Machine learning approaches: From theory to application in schizophrenia

Computational and Mathematical Methods in Medicine

Volumn 2013, Issue , 2013, Pages

  Veronese, Elisa ^a   Castellani, Umberto ^b   Peruzzo, Denis ^a,b   Bellani, Marcella ^b   Brambilla, Paolo ^c,d  

^a SCIENTIFIC INSTITUTE   (Italy)

^b UNIVERSITY OF VERONA   (Italy)

^c UNIVERSITY OF UDINE   (Italy)

^d UNIVERSITY OF TEXAS MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; DISEASES; NEUROIMAGING; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

AUTOMATIC CLASSIFICATION; BIOLOGICAL UNDERPINNINGS; CLINICAL PRACTICES; DISEASE DIAGNOSIS; HEALTHY CONTROLS; MACHINE LEARNING APPROACHES;

LEARNING SYSTEMS;

ALGORITHM; CLINICAL PRACTICE; CONCEPTUAL FRAMEWORK; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; HISTOGRAM; HUMAN; MACHINE LEARNING; MATHEMATICAL MODEL; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; REVIEW; SAMPLE SIZE; SCHIZOPHRENIA; SUPPORT VECTOR MACHINE; ARTIFICIAL INTELLIGENCE; BRAIN; METHODOLOGY; PATHOLOGY; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; HUMANS; MAGNETIC RESONANCE IMAGING; SCHIZOPHRENIA; SUPPORT VECTOR MACHINES;

EID: 84893171253     PISSN: 1748670X     EISSN: 17486718     Source Type: Journal    
DOI: 10.1155/2013/867924     Document Type: Review

References (70)

