Building better biomarkers: brain models in translational neuroimaging
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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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The search for imaging biomarkers for brain disorders is fraught with difficulty related to the complexity and distribution of cognitive processing, generalization from heterogeneous clinical samples, and small effect sizes with limited clinical utility.
“The search for imaging biomarkers for brain disorders is fraught with difficulty related to the complexity and distribution of cognitive processing in the brain, the generalization of findings from heterogeneous clinical samples, and small effect sizes that have limited clinical utility194, 195.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [194]
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Brain systems at the intersection of chronic pain and self-regulation
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- Authors
- Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D.
- Affiliations
- University of Colorado Boulder (US)
- Dartmouth College (US)
- Johns Hopkins University (US)
- Corresponding author
- Wager, Tor D
- Venue
- Nature Neuroscience
- Published
- 2017-02-23
- Volume
- 20
- Issue
- 3
- Pages
- 365-377
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC5988350
- OpenAlex
- W2590328111
- Cited by
- 1,056
- References
- 153
- Citations by year
- 2026: 117 · 2025: 108 · 2024: 125 · 2023: 97 · 2022: 110 · 2021: 140 · 2020: 129 · 2019: 108 · 2018: 85 · 2017: 36
- FWCI
- 10.95
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH076136)
- National Institute on Drug Abuse (R01DA035484)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- translational neuroimaging, biomarkers, brain signatures, multivariate pattern classification, neuroimaging, generalizability, diagnostic value, data sharing, clinical translation, brain models
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Biomarkers, Pattern Recognition, Physiological, Neuroimaging
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