Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing

NeuroImage

Volumn 39, Issue 4, 2008, Pages 1693-1705

  Chung, SungWon ^a,b,c   Pelletier, Daniel ^b,d   Sdika, Michael ^b,d   Lu, Ying ^a,b,c   Berman, Jeffrey I ^b,c   Henry, Roland G ^a,b,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Center for Molecular and Functional Imaging   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Cluster; Diffusion tensor MRI; Fiber tracking; Longitudinal; Multiple sclerosis; Permutation; Serial; Voxel; White matter

Indexed keywords

ARTICLE; CONTROLLED STUDY; DIFFUSION TENSOR IMAGING; HUMAN; IMAGE ANALYSIS; MONTE CARLO METHOD; NEUROIMAGING; NEUROLOGIC DISEASE; PRIORITY JOURNAL;

ADULT; ALGORITHMS; BRAIN; CENTRAL NERVOUS SYSTEM DISEASES; CEREBRAL CORTEX; CLUSTER ANALYSIS; CORPUS CALLOSUM; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MONTE CARLO METHOD; MULTIPLE SCLEROSIS; NONLINEAR DYNAMICS; PYRAMIDAL TRACTS; REFERENCE VALUES;

EID: 38749130449     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2007.10.039     Document Type: Article

References (59)

1. Anderson A.W. Theoretical analysis of the effects of noise on diffusion tensor imaging. Magn. Reson. Med. 46 (2001) 1174-1188
2. Basser P.J., Mattiello J., and LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson., Ser. B 103 (1994) 247-254
3. Basser P.J., Mattiello J., and LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66 (1994) 259-267
4. Bullmore E.T., Suckling J., Overmeyer S., Rabe-Hesketh S., Taylor E., and Brammer M.J. Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Trans. Med. Imag. 18 (1999) 32-42
5. Caramia F., Pantano P., Di Legge S., Piattella M.C., Lenzi D., Paolillo A., et al. A longitudinal study of MR diffusion changes in normal appearing white matter of patients with early multiple sclerosis. Magn. Reson. Imaging 20 (2002) 383-388
6. Castriota-Scanderbeg A., Sabatini U., Fasano F., Floris R., Fraracci L., Mario M.D., et al. Diffusion of water in large demyelinating lesions: a follow-up study. Neuroradiology 44 (2002) 764-767
7. Chang L.C., Koay C.G., Pierpaoli C., and Basser P.J. Variance of estimated DTI-derived parameters via first-order perturbation methods. Magn. Reson. Med. 57 (2007) 141-149
8. Chung S., Lu Y., and Henry R.G. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33 (2006) 531-541
9. Chung S., Pelletier D., Berman J.I., Metcalf M.A., and Henry R.G. Bootstrap-based longitudinal analysis of DTI estimates (BLADE): applications in clinically isolated syndrome patients. 14th Annual Meeting of ISMRM vol. 275 (2006), ISMRM, Seattle, WA
10. Crum W.R., Scahill R.I., and Fox N.C. Automated hippocampal segmentation by regional fluid registration of serial MRI: validation and application in Alzheimer's disease. NeuroImage 13 (2001) 847-855
11. Edgington E.S. Randomization Tests (1995), Marcel Dekker, New York
12. Freeborough P.A., and Fox N.C. The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans. Med. Imag. 16 (1997) 623-629
13. Freeborough P.A., and Fox N.C. Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. J. Comput. Assist. Tomogr. 22 (1998) 838-843
14. Friston K.J., Frith C.D., Liddle P.F., and Frackowiak R.S. Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11 (1991) 690-699
15. Friston K.J., Holmes A.P., Worsley K.J., Poline J.P., Frith C.D., and Frackowiak R.S.J. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2 (1995) 189-210
16. Friston K.J., Holmes A., Poline J.B., Price C.J., and Frith C.D. Detecting activations in PET and fMRI: levels of inference and power. NeuroImage 4 (1996) 223-235
17. Gerig G., Welti D., Guttmann C.R., Colchester A.C., and Szekely G. Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Med. Image Anal. 4 (2000) 31-42
18. Good P.I. Permutation, Parametric and Bootstrap Tests of Hypotheses (2005), Springer, New York
19. Hajnal J.V., Saeed N., Oatridge A., Williams E.J., Young I.R., and Bydder G.M. Detection of subtle brain changes using subvoxel registration and subtraction of serial MR-images. J. Comput. Assist. Tomogr. 19 (1995) 677-691
20. Hajnal J.V., Saeed N., Soar E.J., Oatridge A., Young I.R., and Bydder G.M. A registration and interpolation procedure for subvoxel matching of serially acquired MR-images. J. Comput. Assist. Tomogr. 19 (1995) 289-296
21. Heim S., Hahn K., Samann P.G., Fahrmeir L., and Auer D.P. Assessing DTI data quality using bootstrap analysis. Magn. Reson. Med. 52 (2004) 582-589
22. Henry R.G., Oh J., Nelson S.J., and Pelletier D. Directional diffusion in relapsing-remitting multiple sclerosis: a possible in vivo signature of Wallerian degeneration. J. Magn. Reson. Imaging 18 (2003) 420-426
23. Holmes, A.P., 1994. Statistical issues in functional brain mapping. Vol PhD: University of Glasgow.
24. Holmes A.P., Blair R.C., Watson J.D., and Ford I. Nonparametric analysis of statistic images from functional mapping experiments. J. Cereb. Blood Flow Metab. 16 (1996) 7-22
25. Jenkinson M., and Smith S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5 (2001) 143-156
26. Jones D.K. Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI. Magn. Reson. Med. 49 (2003) 7-12
