Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI

Computers in Biology and Medicine

Volumn 41, Issue 8, 2011, Pages 600-610

  Savio, A ^a   Garcia Sebastian M T ^a   Chyzyk, D ^a   Hernandez, C ^a   Grana M ^a   Sistiaga, A ^c,d,e   Lopez de Munain A ^b,d,e   Villanua J ^b  

^a Grupo de Inteligencia Computacional   (Spain)

^b HOSPITAL DONOSTIA   (Spain)

^c UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^d BIODONOSTIA HEALTH RESEARCH INSTITUTE   (Spain)

^e CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED SOBRE ENFERMEDADES NEURODEGENERATIVAS CIBERNED   (Spain)

Author keywords

Alzheimer's disease; Classification; Feature extraction; Myotonic distrophy of type 1; Structural MRI

Indexed keywords

10-FOLD CROSS-VALIDATION; AGING PROCESS; ALZHEIMER DISEASE; ALZHEIMER'S DISEASE; CLASSIFICATION ALGORITHM; CLASSIFICATION SYSTEM; COGNITIVE IMPAIRMENT; CONTROL SUBJECT; FEATURE EXTRACTION METHODS; FEATURE VECTORS; HIGH SPECIFICITY; MEDICAL EXPERTS; MYOTONIC DISTROPHY OF TYPE 1; MYOTONIC DYSTROPHY; NEUROLOGICAL DISORDERS; NON-INVASIVE DETECTION; STRUCTURAL MRI; VOXEL-BASED MORPHOMETRY; WESTERN SOCIETIES;

COGNITIVE SYSTEMS; FEATURE EXTRACTION; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES; RESONANCE;

MAGNETIC RESONANCE IMAGING;

ADULT; AGED; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL NEURAL NETWORK; COGNITIVE DEFECT; CONTROLLED STUDY; DISEASE CLASSIFICATION; EXTRACTION; FEMALE; HUMAN; IMAGE PROCESSING; MAJOR CLINICAL STUDY; MALE; MYOTONIC DYSTROPHY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; STRUCTURAL MAGNETIC RESONANCE IMAGING; SUPPORT VECTOR MACHINE; VOXEL BASED MORPHOMETRY;

ADULT; ALGORITHMS; ALZHEIMER DISEASE; BRAIN; CLUSTER ANALYSIS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MYOTONIC DYSTROPHY; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS;

EID: 79957902309     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2011.05.010     Document Type: Article

References (65)

