Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features

NeuroImage

Volumn 70, Issue , 2013, Pages 386-401

  Gutman, Boris A ^a   Hua, Xue ^a   Rajagopalan, Priya ^a   Chou, Yi Yu ^a   Wang, Yalin ^b   Yanovsky, Igor ^c   Toga, Arthur W ^a   Jack, Clifford R ^d   Weiner, Michael W ^e,f   Thompson, Paul M ^a,g  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c Los Angeles   (United States)

^d MAYO CLINIC   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

ADNI; Alzheimer's disease; Biomarker; Drug trial; Lateral ventricles; Linear Discriminant Analysis; Machine learning; Mild cognitive impairment; Shape analysis

Indexed keywords

BIOLOGICAL MARKER;

ADULT; ALZHEIMER DISEASE; ARTICLE; ARTIFACT; BRAIN ATROPHY; BRAIN REGION; DISCRIMINANT ANALYSIS; DISEASE COURSE; FEMALE; HUMAN; IMAGE ANALYSIS; LATERAL BRAIN VENTRICLE; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL ANALYSIS; MILD COGNITIVE IMPAIRMENT; MULTIVARIATE ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGICAL ANATOMY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; STATISTICAL ANALYSIS; THREE DIMENSIONAL IMAGING;

AGED; ALZHEIMER DISEASE; CEREBRAL VENTRICLES; DISCRIMINANT ANALYSIS; DISEASE PROGRESSION; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MILD COGNITIVE IMPAIRMENT;

EID: 84873807104     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.12.052     Document Type: Article

References (69)

