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.

  • Review Article
  • Published:

Building better biomarkers: brain models in translational neuroimaging

Nature Neuroscience volume 20, pages 365–377 (2017)Cite this article

Subjects

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.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Standard mapping versus predictive modeling.
Figure 2: A snapshot of translational neuroimaging using multivariate predictive models.
Figure 3: Brain signature development and validation.
Figure 4: Future directions.
Figure 5: Varieties of predictive models.

Similar content being viewed by others

References

  1. Mather, M., Cacioppo, J.T. & Kanwisher, N. Introduction to the special section: 20 years of fMRI-what has it done for understanding cognition? Perspect. Psychol. Sci. 8, 41–43 (2013).

    Article  PubMed  Google Scholar 

  2. Kapur, S., Phillips, A.G. & Insel, T.R. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol. Psychiatry 17, 1174–1179 (2012).

    Article  CAS  PubMed  Google Scholar 

  3. Mayberg, H.S. et al. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am. J. Psychiatry 156, 675–682 (1999).

    CAS  PubMed  Google Scholar 

  4. Keedwell, P.A., Andrew, C., Williams, S.C., Brammer, M.J. & Phillips, M.L. The neural correlates of anhedonia in major depressive disorder. Biol. Psychiatry 58, 843–853 (2005).

    Article  PubMed  Google Scholar 

  5. Tom, S.M., Fox, C.R., Trepel, C. & Poldrack, R.A. The neural basis of loss aversion in decision-making under risk. Science 315, 515–518 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  7. Sanislow, C.A. et al. Developing constructs for psychopathology research: research domain criteria. J. Abnorm. Psychol. 119, 631–639 (2010).

    Article  PubMed  Google Scholar 

  8. Scoville, W.B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11–21 (1957).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fodor, J.A. The Modularity of Mind (MIT Press, 1983).

  10. Hamani, C. et al. Deep brain stimulation for chronic neuropathic pain: long-term outcome and the incidence of insertional effect. Pain 125, 188–196 (2006).

    Article  PubMed  Google Scholar 

  11. Welter, M.L. et al. Basal ganglia dysfunction in OCD: subthalamic neuronal activity correlates with symptoms severity and predicts high-frequency stimulation efficacy. Transl. Psychiatry 1, e5 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Krack, P. et al. Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson's disease. N. Engl. J. Med. 349, 1925–1934 (2003).

    Article  CAS  PubMed  Google Scholar 

  13. Swartz, J.R., Knodt, A.R., Radtke, S.R. & Hariri, A.R. A neural biomarker of psychological vulnerability to future life stress. Neuron 85, 505–511 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dougherty, D.D. et al. A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression. Biol. Psychiatry 78, 240–248 (2015).

    Article  PubMed  Google Scholar 

  15. Morishita, T., Fayad, S.M., Higuchi, M.A., Nestor, K.A. & Foote, K.D. Deep brain stimulation for treatment-resistant depression: systematic review of clinical outcomes. Neurotherapeutics 11, 475–484 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Reddan, M.C., Lindquist, M.A. & Wager, T.D. Effect size estimation in neuroimaging. JAMA Psychiatry http://dx.doi.org/10.1001/jamapsychiatry.2016.3356 (2017).

  17. Logothetis, N.K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Price, J.L. & Drevets, W.C. Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn. Sci. 16, 61–71 (2012).

    Article  PubMed  Google Scholar 

  20. Roy, M., Shohamy, D. & Wager, T.D. Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends Cogn. Sci. 16, 147–156 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wager, T.D. et al. Pain in the ACC? Proc. Natl. Acad. Sci. USA 113, E2474–E2475 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Poldrack, R.A. Can cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10, 59–63 (2006).

    Article  PubMed  Google Scholar 

  23. Wager, T.D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chang, L.J., Gianaros, P.J., Manuck, S.B., Krishnan, A. & Wager, T.D. A sensitive and specific neural signature for picture-induced negative affect. PLoS Biol. 13, e1002180 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Doyle, O.M., Mehta, M.A. & Brammer, M.J. The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology (Berl.) 232, 4179–4189 (2015).

    Article  CAS  Google Scholar 

  26. Haynes, J.D. A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron 87, 257–270 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Orrù, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G. & Mechelli, A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36, 1140–1152 (2012).

    Article  PubMed  Google Scholar 

  28. Hackmack, K., Paul, F., Weygandt, M., Allefeld, C. & Haynes, J.D. Multi-scale classification of disease using structural MRI and wavelet transform. Neuroimage 62, 48–58 (2012).

    Article  PubMed  Google Scholar 

  29. Miyawaki, Y. et al. Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron 60, 915–929 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679–685 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kriegeskorte, N., Cusack, R. & Bandettini, P. How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter? Neuroimage 49, 1965–1976 (2010).

    Article  PubMed  Google Scholar 

  32. Poldrack, R.A. & Gorgolewski, K.J. Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Abi-Dargham, A. & Horga, G. The search for imaging biomarkers in psychiatric disorders. Nat. Med. 22, 1248–1255 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Hastie, T., Tibshirani, R. & Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn (Springer, 2009).

  35. Mohri, M., Rostamizadeh, A. & Talwalkar, A. Foundations of Machine Learning (MIT Press, 2012).

  36. de Leon, M.J. et al. Positron emission tomographic studies of aging and Alzheimer disease. AJNR Am. J. Neuroradiol. 4, 568–571 (1983).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Kippenhan, J.S., Barker, W.W., Pascal, S., Nagel, J. & Duara, R. Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects. J. Nucl. Med. 33, 1459–1467 (1992).

    CAS  PubMed  Google Scholar 

  38. Doyle, O.M. et al. Predicting progression of Alzheimer's disease using ordinal regression. PLoS One 9, e105542 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Singh, G. & Samavedham, L. Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease. J. Neurosci. Methods 256, 30–40 (2015).

    Article  PubMed  Google Scholar 

  40. Koutsouleris, N. et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch. Gen. Psychiatry 66, 700–712 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Sørensen, L. et al. Early detection of Alzheimer's disease using MRI hippocampal texture. Hum. Brain Mapp. 37, 1148–1161 (2016).

    Article  PubMed  Google Scholar 

  42. Moradi, E., Pepe, A., Gaser, C., Huttunen, H. & Tohka, J. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage 104, 398–412 (2015).

    Article  PubMed  Google Scholar 

  43. Beardslee, W.R. et al. Prevention of depression in at-risk adolescents: longer-term effects. JAMA Psychiatry 70, 1161–1170 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Addington, J. & Heinssen, R. Prediction and prevention of psychosis in youth at clinical high risk. Annu. Rev. Clin. Psychol. 8, 269–289 (2012).

    Article  PubMed  Google Scholar 

  45. Davatzikos, C., Xu, F., An, Y., Fan, Y. & Resnick, S.M. Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132, 2026–2035 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Fan, Y., Batmanghelich, N., Clark, C.M., Davatzikos, C. & Alzheimer's Disease Neuroimaging Initiative\par Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39, 1731–1743 (2008).

    Article  PubMed  Google Scholar 

  47. Misra, C., Fan, Y. & Davatzikos, C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44, 1415–1422 (2009).

    Article  PubMed  Google Scholar 

  48. Tang, C.C. et al. Differential diagnosis of Parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 9, 149–158 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Pantazatos, S.P., Talati, A., Schneier, F.R. & Hirsch, J. Reduced anterior temporal and hippocampal functional connectivity during face processing discriminates individuals with social anxiety disorder from healthy controls and panic disorder, and increases following treatment. Neuropsychopharmacology 39, 425–434 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Anticevic, A. et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb. Cortex 24, 3116–3130 (2014).

    Article  PubMed  Google Scholar 

  51. Calhoun, V.D., Maciejewski, P.K., Pearlson, G.D. & Kiehl, K.A. Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum. Brain Mapp. 29, 1265–1275 (2008).

    Article  PubMed  Google Scholar 

  52. Insel, T.R. & Cuthbert, B.N. Medicine. Brain disorders? Precisely. Science 348, 499–500 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Clementz, B.A. et al. Identification of distinct psychosis biotypes using brain-based biomarkers. Am. J. Psychiatry 173, 373–384 (2016).

    Article  PubMed  Google Scholar 

  54. Price, R.B. et al. Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol. Psychiatry S0006-3223(16)32540-9 (2016).

  55. Drysdale, A.T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. (2016).

  56. Weinstein, J.N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Roychowdhury, S. & Chinnaiyan, A.M. Translating genomics for precision cancer medicine. Annu. Rev. Genomics Hum. Genet. 15, 395–415 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Hahn, T. et al. Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiatry 72, 68–74 (2015).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  60. Whitfield-Gabrieli, S. et al. Brain connectomics predict response to treatment in social anxiety disorder. Mol. Psychiatry 21, 680–685 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. van Waarde, J.A. et al. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol. Psychiatry 20, 609–614 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Widge, A.S., Avery, D.H. & Zarkowski, P. Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation. Brain Stimul. 6, 929–931 (2013).

    Article  PubMed  Google Scholar 

  63. Sarpal, D.K. et al. Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. Am. J. Psychiatry 173, 69–77 (2016).

    Article  PubMed  Google Scholar 

  64. Ye, Z. et al. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures. Hum. Brain Mapp. 37, 1026–1037 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Woo, C.W. & Wager, T.D. Neuroimaging-based biomarker discovery and validation. Pain 156, 1379–1381 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Robinson, M., Boissoneault, J., Sevel, L., Letzen, J. & Staud, R. The effect of base rate on the predictive value of brain biomarkers. J. Pain 17, 637–641 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Cronbach, L.J. & Meehl, P.E. Construct validity in psychological tests. Psychol. Bull. 52, 281–302 (1955).

    Article  CAS  PubMed  Google Scholar 

  68. Freedman, R. et al. The initial field trials of DSM-5: new blooms and old thorns. Am. J. Psychiatry 170, 1–5 (2013).

    Article  PubMed  Google Scholar 

  69. Iidaka, T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63, 55–67 (2015).

    Article  PubMed  Google Scholar 

  70. Duffy, F.H. & Als, H. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study. BMC Med. 10, 64 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Deshpande, G., Wang, P., Rangaprakash, D. & Wilamowski, B. Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans. Cybern. 45, 2668–2679 (2015).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  73. Zaki, J., Wager, T.D., Singer, T., Keysers, C. & Gazzola, V. The anatomy of suffering: understanding the relationship between nociceptive and empathic pain. Trends Cogn. Sci. 20, 249–259 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Olivetti, E., Sona, D. & Veeramachaneni, S. Gaussian process regression and recurrent neural networks for fmri image classification. in Proc. 12th Meeting Org. for Human Brain Mapping, Florence, Italy (2006).

  75. Ribeiro, M.T., Singh, S. & Guestrin, C. “Why should I trust you?”: Explaining the predictions of any classifier. Preprint at arXiv https://arxiv.org/abs/1602.04938 (2016).

  76. HD-200 Consortium. The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012).

  77. Eloyan, A. et al. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Front. Syst. Neurosci. 6, 61 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Eldridge, J., Lane, A.E., Belkin, M. & Dennis, S. Robust features for the automatic identification of autism spectrum disorder in children. J. Neurodev. Disord. 6, 12 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Geurts, J.J., Calabrese, M., Fisher, E. & Rudick, R.A. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 11, 1082–1092 (2012).

    Article  PubMed  Google Scholar 

  80. van den Heuvel, M.P. et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70, 783–792 (2013).

    Article  PubMed  Google Scholar 

  81. Yahata, N. et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat. Commun. 7, 11254 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Huth, A.G., de Heer, W.A., Griffiths, T.L., Theunissen, F.E. & Gallant, J.L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Vemuri, P. et al. Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage. Neuroimage 42, 559–567 (2008).

    Article  PubMed  Google Scholar 

  84. Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C. & Wager, T.D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  86. Glasser, M.F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Bota, M., Dong, H.W. & Swanson, L.W. Brain architecture management system. Neuroinformatics 3, 15–48 (2005).

    Article  PubMed  Google Scholar 

  88. Stephan, K.E. The history of CoCoMac. Neuroimage 80, 46–52 (2013).

    Article  PubMed  Google Scholar 

  89. 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  PubMed  Google Scholar 

  90. Gorgolewski, K.J. & Poldrack, R.A. A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biol. 14, e1002506 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N. & Trojanowski, J.Q. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32, 2322.e19–2322.e27 (2011).

    Article  Google Scholar 

  92. Weintraub, D. et al. Alzheimer's disease pattern of brain atrophy predicts cognitive decline in Parkinson's disease. Brain 135, 170–180 (2012).

    Article  PubMed  Google Scholar 

  93. Toledo, J.B. et al. Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings. Neurology 85, 144–153 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Habes, M. et al. White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain 139, 1164–1179 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Asanuma, K. et al. Network modulation in the treatment of Parkinson's disease. Brain 129, 2667–2678 (2006).