1. 〈〉. http://www.fil.ion.ucl.ac.uk/spm/.
2. Antonini G., Mainero C., Romano A., Giubilei F., Ceschin V., Gragnani F., Morino S., Fiorelli M., Soscia F., Di Pasquale A., Caramia F. Cerebral atrophy in myotonic dystrophy: a voxel based morphometric study. Journal of Neurology, Neurosurgery & Psychiatry 2004, 75(11):1611-1613.
3. Ashburner J., Friston K.J. Voxel-based morphometry-the methods. NeuroImage 2000, 11(6):805-821.
4. Baron J.C., Chetelat G., Desgranges B., Perchey G., Landeau B., de la Sayette V., Eustache F. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. NeuroImage 2001, 14(2):298-309.
5. Bonelli R.M., Cummings J.L. Frontal-subcortical circuitry and behavior. Dialogues in Clinical Neurosciences 2007, 9:141-151.
6. Brook J.D., McCurrach M.E., Harley H.G., Buckler A.J., Church D., Aburatani H., Hunter K., Stanton V.P., Thirion J.P., Hudson T. Molecular basis of myotonic dystrophy: expansion of a trinucleotide (CTG) repeat at the 3' end of a transcript encoding a protein kinase family member. Cell 1992, 68(4):799-808. (PMID: 1310900).
7. Broomhead D.S., Lowe D. Multivariable functional interpolation and adaptive networks. Complex Systems 1988, 2:321-355.
8. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2(2):121-167.
9. Busatto G.F., Garrido G.E.J., Almeida O.P., Castro C.C., Camargo C.H.P., Cid C.G., Buchpiguel C.A., Furuie S., Bottino C.M. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer's disease. Neurobiology of Aging 2003, 24(2):221-231.
10. Busatto G. Voxel-based morphometry in Alzheimers disease. Expert Review of Neurotherapeutics 2008, 8(11):1691-1702.
11. Busatto G.F., Garrido G.E., Almeida O.P., Castro C.C., Camargo C.H., Cid C.G., Buchpiguel C.A., Furuie S., Bottino C.M. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer's disease. Neurobiol Aging 2003, 24(2):221-231.
12. Censori B., Provinciali L., Danni M., Chiaramoni L., Maricotti M., Foschi N., Del Pesce M., Salvolini U. Brain involvement in myotonic dystrophy: MRI features and their relationship to clinical and cognitive conditions. Acta Neurologica Scandinavica 1994, 90(3):211-217. (PMID: 7847063).
13. C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines. Software available at 〈〉, 2001. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
14. Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 1991, 2(2):302-309.
15. Damian M.S., Schilling G., Bachmann G., Simon C., Stöppler S., Dorndorf W. White matter lesions and cognitive deficits: relevance of lesion pattern?. Acta Neurologica Scandinavica 1994, 90(6):430-436. (PMID: 7892763).
16. Davatzikos C., Fan Y., Wu X., Shen D., Resnick S.M. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 2008, 29(4):514-523.
17. Lopez de Munain A., Blanco A., Emparanza J.I., Poza J.J., Marti Masso J.F., Cobo A., Martorell L., Baiget M., Martinez Lage J.M. Prevalence of myotonic dystrophy in Guipuzcoa (Basque country spain). Neurology 1993, 43(8):1573.
18. Lopez de Munain A., Emparanza J.I., Blanco A., Cobo A., Poza J.J., Basauri B., et al. Clinical manifestations of myotonic dystrophy: epidemiologic survey. Medical Clinic (Barc.) 1993, 101(August):161-164.
19. Fan Y., Resnick S.M., Wu S., Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. NeuroImage 2008, 41(2):277-285.
20. Y. Fan, D. Shen, C. Davatzikos, Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM, in: Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv, vol. 8, no. Pt 1, 2005, pp. 1-8.
21. Fotenos A.F., Snyder A.Z., Girton L.E., Morris J.C., Buckner R.L. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005, 64(6):1032-1039.
22. Freund Y., Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. European Conference on Computational Learning Theory 1995, 23-37.
23. Frisoni G.B., Testa C., Zorzan A., Sabattoli F., Beltramello A., Soininen H., Laakso M.P. Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry. Journal of Neurology, Neurosurgery & Psychiatry 2002, 73(6):657-664.
24. Fung G., Stoeckel J. SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information. Knowledge and Information Systems 2007, 11(2):243-258.
25. García-Sebastián M.T., Savio A., Graña M., Villanúa J. On the use of morphometry based features for Alzheimer's disease detection on MRI. Bio-Inspired Systems: Computational and Ambient Intelligence-IWANN 2009 (Part I) of Lecture Notes in Computer Science 2009, vol. 5517:957-964. Springer-Verlag.
26. M. Graña, A brief review of Lattice Computing, in: IEEE International Conference on Fuzzy Systems, 2008, FUZZ-IEEE 2008, (IEEE World Congress on Computational Intelligence), 2008, pp. 1777-1781.
27. Graña M., Manhaes-Savio A., Garcia-Sebastian M., Fernandez E. A lattice computing approach for on-line fMRI analysis. Image and Vision Computing 2010, 28(7):1155-1161. (Online pattern recognition and machine learning techniques for computer-vision: theory and applications).
28. Graña M., Sussner P., Ritter G.X. Associative morphological memories for endmember determination in spectral unmixing. The 12th IEEE International Conference on Fuzzy Systems, 2003 (FUZZ '03) 2003, vol. 2:1285-1290.
29. Graña M., Villaverde I., Maldonado J.O., Hernandez C. Two lattice computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomputing 2009, 72(10-12):2111-2120.
30. M. Graña, I. Villaverde, R. Moreno, F.X. Albizuri, Convex coordinates from lattice independent sets for visual pattern recognition, in: Computational Intelligence Based on Lattice Theory, Springer-Verlag, 2007 (pp. 99-126).
31. Grahn J.A., Parkinson J.A., Owen A.M. The cognitive functions of the caudate nucleus. Progress in Neurobiology 2008, 86:141-155.
32. Graña M., Chyzhyk D., García-Sebastián M., Hernández C. Lattice independent component analysis for functional magnetic resonance imaging. Information Sciences 2011, 181:1910-1928.