1. Alcantara D.A., Carmichael O., Harcourt-Smith W., Sterner K., Frost S.R., Dutton R., Thompson P., Delson E., Amenta N. Exploration of shape variation using localized components analysis. IEEE T Pattern Anal 2009, 31:1510-1516.
2. Avants B.B., Epstein C.L., Grossman M., Gee J.C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 2008, 12:26-41.
3. Beckett L.A. Community-based studies of Alzheimer's disease: statistical challenges in design and analysis. Stat. Med. 2000, 19:1469-1480.
4. Brun C.C., Lepore N., Luders E., Chou Y.Y., Madsen S.K., Toga A.W., Thompson P.M. Sex differences in brain structure in auditory and cingulate regions. Neuroreport 2009, 20:930-935.
5. Carmichael O.T., Kuller L.H., Lopez O.L., Thompson P.M., Dutton R.A., Lu A., Lee S.E., Lee J.Y., Aizenstein H.J., Meltzer C.C., Liu Y., Toga A.W., Becker J.T. Ventricular volume and dementia progression in the Cardiovascular Health Study. Neurobiol. Aging 2007, 28:389-397.
6. Carmichael O., Xie J., Fletcher E., Singh B., DeCarli C. Localized hippocampus measures are associated with Alzheimer pathology and cognition independent of total hippocampal volume. Neurobiology of Aging 2012, 33.
7. Chen K., Langbaum J.B., Fleisher A.S., Ayutyanont N., Reschke C., Lee W., Liu X., Bandy D., Alexander G.E., Thompson P.M., Foster N.L., Harvey D.J., de Leon M.J., Koeppe R.A., Jagust W.J., Weiner M.W., Reiman E.M. Twelve-month metabolic declines in probable Alzheimer's disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: findings from the Alzheimer's Disease Neuroimaging Initiative. Neuroimage 2010, 51:654-664.
8. Cho Y., Seong J.K., Jeong Y., Shin S.Y. Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 2012, 59:2217-2230.
9. Chou Y.Y., Lepore N., de Zubicaray G.I., Carmichael O.T., Becker J.T., Toga A.W., Thompson P.M. Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease. Neuroimage 2008, 40:615-630.
10. Chou Y.Y., Lepore N., Avedissian C., Madsen S.K., Parikshak N., Hua X., Shaw L.M., Trojanowski J.Q., Weiner M.W., Toga A.W., Thompson P.M. Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls. Neuroimage 2009, 46:394-410.
11. Cornea N.D., Silver D., Yuan X.S., Balasubramanian R. Computing hierarchical curve-skeletons of 3D objects. Visual Comput. 2005, 21:945-955.
12. Cover K.S., van Schijndel R.A., van Dijk B.W., Redolfi A., Knol D.L., Frisoni G.B., Barkhof F., Vrenken H. Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans. Psychiatry Res. 2011, 193:182-190.
13. Cuingnet R., Chupin M., Benali H., Colliot O. Spatial prior in SVM-based classification of brain images. Medical Imaging 2010: Computer-Aided Diagnosis 2010, 7624.
14. Cummings J.L. Integrating ADNI results into Alzheimer's disease drug development programs. Neurobiol. Aging 2010, 31:1481-1492.
15. DiCicio T.J., Efron B. Bootstrap confidence intervals. Stat. Sci. 1996, 11:10.
16. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, Wiley, New York.
17. Fan Y., Batmanghelich N., Clark C.M., Davatzikos C. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008, 39:1731-1743.
18. Ferrarini L., Palm W.M., Olofsen H., van Buchem M.A., Reiber J.H., Admiraal-Behloul F. Shape differences of the brain ventricles in Alzheimer's disease. Neuroimage 2006, 32:1060-1069.
19. Fischl B., Dale A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 2000, 97:11050-11055.
20. Fox N.C., Ridgway G.R., Schott J.M. Algorithms, atrophy and Alzheimer's disease: cautionary tales for clinical trials. Neuroimage 2011, 57:15-18.
21. Gerardin E., Chetelat G., Chupin M., Cuingnet R., Desgranges B., Kim H.S., Niethammer M., Dubois B., Lehericy S., Garnero L., Eustache F., Colliot O. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. Neuroimage 2009, 47:1476-1486.
22. Golland P., Grimson W.E.L., Kikinis R. Statistical shape analysis using fixed topology skeletons: corpus callosum study. Lect. Notes Comput. Sci. 1999, 1613:382-387.
23. Good C.D., Scahill R.I., Fox N.C., Ashburner J., Friston K.J., Chan D., Crum W.R., Rossor M.N., Frackowiak R.S. Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias. Neuroimage 2002, 17:29-46.
24. Gunter J., Bernstein M., Borowski B., Felmlee J., Blezek D., Mallozzi R., Levy J., Schuff N., Jack C.R. Validation testing of the MRI calibration phantom for the Alzheimer's Disease Neuroimaging Initiative Study. ISMRM 14th Scientific Meeting and Exhibition 2006.