    Article  PubMed  Google Scholar 

  96. Eidelberg, D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 32, 548–557 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wu, P. et al. Metabolic brain network in the Chinese patients with Parkinson's disease based on 18F-FDG PET imaging. Parkinsonism Relat. Disord. 19, 622–627 (2013).

    Article  PubMed  Google Scholar 

  98. Teune, L.K. et al. Validation of parkinsonian disease-related metabolic brain patterns. Mov. Disord. 28, 547–551 (2013).

    Article  CAS  PubMed  Google Scholar 

  99. Westfall, J., Judd, C.M. & Kenny, D.A. Replicating studies in which samples of participants respond to samples of stimuli. Perspect. Psychol. Sci. 10, 390–399 (2015).

    Article  PubMed  Google Scholar 

  100. Hashmi, J.A. et al. Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain 136, 2751–2768 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Petersen, S.E. et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches. J. Cardiovasc. Magn. Reson. 15, 46 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Weiner, M.W. et al. Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement. 11, 865–884 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Tagliazucchi, E. & Laufs, H. Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82, 695–708 (2014).

    Article  CAS  PubMed  Google Scholar 

  104. Buckner, R.L., Krienen, F.M. & Yeo, B.T.T. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci. 16, 832–837 (2013).

    Article  PubMed  Google Scholar 

  105. Glover, G.H. et al. Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J. Magn. Reson. Imaging 36, 39–54 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Landis, J.R. et al. The MAPP research network: design, patient characterization and operations. BMC Urol. 14, 58 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Thompson, P.M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).

    PubMed  PubMed Central  Google Scholar 

  108. Borsook, D., Becerra, L. & Hargreaves, R. Biomarkers for chronic pain and analgesia. Part 1: the need, reality, challenges, and solutions. Discov. Med. 11, 197–207 (2011).

    PubMed  Google Scholar 

  109. Hargreaves, R.J. et al. Optimizing central nervous system drug development using molecular imaging. Clin. Pharmacol. Ther. 98, 47–60 (2015).

    Article  CAS  PubMed  Google Scholar 

  110. López-Solà, M. et al. Towards a neurophysiological signature for fibromyalgia. Pain (2016).

  111. Lombardo, M.V. et al. Different functional neural substrates for good and poor language outcome in autism. Neuron 86, 567–577 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Woolf, C.J. & Salter, M.W. Neuronal plasticity: increasing the gain in pain. Science 288, 1765–1769 (2000).

    Article  CAS  PubMed  Google Scholar 

  113. Diatchenko, L., Nackley, A.G., Slade, G.D., Fillingim, R.B. & Maixner, W. Idiopathic pain disorders--pathways of vulnerability. Pain 123, 226–230 (2006).

    Article  PubMed  Google Scholar 

  114. Adler, G. & Gattaz, W.F. Pain perception threshold in major depression. Biol. Psychiatry 34, 687–689 (1993).

    Article  CAS  PubMed  Google Scholar 

  115. Krishnan, A. et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife 5, e15166 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Woo, C.W., Roy, M., Buhle, J.T. & Wager, T.D. Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain. PLoS Biol. 13, e1002036 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Ma, Y. et al. Serotonin transporter polymorphism alters citalopram effects on human pain responses to physical pain. Neuroimage 135, 186–196 (2016).

    Article  CAS  PubMed  Google Scholar 

  118. Bräscher, A.K., Becker, S., Hoeppli, M.E. & Schweinhardt, P. Different brain circuitries mediating controllable and uncontrollable pain. J. Neurosci. 36, 5013–5025 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Wiecki, T.V., Poland, J. & Frank, M.J. Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clin. Psychol. Sci. 3, 378–399 (2015).

    Article  Google Scholar 

  120. Huys, Q.J., Maia, T.V. & Frank, M.J. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19, 404–413 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Brodersen, K.H. et al. Generative embedding for model-based classification of fMRI data. PLOS Comput. Biol. 7, e1002079 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Friston, K.J., Harrison, L. & Penny, W. Dynamic causal modelling. Neuroimage 19, 1273–1302 (2003).

    Article  CAS  PubMed  Google Scholar 

  123. Hein, G., Morishima, Y., Leiberg, S., Sul, S. & Fehr, E. The brain's functional network architecture reveals human motives. Science 351, 1074–1078 (2016).

    Article  CAS  PubMed  Google Scholar 

  124. Fan, Y., Resnick, S.M., Wu, X. & Davatzikos, C. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. Neuroimage 41, 277–285 (2008).

