  21. Anderson, J.S. et al. Functional connectivity magnetic resonance imaging classification of autism. Brain 134, 3742–3754 (2011).

    Article  Google Scholar 

  22. Arbabshirani, M.R., Kiehl, K.A., Pearlson, G.D. & Calhoun, V.D. Classification of schizophrenia patients based on resting-state functional network connectivity. Front. Neurosci. 7, 133 (2013).

    Article  Google Scholar 

  23. Khazaee, A., Ebrahimzadeh, A. & Babajani-Feremi, A. Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory. Clin. Neurophysiol. 126, 2132–2141 (2015).

    Article  Google Scholar 

  24. Zeng, L.L. et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135, 1498–1507 (2012).

    Article  Google Scholar 

  25. Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).

    Article  Google Scholar 

  26. Van Dijk, K.R., Sabuncu, M.R. & Buckner, R.L. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431–438 (2012).

    Article  Google Scholar 

  27. Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N. & Fischl, B. Spurious group differences due to head motion in a diffusion MRI study. Neuroimage 88C, 79–90 (2013).

    Google Scholar 

  28. Power, J.D., Schlaggar, B.L. & Petersen, S.E. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105, 536–551 (2015).

    Article  Google Scholar 

  29. Yan, C.-G. et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183–201 (2013).

    Article  Google Scholar 

  30. Kohavi, R . A study of cross-validation and bootstrap for accuracy estimation and model selection. in Proceedings of the 14th International Joint Conference on Artificial Intelligence 2, 1137–1143 (1995).

    Google Scholar 

  31. Justice, A.C., Covinsky, K.E. & Berlin, J.A. Assessing the generalizability of prognostic information. Ann. Intern. Med. 130, 515–524 (1999).

    Article  CAS  Google Scholar 

  32. Steyerberg, E.W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128–138 (2010).

    Article  Google Scholar 

  33. Gibbons, J.D. & Chakraborti, S. Nonparametric Statistical Inference (Springer, 2011).

  34. Holland, P.W. & Welsch, R.E. Robust regression using iteratively reweighted least-squares. Commun. Stat.-Theory Methods 6, 813–827 (1977).

    Article  Google Scholar 

  35. Street, J.O., Carroll, R.J. & Ruppert, D. A note on computing robust regression estimates via iteratively reweighted least squares. Am. Stat. 42, 152–154 (1988).

    Google Scholar 

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

    Article  Google Scholar 

  37. Li, N. et al. Resting-state functional connectivity predicts impulsivity in economic decision-making. J. Neurosci. 33, 4886–4895 (2013).

    Article  CAS  Google Scholar 

  38. Supekar, K. et al. Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proc. Natl. Acad. Sci. USA 110, 8230–8235 (2013).

    Article  Google Scholar 

  39. Doehrmann, O. et al. Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry 70, 87–97 (2013).

    Article  Google Scholar 

  40. Ullman, H., Almeida, R. & Klingberg, T. Structural maturation and brain activity predict future working memory capacity during childhood development. J. Neurosci. 34, 1592–1598 (2014).

    Article  CAS  Google Scholar 

Download references

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

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to R Todd Constable.

Ethics declarations

Competing interests

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

Supplementary information

Supplementary Note

Performance comparison between CPM- and SVR-based methods. (PDF 208 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, X., Finn, E., Scheinost, D. et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc 12, 506–518 (2017). https://doi.org/10.1038/nprot.2016.178

Download citation

  • Published:

  • Issue Date:

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

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing