Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain

NeuroImage

Volumn 109, Issue , 2015, Pages 480-492

  Sadeghi, Neda ^a,b   Nayak, Amritha ^a,b   Walker, Lindsay ^d   Okan Irfanoglu, M ^a,b,c   Albert, Paul S ^a   Pierpaoli, Carlo ^a   Brain Development Cooperative Group, ^e  

^a EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT   (United States)

^b Henry Jackson Foundation   (United States)

^c UNIFORMED SERVICES UNIVERSITY OF THE HEALTH SCIENCES   (United States)

^d BROWN UNIVERSITY   (United States)

^e NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Brain development; DTI; Experimental variability; Mixed effects model

Indexed keywords

ADOLESCENT; ADULT; AGE; ARTICLE; BIOLOGICAL FUNCTIONS; BIOLOGICAL VARIABILITY; BRAIN DEVELOPMENT; BRAIN REGION; CHILD; CONTROLLED STUDY; CORPUS CALLOSUM; DIFFUSION WEIGHTED IMAGING; ENVIRONMENTAL FACTOR; EXPERIMENTAL NOISE; FEMALE; FRACTIONAL ANISOTROPY; HEREDITY; HUMAN; MALE; MEAN DIFFUSIVITY; NEUROIMAGING; NOISE; PHYSICAL PARAMETERS; VARIANCE; ANATOMY AND HISTOLOGY; ANISOTROPY; ARTIFACT; BRAIN; DIFFUSION TENSOR IMAGING; GROWTH, DEVELOPMENT AND AGING; INDIVIDUALITY; MONTE CARLO METHOD; OBSERVER VARIATION; PRESCHOOL CHILD; PROCEDURES; REPRODUCIBILITY; YOUNG ADULT;

ADOLESCENT; ADULT; AGE FACTORS; ANISOTROPY; ARTIFACTS; BRAIN; CHILD; CHILD, PRESCHOOL; DIFFUSION MAGNETIC RESONANCE IMAGING; DIFFUSION TENSOR IMAGING; FEMALE; HUMANS; INDIVIDUALITY; MALE; MONTE CARLO METHOD; OBSERVER VARIATION; REPRODUCIBILITY OF RESULTS; YOUNG ADULT;

EID: 84923012409     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.12.084     Document Type: Article

References (46)