25. Gutman B., Wang Y., Morra J., Toga A.W., Thompson P.M. Disease classification with hippocampal shape invariants. Hippocampus 2009, 19:572-578.
26. Gutman B.A., Yalin W., Rajagopalan P., Toga A.W., Thompson P.M. Shape matching with medial curves and 1-D group-wise registration. Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on 2012, 716-719.
27. Hobbs N.Z., Henley S.M.D., Ridgway G.R., Wild E.J., Barker R.A., Scahill R.I., Barnes J., Fox N.C., Tabrizi S.J. The progression of regional atrophy in premanifest and early Huntington's disease: a longitudinal voxel-based morphometry study. J. Neurol. Neurosurg. Psychiatry 2010, 81:756-763.
28. Holland D., Dale A.M. Nonlinear registration of longitudinal images and measurement of change in regions of interest. Med. Image Anal. 2011, 15:489-497.
29. Holland D., McEvoy L.K., Dale A.M. Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI. Hum. Brain Mapp. 2012, 33. 2586-s2602.
30. Hua X., Lee S., Yanovsky I., Leow A.D., Chou Y.Y., Ho A.J., Gutman B., Toga A.W., Jack C.R., Bernstein M.A., Reiman E.M., Harvey D.J., Kornak J., Schuff N., Alexander G.E., Weiner M.W., Thompson P.M. Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. Neuroimage 2009, 48:668-681.
31. Hua X., Gutman B., Boyle C.P., Rajagopalan P., Leow A.D., Yanovsky I., Kumar A.R., Toga A.W., Jack C.R., Schuff N., Alexander G.E., Chen K., Reiman E.M., Weiner M.W., Thompson P.M. Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry. Neuroimage 2011, 57:5-14.
32. Hua X., Hibar D.P., Ching C.R., Boyle C.P., Rajagopalan P., Gutman B.A., Leow A.D., Toga A.W., Jack C.R., Harvey D., Weiner M.W., Thompson P.M. Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials. Neuroimage 2013, 66C:648-661.
33. 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., Dale A.M., Felmlee J.P., Gunter J.L., Hill D.L., Killiany R., Schuff N., Fox-Bosetti S., Lin C., Studholme C., DeCarli C.S., Krueger G., Ward H.A., Metzger G.J., Scott K.T., Mallozzi R., Blezek D., Levy J., Debbins J.P., Fleisher A.S., Albert M., Green R., Bartzokis G., Glover G., Mugler J., Weiner M.W. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 2008, 27:685-691.
34. Jovicich J., Czanner S., Greve D., Haley E., van der Kouwe A., Gollub R., Kennedy D., Schmitt F., Brown G., Macfall J., Fischl B., Dale A. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 2006, 30:436-443.
35. Kloppel S., Stonnington C.M., Barnes J., Chen F., Chu C., Good C.D., Mader I., Mitchell L.A., Patel A.C., Roberts C.C., Fox N.C., Jack C.R., Ashburner J., Frackowiak R.S. Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain 2008, 131:2969-2974.
36. Kloppel S., Abdulkadir A., Jack C.R., Koutsouleris N., Mourao-Miranda J., Vemuri P. Diagnostic neuroimaging across diseases. Neuroimage 2012, 61:457-463.
37. Kohannim O., Hua X., Hibar D.P., Lee S., Chou Y.Y., Toga A.W., Jack C.R., Weiner M.W., Thompson P.M. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol. Aging 2010, 31:1429-1442.
38. Leow A.D., Yanovsky I., Chiang M.C., Lee A.D., Klunder A.D., Lu A., Becker J.T., Davis S.W., Toga A.W., Thompson P.M. Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration. IEEE Trans. Med. Imaging 2007, 26:822-832.
39. Leung K.K., Ridgway G.R., Ourselin S., Fox N.C. Consistent multi-time-point brain atrophy estimation from the boundary shift integral. Neuroimage 2012, 59:3995-4005.
40. Lu J., Plataniotis K.N., Venetsanopoulos A.N. Boosting linear discriminant analysis for face recognition. ICIP 2003, 657-660.
41. McKhann G., Drachman D., Folstein M., Katzman R., Price D., Stadlan E.M. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984, 34:939-944.
42. Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C.R., Jagust W., Trojanowski J.Q., Toga A.W., Beckett L. Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 2005, 1:55-66.
43. Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C., Jagust W., Trojanowski J.Q., Toga A.W., Beckett L. The Alzheimer's Disease Neuroimaging Initiative. Neuroimaging Clin. N. Am. 2005, 15:869-877. (xi-xii).
44. Nestor S.M., Rupsingh R., Borrie M., Smith M., Accomazzi V., Wells J.L., Fogarty J., Bartha R. Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database. Brain 2008, 131:2443-2454.
45. Ott B.R., Cohen R.A., Gongvatana A., Okonkwo O.C., Johanson C.E., Stopa E.G., Donahue J.E., Silverberg G.D. Brain ventricular volume and cerebrospinal fluid biomarkers of Alzheimer's disease. J. Alzheimers Dis. 2010, 20:647-657.
