Three-class differential diagnosis among Alzheimer disease, frontotemporal dementia, and controls

Frontiers in Neurology

Volumn 5 MAY, Issue , 2014, Pages

  Raamana, Pradeep Reddy ^a   Rosen, Howard ^b   Miller, Bruce ^b   Weiner, Michael W ^c   Wang, Lei ^d   Beg, Mirza Faisal ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Alzheimer; Differential diagnosis; Frontotemporal disease; Multi class; Ventricle

Indexed keywords

ADULT; ALZHEIMER DISEASE; ARTICLE; BRAIN ATROPHY; BRAIN SIZE; COMPARATIVE STUDY; CONTROLLED STUDY; DIFFERENTIAL DIAGNOSIS; DISEASE CLASSIFICATION; DISEASE MARKER; FEMALE; FRONTOTEMPORAL DEMENTIA; HEMISPHERE; HIPPOCAMPUS; HUMAN; LATERAL BRAIN VENTRICLE; MAJOR CLINICAL STUDY; MALE; NEUROANATOMY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; RADIODIAGNOSIS; VALIDATION PROCESS; VOXEL BASED MORPHOMETRY;

EID: 84904420534     PISSN: None     EISSN: 16642295     Source Type: Journal    
DOI: 10.3389/fneur.2014.00071     Document Type: Article

References (73)

