Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter

Imagem de Miniatura
Arquivos
art_JACOBACCI_Improving_Spatial_Normalization_of_Brain_Diffusion_MRI_to_2020.PDF (1.11 MB)
Citações na Scopus
5
Tipo de produção
article
Data de publicação
2020
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
WILEY
Autores
JACOBACCI, Florencia
JOVICICH, Jorge
LERNER, Gonzalo
ARMONY, Jorge L.
DOYON, Julien
DELLA-MAGGIORE, Valeria
Citação
JOURNAL OF MAGNETIC RESONANCE IMAGING, v.52, n.3, p.766-775, 2020
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Background Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure. Purpose To optimize normalization of diffusion tensor images (DTI) to improve registration in gray matter and reduce variability associated with multisession registrations. Study Type Prospective longitudinal study. Subjects Twenty-one healthy subjects (18-31 years old) underwent nine MRI scanning sessions each. Field Strength/Sequence 3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence. Assessment Diffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely, the nonlinear registration algorithm (FSL vs. ANTs), the registration target (FA-based vs. T-1-based templates), and the use of intermediate individual (FA-based or T-1-based) targets. We compared the across-session test-retest reproducibility error of these normalization approaches for FA and MD in white and gray matter. Statistical Tests Reproducibility errors were compared using a repeated-measures analysis of variance with pipeline as the within-subject factor. Results The registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter (P < 0.0001) with respect to FSL. Moreover, using the MNI152 T-1 template as the target of registration resulted in lower reproducibility errors for MD (P < 0.0001), whereas the FMRIB58 FA template performed better for FA (P < 0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152 T-1 was carried out within modality (FA-FA) (P < 0.05), but not via a T-1-based individual template. Data Conclusion A normalization approach using ANTs to register FA images to the MNI152 T-1 template via an individual FA template minimized test-retest reproducibility errors both for gray and white matter. Level of Evidence 1 Technical Efficacy Stage 1
Palavras-chave
diffusion tensor imaging, normalization, reproducibility, ANTs, FSL, longitudinal design
URI
https://observatorio.fm.usp.br/handle/OPI/37719
Referências
  1. Andersson JLR, 2016, NEUROIMAGE, V125, P1063, DOI 10.1016/j.neuroimage.2015.10.019
  2. Andersson JLR, 2003, NEUROIMAGE, V20, P870, DOI 10.1016/S1053-8119(03)00336-7
  3. Andersson JLR, 2007, TR07JA2 FMRIB
  4. Avants BB, 2011, NEUROIMAGE, V54, P2033, DOI 10.1016/j.neuroimage.2010.09.025
  5. Bach M, 2014, NEUROIMAGE, V100, P358, DOI 10.1016/j.neuroimage.2014.06.021
  6. Bastin ME, 1999, MAGN RESON IMAGING, V17, P1011, DOI 10.1016/S0730-725X(99)00026-0
  7. Brodt S, 2018, SCIENCE, V362, P1045, DOI 10.1126/science.aau2528
  8. Farrell JAD, 2007, J MAGN RESON IMAGING, V26, P756, DOI 10.1002/jmri.21053
  9. Fischl B, 2012, NEUROIMAGE, V62, P774, DOI 10.1016/j.neuroimage.2012.01.021
  10. Jacobacci F, 2019, ORGAN HUM BRAIN MAPP, V2019, pT
  11. Jansen JFA, 2007, INVEST RADIOL, V42, P327, DOI 10.1097/01.rli.0000262757.10271.e5
  12. Jones DK, 2010, NMR BIOMED, V23, P803, DOI 10.1002/nbm.1543
  13. Jones DK, 2004, MAGNET RESON MED, V51, P807, DOI 10.1002/mrm.20033
  14. Jones DK, 1999, MAGNET RESON MED, V42, P515, DOI 10.1002/(SICI)1522-2594(199909)42:3<515::AID-MRM14>3.3.CO;2-H
  15. Keihaninejad S, 2013, NEUROIMAGE, V72, P153, DOI 10.1016/j.neuroimage.2013.01.044
  16. Klein A, 2010, NEUROIMAGE, V51, P214, DOI 10.1016/j.neuroimage.2010.01.091
  17. Klein A, 2009, NEUROIMAGE, V46, P786, DOI 10.1016/j.neuroimage.2008.12.037
  18. Landi SM, 2011, J NEUROSCI, V31, P11808, DOI 10.1523/JNEUROSCI.2253-11.2011
  19. Lerner G, 2019, ORIGINS ANTEROGRADE, P1
  20. Leys C, 2013, J EXP SOC PSYCHOL, V49, P764, DOI 10.1016/j.jesp.2013.03.013
  21. Li XR, 2016, J NEUROSCI METH, V264, P47, DOI 10.1016/j.jneumeth.2016.03.001
  22. Liu X, 2014, NEURORADIOLOGY, V56, P497, DOI 10.1007/s00234-014-1342-2
  23. Madhyastha T, 2014, HUM BRAIN MAPP, V35, P4544, DOI 10.1002/hbm.22493
  24. Marenco S, 2006, PSYCHIAT RES-NEUROIM, V147, P69, DOI 10.1016/j.pscychresns.2006.01.008
  25. Marques JP, 2010, NEUROIMAGE, V49, P1271, DOI 10.1016/j.neuroimage.2009.10.002
  26. Papinutto ND, 2013, MAGN RESON IMAGING, V31, P827, DOI 10.1016/j.mri.2013.03.004
  27. Reuter M, 2012, NEUROIMAGE, V61, P1402, DOI 10.1016/j.neuroimage.2012.02.084
  28. Sagi Y, 2012, NEURON, V73, P1195, DOI 10.1016/j.neuron.2012.01.025
  29. Scholz J, 2009, NAT NEUROSCI, V12, P1370, DOI 10.1038/nn.2412
  30. Schwarz CG, 2014, NEUROIMAGE, V94, P65, DOI 10.1016/j.neuroimage.2014.03.026
  31. Smith SM, 2004, NEUROIMAGE, V23, pS208, DOI 10.1016/j.neuroimage.2004.07.051
  32. Smith SM, 2007, NAT PROTOC, V2, P499, DOI 10.1038/nprot.2007.45
  33. Smith SM, 2006, NEUROIMAGE, V31, P1487, DOI 10.1016/j.neuroimage.2006.02.024
  34. Tustison NJ, 2014, HUM BRAIN MAPP, V35, P745, DOI 10.1002/hbm.22211
  35. Ugurbil K, 2013, NEUROIMAGE, V80, P80, DOI 10.1016/j.neuroimage.2013.05.012
  36. Vollmar C, 2010, NEUROIMAGE, V51, P1384, DOI 10.1016/j.neuroimage.2010.03.046
  37. Wang YL, 2017, QUANTUM INF PROCESS, V16, DOI 10.1007/s11128-016-1477-7
  38. Xu JQ, 2013, NEUROIMAGE, V83, P991, DOI 10.1016/j.neuroimage.2013.07.055
  39. Yendiki A, 2016, NEUROIMAGE, V127, P277, DOI 10.1016/j.neuroimage.2015.12.003
  40. Zhang SW, 2018, NEUROIMAGE, V172, P40, DOI 10.1016/j.neuroimage.2018.01.046

Coleções
Artigos e Materiais de Revistas Científicas - FM/MDR
Artigos e Materiais de Revistas Científicas - LIM/44