46. Pizer S.M., Fletcher P.T., Joshi S., Gash A.G., Stough J., Thall A., Tracton G., Chaney E.L. A method and software for segmentation of anatomic object ensembles by deformable m-reps. Med. Phys. 2005, 32:1335-1345.
47. Qazi A.A., Jorgensen D.R., Lillholm M., Loog M., Nielsen M., Dam E.B. A framework for optimizing measurement weight maps to minimize the required sample size. Med. Image Anal. 2010, 14:255-264.
48. Reuter M., Schmansky N.J., Rosas H.D., Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012, 61:1402-1418.
49. Ross J., Thompson P.M., Tariot P., Reiman E.M., Schneider L., Frigerio E., Fiorentini F., Giardino L., Calzà L., Norris D., Cicirello H., Casula D., Imbimbo B.P. Primary and Secondary Prevention Trials in Subjects at Risk of Developing Alzheimer's Disease: the GEPARD-AD (Genetically Enriched Population At Risk of Developing Alzheimer's Disease) Studies. CTAD conference, Monte Carlo, Monaco 2012.
50. Schott J.M., Bartlett J.W., Barnes J., Leung K.K., Ourselin S., Fox N.C. Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: baseline adjustment. Neurobiol. Aging 2010, 31:1452-1462. (1462 e1451-1452).
51. Shen L., Qi Y., Kim S., Nho K., Wan J., Risacher S.L., Saykin A.J. Sparse Bayesian learning for identifying imaging biomarkers in AD prediction. Med. Image Comput. Comput. Assist. Interv. 2010, 13:611-618.
52. Shi Y., Morra J.H., Thompson P.M., Toga A.W. Inverse-consistent surface mapping with Laplace-Beltrami eigen-features. Information Processing in Medical Imaging: Proceedings of the Conference 2009, 21:467-478.
53. Sled J.G., Zijdenbos A.P., Evans A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 1998, 17:87-97.
54. Smith S.M., Zhang Y., Jenkinson M., Chen J., Matthews P.M., Federico A., De Stefano N. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002, 17:479-489.
55. Styner M., Lieberman J.A., McClure R.K., Weinberger D.R., Jones D.W., Gerig G. Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors. Proc. Natl. Acad. Sci. U.S.A. 2005, 102:4872-4877.
56. Tagare H.D., Groisser D., Skrinjar O. Symmetric non-rigid registration: a geometric theory and some numerical techniques. J. Math. Imaging Vision 2009, 34:61-88.
57. Thompson W.K., Holland D. Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimates. Neuroimage 2011, 57:1-4.
58. Thompson P.M., Hayashi K.M., De Zubicaray G.I., Janke A.L., Rose S.E., Semple J., Hong M.S., Herman D.H., Gravano D., Doddrell D.M., Toga A.W. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 2004, 22:1754-1766.
59. 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, 39:1186-1197.
60. Wang Y., Song Y., Rajagopalan P., An T., Liu K., Chou Y.Y., Gutman B., Toga A.W., Thompson P.M. Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study. Neuroimage 2011, 56:1993-2010.
61. Weiner M.W. Expanding ventricles may detect preclinical Alzheimer disease. Neurology 2008, 70:824-825.
62. Wyman, B.T., Harvey, D.J., Crawford, K., Bernstein, M.A., Carmichael, O., Cole, P.E., Crane, P., DeCarli, C., Fox, N.C., Gunter, J., Hill, D., Killiany, R., Pachai, C., Schwarz, A., Schuff, N., Senjem, M., Suhy, J., Thompson, P.M., Weiner, M.W., Jack, C.R., accepted for publication. Standardization of analysis sets for reporting results from ADNI MRI data, Alzheimers Dement. (epub ahead of print).
63. Yang W., Chen X., Hong X., Huang X. ICA-Based Automatic Classification of Magnetic Resonance Images from ADNI Data. Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III 8 2010.
64. Yuan L., Wang Y., Thompson P.M., Narayan V.A., Ye J. Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. Neuroimage 2012, 61:622-632.
65. Yushkevich P.A., Zhang H., Gee J.C. Parametric medial shape representation in 3-D via the Poisson partial differential equation with non-linear boundary conditions. Inf. Process. Med. Imaging 2005, 19:162-173.
66. Yushkevich P.A., Avants B.B., Das S.R., Pluta J., Altinay M., Craige C. Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3 T MRI data. Neuroimage 2010, 50:434-445.
67. Zhang K., Sejnowski T.J. A universal scaling law between gray matter and white matter of cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 2000, 97:5621-5626.
68. Zhang D., Wang Y., Zhou L., Yuan H., Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 55:856-867.
69. Zou H., Hastie T., Tibshirani R. Sparse principal component analysis. J. Comput. Graph. Stat. 2006, 15:265-286.