1. Neary D, Snowden JS, Gustafson L, Passant U, Stuss D, Black S, et al. Frontotemporal lobar degeneration. Neurology (1998) 51(6):1546-54. doi: 10.1212/WNL.51.6.1546
2. Womack K, Diaz-Arrastia R, Aizenstein H, Arnold S, Barbas N, Boeve B, et al. Temporoparietal hypometabolism in frontotemporal lobar degeneration and associated imaging diagnostic errors. Arch Neurol (2011) 68(3):329. doi:10.1001/archneurol.2010.295
3. Hutchinson AD, Mathias JL. Neuropsychological deficits in frontotemporal dementia and Alzheimer's disease: a meta-analytic review. J Neurol Neurosurg Psychiatry (2007) 78(9):917-28. doi:10.1136/jnnp.2006.100669
4. Graham A, Davies R, Xuereb J, Halliday G, Kril J, Creasey H, et al. Pathologically proven frontotemporal dementia presenting with severe amnesia. Brain (2005) 128(3):597. doi:10.1093/brain/awh348
5. Siri S, Benaglio I, Frigerio A, Binetti G, Cappa SF. A brief neuropsychological assessment for the differential diagnosis between frontotemporal dementia and Alzheimer's disease. Eur J Neurol (2001) 8(2):125-32. doi:10.1046/j.1468-1331.2001.00179.x
6. McKhann GM, Albert MS, Grossman M, Miller B, Dickson D, Trojanowski JQ. Clinical and pathological diagnosis of frontotemporal dementia: report of the work group on frontotemporal dementia and pick's disease. Arch Neurol (2001) 58(11):1803-9. doi:10.1001/archneur.58.11.1803
7. Forman M, Farmer J, Johnson J, Clark C, Arnold S, Coslett H, et al. Frontotemporal dementia: clinicopathological correlations. Ann Neurol (2006) 59(6):952-62. doi:10.1002/ana.20873
8. Alladi S, Xuereb J, Bak T, Nestor P, Knibb J, Patterson K, et al. Focal cortical presentations of Alzheimer's disease. Brain (2007) 130(10):2636. doi:10.1093/brain/awm213
9. Varma AR, Snowden JS, Lloyd JJ, Talbot PR, Mann DM, Neary D. Evaluation of the NINCDS-ADRDA criteria in the differentiation of Alzheimer's disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry (1999) 66:184-8. doi:10.1136/jnnp.66.2.184
10. Roberson E, Hesse J, Rose K, Slama H, Johnson J, Yaffe K, et al. Frontotemporal dementia progresses to death faster than Alzheimer disease. Neurology (2005) 65(5):719-25. doi:10.1212/01.wnl.0000173837.82820.9f
11. Pressman PS, Miller BL. Diagnosis and management of behavioral variant frontotemporal dementia. Biol Psychiatry (2014) 75(7):574-81. doi:10.1016/j.biopsych.2013.11.006
12. Woolley JD, Khan BK, Murthy NK, Miller BL, Rankin KP. The diagnostic challenge of psychiatric symptoms in neurodegenerative disease; rates of and risk factors for prior psychiatric diagnosis in patients with early neurodegenerative disease. J Clin Psychiatry (2011) 72(2):126. doi:10.4088/JCP.10m06382oli
13. Pasquier F. Telling the difference between frontotemporal dementia and Alzheimer's disease. Curr Opin Psychiatry (2005) 18(6):628-32. doi:10.1097/01.yco.0000185988.05741.2a
14. Davatzikos C, Resnick S, Wu X, Parmpi P, Clark C. Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage (2008) 41(4):1220-7. doi:10.1016/j.neuroimage.2008.03.050
15. Rabinovici G, Seeley W, Kim E, Gorno-Tempini M, Rascovsky K, Pagliaro T, et al. Distinct MRI atrophy patterns in autopsy-proven Alzheimer's disease and frontotemporal lobar degeneration. Am J Alzheimers Dis Other Demen (2008) 22(6):474-88. doi:10.1177/1533317507308779
16. Boxer A, Rankin K, Miller B, Schuff N, Weiner M, Gorno-Tempini M, et al. Cinguloparietal atrophy distinguishes Alzheimer disease from semantic dementia. Arch Neurol (2003) 60(7):949. doi:10.1001/archneur.60.7.949
17. Du A-T, Schuff N, Kramer JH, Rosen HJ, Gorno-Tempini ML, Rankin K, et al. Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. Brain (2007) 130(4):1159-66. doi:10.1093/brain/awm016
18. Whitwell J, Jack C Jr, Pankratz V, Parisi J, Knopman D, Boeve B, et al. Rates of brain atrophy over time in autopsy-proven frontotemporal dementia and Alzheimer disease. Neuroimage (2008) 39(3):1034-40. doi:10.1016/j.neuroimage.2007.10.001
19. Barnes J, Godbolt A, Frost C, Boyes R, Jones B, Scahill R, et al. Atrophy rates of the cingulate gyrus and hippocampus in ad and ftld. Neurobiol Aging (2007) 28(1):20-8. doi:10.1016/j.neurobiolaging.2005.11.012
20. Galton C, Patterson K, Graham K, Lambon-Ralph M, Williams G, Antoun N, et al. Differing patterns of temporal atrophy in Alzheimers disease and semantic dementia. Neurology (2001) 57(2):216-25. doi:10.1212/WNL.57.2.216
21. Whitwell J, Sampson E, Watt H, Harvey R, Rossor M, Fox N. A volumetric magnetic resonance imaging study of the amygdala in frontotemporal lobar degeneration and Alzheimers disease. Dement Geriatr Cogn Disord (2005) 20(4):238-44. doi:10.1159/000087343
