Combining PET Images and neuropsychological test data for automatic diagnosis of alzheimer's disease

PLoS ONE

Volumn 9, Issue 2, 2014, Pages

  Segovia, Fermín ^a   Bastin, Christine ^a   Salmon, Eric ^a   Goŕriz, Juan Manuel ^b   Ramiŕez, Javier ^b   Phillips, Christophe ^a  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

AGED; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; CLASSIFIER; CLINICAL ARTICLE; COMPUTER SYSTEM; DIAGNOSTIC ACCURACY; EARLY DIAGNOSIS; FEMALE; HUMAN; MALE; NEUROPSYCHOLOGICAL TEST; POSITRON EMISSION TOMOGRAPHY; SCORING SYSTEM; SUPPORT VECTOR MACHINE;

AGED; ALZHEIMER DISEASE; BRAIN; DIAGNOSIS, COMPUTER-ASSISTED; DIMENSIONAL MEASUREMENT ACCURACY; FEMALE; HUMANS; MALE; MIDDLE AGED; NEUROIMAGING; NEUROPSYCHOLOGICAL TESTS; POSITRON-EMISSION TOMOGRAPHY; SUPPORT VECTOR MACHINES;

EID: 84895827060     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0088687     Document Type: Article

References (55)

1. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi H (2007) Forecasting the global burden of Alzheimer's disease. Alzheimer's and Dementia 3: 186-191. (Pubitemid 46825511)
2. Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, et al. (2008) Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. NeuroImage 39: 1186-1197.
3. Horn JF, Habert MO, Kas A, Malek Z, Maksud P, et al. (2009) Differential automatic diagnosis between Alzheimer's disease and frontotemporal dementia based on perfusion SPECT images. Artificial Intelligence in Medicine 47: 147-158.
4. Kloppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, et al. (2008) Automatic classification of MR scans in Alzheimer's disease. Brain: a journal of neurology 131: 681-689.
5. Segovia F, Górriz JM, Ramírez J, Salas-Gonzalez D, Álvarez I, et al. (2012) A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database. Neurocomput 75: 64-71.
6. Signorini M, Paulesu E, Friston K, Perani D, Lucignani G, et al. (1999) Assessment of 18F-FDG PET brain scans in individual patients with statistical parametric mapping. A clinical validation. NeuroImage 9: 63-80.
7. Lopez M, Ramirez J, Gorriz J, Salas-Gonzalez D, Alvarez I, et al. (2009) Automatic tool for alzheimer's disease diagnosis using PCA and bayesian classification rules. Electronics Letters 45: 389-391.
8. Fan Y, Batmanghelich N, Clark CM, Davatzikos C, Alzheimer's Disease Neuroimaging Initiative (2008) Spatial patterns of brain atrophy in MCI patients, identified via highdimensional pattern classification, predict subsequent cognitive decline. NeuroImage 39: 1731-1743. (Pubitemid 351179414)
9. Mourão-Miranda J, Bokde ALW, 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.
10. Schrouff J, Rosa MJ, Rondina JM, Marquand AF, Chu C, et al. (2013) PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics.
11. Canu E, McLaren DG, Fitzgerald ME, Bendlin BB, Zoccatelli G, et al. (2010) Microstructural diffusion changes are independent of macrostructural volume loss in moderate to severe Alzheimer's disease. The Journal of Alzheimer's Disease 19: 963-976.
12. Whitwell JL, Przybelski SA, Weigand SD, Knopman DS, Boeve BF, et al. (2007) 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease. Brain 130: 1777-1786. (Pubitemid 47343806)
13. Friston K, Ashburner J, Kiebel S, Nichols T, Penny W, editors (2007) Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press.
14. Stoeckel J, Malandain G, Migneco O, Koulibaly PM, Robert P, et al. (2001) Classification of SPECT images of normal subjects versus images of Alzheimer's disease patients. In: Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention. London, UK, UK: Springer-Verlag, MICCAI '01, p.666-674.
15. Phillips CL, Bruno MA, Maquet P, Boly M, Noirhomme Q, et al. (2011) "Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients. NeuroImage 56: 797-808.
16. Garraux G, Phillips C, Schrouff J, Kreisler A, Lemaire C, et al. (2013) Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes. NeuroImage: Clinical 2: 883-893.
17. Vapnik V (1999) The Nature of Statistical Learning Theory. Springer, 2nd edition.
18. Duin RPW (2000) Classifiers in almost empty spaces. In: Proceedings 15th International Conference on Pattern Recognition. IEEE, volume 2, pp. 1-7.
19. Chincarini A, Bosco P, Calvini P, Gemme G, Esposito M, et al. (2011) Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease. NeuroImage 58: 469-480.
20. Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, et al. (2002) Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. NeuroImage 17: 302-316.
21. Hinrichs C, Singh V, Xu G, Johnson SC, Alzheimers Disease Neuroimaging Initiative (2011) Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage 55: 574-589.
22. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, et al. (2011) The diagnosis of dementia due to alzheimer's disease: recommendations from the national institute on agingalzheimer's association workgroups on diagnostic guidelines for alzheimer's disease. Alzheimer's & dementia: the journal of the Alzheimer's Association 7: 263-269.
23. Folstein MF, Folstein SE, McHugh PR (1975) "Mini-mental state": A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12: 189-198.
