Quantifying anatomical shape variations in neurological disorders

Medical Image Analysis

Volumn 18, Issue 3, 2014, Pages 616-633

  Singh, Nikhil ^a   Thomas Fletcher, P ^a   Samuel Preston, J ^a   King, Richard D ^a   Marron, J S ^b   Weiner, Michael W ^c   Joshi, Sarang ^a  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Alzheimer's disease; Computational anatomy; Deformation momenta; Kernel partial least squares (PLS); Prediction

Indexed keywords

BRAIN; FORECASTING; FUNCTION EVALUATION; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; NEURODEGENERATIVE DISEASES; NEUROIMAGING; NEUROLOGY; REGRESSION ANALYSIS;

ALZHEIMER'S DISEASE; ANATOMICAL VARIABILITY; COMPUTATIONAL ANATOMY; DIFFEOMORPHIC CHANGES; INDEPENDENT VARIABLES; KERNEL PARTIAL LEAST SQUARES; MULTI VARIATE ANALYSIS; NEUROLOGICAL DISORDERS;

DEFORMATION;

APOLIPOPROTEIN E;

AGED; ALZHEIMER DISEASE; AMYGDALOID NUCLEUS; ANATOMICAL VARIATION; ARTICLE; CONTROLLED STUDY; EXECUTIVE FUNCTION; FEMALE; GENETIC MARKER; GENETIC RISK; HIPPOCAMPUS; HUMAN; INDEPENDENT VARIABLE; KERNEL METHOD; MAJOR CLINICAL STUDY; MALE; MEMORY; NEUROANATOMY; NEUROIMAGING; NEUROLOGIC DISEASE; NEUROPSYCHOLOGICAL TEST; PRIORITY JOURNAL; PUTAMEN; RESPONSE VARIABLE; THALAMUS; AGING; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

AGING; ALGORITHMS; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84896471423     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.01.001     Document Type: Article

References (57)

