Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study

Neuroinformatics

Volumn 13, Issue 1, 2015, Pages 31-46

  Sabuncu, Mert R ^a,b   Konukoglu, Ender ^a  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Computer aided diagnosis; Image based prediction; Machine learning; MRI

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN MAPPING; HUMAN; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; BRAIN MAPPING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING;

EID: 84933047930     PISSN: 15392791     EISSN: 15590089     Source Type: Journal    
DOI: 10.1007/s12021-014-9238-1     Document Type: Article

References (85)

1. Ashburner, J., & Friston, K. J. (2000). VVoxel-based morphometry: the methods. NeuroImage, 11, 805–821.
2. Batmanghelich, N., Taskar, B., Davatzikos, C. (2009). A general and unifying framework for feature construction, in image-based pattern classification. Information Processing in Medical Imaging. Springer, pp. 423–434.
3. Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 289–300.
4. Brown, M.R., Sidhu, G.S., Greiner, R., Asgarian, N., Bastani, M., Silverstone, P.H., Greenshaw, A.J., Dursun, S.M. (2012). ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Frontiers in systems neuroscience 6.
5. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, 27.
6. Cho, Y., Seong, J.-K., Jeong, Y., & Shin, S. Y. (2012). Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. NeuroImage, 59, 2217–2230.
7. Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012). Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. NeuroImage, 60, 59–70.
8. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
9. Costafreda, S. G., Chu, C., Ashburner, J., & Fu, C. H. (2009). Prognostic and diagnostic potential of the structural neuroanatomy of depression. PloS One, 4, e6353.
10. Criminisi, A., Shotton, J., Konukoglu, E., (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Microsoft Research Cambridge, Tech. Rep. MSRTR-2011-114 5, 12.
11. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehericy, S., Habert, M.-O., Chupin, M., Benali, H., & Colliot, O. (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage, 56, 766–781.
12. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9, 179–194.
13. Davatzikos, C., Resnick, S. M., Wu, X., Parmpi, P., & Clark, C. (2008). Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. NeuroImage, 41, 1220–1227.
14. Davatzikos, C., Xu, F., An, Y., Fan, Y., & Resnick, S. M. (2009). Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain, 132, 2026–2035.
15. DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 837–845.
16. Duchesnay, E., Cachia, A., Roche, A., Rivière, D., Cointepas, Y., Papadopoulos-Orfanos, D., Zilbovicius, M., Martinot, J.-L., & Mangin, J. F. (2007). Classification based on cortical folding patterns. Medical Imaging, IEEE Transactions, 26(4), 553–565.
17. Duchesne, S., Rolland, Y., & Verin, M. (2009). Automated computer differential classification in Parkinsonian syndromes via pattern analysis on MRI. Academic Radiology, 16, 61–70.
18. Ecker, C., Rocha-Rego, V., Johnston, P., Mourao-Miranda, J., Marquand, A., Daly, E. M., Brammer, M. J., Murphy, C., & Murphy, D. G. (2010). Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage, 49, 44–56.
19. Fan, Y., Shen, D., Gur, R. C., Gur, R. E., & Davatzikos, C. (2007). COMPARE: classification of morphological patterns using adaptive regional elements. Medical Imaging, IEEE Transactions, 26, 93–105.
20. Feinstein, A., Roy, P., Lobaugh, N., Feinstein, K., O’Connor, P., & Black, S. (2004). Structural brain abnormalities in multiple sclerosis patients with major depression. Neurology, 62(4), 586–590.
21. Fischl, B. (2012). Free surfer. NeuroImage, 62, 774–781.
22. Fischl, B., Sereno, M. I., & Dale, A. M. (1999a). Cortical surface-based analysis: II: Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9, 195–207.
23. Fischl, B., Sereno, M. I., Tootell, R. B., & Dale, A. M. (1999b). High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8, 272–284.
24. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., & Klaveness, S. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341–355.
25. Fischl, B., van Der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., Busa, E., Seidman, L. J., Goldstein, J., Kennedy, D. & Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14(1), 11–22.
26. Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology, 6, 67–77.
27. Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. Ä., Frith, C. D., & Frackowiak, R. S. (1994). Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2, 189–210.
28. Friston, K., Chu, C., Mourao-Miranda, J., Hulme, O., Rees, G., Penny, W., & Ashburner, J. (2008). Bayesian decoding of brain images. NeuroImage, 39, 181–205.