27. Jones D.K. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn. Reson. Med. 51 (2004) 807-815
28. Lazar M., and Alexander A.L. Bootstrap white matter tractography (BOOT-TRAC). NeuroImage 24 (2005) 524-532
29. 2-weighted difference imaging. Brain 121 Pt 11 (1998) 2095-2102
30. Lemieux L., Wieshmann U.C., Moran N.F., Fish D.R., and Shorvon S.D. The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization. Med. Image Anal. 2 (1998) 227-242
31. Liu D.C., and Nocedal J. On the limited memory BFGS method for large-scale optimization. Math. Program. 45 (1989) 503-528
32. Locascio J.J., Jennings P.J., Moore C.I., and Corkin S. Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging. Hum. Brain Mapp. 5 (1997) 168-193
33. Manly B.F.J. Randomization, Bootstrap, and Monte Carlo Methods in Biology (2007), Chapman & Hall/CRC, Boca Raton, FL
34. Meier D.S., and Guttmann C.R. Time-series analysis of MRI intensity patterns in multiple sclerosis. NeuroImage 20 (2003) 1193-1209
35. Mori S., Crain B.J., Chacko V.P., and van Zijl P.C. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45 (1999) 265-269
36. Nichols T.E., and Holmes A.P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15 (2002) 1-25
37. Oreja-Guevara C., Rovaris M., Iannucci G., Valsasina P., Caputo D., Cavarretta R., et al. Progressive gray matter damage in patients with relapsing-remitting multiple sclerosis: a longitudinal diffusion tensor magnetic resonance imaging study. Arch. Neurol. 62 (2005) 578-584
38. Pajevic S., and Basser P.J. Parametric and non-parametric statistical analysis of DT-MRI data. J. Magn. Reson. 161 (2003) 1-14
39. Petersson K.M., Nichols T.E., Poline J.B., and Holmes A.P. Statistical limitations in functional neuroimaging: II. Signal detection and statistical inference. Philos. Trans. R. Soc. Lond., B Biol. Sci. 354 (1999) 1261-1281
40. Poline J.B., Worsley K.J., Evans A.C., and Friston K.J. Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage 5 (1997) 83-96
41. Qiu D., Leung L.H., Kwong D.L., Chan G.C., and Khong P.L. Mapping radiation dose distribution on the fractional anisotropy map: applications in the assessment of treatment-induced white matter injury. NeuroImage 31 (2006) 109-115
42. Rey D., Subsol G., Delingette H., and Ayache N. Automatic detection and segmentation of evolving processes in 3D medical images: application to multiple sclerosis. Med. Image Anal. 6 (2002) 163-179
43. Rocca M.A., Cercignani M., Iannucci G., Comi G., and Filippi M. Weekly diffusion-weighted imaging of normal-appearing white matter in MS. Neurology 55 (2000) 882-884
44. Roland P.E., Levin B., Kawashima R., and Akerman S. Three-dimensional analysis of clustered voxels in O-butanol brain activation images. Hum. Brain Mapp. 1 (1993) 3-19
45. Rovaris M., Gass A., Bammer R., Hickman S.J., Ciccarelli O., Miller D.H., et al. Diffusion MRI in multiple sclerosis. Neurology 65 (2005) 1526-1532
46. Schwartzman A., Dougherty R.F., and Taylor J.E. Cross-subject comparison of principal diffusion direction maps. Magn. Reson. Med. 53 (2005) 1423-1431
47. Sdika, M., in press. A fast non rigid image registration with constraints on the Jacobian using large scale constrained optimization. IEEE on Medical Imaging.
48. Skare S., Li T., Nordell B., and Ingvar M. Noise considerations in the determination of diffusion tensor anisotropy. Magn. Reson. Imaging 18 (2000) 659-669
49. Smith S.M. Fast robust automated brain extraction. Hum. Brain Mapp. 17 (2002) 143-155
50. Smith S.M., De Stefano N., Jenkinson M., and Matthews P.M. Normalized accurate measurement of longitudinal brain change. J. Comput. Assist. Tomogr. 25 (2001) 466-475
51. Smith S.M., Jenkinson M., Johansen-Berg H., Rueckert D., Nichols T.E., Mackay C.E., et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505
52. Studholme C., Cardenas V., Maudsley A., and Weiner M. An intensity consistent filtering approach to the analysis of deformation tensor derived maps of brain shape. NeuroImage 19 (2003) 1638-1649
53. Thompson P.M., Hayashi K.M., de Zubicaray G., Janke A.L., Rose S.E., Semple J., et al. Dynamics of gray matter loss in Alzheimer's disease. J. Neurosci. 23 (2003) 994-1005
54. Tuch D.S. Q-ball imaging. Magn. Reson. Med. 52 (2004) 1358-1372
55. Wang D., and Doddrell D.M. MR image-based measurement of rates of change in volumes of brain structures: Part I. Method and validation. Magn. Reson. Imaging 20 (2002) 27-40
56. Werring D.J., Brassat D., Droogan A.G., Clark C.A., Symms M.R., Barker G.J., et al. The pathogenesis of lesions and normal-appearing white matter changes in multiple sclerosis: a serial diffusion MRI study. Brain 123 Pt 8 (2000) 1667-1676
57. Whitcher B., Tuch D.S., Wisco J.J., Sorensen A.G., and Wang L. Using the wild bootstrap to quantify uncertainty in diffusion tensor imaging. Hum. Brain Mapp. (2007)
58. Worsley K.J., Evans A.C., Marrett S., and Neelin P. A three-dimensional statistical analysis for CBF activation studies in human brain. J. Cereb. Blood Flow Metab. 12 (1992) 900-918
59. Zhang Y., Brady M., and Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 20 (2001) 45-57