1. Ashburner J., Csernansk J., Davatzikos C., Fox N., Frisoni G., Thompson P. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol. 2003, 2(2):79-88.
2. Ashburner J., Hutton C., Frackowiak R., Johnsrude I., Price C., Friston K. Identifying global anatomical differences: deformation-based morphometry. Human Brain Mapp. 1998, 6(5-6):348-357.
3. Bai Z.D., Yin Y.Q. Limit of the smallest eigenvalue of a large dimensional sample covariance matrix. Ann. Probab. 1993, 21(3):1275-1294.
4. Batmanghelich N., Dalca A., Sabuncu M., Golland P. Joint modeling of imaging and genetics. Information Processing in Medical Imaging. Lecture Notes in Computer Science 2013, vol. 7917:766-777. Springer, Berlin Heidelberg.
5. Beg M., Miller M., Trouvé A., Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 2005, 61(2):139-157.
6. Boulesteix A., Strimmer K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings Bioinform. 2007, 8(1):32.
7. Braak H., Braak E. Alzheimer's disease affects limbic nuclei of the thalamus. Acta Neuropathol. (Berl.) 1991, 81(3):261-268.
8. Christensen G., Joshi S., Miller M. Individualizing anatomical atlases of the head. Visualization in Biomedical Computing 1996, 343-348. Springer.
9. Cohen J., Asarnow R., Sabb F., Bilder R., Bookheimer S., Knowlton B., Poldrack R. Decoding continuous variables from neuroimaging data: basic and clinical applications. Front. Neurosci. 2011, 5.
10. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehricy S., Habert M.-O., Chupin M., Benali H., Colliot O. Automatic classification of patients with alzheimer's disease from structural mri: a comparison of ten methods using the ADNI database. NeuroImage 2011, 56(2):766-781.
11. Dale A., Fischl B., Sereno M. Cortical surface-based analysis I. Segmentation and surface reconstruction. NeuroImage 1999, 9(2):179-194.
12. 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):1220-1227.
13. Davis, B., Fletcher, P., Bullitt, E., Joshi, S., 2007. Population shape regression from random design data. In: Proceeding of ICCV.
14. de Jong L.W., van der Hiele K., Veer I.M., Houwing J.J., Westendorp R.G., Bollen E.L., de Bruin P.W., Middelkoop H.A., van Buchem M.A., van der Grond J. Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: an MRI study. Brain: A J. Neurol. 2008, 131(Pt. 12):3277-3285.
15. Duchesne S., Caroli A., Geroldi C., Collins D., Frisoni G. Relating one-year cognitive change in mild cognitive impairment to baseline mri features. NeuroImage 2009, 47(4):1363-1370.
16. Dupuis P., Grenander U., Miller M.I. Variational problems on flows of diffeomorphisms for image matching. Quart. Appl. Math. 1998, 56(3):587.
17. Efron B., Tibshirani R.J. An Introduction to the Bootstrap 1993, Chapman & Hall, New York.
18. 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(4):1731-1743.
19. Filipovych R., Wang Y., Davatzikos C. Pattern analysis in neuroimaging: beyond two-class categorization. Int. J. Imag. Syst. Technol. 2011, 21(2):173-178.
20. Frank K. Impact of a confounding variable on a regression coefficient. Sociol. Methods Res. 2000, 29(2):147.
21. Ha, L., Kruger, J., Fletcher, P.T., Joshi, S., Silva, C.T., 2009. Fast parallel unbiased diffeomorphic atlas construction on multi-graphics processing units. In: Proceedings of Eurographic Symposium on Parallel Graphic and Visualization (EGPGV), pp. 41-48.
22. Hall P., Marron J.S., Neeman A. Geometric representation of high dimension, low sample size data. J. Roy. Stat. Soc. Ser. B 2005, 67(3):427-444.
23. Holden M. A review of geometric transformations for nonrigid body registration. IEEE Trans. Med. Imag. 2008, 27(1):111-128.
24. Hong Y., Joshi S., Sanchez M., Styner M., Niethammer M. Metamorphic geodesic regression. Lecture Notes in Computer Science 2012, vol. 7512:197-205. Springer, Berlin,Heidelberg. N. Ayache, H. Delingette, P. Golland, K. Mori (Eds.).
25. Höskuldsson A. Pls regression methods. J. Chemometr. 1988, 2(3):211-228.
26. Jolliffe I.T. A note on the use of principal components in regression. J. Roy. Statist. Soc. Ser. C (Appl. Statist.) 1982, 31(3):300-303.
27. Joshi S., Davis B., Jomier M., Gerig G. Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 2004, 23:151-160.
28. Kassubek J., Juengling F.D., Ecker D., Landwehrmeyer G.B. Thalamic atrophy in Huntington's disease co-varies with cognitive performance: a morphometric MRI analysis. Cereb. Cortex 2005, 15(6):846-853.
29. Li, T., Wana, J., Zhang, Z., Yan, J., Kim, S., Risacher, S.L., Fang, S., Beg, M.F., Wang, L., Saykin, A.J., Shen, L., 2012. Hippocampus as a predictor of cognitive performance: comparative evaluation of analytical methods and morphometric measures. In: MICCAI Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders (NIBAD'12), pp. 133-144.
30. Lorenzi M., Ayache N., Frisoni G., Pennec X. Mapping the effects of Aβ1-42 levels on the longitudinal changes in healthy aging: hierarchical modeling based on stationary velocity fields. Lecture Notes in Computer Science 2011, vol. 6892:663-670. Springer, Berlin,Heidelberg. G. Fichtinger, A. Martel, T. Peters (Eds.).