29. Gaonkar, B., & Davatzikos, C. (2013). Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification. NeuroImage, 78, 270–283.
30. Gollub, R.L., Shoemaker, J.M., King, M.D., White, T., Ehrlich, S., Sponheim, S.R., Clark, V.P., Turner, J.A., Mueller, B.A., Magnotta, V. (2013). The MCIC Collection: A Shared Repository of Multi-Modal, Multi-Site Brain Image Data from a Clinical Investigation of Schizophrenia. Neuroinformatics, 1–22.
31. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.
32. Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., & Killiany, R. (2006). Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage, 32, 180–194.
33. Ho, B.-C., Andreasen, N. C., Nopoulos, P., Arndt, S., Magnotta, V., & Flaum, M. (2003). Progressive structural brain abnormalities and their relationship to clinical outcome: a longitudinal magnetic resonance imaging study early in schizophrenia. Archives of General Psychiatry, 60, 585.
34. Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., Whitwell, J. L., & Ward, C. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27, 685–691.
35. Jain, A., & Zongker, D. (1997). Feature selection: evaluation, application, and small sample performance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19, 153–158.
36. Japkowicz, N., Shah, M. (2011). Evaluating learning algorithms: a classification perspective. Cambridge University Press.
37. Kawasaki, Y., Suzuki, M., Kherif, F., Takahashi, T., Zhou, S.-Y., Nakamura, K., Matsui, M., Sumiyoshi, T., Seto, H., & Kurachi, M. (2007). Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls. NeuroImage, 34, 235–242.
38. Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain, 131, 681–689.
39. Kloppel, S., Chu, C., Tan, G., Draganski, B., Johnson, H., Paulsen, J., Kienzle, W., Tabrizi, S., Ashburner, J., & Frackowiak, R. (2009). Automatic detection of preclinical neurodegeneration Presymptomatic Huntington disease. Neurology, 72, 426–431.
40. Kloppel, S., Abdulkadir, A., Jack, C. R., Jr., Koutsouleris, N., Mour√£o-Miranda, J., & Vemuri, P. (2012). Diagnostic neuroimaging across diseases. NeuroImage, 61, 457–463.
41. Konukoglu, E., Glocker, B., Zikic, D., & Criminisi, A., (2013). Neighbourhood Approximation using randomized forests. Medical Image Analysis 17(7), 790–804.
42. Koutsouleris, N., Meisenzahl, E. M., Davatzikos, C., Bottlender, R., Frodl, T., Scheuerecker, J., Schmitt, G., Zetzsche, T., Decker, P., & Reiser, M. (2009). Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Archives of General Psychiatry, 66, 700.
43. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103, 3863–3868.
44. Lao, Z., Shen, D., Xue, Z., Karacali, B., Resnick, S. M., & Davatzikos, C. (2004). Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage, 21, 46–57.
45. Lerch, J. P., Pruessner, J., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H., & Evans, A. C. (2008). Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiology of Aging, 29, 23–30.
46. Liu, F., Guo, W., Yu, D., Gao, Q., Gao, K., Xue, Z., Du, H., Zhang, J., Tan, C., & Liu, Z. (2012). Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PloS One, 7, e40968.
47. Lockhart, R., Taylor, J., Tibshirani, R.J., Tibshirani, R., 2012. A significance test for the lasso.
48. MacKay, D. J. (1992). The evidence framework applied to classification networks. Neural Computation, 4, 720–736.
49. Marcus, D. S., Olsen, T. R., Ramaratnam, M., & Buckner, R. L. (2007a). The extensible neuroimaging archive toolkit. Neuroinformatics, 5, 11–33.
50. Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007b). Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19, 1498–1507.
51. Meinshausen, N., & Buhlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 72, 417–473.
52. Milham, M. P., Fair, D., Mennes, M., & Mostofsky, S. H. (2012). The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 62.
53. Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., & Newman, S. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57, 145–175.
54. Mourao-Miranda, J., Bokde, A. L., Born, C., Hampel, H., & Stetter, M. (2005). Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage, 28, 980–995.
55. Mourao-Miranda, J., Reinders, A., Rocha-Rego, V., Lappin, J., Rondina, J., Morgan, C., Morgan, K., Fearon, P., Jones, P., & Doody, G. (2012). Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. Psychological Medicine, 42, 1037.
56. Mwangi, B., Matthews, K., & Steele, J. D. (2012). Prediction of illness severity in patients with major depression using structural MR brain scans. Journal of Magnetic Resonance Imaging, 35, 64–71.
57. Nie, K., Chen, J.-H., Yu, H. J., Chu, Y., Nalcioglu, O., & Su, M.-Y. (2008). Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Academic Radiology, 15, 1513–1525.