1. Ashtari M., Cervellione K.L., Hasan K.M., Wu J., McIlree C., Kester H., Ardekani B.A., Roofeh D., Szeszko P.R., Kumra S. White matter development during late adolescence in healthy males: a cross-sectional diffusion tensor imaging study. NeuroImage 2007, 35:501-510.
2. Barnea-Goraly N., Menon V., Eckert M., Tamm L., Bammer R., Karchemskiy A., Dant C., Reiss A. White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. Cereb. Cortex 2005, 15:1848-1854.
3. Basser P.J., Mattiello J., LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. Ser. B 1994, 103:247-254.
4. Bava S., Thayer R., Jacobus J., Ward M., Jernigan T.L., Tapert S.F. Longitudinal characterization of white matter maturation during adolescence. Brain Res. 2010, 1327:38-46.
5. Bazin P.L., Cuzzocreo J.L., Yassa M.A., Gandler W., McAuliffe M.J., Bassett S.S., Pham D.L. Volumetric neuroimage analysis extensions for the MIPAV software package. J. Neurosci. Methods 2007, 165:111-121.
6. Benjamini Y., Hochberg Y.B. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 1995, 289-300.
7. Bonekamp D., Nagae L.M., Degaonkar M., Matson M., Abdalla W.M., Barker P.B., Mori S., Horska A. Diffusion tensor imaging in children and adolescents: reproducibility, hemispheric, and age-related differences. NeuroImage 2007, 34:733-742.
8. Cassol E., Ranjeva J.P., Ibarrola D., Mekies C., Manelfe C., Clanet M., Berry I. Diffusion tensor imaging in multiple sclerosis: a tool for monitoring changes in normal-appearing white matter. Mult. Scler. 2004, 10:188-196.
9. Chang L.C., Walker L., Pierpaoli C. Informed RESTORE: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magn. Reson. Med. 2012, 68:1654-1663.
10. Eluvathingal T.J., Hasan K.M., Kramer L., Fletcher J.M., Ewing-Cobbs L. Quantitative diffusion tensor tractography of association and projection fibers in normally developing children and adolescents. Cereb. Cortex 2007, 17:2760-2768.
11. Faria A.V., Zhang J., Oishi K., Li X., Jiang H., Akhter K., Hermoye L., Lee S.K., Hoon A., Stashinko E., Miller M.I., van Zijl P.C., Mori S. Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. NeuroImage 2010, 52:415-428.
12. Farrell J.A., Landman B.A., Jones C.K., Smith S.A., Prince J.L., van Zijl P.C., Mori S. Effects of SNR on the accuracy and reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. J. Magn. Reson. Imaging 2007, 26:756.
13. Gao W., Zhu H., Lin W. A unified optimization approach for diffusion tensor imaging technique. NeuroImage 2009, 44:729-741.
14. Giorgio A., Watkins K., Chadwick M., James S., Winmill L., Douaud G., De Stefano N., Matthews P.M., Smith S.M., Johansen-Berg H., James A. Longitudinal changes in grey and white matter during adolescence. NeuroImage 2010, 49:94-103.
15. Heng S., Song A.W., Sim K. White matter abnormalities in bipolar disorder: insights from diffusion tensor imaging studies. J. Neural Transm. 2010, 117:639-654.
16. Hermoye L., Saint-Martin C., Cosnard G., Lee S.K., Kim J., Nassogne M.C., Menten R., Clapuyt P., Donohue P.K., Hua K., Wakana S., Jiang H., van Zijl P.C., Mori S. Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. NeuroImage 2006, 29:493-504.
17. Huppi P.S., Maier S.E., Peled S., Zientara G.P., Barnes P.D., Jolesz F.A., Volpe J.J. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr. Res. 1998, 44:584-590.
18. Irfanoglu M., Nayak A., Walker L., Pierpaoli C., the Brain Development Cooperative Group Optimization of ROI transposition for atlas-based analysis of MRI quantitative metrics in neuroimaging studies. ISMRM 22nd annual meeting, Milan, Italy 2014.
19. Jones D.K. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn. Reson. Med. 2004, 51:807-815.
20. Jones D.K., Horsfield M.A., Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 1999, 42:515-525.
21. Kubicki M., McCarley R., Westin C.F., Park H.J., Maier S., Kikinis R., Jolesz F.A., Shenton M.E. A review of diffusion tensor imaging studies in schizophrenia. J. Psychiatr. Res. 2007, 41:15-30.
22. Laird N.M., Ware J.H. Random-effects models for longitudinal data. Biometrics 1982, 38:963-974.
23. Lebel C., Beaulieu C. Longitudinal development of human brain wiring continues from childhood into adulthood. J. Neurosci. 2011, 31:10937-10947.
24. Lebel C., Walker L., Leemans A., Phillips L., Beaulieu C. Microstructural maturation of the human brain from childhood to adulthood. NeuroImage 2008, 40:1044-1055.