    Article  PubMed  Google Scholar 

  125. Casanova, R. et al. Alzheimer's disease risk assessment using large-scale machine learning methods. PLoS One 8, e77949 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Tosun, D., Joshi, S. & Weiner, M.W. Neuroimaging predictors of brain amyloidosis in mild cognitive impairment. Ann. Neurol. 74, 188–198 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Vemuri, P. et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186–1197 (2008).

    Article  PubMed  Google Scholar 

  128. Huang, C. et al. Metabolic brain networks associated with cognitive function in Parkinson's disease. Neuroimage 34, 714–723 (2007).

    Article  PubMed  Google Scholar 

  129. Mure, H. et al. Parkinson's disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage 54, 1244–1253 (2011).

    Article  PubMed  Google Scholar 

  130. Eckert, T. et al. Abnormal metabolic networks in atypical parkinsonism. Mov. Disord. 23, 727–733 (2008).

    Article  PubMed  Google Scholar 

  131. Niethammer, M. et al. A disease-specific metabolic brain network associated with corticobasal degeneration. Brain 137, 3036–3046 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Geman, S., Bienenstock, E. & Doursat, R. Neural networks and the bias/variance dilemma. Neural Comput. 4, 1–58 (1992).

    Article  Google Scholar 

  133. Haxby, J.V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).

    Article  CAS  PubMed  Google Scholar 

  134. Kriegeskorte, N., Goebel, R. & Bandettini, P. Information-based functional brain mapping. Proc. Natl. Acad. Sci. USA 103, 3863–3868 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Sato, J.R. et al. Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res. 233, 289–291 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Wager, T.D., Atlas, L.Y., Leotti, L.A. & Rilling, J.K. Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. J. Neurosci. 31, 439–452 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Dukart, J., Schroeter, M.L. & Mueller, K. Age correction in dementia--matching to a healthy brain. PLoS One 6, e22193 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Naselaris, T., Kay, K.N., Nishimoto, S. & Gallant, J.L. Encoding and decoding in fMRI. Neuroimage 56, 400–410 (2011).

    Article  PubMed  Google Scholar 

  139. Mitchell, T.M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).

    Article  CAS  PubMed  Google Scholar 

  140. Krishnan, A., Williams, L.J., McIntosh, A.R. & Abdi, H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 56, 455–475 (2011).

    Article  PubMed  Google Scholar 

  141. Nishimoto, S. et al. Reconstructing visual experiences from brain activity evoked by natural movies. Curr. Biol. 21, 1641–1646 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Kay, K.N., Naselaris, T., Prenger, R.J. & Gallant, J.L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Ketz, N., O'Reilly, R.C. & Curran, T. Classification aided analysis of oscillatory signatures in controlled retrieval. Neuroimage 85, 749–760 (2014).

    Article  PubMed  Google Scholar 

  144. Kim, J., Calhoun, V.D., Shim, E. & Lee, J.H. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage 124 Pt A: 127–146 (2016).

    Article  PubMed  Google Scholar 

  145. Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science 1, 417–446 (2015).

    Article  PubMed  Google Scholar 

  146. O'Reilly, R.C. Biologically based computational models of high-level cognition. Science 314, 91–94 (2006).

    Article  CAS  PubMed  Google Scholar 

  147. Poldrack, R.A., Halchenko, Y.O. & Hanson, S.J. Decoding the large-scale structure of brain function by classifying mental States across individuals. Psychol. Sci. 20, 1364–1372 (2009).

    Article  PubMed  Google Scholar 

  148. Todd, M.T., Nystrom, L.E. & Cohen, J.D. Confounds in multivariate pattern analysis: Theory and rule representation case study. Neuroimage 77, 157–165 (2013).

    Article  PubMed  Google Scholar 

  149. Etzel, J.A., Zacks, J.M. & Braver, T.S. Searchlight analysis: promise, pitfalls, and potential. Neuroimage 78, 261–269 (2013).

    Article  PubMed  Google Scholar 

  150. Haxby, J.V. et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72, 404–416 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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).

Author information

Authors and 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

Authors
  1. Choong-Wan Woo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luke J Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin A Lindquist
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tor D Wager
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tor D Wager.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Woo, CW., Chang, L., Lindquist, M. et al. Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 20, 365–377 (2017). https://doi.org/10.1038/nn.4478

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research