Top

Neuroradiology

Published in:

01-08-2010 | Topic Article

Computational analysis of cerebral cortex

Authors: Hidemasa Takao, Osamu Abe, Kuni Ohtomo

Published in: Neuroradiology | Issue 8/2010

Abstract

Introduction

Magnetic resonance imaging (MRI) has been used in many in vivo anatomical studies of the brain. Computational neuroanatomy is an expanding field of research, and a number of automated, unbiased, objective techniques have been developed to characterize structural changes in the brain using structural MRI without the need for time-consuming manual measurements. Voxel-based morphometry is one of the most widely used automated techniques to examine patterns of brain changes. Cortical thickness analysis is also becoming increasingly used as a tool for the study of cortical anatomy. Both techniques can be relatively easily used with freely available software packages. MRI data quality is important in order for the processed data to be accurate.

Discussion

In this review, we describe MRI data acquisition and preprocessing for morphometric analysis of the brain and present a brief summary of voxel-based morphometry and cortical thickness analysis.

Abe O, Yamasue H, Aoki S, Suga M, Yamada H, Kasai K, Masutani Y, Kato N, Ohtomo K (2008) Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data. Neurobiol Aging 29(1):102–116. doi:10.1016/j.neurobiolaging.2006.09.003 CrossRefPubMed

Abe O, Yamasue H, Yamada H, Masutani Y, Kabasawa H, Sasaki H, Takei K, Suga M, Kasai K, Aoki S, Ohtomo K Sex dimorphism in gray/white matter volume and diffusion tensor during normal aging. NMR Biomed. doi:10.1002/nbm.1479 (in press)

Abe O, Yamasue H, Kasai K, Yamada H, Aoki S, Inoue H, Takei K, Suga M, Matsuo K, Kato T, Masutani Y, Ohtomo K (2010) Voxel-based analyses of gray/white matter volume and diffusion tensor data in major depression. Psychiatry Res 181(1):64–70. doi:10.1016/j.pscychresns.2009.07.007 CrossRefPubMed

Takao H, Abe O, Yamasue H, Aoki S, Kasai K, Ohtomo K (2010) Cerebral asymmetry in patients with schizophrenia: a voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) study. J Magn Reson Imaging 31(1):221–226. doi:10.1002/jmri.22017 CrossRefPubMed

Takao H, Abe O, Yamasue H, Aoki S, Kasai K, Sasaki H, Ohtomo K (2010) Aging effects on cerebral asymmetry: a voxel-based morphometry and diffusion tensor imaging study. Magn Reson Imaging 28(1):65–69. doi:10.1016/j.mri.2009.05.020 CrossRefPubMed

Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM (2003) Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol 2(2):79–88. doi:S1474442203003041 CrossRefPubMed

Davatzikos C (2005) Voxel-based morphometric analysis using shape transformations. Int Rev Neurobiol 66:125–146. doi:10.1016/S0074-7742(05)66004-7 CrossRefPubMed

Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11(6 Pt 1):805–821. doi:10.1006/nimg.2000.0582S1053-8119(00)90582-2 CrossRefPubMed

Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97(20):11050–11055. doi:10.1073/pnas.200033797200033797 CrossRefPubMed

10.

Preboske GM, Gunter JL, Ward CP, Jack CR Jr (2006) Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI. Neuroimage 30(4):1196–1202. doi:10.1016/j.neuroimage.2005.10.049 CrossRefPubMed

11.

Littmann A, Guehring J, Buechel C, Stiehl HS (2006) Acquisition-related morphological variability in structural MRI. Acad Radiol 13(9):1055–1061. doi:10.1016/j.acra.2006.05.001 CrossRefPubMed

12.

Camara-Rey O, Sneller BI, Ridgway GR, Garde E, Fox NC, Hill DL (2006) Simulation of acquisition artefacts in MR scans: effects on automatic measures of brain atrophy. Med Image Comput Comput Assist Interv 9(Pt 1):272–280CrossRefPubMed

13.

Shuter B, Yeh IB, Graham S, Au C, Wang SC (2008) Reproducibility of brain tissue volumes in longitudinal studies: effects of changes in signal-to-noise ratio and scanner software. Neuroimage 41(2):371–379. doi:10.1016/j.neuroimage.2008.02.003 CrossRefPubMed

14.

Ashburner J (2009) Computational anatomy with the SPM software. Magn Reson Imaging 27(8):1163–1174. doi:10.1016/j.mri.2009.01.006 CrossRefPubMed

15.

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 (2006) Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30(2):436–443. doi:10.1016/j.neuroimage.2005.09.046 CrossRefPubMed

16.

