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

European Child and Adolescent Psychiatry

Volumn 22, Issue 12, 2013, Pages 733-744

  Johnston, B A ^a   Mwangi, B ^a   Matthews, K ^a   Coghill, D ^a   Steele, J D ^a  

^a UNIVERSITY OF DUNDEE   (United Kingdom)

Author keywords

DARTEL; Feature selection; Gaussian processes; Individual scan classification; Relevance vector machine; SPM; Support vector machine

Indexed keywords

METHYLPHENIDATE;

ATTENTION DEFICIT DISORDER; AUTISM; CLINICAL FEATURE; DIFFUSION TENSOR IMAGING; DISEASE SEVERITY; EPILEPSY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GRAY MATTER; HEAD MOVEMENT; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION; QUALITY CONTROL; RADIOFREQUENCY; REVIEW; SIGNAL NOISE RATIO; SUPPORT VECTOR MACHINE; SYMPTOM; TASK PERFORMANCE; WHITE MATTER;

ADOLESCENT; BRAIN; CHILD; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MENTAL DISORDERS; NEUROIMAGING;

EID: 84890859314     PISSN: 10188827     EISSN: 1435165X     Source Type: Journal    
DOI: 10.1007/s00787-012-0319-0     Document Type: Review

References (53)

1. Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge, MA
2. Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38:95-113
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
5. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin
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
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
8. Costafreda SG, Chu C, Ashburner J, Fu CHY (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4:e6353
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
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
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
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
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
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
15. Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD (2007) Statistical parametric mapping: the analysis of functional brain images. Academic Press, London
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
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
19. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182
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
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
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
25. Linden David EJ (2012) The challenges and promise of neuroimaging in psychiatry. Neuron 73:8-22
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
27. McRobbie DW, Moore EA, Graves MJ, Prince MR (2010) MRI from picture to proton. Cambridge University Press, New York
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
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
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
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
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
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
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
35. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge
36. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507-2517 (Oxford, England)
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
38. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge
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
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
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
43. Theodoridis S, Koutroumbas K (2006) Pattern recognition. Elsevier, Amsterdam
44. Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211-244
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
46. Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin
47. Vapnik VN (1998) Statistical learning theory. Wiley, New York
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
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
50. Watkins AE, Scheaffer RL, Cobb GW (2009) Statistics: from data to decision. Wiley, New York
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
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
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