1. Szeszko P. R., Narr K. L., Phillips O. R., McCormack J., Sevy S., Gunduz-Bruce H., Kane J. M., Bilder R. M., Robinson D. G., Magnetic resonance imaging predictors of treatment response in first-episode schizophrenia. Schizophrenia Bulletin 2012 38 3 569 578 2-s2.0-84860110957 10.1093/schbul/ sbq126
2. Olabi B., Ellison-Wright I., McIntosh A. M., Wood S. J., Bullmore E., Lawrie S. M., Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies. Biological Psychiatry 2011 70 1 88 96 2-s2.0-79958858244 10.1016/j.biopsych.2011.01.032
3. Bodnar M., Achim A. M., Malla A. K., Joober R., Benoit A., Lepage M., Functional magnetic resonance imaging correlates of memory encoding in relation to achieving remission in first-episode schizophrenia. The British Journal of Psychiatry 2012 200 4 300 307 2-s2.0-84859600587 10.1192/bjp.bp.111.098046
4. Klöppel S., Abdulkadir A., Jack C. R. Jr., Koutsouleris N., Mourão-Miranda J., Vemuri P., Diagnostic neuroimaging across diseases. Neuroimage 2012 61 2 457 463 10.1016/j.neuroimage.2011.11.002
5. Lao Z., Shen D., Xue Z., Karacali B., Resnick S. M., Davatzikos C., Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 2004 21 1 46 57 2-s2.0-1642574430 10.1016/j.neuroimage.2003.09.027 (Pubitemid 38114995)
6. Pereira F., Mitchell T., Botvinick M., Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 2009 45 1 S199 S209 2-s2.0-65549168742 10.1016/j.neuroimage.2008.11.007
7. Lemm S., Blankertz B., Dickhaus T., Müller K.-R., Introduction to machine learning for brain imaging. NeuroImage 2011 56 2 387 399 2-s2.0-79954990666 10.1016/j.neuroimage.2010.11.004
8. Vapnik V. N., The Nature of Statistical Learning Theory 1995 New York, NY, USA Springer
9. Burges C. J. C., A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998 2 2 121 167 2-s2.0-27144489164 (Pubitemid 128695475)
10. Pontil M., Verri A., Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998 20 6 637 646 2-s2.0-0032095724 10.1109/34.683777 (Pubitemid 128744848)
11. Modak S., Sharma S., Prabhakar P., Yadav A., Jayaraman V. K., Gupta V. K., Tuohy M. G., Ayyachamy M., Turner K. M., O'Donovan A., Application of support vector machines in fungal genome and proteome annotation. Laboratory Protocols in Fungal Biology 2013 New York, NY, USA Springer 565 577 Fungal Biology 10.1007/978-1-4614-2356-0-56
12. Heisele B., Ho P., Wu J., Poggio T., Face recognition: component-based versus global approaches. Computer Vision and Image Understanding 2003 91 1-2 6 21 2-s2.0-0141526184 10.1016/S1077-3142(03)00073-0
13. Niu X.-X., Suen C. Y., A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition 2012 45 4 1318 1325 2-s2.0-83655163714 10.1016/j.patcog.2011.09.021
14. Boser B. E., Guyon I. M., Vapnik V. N., Training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory July 1992 ACM Press 144 152 2-s2.0-0026966646
15. Cox D. D., Savoy R. L., Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 2003 19 2 261 270 2-s2.0-0041737619 10.1016/S1053-8119(03)00049-1 (Pubitemid 39665678)
16. Wang Z., Childress A. R., Wang J., Detre J. A., Support vector machine learning-based fMRI data group analysis. NeuroImage 2007 36 4 1139 1151 2-s2.0-34347333571 10.1016/j.neuroimage.2007.03.072 (Pubitemid 47008958)
17. 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. Neuroscience and Biobehavioral Reviews 2012 36 4 1140 1152 2-s2.0-84857000430 10.1016/j.neubiorev.2012.01.004
18. Ashburner J., Friston K. J., Voxel-based morphometry-the methods. NeuroImage 2000 11 6 805 821 2-s2.0-0033933649 10.1006/nimg.2000.0582 (Pubitemid 30437668)
19. Nordahl C. W., Dierker D., Mostafavi I., Schumann C. M., Rivera S. M., Amaral D. G., van Essen D. C., Cortical folding abnormalities in autism revealed by surface-based morphometry. Journal of Neuroscience 2007 27 43 11725 11735 2-s2.0-35548949445 10.1523/JNEUROSCI.0777-07.2007 (Pubitemid 350006915)
20. Pantazis D., Leahy R. M., Nichols T. E., Styne M., Statistical surface-based morphometry using a non-parametric approach. Proceedings of the 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano April 2004 1283 1286 2-s2.0-4444299299
21. Shenton M. E., Dickey C. C., Frumin M., McCarley R. W., A review of MRI findings in schizophrenia. Schizophrenia Research 2001 49 1-2 1 52 2-s2.0-0035870757 10.1016/S0920-9964(01)00163-3 (Pubitemid 32451739)
22. Rujescu D. A. N., Collier D. A., Dissecting the many genetic faces of schizophrenia. Epidemiologia e Psichiatria Sociale 2009 18 2 91 95 2-s2.0-70449730211
23. Fan Y., Shen D., Gur R. C., Gur R. E., Davatzikos C., COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Transactions on Medical Imaging 2007 26 1 93 105 2-s2.0-33846264886 10.1109/TMI.2006.886812 (Pubitemid 46096354)