22. Looi J, Walterfang M, Styner M, Svensson L, Lindberg O, Østberg P, et al. Shape analysis of the neostriatum in frontotemporal lobar degeneration, Alzheimer's disease, and controls. Neuroimage (2010) 51(3):970-86. doi:10.1016/j.neuroimage.2010.02.017
23. Zhang Y, Schuff N, Du A, Rosen H, Kramer J, Gorno-Tempini M, et al. White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI. Brain (2009) 132(9):2579-92. doi:10.1093/brain/awp071
24. Rabinovici GD, Rosen HJ, Alkalay A, Kornak J, Furst AJ, Agarwal N, et al. Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. Neurology (2011) 77:2034-42. doi:10.1212/WNL.0b013e31823b9c5e
25. Jagust W, Reed B, Mungas D, Ellis W, Decarli C. What does fluorodeoxyglucose pet imaging add to a clinical diagnosis of dementia? Neurology (2007) 69(9):871-7. doi:10.1212/01.wnl.0000269790.05105.16
26. Foster NL, Heidebrink JL, Clark CM, Jagust WJ, Arnold SE, Barbas NR, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease. Brain (2007) 130(10):2616-35. doi:10.1093/brain/awm177
27. Rabinovici G, Furst A, O'Neil J, Racine C, Mormino E, Baker S, et al. 11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurology (2007) 68(15):1205-12. doi:10.1212/01.wnl.0000259035.98480.ed
28. Sadeghi N, Foster N, Wang A, Minoshima S, Lieberman A, Tasdizen T. Automatic classification of Alzheimer's disease vs. frontotemporal dementia: a spatial decision tree approach with FDG-PET. Biomedical Imaging: From Nano to Macro, 2008, 5th IEEE International Symposium. Paris: IEEE (2008). p. 408-11. ISBI 2008.
29. Higdon R, Foster NL, Koeppe RA, DeCarli CS, Jagust WJ, Clark CM, et al. A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer's disease using FDG-PET imaging. Stat Med (2004) 23:315-26. doi:10.1002/sim.1719
30. Krueger C, Dean D, Rosen H, Halabi C, Weiner M, Miller B, et al. Longitudinal rates of lobar atrophy in frontotemporal dementia, semantic dementia, and Alzheimer's disease. Alzheimer Dis Assoc Disord (2010) 24(1):43. doi:10.1097/WAD.0b013e3181a6f101
31. Zhang Y, Schuff N, Ching C, Tosun D, Zhan W, Nezamzadeh M, et al. Joint assessment of structural, perfusion, and diffusion MRI in Alzheimer's disease and frontotemporal dementia. Int J Alzheimers Dis (2011) 2011:11. doi:10.4061/2011/546871
32. Grossman M, McMillan C, Moore P, Ding L, Glosser G, Work M, et al. What's in a name: voxel-based morphometric analyses of MRI and naming difficulty in Alzheimer's disease, frontotemporal dementia and corticobasal degeneration. Brain (2004) 127(3):628-49. doi:10.1093/brain/awh075
33. Lehmann M, Douiri A, Kim L, Modat M, Chan D, Ourselin S, et al. Atrophy patterns in Alzheimer's disease and semantic dementia: a comparison of freesurfer and manual volumetric measurements. Neuroimage (2010) 49(3):2264-2274. doi:10.1016/j.neuroimage.2009.10.056
34. Chan D, Fox N, Scahill R, Crum W, Whitwell J, Leschziner G, et al. Patterns of temporal lobe atrophy in semantic dementia and Alzheimer's disease. Ann Neurol (2001) 49(4):433-42. doi:10.1002/ana.92.abs
35. Zahn R, Buechert M, Overmans J, Talazko J, Specht K, Ko C, et al. Mapping of temporal and parietal cortex in progressive nonfluent aphasia and Alzheimer's disease using chemical shift imaging, voxel-based morphometry and positron emission tomography. Psychiatry Res (2005) 140(2):115-31. doi:10.1016/j.pscychresns.2005.08.001
36. Whitwell J, Jack C Jr. Comparisons between Alzheimer disease, frontotemporal lobar degeneration, and normal aging with brain mapping. Top Magn Reson Imaging (2005) 16(6):409. doi:10.1097/01.rmr.0000245457.98029.e1
37. Vemuri P, Simon G, Kantarci K, Whitwell J, Senjem M, Przybelski S, et al. Antemortem differential diagnosis of dementia pathology using structural MRI: differential-stand. Neuroimage (2011) 55(2):522-31. doi:10.1016/j.neuroimage.2010.12.073
38. Hastie T, Tibshirani R. Classification by pair-wise coupling. Ann Stat (1998) 26(2):451-71. doi:10.1214/aos/1028144844
39. Cutzu F. Polychotomous Classification with Pairwise Classifiers: A New Voting Principle. Heidelberg: Springer (2003).
40. Forman G, Scholz M. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newsletter. New York: ACM (2010) 12(1):49-57. doi:10.1145/1882471.1882479
41. Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics (2006) 7:91. doi:10.1186/1471-2105-7-91
42. Parker B, Gunter S. Stratification bias in low signal microarray studies. BMC Bioinformatics (2007) 8:326. doi:10.1186/1471-2105-8-326
43. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease. Neurology (1984) 34(7):939. doi:10.1212/WNL.34.7.939
44. Folstein M, Folstein S, McHugh P. Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res (1975) 12(3):189-98.
45. Morris JC. The clinical dementia rating (CDR): current version and scoring rules. Neurology (1993) 43:2412-4. doi:10.1212/WNL.43.11.2412-a