58. Nielsen, J.A., Zielinski, B.A., Fletcher, P.T., Alexander, A.L., Lange, N., Bigler, E.D., Lainhart, J.E., Anderson, J.S., 2013. Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in human neuroscience 7.
59. Nieuwenhuis, M., van Haren, N. E., Hulshoff Pol, H. E., Cahn, W., Kahn, R. S., & Schnack, H. G. (2012). Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. NeuroImage, 61, 606–612.
60. Nouretdinov, I., Costafreda, S. G., Gammerman, A., Chervonenkis, A., Vovk, V., Vapnik, V., & Fu, C. H. (2011). Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage, 56, 809–813.
61. Parker, B. J., Günter, S., & Bedo, J. (2007). Stratification bias in low signal microarray studies. BMC Bioinformatics, 8(1), 326.
62. Pereira, F., Botvinick, M., (2011). Classification of functional magnetic resonance imaging data using informative pattern features. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 940–946.
63. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56, 303.
64. Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J., Bokde, A. W., Hampel, H., & Ewers, M. (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. NeuroImage, 50(1), 162–174.
65. Rondina, J., Hahn, T., de Oliveira, L., Marquand, A., Dresler, T., Leitner, T., Fallgatter, A., Shawe-Taylor, J., Mourao-Miranda, J. (2013). SCoRS-a method based on stability for feature selection and mapping in neuroimaging.
66. Sabuncu, M. R., Van Leemput, K. (2012). The Relevance Voxel Machine (RVoxM): A self-tuning bayesian model for informative image-based prediction. Medical Imaging, IEEE Transactions on Medical Imaging, 31(12), 2290–2306.
67. Saeys, Y., Inza, I. a., & Larra√±aga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507–2517.
68. Schnack, H. G., Nieuwenhuis, M., van Haren, N. E., Abramovic, L., Scheewe, T. W., Brouwer, R. M., Hulshoff Pol, H. E., & Kahn, R. S. (2014). Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. NeuroImage, 84, 299–306.
69. Scholkopf, B., Smola, A.J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. the MIT Press.
70. Scott, A., Courtney, W., Wood, D., De la Garza, R., Lane, S., King, M., Wang, R., Roberts, J., Turner, J.A., Calhoun, V.D., 2011. COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Frontiers in neuroinformatics 5.
71. Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron, 62, 42–52.
72. Sonnenburg, S., Zien, A., Philips, P., & Rätsch, G. (2008). POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors. Bioinformatics, 24(13), i6–i14.
73. Soriano-Mas, C., Pujol, J., Alonso, P., Cardoner, N., Menchon, J. M., Harrison, B. J., Deus, J., Vallejo, J., & Gaser, C. (2007). Identifying patients with obsessive-compulsive disorder using whole-brain anatomy. NeuroImage, 35, 1028–1037.
74. Stonnington, C. M., Chu, C., Kl√∂ppel, S., Jack, C. R., Jr., Ashburner, J., & Frackowiak, R. S. (2010). Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease. NeuroImage, 51, 1405–1413.
75. Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9, 307.
76. Teipel, S. J., Born, C., Ewers, M., Bokde, A. L., Reiser, M. F., M√∂ller, H.-J. R., & Hampel, H. (2007). Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. NeuroImage, 38, 13–24.
77. Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. The Journal of Machine Learning Research, 1, 211–244.
78. Vemuri, P., Whitwell, J. L., Kantarci, K., Josephs, K. A., Parisi, J. E., Shiung, M. S., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Dickson, D. W. (2008). Antemortem MRI based Structural abnormality iNDex (STAND)-scores correlate with postmortem braak neurofibrillary tangle stage. NeuroImage, 42, 559–567.
79. Wang, X., Yang, J., Jensen, R., & Liu, X. (2006). Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Computer Methods and Programs in Biomedicine, 83, 147–156.
80. 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.
81. Wang, H., Nie, F., Huang, H., Risacher, S., Ding, C., Saykin, A.J., Shen, L. (2011). Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. Computer Vision (ICCV), 2011 I.E. International Conference on. IEEE, pp. 557–562.
82. Westman, E., Simmons, A., Zhang, Y., Muehlboeck, J., Tunnard, C., Liu, Y., Collins, L., Evans, A., Mecocci, P., & Vellas, B. (2011). Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls. NeuroImage, 54, 1178–1187.
83. Wilson, S. M., Ogar, J. M., Laluz, V., Growdon, M., Jang, J., Glenn, S., Miller, B. L., Weiner, M. W., & Gorno-Tempini, M. L. (2009). Automated MRI-based classification of primary progressive aphasia variants. NeuroImage, 47, 1558–1567.
84. Wolfe, D.A., Hollander, M., 1973. Nonparametric statistical methods. Nonparametric statistical methods.
85. Zien, A., Krämer, N., Sonnenburg, S.r., Rätsch, G. (2009). The feature importance ranking measure. In Machine Learning and Knowledge Discovery in Databases (pp. 694–709). Springer Berlin Heidelberg.