33. M.T. Hagan, H.B. Demuth, M.H. Beale, Neural Network Design, PWS Pub. Co., Har/Dsk edition, December 1995.
34. Harper P. Myotonic Dystrophy 2009, Oxford University Press, USA, October. second ed.
35. Hashimoto T., Tayama M., Miyazaki M., Murakawa K., Kawai H., Nishitani H., Kuroda Y. Neuroimaging study of myotonic dystrophy. II. MRI measurements of the brain. Brain and Development 1995, 17(1):28-32.
36. Haykin S. Neural Networks: A Comprehensive Foundation 1998, Prentice Hall, July. second ed.
37. C. Huang, B. Yan, H. Jiang, D. Wang, Combining voxel-based morphometry with artificial neural network theory in the application research of diagnosing Alzheimer's disease, in: International Conference on BioMedical Engineering and Informatics, 2008 (BMEI 2008), vol. 1, pp. 250-254, May 2008.
38. Illan I.A., Gorriz J.M., Ramirez J., Salas-Gonzalez D., Lopez M.M., Segovia F., Chaves R., Gomez-Rio M., Puntonet C.G. 18f-fdg pet imaging analysis for computer aided Alzheimer's diagnosis. Information Sciences 2011, 181(4):903-916.
39. Kloppel S. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 131(3):681-689.
40. Kohonen T. Self-organization and associative memory 1989, Springer-Verlag, New York, Inc. third ed.
41. T. Kohonen, Learning vector quantization, in: The Handbook of Brain Theory and Neural Networks, MIT Press, 1998 (pp. 537-540).
42. 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.
43. 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.
44. Li X., Wang L., Sung E. A study of AdaBoost with SVM based weak learners. Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005 (IJCNN '05) 2005, vol. 1:196-201.
45. Liu Y., Teverovskiy L., Carmichael O., Kikinis R., Shenton M., Carter C.S., Stenger V.A., Davis S., Aizenstein H., Becker J.T., Lopez O.L., Meltzer C.C. Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification. Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2004, 3216:393-401.
46. Lopez M., Ramirez J., Gorriz J.M., Salas-Gonzalez D., Alvarez I., Segovia F., Puntonet C.G. Automatic tool for Alzheimer's disease diagnosis using PCA and Bayesian classification rules. Electronics Letters 2009, 45(8):389-391.
47. Luaces O., Taboada F., Albaiceta G.M., Domínguez L.A., Enriquez P., Bahamonde A. Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples. Artificial Intelligence in Medicine 2009, 45(1):63-76.
48. Marcus D.S., Wang T.H., Parker J., Csernansky J.G., Morris J.C., Buckner R.L. Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 2007, 19(9):1498-1507. (PMID: 17714011).
49. Middleton F.A., Strick P.L. Basal ganglia output and cognition: evidence from anatomical, behavioral, and clinical studies. Brain and Cognition 2000, 42:183-200.
50. Mostacciuolo M.L., Barbujani G., Armani M., Danieli G.A., Angelini C., Rao D.C. Genetic epidemiology of myotonic dystrophy. Genetic Epidemiology 1987, 4(4):289-298.
51. Ota M., Sato N., Ohya Y., Aoki Y., Mizukami K., Mori T., Asada T. Relationship between diffusion tensor imaging and brain morphology in patients with myotonic dystrophy. Neuroscience Letters 2006, 407(3):234-239.
52. Pell G.S., Briellmann R.S., Chan C.H.P., Pardoe H., Abbott D.F., Jackson G.D. Selection of the control group for VBM analysis: influence of covariates, matching and sample size. NeuroImage 2008, 41(4):1324-1335. (PMID: 18467131).
53. J. Ramirez, J.M. Gorriz, D. Salas-Gonzalez, A. Romero, M. Lopez, I. Alvarez, M. Gomez-Rio, Computer-aided diagnosis of Alzheimer's type dementia combining support vector machines and discriminant set of features, Information Sciences, in press, doi:. http://10.1016/j.ins.2009.05.012.
54. Rumelhart D.E., Hinton G.E., Williams R.J. Learning Internal Representations by Error Propagation 1986, MIT Press, (pp. 318-362).
55. Savio A., García-Sebastián M.T., Graña M., Villanúa J. Results of an AdaBoost approach on Alzheimer's disease detection on MRI. Bioinspired Applications in Artificial and Natural Computation, Proc. IWINAC 2009, Part II, Lecture Notes in Computer Science 2009, vol. 5602. Springer-Verlag.
56. Savio A., García-Sebastián M.T., Hernandez C., Graña M., Villanúa J. Classification results of artificial neural networks for Alzheimer's disease detection. Intelligent Data Engineering and Automated Learning - IDEAL 2009 2009, vol. 5788:641-648. Springer-Verlag.
57. Scahill R.I., Schott J.M., Stevens J.M., Rossor M.N., Fox N.C. Mapping the evolution of regional atrophy in Alzheimer's disease: unbiased analysis of fluid-registered serial MRI. Proceedings of the National Academy of Sciences 2002, 99(7):4703.
58. Schapire R.E., Singer Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning 1999, 37(3):297-336.
59. Sistiaga A., Urreta I., Jodar M., Cobo A.M., Emparanza J., Otaegui D., Poza J.J., Merino J.J., Imaz H., Marti-Masso J.F., Lopez de Munain A. Cognitive/personality pattern and triplet expansion size in adult myotonic dystrophy type 1 (DM1): CTG repeats, cognition and personality in DM1. Psychological Medicine 2009, 1-9. (first view).
60. Somervuo P., Kohonen T. Self-organizing maps and learning vector quantization for feature sequences. Neural Processing Letters 1999, 10(2):151-159.
61. Specht D.F. Probabilistic neural networks. Neural Networks 1990, 3(1):109-118.
62. Tao D., Tang X., Li X., Wu X. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 28(7):1088-1099.
63. Vapnik V. Statistical Learning Theory 1998, Wiley-Interscience.
64. Vemuri P., Gunter J.L., Senjem M.L., Whitwell J.L., Kantarci K., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. NeuroImage 2008, 39(3):1186-1197.
65. 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 2008 Fourth International Conference on Natural Computation (ICNC) 2008, 197-201. IEEE Computer Society, Washington, DC, USA.