25. Lim K.O., Hedehus M., Moseley M., de Crespigny A., Sullivan E.V., Pfefferbaum A. Compromised white matter tract integrity in schizophrenia inferred from diffusion tensor imaging. Arch. Gen. Psychiatry 1999, 56:367-374.
26. Mukherjee P., Miller J.H., Shimony J.S., Conturo T.E., Lee B.C., Almli C.R., McKinstry R.C. Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR imaging. Radiology 2001, 221:349-358.
27. Paus T., Keshavan M., Giedd J.N. Why do many psychiatric disorders emerge during adolescence?. Nat. Rev. Neurosci. 2008, 9:947-957.
28. Pfefferbaum A., Adalsteinsson E., Sullivan E.V. Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain. J. Magn. Reson. Imaging 2003, 18:427-433.
29. Pierpaoli C., Basser P.J. Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 1996, 36:893-906.
30. Pierpaoli C., Jezzard P., Basser P.J., Barnett A., Di Chiro G. Diffusion tensor MR imaging of the human brain. Radiology 1996, 201:637-648.
31. Pierpaoli C., Marenco S., Rohde G., Jones D.K., Barnett A.S. Analyzing the contribution of cardiac pulsation to the variability of quantities derived from the diffusion tensor. ISMRM 11th annual meeting, Toronto, Canada 2003.
32. Pierpaoli C., Walker L., Irfanoglu M., Barnett A., Chang L.C., Koay C., Pajevic S., Rohde G., Sarlls J., Wu M. TORTOISE: an integrated software package for processing of diffusion MRI data. ISMRM 19th annual meeting, Stockholm, Sweden 2010.
33. Pierpaoli C., Walker L., Nayak A., Chang L.C., Ball W., Botterson K., McCracken J., McKinstry R., Rivkin M., Group, T.B.D.C. Age specific DTI average brain atlases from the NIH MRI study of normal brain development. ISMRM 20th annual meeting, Melbourne, Australia 2010.
34. Pinheiro J., Bates D., DebRoy S., Sarkar D., Core Team R. nlme: linear and nonlinear mixed effects models. R package version 31-104 2012.
35. Rohde G.K., Barnett A.S., Basser P.J., Marenco S., Pierpaoli C. Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn. Reson. Med. 2004, 51:103-114.
36. Sadeghi N., Prastawa M., Fletcher P.T., Vachet C., Wang B., Gilmore J., Gerig G. Multivariate modeling of longitudinal MRI in early brain development with confidence measures. Proceedings of the 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI): From Nano to Macro 2013, 1400-1403.
37. Sadeghi N., Prastawa M., Fletcher P.T., Wolff J., Gilmore J.H., Gerig G. Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain. NeuroImage 2013, 68:236-247.
38. Schmithorst V.J., Wilke M., Dardzinski B.J., Holland S.K. Correlation of white matter diffusivity and anisotropy with age during childhood and adolescence: a cross-sectional diffusion-tensor MR imaging study. Radiology 2002, 222:212-218.
39. Skare S., Hedehus M., Moseley M.E., Li T.Q. Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. J. Magn. Reson. 2000, 147:340-352.
40. Snook L., Paulson L.A., Roy D., Phillips L., Beaulieu C. Diffusion tensor imaging of neurodevelopment in children and young adults. NeuroImage 2005, 26:1164-1173.
41. Vollmar C., O'Muircheartaigh J., Barker G.J., Symms M.R., Thompson P., Kumari V., Duncan J.S., Richardson M.P., Koepp M.J. Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0T scanners. NeuroImage 2010, 51:1384-1394.
42. Walker L., Chang L.C., Koay C.G., Sharma N., Cohen L., Verma R., Pierpaoli C. Effects of physiological noise in population analysis of diffusion tensor MRI data. NeuroImage 2011, 54:1168-1177.
43. Walker L., Gozzi M., Lenroot R., Thurm A., Behseta B., Swedo S., Pierpaoli C. Diffusion tensor imaging in young children with autism: biological effects and potential confounds. Biol. Psychiatry 2012, 72:1043-1051.
44. Walker L., Curry M., Nayak A., Lange N., Pierpaoli C. A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies. Hum. Brain Mapp. 2013, 34:2439-2454.
45. Wu M., Chang L.C., Walker L., Lemaitre H., Barnett A.S., Marenco S., Pierpaoli C. Comparison of EPI distortion correction methods in diffusion tensor MRI using a novel framework. Med. Image Comput. Comput. Assist. Interv. 2008, 11:321-329.
46. Zhang H., Avants B.B., Yushkevich P.A., Woo J.H., Wang S., McCluskey L.F., Elman L.B., Melhem E.R., Gee J.C. High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis. IEEE Trans. Med. Imaging 2007, 26:1585-1597.