Skip to main content
Log in

Predictive classification of individual magnetic resonance imaging scans from children and adolescents

  • Review
  • Published:
European Child & Adolescent Psychiatry Aims and scope Submit manuscript

Abstract

Neuroimaging techniques are increasingly being explored as potential tools for clinical prediction in psychiatry. There are a wide range of approaches which can be applied to make individual predictions for various aspects of disorders such as diagnostic status, symptom severity scores, identification of patients at risk of developing disorders and estimation of the likelihood of response to treatment. This selective review highlights a popular group of pattern recognition techniques, support vector machines (SVMs) for use with structural magnetic resonance imaging scans. First, however, we outline various practical issues, limitations and techniques which need to be considered before SVM’s can be applied. We begin with a discussion on the practicalities of scanning children and adolescent participants and the importance of acquiring high quality images. Scan processing required for inter-subject comparisons is then discussed. We then briefly discuss feature selection and other considerations when applying pattern recognition techniques. Finally, SVMs are described and various studies highlighted to indicate the potential of these techniques for child and adolescent psychiatric research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge, MA

    Google Scholar 

  2. Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38:95–113

    Article  PubMed  Google Scholar 

  3. Ashburner J, Barnes G, Chen C-C, Daunizeau J, Flandin G, Friston K, Kiebel S, Kilner J, Litvak V, Moran R, Penny W, Rosa M, Stephan K, Gitelman D, Henson R, Hutton C, Glauche V, Mattout J, Phillips C (2012) SPM8 manual. In: Functional imaging laboratory. University College London, p 475

  4. Banaschewski T, Coghill D, Danckaerts M, Dopfner M, Rohde L (2010) ADHD and hyperkinetic disorder. Oxford University Press, Oxford

    Google Scholar 

  5. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

    Google Scholar 

  6. Bray S, Chang C, Hoeft F (2009) Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations. Front Hum Neurosci 3:32

    PubMed  Google Scholar 

  7. Chaves R, Ramirez J, Gorriz JM, Lopez M, Salas-Gonzalez D, Alvarez I, Segovia F (2009) SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neurosci Lett 461:293–297

    Article  PubMed  CAS  Google Scholar 

  8. Costafreda SG, Chu C, Ashburner J, Fu CHY (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4:e6353

    Article  PubMed  Google Scholar 

  9. Craddock RC, Holtzheimer PE III, Hu XP, Mayberg HS et al (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62:1619–1628

    Article  PubMed  Google Scholar 

  10. De Martino F, Valente G, Staeren Nl, Ashburner J, Goebel R, Formisano E et al (2008) Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43:44–58

    Article  PubMed  Google Scholar 

  11. Duchesnay E, Cachia A, Boddaert N, Chabane N, Mangin J-F, Martinot J-L, Brunelle F, Zilbovicius M (2011) Feature selection and classification of imbalanced datasets: application to PET images of children with autistic spectrum disorders. Neuroimage 57:1003–1014

    Article  PubMed  Google Scholar 

  12. Durston S, Tottenham NT, Thomas KM, Davidson MC, Eigsti I-M, Yang Y, Ulug AM, Casey BJ (2003) Differential patterns of striatal activation in young children with and without ADHD. Biol Psychiatry 53:871–878

    Article  PubMed  Google Scholar 

  13. Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, Brammer MJ, Murphy C, Murphy DG (2010) Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49:44–56

    Article  PubMed  Google Scholar 

  14. Fassbender C, Zhang H, Buzy WM, Cortes CR, Mizuiri D, Beckett L, Schweitzer JB (2009) A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res 1273:114–128

    Article  PubMed  CAS  Google Scholar 

  15. Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD (2007) Statistical parametric mapping: the analysis of functional brain images. Academic Press, London

    Google Scholar 

  16. Fu CHY, Russell T, Murray R, Weinberger DR (2003) Neuroimaging in psychiatry. Martin Dunitz

  17. Gong Q, Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, Huang X, McGuire P, Mechelli A (2011) Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage 55:1497–1503

    Article  PubMed  Google Scholar 

  18. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14:21–36

    Article  PubMed  CAS  Google Scholar 

  19. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  20. Ingalhalikar M, Kanterakis S, Gur R, Roberts T, Verma R, Jiang T, Navab N, Pluim J, Viergever M (2010) DTI based diagnostic prediction of a disease via pattern classification. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) Medical image computing and computer-assisted intervention—MICCAI 2010, Springer, Berlin, pp 558–565

  21. Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR Jr, Ashburner J, Frackowiak RS (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131:681–689

    Article  PubMed  Google Scholar 

  22. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Moller H-J, Riecher-Rössler A (2011) Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull [Epub ahead of print]

  23. Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Moller HJ, Gaser C (2009) 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

    Article  PubMed  Google Scholar 

  24. Kriegeskorte N, Simmons WK, Bellgowan PSF, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12:535–540

    Article  PubMed  CAS  Google Scholar 

  25. Linden David EJ (2012) The challenges and promise of neuroimaging in psychiatry. Neuron 73:8–22

    Article  PubMed  CAS  Google Scholar 

  26. Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J (1995) A probabilistic atlas of the human brain: theory and rationale for its development: the international consortium for brain mapping (ICBM). Neuroimage 2:89–101

    Article  PubMed  CAS  Google Scholar 

  27. McRobbie DW, Moore EA, Graves MJ, Prince MR (2010) MRI from picture to proton. Cambridge University Press, New York

    Google Scholar 

  28. Meyfroidt G, Güiza F, Ramon J, Bruynooghe M (2009) Machine learning techniques to examine large patient databases. Best Pract Res Clin Anaesthesiol 23:127–143

