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
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
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).
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).
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).
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).
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).
Rosenberg, M.D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).
Sanislow, C.A. et al. Developing constructs for psychopathology research: research domain criteria. J. Abnorm. Psychol. 119, 631–639 (2010).
Scoville, W.B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11–21 (1957).
Fodor, J.A. The Modularity of Mind (MIT Press, 1983).
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).
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).
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).
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).
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).
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).
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).
Logothetis, N.K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).
Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).
Price, J.L. & Drevets, W.C. Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn. Sci. 16, 61–71 (2012).
Roy, M., Shohamy, D. & Wager, T.D. Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends Cogn. Sci. 16, 147–156 (2012).
Wager, T.D. et al. Pain in the ACC? Proc. Natl. Acad. Sci. USA 113, E2474–E2475 (2016).
Poldrack, R.A. Can cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10, 59–63 (2006).
Wager, T.D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013).
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).
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).
Haynes, J.D. A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron 87, 257–270 (2015).
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).
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).
Miyawaki, Y. et al. Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron 60, 915–929 (2008).
Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679–685 (2005).
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).
Poldrack, R.A. & Gorgolewski, K.J. Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517 (2014).
Abi-Dargham, A. & Horga, G. The search for imaging biomarkers in psychiatric disorders. Nat. Med. 22, 1248–1255 (2016).
Hastie, T., Tibshirani, R. & Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn (Springer, 2009).
Mohri, M., Rostamizadeh, A. & Talwalkar, A. Foundations of Machine Learning (MIT Press, 2012).
de Leon, M.J. et al. Positron emission tomographic studies of aging and Alzheimer disease. AJNR Am. J. Neuroradiol. 4, 568–571 (1983).
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).
Doyle, O.M. et al. Predicting progression of Alzheimer's disease using ordinal regression. PLoS One 9, e105542 (2014).
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).
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).
Sørensen, L. et al. Early detection of Alzheimer's disease using MRI hippocampal texture. Hum. Brain Mapp. 37, 1148–1161 (2016).
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).
Beardslee, W.R. et al. Prevention of depression in at-risk adolescents: longer-term effects. JAMA Psychiatry 70, 1161–1170 (2013).
Addington, J. & Heinssen, R. Prediction and prevention of psychosis in youth at clinical high risk. Annu. Rev. Clin. Psychol. 8, 269–289 (2012).
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).
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).
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).
Tang, C.C. et al. Differential diagnosis of Parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 9, 149–158 (2010).
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).
Anticevic, A. et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb. Cortex 24, 3116–3130 (2014).
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).
Insel, T.R. & Cuthbert, B.N. Medicine. Brain disorders? Precisely. Science 348, 499–500 (2015).
Clementz, B.A. et al. Identification of distinct psychosis biotypes using brain-based biomarkers. Am. J. Psychiatry 173, 373–384 (2016).
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).
Drysdale, A.T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. (2016).
Weinstein, J.N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Roychowdhury, S. & Chinnaiyan, A.M. Translating genomics for precision cancer medicine. Annu. Rev. Genomics Hum. Genet. 15, 395–415 (2014).
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).
Doehrmann, O. et al. Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry 70, 87–97 (2013).
Whitfield-Gabrieli, S. et al. Brain connectomics predict response to treatment in social anxiety disorder. Mol. Psychiatry 21, 680–685 (2016).
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).
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).
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).
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).
Woo, C.W. & Wager, T.D. Neuroimaging-based biomarker discovery and validation. Pain 156, 1379–1381 (2015).
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).
Cronbach, L.J. & Meehl, P.E. Construct validity in psychological tests. Psychol. Bull. 52, 281–302 (1955).
Freedman, R. et al. The initial field trials of DSM-5: new blooms and old thorns. Am. J. Psychiatry 170, 1–5 (2013).
Iidaka, T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63, 55–67 (2015).
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).
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).
Whelan, R. & Garavan, H. When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol. Psychiatry 75, 746–748 (2014).
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).
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).
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).
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).
Eloyan, A. et al. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Front. Syst. Neurosci. 6, 61 (2012).
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).
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).
van den Heuvel, M.P. et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70, 783–792 (2013).
Yahata, N. et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat. Commun. 7, 11254 (2016).
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).
Vemuri, P. et al. Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage. Neuroimage 42, 559–567 (2008).
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).
Yeo, B.T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Glasser, M.F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Bota, M., Dong, H.W. & Swanson, L.W. Brain architecture management system. Neuroinformatics 3, 15–48 (2005).
Stephan, K.E. The history of CoCoMac. Neuroimage 80, 46–52 (2013).
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).
Gorgolewski, K.J. & Poldrack, R.A. A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biol. 14, e1002506 (2016).
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).
Weintraub, D. et al. Alzheimer's disease pattern of brain atrophy predicts cognitive decline in Parkinson's disease. Brain 135, 170–180 (2012).
Toledo, J.B. et al. Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings. Neurology 85, 144–153 (2015).
Habes, M. et al. White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain 139, 1164–1179 (2016).
Asanuma, K. et al. Network modulation in the treatment of Parkinson's disease. Brain 129, 2667–2678 (2006).
Eidelberg, D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 32, 548–557 (2009).