24. Adam S, Van der Linden M, Ivanoiu A, Juillerat AC, Bechet S, et al. (2007) Optimization of encoding specificity for the diagnosis of early AD: the RI-48 task. Journal of clinical and experimental neuropsychology 29: 477-487. (Pubitemid 46918930)
25. Ivanoiu A, Adam S, Van der Linden M, Salmon E, Juillerat AC, et al. (2005) Memory evaluation with a new cued recall test in patients with mild cognitive impairment and alzheimer's disease. Journal of neurology 252: 47-55. (Pubitemid 40179018)
26. Artero S, Petersen R, Touchon J, Ritchie K (2006) Revised criteria for mild cognitive impairment: validation within a longitudinal population study. Dementia and geriatric cognitive disorders 22: 465-470. (Pubitemid 44726018)
27. Perry RJ, Watson P, Hodges JR (2000) The nature and staging of attention dysfunction in early (minimal and mild) alzheimer's disease: relationship to episodic and semantic memory impairment. Neuropsychologia 38: 252-271.
28. Woods RP (2000) Spatial transformation models. In: Bankman IN, editor, Handbook of Medical Imaging, San Diego: Academic Press, chapter 29. 465-490.
29. Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis functions. Human Brain Mapping 7: 254-266. (Pubitemid 29269964)
30. Dukart J, Mueller K, Horstmann A, Vogt B, Frisch S, et al. (2010) Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies. NeuroImage 49: 1490-1495.
31. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15: 273-289. (Pubitemid 34852226)
32. Dukart J, Perneczky R, Förster S, Barthel H, Diehl-Schmid J, et al. (2013) Reference cluster normalization improves detection of frontotemporal lobar degeneration by means of FDG-PET. PLoS ONE 8: e55415.
33. López M, Ramírez J, Górriz JM, Álvarez I, Salas-Gonzalez D, et al. (2009) SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA. Neuroscience Letters.
34. Illán I, Górriz J, Ramírez J, Salas-Gonzalez D, López M, et al. (2011) 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis. Information Sciences 181: 903-916.
35. Jolliffe IT (2002) Principal Component Analysis. Springer, 2nd ed. 2002 edition.
36. Turk M, Pentland A (1991) Eigenfaces for recognition. Journal of Cognitive Neuroscience 3: 71-86.
37. Varmuza K, Filzmoser P (2009) Introduction to Multivariate Statistical Analysis in Chemometrics. Boca Raton, FL: Taylor and Francis - CRC Press.
38. Webb AR (2002) Statistical Pattern Recognition, 2nd Edition. Wiley, 2 edition.
39. Wang S, Li D, Song X, Wei Y, Li H (2011) A feature selection method based on improved fisher's discriminant ratio for text sentiment classification. Expert Systems with Applications 38: 8696-8702.
40. Segovia F, Górriz JM, Ramírez J, Chaves R, Illán IÁ (2012) Automatic differentiation between controls and parkinson's disease datscan images using a partial least squares scheme and the fisher discriminant ratio. In: Advances in Knowledge-Based and Intelligent Information and Engineering Systems - 16th Annual KES Conference. 2241-2250.
41. Noble WS (2004) Kernel Methods in Computational Biology, MIT Press, chapter Support vector machine applications in computational biology. pp. 71-92.
42. Gönen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12: 2211-2268.
43. Igel C, Glasmachers T, Mersch B, Pfeifer N, Meinicke P (2007) Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection. IEEE/ACM transactions on computational biology and bioinformatics/IEEE, ACM 4: 216-226. (Pubitemid 46733326)
44. Cortes C, Mohri M, Rostamizadeh A (2010) Two-stage learning kernel algorithms. In: Proceedings of the 27th Annual International Conference on Machine Learning (ICML 2010). 239-246.
45. Lee WJ, Verzakov S, Duin RPW (2007) Kernel combination versus classifier combination. In: Proceedings of the 7th international conference on Multiple classifier systems. Berlin, Heidelberg: Springer-Verlag, MCS'07, p.22-31.
46. Varma M, Babu BR (2009) More generality in efficient multiple kernel learning. In: Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY, USA: ACM, ICML '09, p.1065-1072. doi:10.1145/1553374. 1553510.
47. Gönen M, Alpaydin E (2008) Localized multiple kernel learning. In: Proceedings of the 25th international conference on Machine learning. New York, NY, USA: ACM, ICML '08, p.352-359. doi:10.1145/1390156.1390201.
48. Yang J, Li Y, Tian Y, Duan L, Gao W (2009) Group-sensitive multiple kernel learning for object categorization. In: 2009 IEEE 12th International Conference on Computer Vision. 436-443. doi:10.1109/ICCV.2009.5459172.
49. Vapnik VN (1998) Statistical Learning Theory. John Wiley and Sons, Inc., New York.
50. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39: 561-77. (Pubitemid 23118304)
51. Good PI (2000) Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer.
52. Miller GA, Chapman JP (2001) Misunderstanding analysis of covariance. Journal of abnormal psychology 110: 40-48. (Pubitemid 32205156)
53. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ (2011) Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 32: 2322.e19-2322.e27.
54. Jhoo JH, Lee DY, Choo IH, Seo EH, Oh JS, et al. (2010) Discrimination of normal aging, MCI and AD with multimodal imaging measures on the medial temporal lobe. Psychiatry Research: Neuroimaging 183: 237-243.
55. Misra C, Fan Y, Davatzikos C (2009) Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage 44: 1415-1422.