31. Lorenzi, M., Pennec, X., Ayache, N., Frisoni, G., 2012. Disentangling the normal aging from the pathological Alzheimer's disease progression on cross-sectional structural MR images. In: MICCAI workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders (NIBAD'12). Nice, France, pp. 145-154.
32. Manne R. Analysis of two partial-least-squares algorithms for multivariate calibration. Chemometr. Intell. Lab. Syst. 1987, 2(1-3):187-197.
33. Mansi T., Voigt I., Leonardi B., Pennec X., Durrleman S., Sermesant M., Delingette H., Taylor A.M., Boudjemline Y., Pongiglione G., Ayache N. A statistical model for quantification and prediction of cardiac remodelling: application to tetralogy of fallot. IEEE Trans. Med. Imag. 2011, 30(9):1605-1616.
34. Mechelli A., Price C.J., Friston K.J., Ashburner J. Voxel-based morphometry of the human brain: methods and applications. Current Med. Imag. Rev. 2005, 1:105-113.
35. Miller M., Younes L. Group actions, homeomorphisms, and matching: a general framework. Int. J. Comput. Vis. 2001, 41(1-2):61-84.
36. Miller M.I., Beg M.F., Ceritoglu C., Stark C. Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. Proc. Natl. Acad. Sci. USA 2005, 102(27):9685-9690.
37. Miller M.I., Trouv A., Younes L. On the metrics and Euler-Lagrange equations of computational anatomy. Annu. Rev. Biomed. Eng. 2002, 4(1):375-405. pMID: 12117763.
38. Miller, M.I., Younes, L., Ratnanather, J.T., Brown, T., Reigel, T., Trinh, H., Tang, X., Barker, P., Mori, S., Albert, M., 2012. Amygdala atrophy in MCI/Alzheimers disease in the BIOCARD cohort based on diffeomorphic morphometry. In: MICCAI workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders (NIBAD'12), pp. 155-166.
39. Niethammer M., Huang Y., Vialard F.-X. Geodesic regression for image time-series. MICCAI 2011 2011, vol. 6892:655-662. Springer, Berlin, Heidelberg.
40. Phatak A., Jong S.D. The geometry of partial least squares. J. Chemometr. 1997, 11(4):311-338.
41. Portnoy S. Asymptotic behavior of M-estimators of p regression parameters when p2/n is large. i. Consistency. Ann. Statist. 1984, 12(4):1298-1309.
42. Rännar S., Lindgren F., Geladi P., Wold S. A pls kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm. J. Chemometr. 1994, 8(2):111-125.
43. Rosipal R., Trejo L. Kernel partial least squares regression in reproducing kernel hilbert space. J. Machine Learn. Res. 2002, 2:97-123.
44. Singh N., Fletcher P.T., Preston J.S., Ha L., King R., Marron J.S., Wiener M., Joshi S. Multivariate statistical analysis of deformation momenta relating anatomical shape to neuropsychological measures. Medical Image Computing and Computer-assisted Intervention: Part III. MICCAI'10 2010, 529-537. Springer-Verlag, Berlin, Heidelberg.
45. Singh, N., Hinkle, J., Joshi, S., Fletcher, P., 2013. A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1219-1222.
46. Singh N., Wang A., Sankaranarayanan P., Fletcher P., Joshi S. Genetic, structural and functional imaging biomarkers for early detection of conversion from mci to ad. Lecture Notes in Computer Science 2012, vol. 7510:132-140. Springer, Berlin, Heidelberg. N. Ayache, H. Delingette, P. Golland, K. Mori (Eds.).
47. Stonnington C., Chu C., Klöppel S., Jack Jr C., Ashburner J., Frackowiak R. Predicting clinical scores from magnetic resonance scans in alzheimer's disease. NeuroImage 2010, 51(4):1405-1413.
48. Tipping M.E. Sparse bayesian learning and the relevance vector machine. J. Machine Learn. Res. 2001, 1:211-244.
49. Twining C., Marsland S. Constructing diffeomorphic representations of non-rigid registrations of medical images. Lecture Notes in Computer Science 2003, vol. 2732:413-425. Springer, Berlin Heidelberg. C. Taylor, J. Noble (Eds.).
50. 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.
51. Vialard F.-X., Risser L., Rueckert D., Cotter C. Diffeomorphic 3d image registration via geodesic shooting using an efficient adjoint calculation. Int. J. Comput. Vis. 2011, 1-1310.1007/s11263-011-0481-8.
52. Wang L., Beg M., Ratnanather J., Ceritoglu C., Younes L., Morris J., Csernansky J., Miller M. Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. IEEE Trans. Med. Imag. 2007, 26(4):462.
53. Wang Y., Fan Y., Bhatt P., Davatzikos C. High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. NeuroImage 2010, 50(4):1519-1535.
54. Weiner M.W., Veitch D.P., Aisen P.S., Beckett L.A., Cairns N.J., Green R.C., Harvey D., Jack C.R., Jagust W., Liu E., Morris J.C., Petersen R.C., Saykin A.J., Schmidt M.E., Shaw L., Siuciak J.A., Soares H., Toga A.W., Trojanowski J.Q. The alzheimers disease neuroimaging initiative: a review of papers published since its inception. Alzheimer's Dementia 2012, 8(Suppl. 1):S1-S68.
55. Wold H. Path models with latent variables: the NIPALS approach. Quantitative Sociology: International Perspectives on Mathematical and Statistical Model Building 1975.
56. Younes L., Arrate F., Miller M. Evolutions equations in computational anatomy. NeuroImage 2009, 45(1S1):40-50.
57. Zhang D., Wang Y., Zhou L., Yuan H., Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 2011, 55(3):856-867.