24. Gerig G., Styner M., Shenton M. E., Lieberman J. A., Shape versus size: improved understanding of the morphology of brain structures. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI '01) 2001 24 32 (Pubitemid 33352393)
25. Koutsouleris N., Meisenzahl E. M., Davatzikos C., Bottlender R., Frodl T., Scheuerecker J., Schmitt G., Zetzsche T., Decker P., Reiser M., Möller H.-J., Gaser C., Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Archives of General Psychiatry 2009 66 7 700 712 2-s2.0-67650457029 10.1001/archgenpsychiatry.2009.62
26. Yoon U., Lee J.-M., Im K., Shin Y.-W., Cho B. H., Kim I. Y., Kwon J. S., Kim S. I., Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. NeuroImage 2007 34 4 1405 1415 2-s2.0-33846575157 10.1016/j.neuroimage.2006.11.021 (Pubitemid 46187086)
27. Palaniyappan L., Liddle P. F., Aberrant cortical gyrification in schizophrenia: a surface-based morphometry study. Journal of Psychiatry & Neuroscience 2012 37 6 399 406 10.1503/jpn.110119
28. Castellani U., Rossato E., Murino V., Bellani M., Rambaldelli G., Perlini C., Tomelleri L., Tansella M., Brambilla P., Classification of schizophrenia using feature-based morphometry. Journal of Neural Transmission 2012 119 3 395 404 2-s2.0-84857630976 10.1007/s00702-011-0693-7
29. Castellani U., Perina A., Murino V., Bellani M., Rambaldelli G., Tansella M., Brambilla P., Brain morphometry by probabilistic latent semantic analysis. Proceedings of the 13th international conference on Medical Image Computing and Computer-Assisted Intervention: Part II (MICCAI '10) September 2010 2-s2.0-84873984548
30. Ulaş A., Duin R. P. W., Castellani U., Loog M., Bicego M., Murino V., Bellani M., Cerruti S., Tansella M., Brambilla P., Dissimilarity-based detection of schizophrenia. Proceedings of the 1st Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD '10) August 2010 32 35 2-s2.0-78049493721 10.1109/WBD.2010.10
31. Castellani U., Mirtuono P., Murino V., Bellani M., Rambaldelli G., Tansella M., Brambilla P., A new shape diffusion descriptor for brain classification. Proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II (MICCAI '11) 2011 426 433 2-s2.0-80053483511
32. Ulaş A., Duin R. P. W., Castellani U., Dissimilarity-based detection of schizophrenia. International Journal of Imaging Systems and Technology 2011 21 2 179 192 2-s2.0-79955927394 10.1002/ima.20279
33. Ulaş A., Castellani U., Murino V., Bellani M., Tansella M., Brambilla P., Biomarker evaluation by multiple Kernel learning for schizophrenia detection. Proceedings of the International Workshop on Pattern Recognition in NeuroImaging (PRNI '12) July 2012 89 92 10.1109/PRNI.2012.12
34. Ulaş A., Gönen M., Castellani U., Murino V., Bellani M., Tansella M., Brambilla P., Wang F., Shen D., Yan P., Suzuki K., A localized MKL method for brain classification with known intra-class variability. Machine Learning in Medical Imaging 2012 7588 152 159 Lecture Notes in Computer Science 2-s2.0-84870039340 10.1007/978-3-642-35428-1-19
35. Yang H., Liu J., Sui J., Pearlson G., Calhoun V. D., A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Frontiers in Human Neuroscience 2010 4, article 192 2-s2.0-79953008341 10.3389/fnhum.2010.00192
36. Duda R. O., Hart P. E., Stork D. G., Pattern Classification 2001 2nd New York, NY, USA Springer
37. Zhang J., Yan B., Huang X., Yang P., Huang C., The diagnosis of Alzheimer's disease based on voxel-based morphometry and support vector machine. Proceedings of the 4th International Conference on Natural Computation (ICNC '08) October 2008 197 201 2-s2.0-57649137241 10.1109/ICNC.2008.804
38. Selvaraj H., Selvi S. T., Selvathi D., Gewali L., Brain MRI slices classification using least squares support vector machine. International Journal of Intelligent Computing in Medical Sciences and Image Processing 2007 1 1 21 33 10.1080/1931308X.2007.10644134
39. Kloppel S., Abdulkadir A., Jack C. R. Jr., Koutsouleris N., Mourão-Miranda J., Vemuri P., Diagnostic neuroimaging across diseases. Neuroimage 2012 61 2 457 463 10.1016/j.neuroimage.2011.11.002
40. Schutze H., Hull D. A., Pedersen J. O., Comparison of classifiers and document representations for the routing problem. Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval July 1995 229 237 2-s2.0-0029206376
41. Bradley P. S., Mangasarian O. L., Street W. N., Feature selection via mathematical programming. INFORMS Journal on Computing 1998 10 2 209 217 2-s2.0-0001010266 (Pubitemid 128669128)
42. Whitney A. W., A direct method of nonparametric measurement selection. IEEE Transactions on Computers 1971 20 9 1100 1103 2-s2.0-0015125457
43. Marill T., Green D. M., On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory 1963 9 11 17 10.1109/TIT.1963.1057810