Leow AD, Klunder AD, Jack CR Jr, Toga AW, Dale AM, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Whitwell JL, Borowski BJ, Fleisher AS, Fox NC, Harvey D, Kornak J, Schuff N, Studholme C, Alexander GE, Weiner MW, Thompson PM (2006) Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage 31(2):627–640. doi:10.1016/j.neuroimage.2005.12.013 CrossRefPubMed

17.

Boyes RG, Gunter JL, Frost C, Janke AL, Yeatman T, Hill DL, Bernstein MA, Thompson PM, Weiner MW, Schuff N, Alexander GE, Killiany RJ, DeCarli C, Jack CR, Fox NC (2008) Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. Neuroimage 39(4):1752–1762. doi:10.1016/j.neuroimage.2007.10.026 CrossRefPubMed

18.

Acosta-Cabronero J, Williams GB, Pereira JM, Pengas G, Nestor PJ (2008) The impact of skull-stripping and radio-frequency bias correction on grey-matter segmentation for voxel-based morphometry. Neuroimage 39(4):1654–1665. doi:10.1016/j.neuroimage.2007.10.051 CrossRefPubMed

19.

Chard DT, Parker GJ, Griffin CM, Thompson AJ, Miller DH (2002) The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM-based segmentation methodology. J Magn Reson Imaging 15(3):259–267. doi:10.1002/jmri.10064 CrossRefPubMed

20.

Clark KA, Woods RP, Rottenberg DA, Toga AW, Mazziotta JC (2006) Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images. Neuroimage 29(1):185–202. doi:10.1016/j.neuroimage.2005.07.035 CrossRefPubMed

21.

Zheng W, Chee MW, Zagorodnov V (2009) Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3. Neuroimage 48(1):73–83. doi:10.1016/j.neuroimage.2009.06.039 CrossRefPubMed

22.

Lewis EB, Fox NC (2004) Correction of differential intensity inhomogeneity in longitudinal MR images. Neuroimage 23(1):75–83. doi:10.1016/j.neuroimage.2004.04.030 CrossRefPubMed

23.

Takao H, Abe O, Hayashi N, Kabasawa H, Ohtomo K (2010) Effects of gradient non-linearity correction and intensity non-uniformity correction in longitudinal studies using structural image evaluation using normalization of atrophy (SIENA). J Magn Reson Imaging (in press)

24.

Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3):405–421. doi:10.1109/TMI.2006.891486 CrossRefPubMed

25.

Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17(1):87–97. doi:10.1109/42.668698 CrossRefPubMed

26.

Mechelli A, Price CJ, Friston KJ, Ashburner J (2005) Voxel-based morphometry of the human brain: methods and applications. Curr Med Imaging Rev 1(2):105–113CrossRef

27.

Bookstein FL (2001) “Voxel-based morphometry” should not be used with imperfectly registered images. Neuroimage 14(6):1454–1462. doi:10.1006/nimg.2001.0770 CrossRefPubMed

28.

Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. Neuroimage 14(6):1238–1243. doi:10.1006/nimg.2001.0961 CrossRefPubMed

29.

Crum WR, Griffin LD, Hill DL, Hawkes DJ (2003) Zen and the art of medical image registration: correspondence, homology, and quality. Neuroimage 20(3):1425–1437. doi:S1053811903004154 CrossRefPubMed

30.

Davatzikos C (2004) Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage 23(1):17–20. doi:10.1016/j.neuroimage.2004.05.010 CrossRefPubMed

31.

Friston KJ, Ashburner J (2004) Generative and recognition models for neuroanatomy. Neuroimage 23(1):21–24. doi:10.1016/j.neuroimage.2004.04.021 CrossRefPubMed

32.

Good CD, Scahill RI, Fox NC, Ashburner J, Friston KJ, Chan D, Crum WR, Rossor MN, Frackowiak RS (2002) Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias. Neuroimage 17(1):29–46. doi:S1053811902912024 CrossRefPubMed

33.

Douaud G, Gaura V, Ribeiro MJ, Lethimonnier F, Maroy R, Verny C, Krystkowiak P, Damier P, Bachoud-Levi AC, Hantraye P, Remy P (2006) Distribution of grey matter atrophy in Huntington’s disease patients: a combined ROI-based and voxel-based morphometric study. Neuroimage 32(4):1562–1575. doi:10.1016/j.neuroimage.2006.05.057 CrossRefPubMed

34.

Kennedy KM, Erickson KI, Rodrigue KM, Voss MW, Colcombe SJ, Kramer AF, Acker JD, Raz N (2009) Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry. Neurobiol Aging 30(10):1657–1676. doi:10.1016/j.neurobiolaging.2007.12.020 CrossRefPubMed

35.

