Review Article | Published:

Building better biomarkers: brain models in translational neuroimaging

Nature Neuroscience volume 20, pages 365377 (2017) | Download Citation

Abstract

Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.

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Acknowledgements

We thank our colleagues for discussion of issues surrounding biomarker development and consortium data, including V. Apkarian, M. Banich, D. Barch, P. Bellec, R. Casanova, C. Davatzikos, O. Doyle, D. Eidelberg, G. Glover, S. Mackey, E. Mayer, R. Poldrack, V. Prashanthi, M. Rosenberg, S. Smith, I. Tracey and others. We also thank J. Buhle, L. Van Oudenhove, M. Kano, P. Kragel, H. Giao Ly, P. Dupont, A. Rubio, C. Delon-Martin and B.L. Bonaz for contributing to work discussed in Figure 4 and the authors of published manuscripts using the Neurologic Pain Signature. This work was funded by NIH R01DA035484 and R01MH076136 (T.D.W., PI).

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Affiliations

  1. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.

    • Choong-Wan Woo
  2. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.

    • Choong-Wan Woo
  3. Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado, USA.

    • Choong-Wan Woo
    •  & Tor D Wager
  4. Institute of Cognitive Science, University of Colorado, Boulder, Colorado, USA.

    • Choong-Wan Woo
    •  & Tor D Wager
  5. Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, USA.

    • Luke J Chang
  6. Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.

    • Martin A Lindquist

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Tor D Wager.

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https://doi.org/10.1038/nn.4478

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