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Building Better Biomarkers: Brain Models in Translational Neuroimaging

Overview
Journal Nat Neurosci
Date 2017 Feb 24
PMID 28230847
Citations 410
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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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References
1.
Doyle O, Westman E, Marquand A, Mecocci P, Vellas B, Tsolaki M . Predicting progression of Alzheimer's disease using ordinal regression. PLoS One. 2014; 9(8):e105542. PMC: 4139338. DOI: 10.1371/journal.pone.0105542. View

2.
Chang L, Gianaros P, Manuck S, Krishnan A, Wager T . A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect. PLoS Biol. 2015; 13(6):e1002180. PMC: 4476709. DOI: 10.1371/journal.pbio.1002180. View

3.
Eloyan A, Muschelli J, Nebel M, Liu H, Han F, Zhao T . Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Front Syst Neurosci. 2012; 6:61. PMC: 3431009. DOI: 10.3389/fnsys.2012.00061. View

4.
Casanova R, Hsu F, Sink K, Rapp S, Williamson J, Resnick S . Alzheimer's disease risk assessment using large-scale machine learning methods. PLoS One. 2013; 8(11):e77949. PMC: 3826736. DOI: 10.1371/journal.pone.0077949. View

5.
. The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience. Front Syst Neurosci. 2012; 6:62. PMC: 3433679. DOI: 10.3389/fnsys.2012.00062. View