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).
Teune, L.K. et al. Validation of parkinsonian disease-related metabolic brain patterns. Mov. Disord. 28, 547–551 (2013).
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).
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).
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).
Weiner, M.W. et al. Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement. 11, 865–884 (2015).
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).
Buckner, R.L., Krienen, F.M. & Yeo, B.T.T. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci. 16, 832–837 (2013).
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).
Landis, J.R. et al. The MAPP research network: design, patient characterization and operations. BMC Urol. 14, 58 (2014).
Thompson, P.M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).
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).
Hargreaves, R.J. et al. Optimizing central nervous system drug development using molecular imaging. Clin. Pharmacol. Ther. 98, 47–60 (2015).
López-Solà, M. et al. Towards a neurophysiological signature for fibromyalgia. Pain (2016).
Lombardo, M.V. et al. Different functional neural substrates for good and poor language outcome in autism. Neuron 86, 567–577 (2015).
Woolf, C.J. & Salter, M.W. Neuronal plasticity: increasing the gain in pain. Science 288, 1765–1769 (2000).
Diatchenko, L., Nackley, A.G., Slade, G.D., Fillingim, R.B. & Maixner, W. Idiopathic pain disorders--pathways of vulnerability. Pain 123, 226–230 (2006).
Adler, G. & Gattaz, W.F. Pain perception threshold in major depression. Biol. Psychiatry 34, 687–689 (1993).
Krishnan, A. et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife 5, e15166 (2016).
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).
Ma, Y. et al. Serotonin transporter polymorphism alters citalopram effects on human pain responses to physical pain. Neuroimage 135, 186–196 (2016).
Bräscher, A.K., Becker, S., Hoeppli, M.E. & Schweinhardt, P. Different brain circuitries mediating controllable and uncontrollable pain. J. Neurosci. 36, 5013–5025 (2016).
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).
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).
Brodersen, K.H. et al. Generative embedding for model-based classification of fMRI data. PLOS Comput. Biol. 7, e1002079 (2011).
Friston, K.J., Harrison, L. & Penny, W. Dynamic causal modelling. Neuroimage 19, 1273–1302 (2003).
Hein, G., Morishima, Y., Leiberg, S., Sul, S. & Fehr, E. The brain's functional network architecture reveals human motives. Science 351, 1074–1078 (2016).
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).
Casanova, R. et al. Alzheimer's disease risk assessment using large-scale machine learning methods. PLoS One 8, e77949 (2013).
Tosun, D., Joshi, S. & Weiner, M.W. Neuroimaging predictors of brain amyloidosis in mild cognitive impairment. Ann. Neurol. 74, 188–198 (2013).
Vemuri, P. et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186–1197 (2008).
Huang, C. et al. Metabolic brain networks associated with cognitive function in Parkinson's disease. Neuroimage 34, 714–723 (2007).
Mure, H. et al. Parkinson's disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage 54, 1244–1253 (2011).
Eckert, T. et al. Abnormal metabolic networks in atypical parkinsonism. Mov. Disord. 23, 727–733 (2008).
Niethammer, M. et al. A disease-specific metabolic brain network associated with corticobasal degeneration. Brain 137, 3036–3046 (2014).
Geman, S., Bienenstock, E. & Doursat, R. Neural networks and the bias/variance dilemma. Neural Comput. 4, 1–58 (1992).
Haxby, J.V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).
Kriegeskorte, N., Goebel, R. & Bandettini, P. Information-based functional brain mapping. Proc. Natl. Acad. Sci. USA 103, 3863–3868 (2006).
Sato, J.R. et al. Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res. 233, 289–291 (2015).
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).
Dukart, J., Schroeter, M.L. & Mueller, K. Age correction in dementia--matching to a healthy brain. PLoS One 6, e22193 (2011).
Naselaris, T., Kay, K.N., Nishimoto, S. & Gallant, J.L. Encoding and decoding in fMRI. Neuroimage 56, 400–410 (2011).
Mitchell, T.M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).
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).
Nishimoto, S. et al. Reconstructing visual experiences from brain activity evoked by natural movies. Curr. Biol. 21, 1641–1646 (2011).
Kay, K.N., Naselaris, T., Prenger, R.J. & Gallant, J.L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).
Ketz, N., O'Reilly, R.C. & Curran, T. Classification aided analysis of oscillatory signatures in controlled retrieval. Neuroimage 85, 749–760 (2014).
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).
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).
O'Reilly, R.C. Biologically based computational models of high-level cognition. Science 314, 91–94 (2006).
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).
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).
Etzel, J.A., Zacks, J.M. & Braver, T.S. Searchlight analysis: promise, pitfalls, and potential. Neuroimage 78, 261–269 (2013).
Haxby, J.V. et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72, 404–416 (2011).
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
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nn.4478
This article is cited by
-
Distributed neural representations of conditioned threat in the human brain
Nature Communications (2024)
-
The psychological, computational, and neural foundations of indebtedness
Nature Communications (2024)
-
A neural signature for the subjective experience of threat anticipation under uncertainty
Nature Communications (2024)
-
Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning
Nature Mental Health (2024)
-
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
BMC Medicine (2023)