44. Efron B., Bootstrap methods: another look at the jackknife. The Annals of Statistics 1979 7 1 1 26 10.1214/aos/1176344552
45. Chang C.-C., Lin C.-J., LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2011 2 3, article 27 2-s2.0-79955702502 10.1145/1961189.1961199
46. Bottou L., Chapelle O., DeCoste D., Weston J., Large Scale Kernel Machines 2007 Cambridge, Mass, USA MIT Press
47. Joachims T., Training linear SVMs in linear time. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '06) August 2006 217 226 2-s2.0-33749563073
48. Hastie T., Tibshirani R., Friedman J., Franklin J., The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer 2005 27 2 83 85 10.1007/BF02985802
49. Davatzikos C., Ruparel K., Fan Y., Shen D. G., Acharyya M., Loughead J. W., Gur R. C., Langleben D. D., Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 2005 28 3 663 668 2-s2.0-27544453543 10.1016/j.neuroimage.2005.08.009 (Pubitemid 41540374)
50. Ecker C., Rocha-Rego V., Johnston P., Mourao-Miranda J., Marquand A., Daly E. M., Brammer M. J., Murphy C., Murphy D. G., Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage 2010 49 1 44 56 2-s2.0-70349964707 10.1016/j.neuroimage. 2009.08.024
51. Koenderink J. J., van Doorn A. J., Surface shape and curvature scales. Image and Vision Computing 1992 10 8 557 564 2-s2.0-0000113585
52. Hofmann T., Unsupervised learning by probabilistic Latent semantic analysis. Machine Learning 2001 42 1-2 177 196 2-s2.0-0034818212 10.1023/A:1007617005950 (Pubitemid 32872403)
53. Sun J., Ovsjanikov M., Guibas L., A concise and provably informative multi-scale signature based on heat diffusion. Eurographics Symposium on Geometry Processing 2009 28 5 1383 1392 2-s2.0-78651295003
54. Reuter M., Wolter F.-E., Shenton M., Niethammer M., Laplace-Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis. Computer Aided Design 2009 41 10 739 755 2-s2.0-69849109283 10.1016/j.cad.2009.02.007
55. Lowe D. G., Object recognition from local scale-invariant features. Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV '99) September 1999 1150 1157 2-s2.0-0033284915
56. Cruska G., Dance C. R., Fan L., Willamowski J., Bray C., Visual categorization with bags of keypoints. Proceedings of the Workshop on Statistical Learning in Computer Vision (ECCV '04) 2004 1 22
57. Grauman K., Darrell T., The pyramid match kernel: efficient learning with sets of features. Journal of Machine Learning Research 2007 8 725 760 2-s2.0-34247576789 (Pubitemid 46677049)
58. Bach F. R., Lanckriet G. R. G., Jordan M. I., Multiple kernel learning, conic duality, and the SMO algorithm. Proceedings of the 21st International Conference on Machine Learning (ICML '04) July 2004 41 48 2-s2.0-14344252374
59. Lanckriet G. R. G., Cristianini N., Bartlett P., El Ghaoui L., Jordan M. I., Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research 2004 5 27 72 2-s2.0-8844278523
60. Gonen M., Alpaydn E., Multiple kernel learning algorithms. Journal of Machine Learning Research 2011 12 2181 2238
61. Cristianini N., Shawe-Taylor J., An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000 Cambridge University Press
62. Kloft M., Brefeld U., Sonnenburg S., Zien A., ℓ p-norm multiple kernel learning. Journal of Machine Learning Research 2011 12 953 997 2-s2.0-79955848223
63. Rakotomamonjy A., Bach F. R., Canu S., Grandvalet Y., SimpleMKL. Journal of Machine Learning Research 2008 9 2491 2521 2-s2.0-57249084590
64. Nyul L. G., Udupa J. K., Zhang X., New variants of a method of MRI scale standardization. IEEE Transactions on Medical Imaging 2000 19 2 143 150 2-s2.0-0033624997 10.1109/42.836373 (Pubitemid 30594807)
65. Gönen M., Alpaydin E., Localized multiple kernel learning. Proceedings of the 25th International Conference on Machine Learning July 2008 Helsinki, Finland 352 359 2-s2.0-56449124689
66. Jacobs R. A., Jordan M. I., Nowlan S. J., Hinton G. E., Adaptive mixtures of local experts. Neural Computation 1991 3 1 79 87 10.1162/neco.1991.3.1.79
67. Pekalska E., Duin R. P. W., The Dissimilarity Representation for Pattern Recognition. Foundations and Applications 2005 World Scientific Publishing
68. Scholkopf B., Smola A. J., Learning with Kernels 2002 The MIT Press
69. Freudenmann R. W., Kölle M., Huwe A., Luster M., Reske S. N., Huber M., Lepping P., Wolf R. C., Schönfeldt-Lecuona C., Delusional infestation: neural correlates and antipsychotic therapy investigated by multimodal. Progress in Neuro-Psychopharmacology and Biological Psychiatry 2010 34 7 1215 1222 10.1016/j.pnpbp.2010.06.022
70. Friston K. J., Dolan R. J., Computational and dynamic models in neuroimaging. NeuroImage 2010 52 3 752 765 2-s2.0-77954195285 10.1016/j.neuroimage.2009.12.068