46. Rosen HJ, Narvaez JM, Hallam B, Kramer JH, Wyss-Coray C, Gearhart R, et al. Neuropsychological and functional measures of severity in Alzheimer disease, frontotemporal dementia, and semantic dementia. Alzheimer Dis Assoc Disord (2004) 18(4):202-7.
47. Aljabar P, Heckemann R, Hammers A, Hajnal J, Rueckert D. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage (2009) 46(3):726-38. doi:10.1016/j.neuroimage.2009.02.018
48. Kittler J, Hatef M, Duin R, Matas J. On combining classifiers. In: Kasturi R, editor. Pattern Analysis and Machine Intelligence, IEEE Transactions (Vol. 20). Washington, DC: IEEE (1998). p. 226-39.
49. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage (2006) 33(1):115-26. doi:10.1016/j.neuroimage.2006.05.061
50. Khan A, Wang L, Beg M. Freesurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping. Neuroimage (2008) 41(2):735-46. doi:10.1016/j.neuroimage.2008.03.024
51. Beg MF, Miller MI, Trouve A, Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphism. Int J Comput Vis (2005) 61:139-57. doi:10.1016/j.neuroimage.2009.08.026
52. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron (2002) 33(3):341-55. doi:10.1016/S0896-6273(02)00569-X
53. Eckerstrom C, Olsson E, Borga M, Ekholm S, Ribbelin S, Rolstad S, et al. Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: the Goteborg MCI study. J Neurol Sci (2008) 272(1-2):48-59. doi:10.1016/j.jns.2008.04.024
54. Saito N. Data analysis and representation on a general domain using eigenfunctions of Laplacian. Appl Comput Harmon Anal (2007) 25:68-97. doi:10.1016/j.acha.2007.09.005
55. Gordon C, Webb D, Wolpert S. One cannot hear the shape of a drum. Bull Am Math Soc (1992) 27:134-8. doi:10.1090/S0273-0979-1992-00289-6
56. Khabou MA, Hermi L, Rhouma MB. Shape recognition using eigenvalues of the Dirichlet Laplacian. J Pattern Recognit (2007) 40:141-53. doi:10.1016/j.patcog.2006.01.002
57. Reuter M, Wolter F-E, Peinecke N. Laplace-Beltrami spectra as 'shape-DNA' of surfaces and solids. Comput Aided Des (2006) 38(4):342-66. doi:10.1016/j.cad.2005.10.011 Symposium on Solid and Physical Modeling 2005
58. Levy B. Laplace-Beltrami eigenfunctions towards an algorithm that Understands geometry. SMI'06: Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006. Matsushima: IEEE (2006). 13 p.
59. Rustamov RM. Laplace-Beltrami eigenfunctions for deformation invariant shape representation. SGP'07: Proceedings of the Fifth Eurographics Symposium on Geometry processing. Aire-la-Ville: Eurographics Association (2007). p. 225-33.
60. Zuliani M, Kenney C, Bhagavathy S, Manjunath BS. Drums and curve descriptors. Tech Report. UCSB Vision Research Lab Preprint (2004). Available from: http://vision.ece.ucsb.edu/publications/04BMVCMarco.pdf
61. Courant R, Hilbert D. Methods of Mathematical Physics. 2nd ed. New York: Interscience Publishers (1965).
62. Beg MF, Raamana PR, Barbieri S, Wang L. Comparison of four shape features for detecting hippocampal shape changes in early Alzheimer's. Stat Methods Med Res (2012) 22(4):439-62. doi:10.1177/0962280212448975
63. Qiu A, Miller M. Multi-structure network shape analysis via normal surface momentum maps. Neuroimage (2008) 42(4):1430-8. doi:10.1016/j.neuroimage.2008.04.257
64. Cortes C, Vapnik V. Support vector networks. Mach Learn (1995) 20:273-97. doi:10.1023/A:1022627411411
65. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The weka data mining software: an update. SIGKDD Explor (2009) 11(1):10-8. doi:10.1145/1656274.1656278
66. Fitzpatrick M, Sonka M. Handbook of Medical Imaging 2: Medical Image Processing & Analysis (PM80). Bellingham, WA: SPIE-International Society for Optical Engineering (2000).
67. Woodward M, Mackenzie IRA, Hsiung GYR, Jacova C, Feldman H. Multiple brain pathologies in dementia are common. Eur Geriatr Med (2010) 1(5):259-65. doi:10.1016/j.eurger.2010.07.012
68. Metz C. Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol (2006) 3(6):413-422. doi:10.1016/j.jacr.2006.02.021
69. Fawcett T. An introduction to roc analysis. Pattern Recognit Lett (2006) 27(8):861-74. doi:10.1016/j.patrec.2005.10.010
70. Rogers S, Girolami M. A First Course in Machine Learning. Taylor and Francis (2011). Available from: http://books.google.ca/books?id=rdQ1daD8BH8C
71. Chung MK, Worsley KJ, Nacewicz BM, Dalton KM, Davidson RJ. General multivariate linear modeling of surface shapes using SurfStat. Neuroimage (2010) 53(2):491-505. doi:10.1016/j.neuroimage.2010.06.032
72. Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc (1983) 78(382):316-31. doi:10.1080/01621459.1983.10477973
73. Provost F, Fawcett T. The case against accuracy estimation for comparing induction algorithms. Proceedings of the Fifteenth International Conference on Machine Learning. Madison, WI: Morgan Kaufmann (1998).