    Article  PubMed  Google Scholar 

  29. Mourão-Miranda J, Hardoon DR, Hahn T, Marquand AF, Williams SCR, Shawe-Taylor J, Brammer M (2011) Patient classification as an outlier detection problem: an application of the one-class support vector machine. Neuroimage 58:793–804

    Article  PubMed  Google Scholar 

  30. Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera G-M (2011) Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear Biomedical Physics 5:5

    Article  PubMed  Google Scholar 

  31. Mwangi B, Ebmeier K, Matthews K, Steele JD (2012) Multicentre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 135:1508–1521

    Google Scholar 

  32. Mwangi B, Matthews K, Steele JD (2012) Prediction of illness severity in patients with major depression using structural MR brain scans. J Magn Reson Imaging 35:64–71

    Article  PubMed  Google Scholar 

  33. Park MY, Hastie T (2007) L1-regularization path algorithm for generalized linear models. J R Stat Soc Series B Stat Methodol 69:659–677

    Article  Google Scholar 

  34. Plant C, Teipel SJ, Oswald A, Böhm C, Meindl T, Mourao-Miranda J, Bokde AW, Hampel H, Ewers M (2010) Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage 50:162–174

    Article  PubMed  Google Scholar 

  35. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge

    Google Scholar 

  36. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517 (Oxford, England)

    Article  PubMed  CAS  Google Scholar 

  37. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1471

    Article  PubMed  Google Scholar 

  38. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  39. Shen L, Kim S, Qi Y, Inlow M, Swaminathan S, Nho K, Wan J, Risacher S, Shaw L, Trojanowski J, Weiner M, Saykin A, Liu T, Shen D, Ibanez L, Tao X (2011) Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. In: Liu T, Shen D, Ibanez L, Tao X (eds) Multimodal brain image analysis, Springer, Berlin, pp 27–34

  40. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    Article  Google Scholar 

  41. Stonnington CM, Chu C, Klöppel S, Jack CR Jr, Ashburner J, Frackowiak RSJ (2010) Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease. Neuroimage 51:1405–1413

    Article  PubMed  Google Scholar 

  42. Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system—an approach to cerebral imaging. Thieme Medical Publishers, New York

    Google Scholar 

  43. Theodoridis S, Koutroumbas K (2006) Pattern recognition. Elsevier, Amsterdam

    Google Scholar 

  44. Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244

    Google Scholar 

  45. Vapnik V, Golowich SE, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Adv Neural Inf Process Syst 9:281–287

    Google Scholar 

  46. Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin

    Book  Google Scholar 

  47. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  48. Wang L, Zhu C, He Y, Zang Y, Cao Q, Zhang H, Zhong Q, Wang Y (2009) Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Hum Brain Mapp 30:638–649

    Article  PubMed  CAS  Google Scholar 

  49. Wang Y, Fan Y, Bhatt P, Davatzikos C (2010) High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 50:1519–1535

    Article  PubMed  Google Scholar 

  50. Watkins AE, Scheaffer RL, Cobb GW (2009) Statistics: from data to decision. Wiley, New York

    Google Scholar 

  51. Yerys BE, Jankowski KF, Shook D, Rosenberger LR, Barnes KA, Berl MM, Ritzl EK, Vanmeter J, Vaidya CJ, Gaillard WD (2009) The fMRI success rate of children and adolescents: typical development, epilepsy, attention deficit/hyperactivity disorder, and autism spectrum disorders. Hum Brain Mapp 30:3426–3435

    Article  PubMed  Google Scholar 

  52. Zhu C-Z, Zang Y-F, Cao Q-J, Yan C-G, He Y, Jiang T-Z, Sui M-Q, Wang Y-F (2008) Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage 40:110–120

    Article  PubMed  Google Scholar 

  53. Zhu CZ, Zang YF, Liang M, Tian LX, He Y, Li XB, Sui MQ, Wang YF, Jiang TZ (2005) Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder. Med Image Comput Comput Assist Interv 8:468–475

    PubMed  CAS  Google Scholar 

Download references

Acknowledgments

BAJ and BMI were partially supported by SINAPSE (www.sinapse.ac.uk) studentships. BAJ support includes a SINAPSE-SPIRIT industry partnership with Siemens Medical.

Conflict of interest

KM has received research funding from St Jude Medical (BROADEN study), is on the advisory board of the Medtronic OCD DBS PM Study, has received educational grants from Cyberonics and Schering Plough, has received travel/meetings support from Medtronic/SJM and is the clinical lead of the Advanced Interventions Service for NHS Tayside. DC has received research funding from Lilly, Shire, Janssen and Vifor, honoraria for consultancy, advisory boards and speaker fees from Lilly, Shire, Janssen, Medice, Flynn, Novartis and Vifor. JDS has received research funding via an honorarium associated with a lecture from Wyeth. All other authors reported no potential conflicts of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. A. Johnston.

Additional information

The data included in the current study are available online as part of the ADHD-200 Sample and the International Neuroimaging Data sharing Initiative. They can be downloaded through http://fcon_1000.projects.nitrc.org/indi/adhd200. We included data from the NeuroImage, Oregon Health & Science University, and New York University Child Study Centre samples that are available within the larger ADHD-200 sample.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 186 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Johnston, B.A., Mwangi, B., Matthews, K. et al. Predictive classification of individual magnetic resonance imaging scans from children and adolescents. Eur Child Adolesc Psychiatry 22, 733–744 (2013). https://doi.org/10.1007/s00787-012-0319-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00787-012-0319-0

Keywords

Navigation