Bergouignan L, Chupin M, Czechowska Y, Kinkingnehun S, Lemogne C, Le Bastard G, Lepage M, Garnero L, Colliot O, Fossati P (2009) Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? Neuroimage 45(1):29–37. doi:10.1016/j.neuroimage.2008.11.006 CrossRefPubMed

36.

Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RSJ (1994) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2(4):189–210CrossRef

37.

Shen S, Szameitat AJ, Sterr A (2007) VBM lesion detection depends on the normalization template: a study using simulated atrophy. Magn Reson Imaging 25(10):1385–1396. doi:10.1016/j.mri.2007.03.025 CrossRefPubMed

38.

Rosario BL, Ziolko SK, Weissfeld LA, Price JC (2008) Assessment of parameter settings for SPM5 spatial normalization of structural MRI data: application to type 2 diabetes. Neuroimage 41(2):363–370. doi:10.1016/j.neuroimage.2008.02.004 CrossRefPubMed

39.

Henley SM, Ridgway GR, Scahill RI, Kloppel S, Tabrizi SJ, Fox NC, Kassubek J (2009) Pitfalls in the use of voxel-based morphometry as a biomarker: examples from Huntington disease. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A1939 PubMed

40.

Ridgway GR, Henley SM, Rohrer JD, Scahill RI, Warren JD, Fox NC (2008) Ten simple rules for reporting voxel-based morphometry studies. Neuroimage 40(4):1429–1435. doi:10.1016/j.neuroimage.2008.01.003 CrossRefPubMed

41.

Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851. doi:10.1016/j.neuroimage.2005.02.018 CrossRefPubMed

42.

Pereira JM, Xiong L, Acosta-Cabronero J, Pengas G, Williams GB, Nestor PJ (2009) Registration accuracy for VBM studies varies according to region and degenerative disease grouping. Neuroimage. doi:10.1016/j.neuroimage.2009.10.068

43.

Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38(1):95–113. doi:10.1016/j.neuroimage.2007.07.007 CrossRefPubMed

44.

Ashburner J, Friston KJ (2009) Computing average shaped tissue probability templates. Neuroimage 45(2):333–341. doi:10.1016/j.neuroimage.2008.12.008 CrossRefPubMed

45.

Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14(1 Pt 1):21–36. doi:10.1006/nimg.2001.0786 CrossRefPubMed

46.

Davatzikos C, Genc A, Xu D, Resnick SM (2001) Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy. Neuroimage 14(6):1361–1369. doi:10.1006/nimg.2001.0937 CrossRefPubMed

47.

Yassa MA, Stark CE (2009) A quantitative evaluation of cross-participant registration techniques for MRI studies of the medial temporal lobe. Neuroimage 44(2):319–327. doi:10.1016/j.neuroimage.2008.09.016 CrossRefPubMed

48.

Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang MC, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3):786–802. doi:10.1016/j.neuroimage.2008.12.037S1053-8119(08)01297-4 CrossRefPubMed

49.

Salmond CH, Ashburner J, Vargha-Khadem F, Connelly A, Gadian DG, Friston KJ (2002) Distributional assumptions in voxel-based morphometry. Neuroimage 17(2):1027–1030. doi:S1053811902911535 CrossRefPubMed

50.

Jones DK, Symms MR, Cercignani M, Howard RJ (2005) The effect of filter size on VBM analyses of DT-MRI data. Neuroimage 26(2):546–554. doi:10.1016/j.neuroimage.2005.02.013 CrossRefPubMed

51.

Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15(1):1–25. doi:10.1002/hbm.1058 CrossRefPubMed

52.

Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15(4):870–878. doi:10.1006/nimg.2001.1037 CrossRefPubMed

53.

Chumbley JR, Friston KJ (2009) False discovery rate revisited: FDR and topological inference using Gaussian random fields. Neuroimage 44(1):62–70. doi:10.1016/j.neuroimage.2008.05.021 CrossRefPubMed

54.

Chumbley J, Worsley K, Flandin G, Friston K (2010) Topological FDR for neuroimaging. Neuroimage 49(4):3057–3064. doi:10.1016/j.neuroimage.2009.10.090 CrossRefPubMed

55.

Hutton C, De Vita E, Ashburner J, Deichmann R, Turner R (2008) Voxel-based cortical thickness measurements in MRI. Neuroimage 40(4):1701–1710. doi:10.1016/j.neuroimage.2008.01.027 CrossRefPubMed

56.

Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9(2):179–194. doi:10.1006/nimg.1998.0395 CrossRefPubMed

57.

Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9(2):195–207. doi:10.1006/nimg.1998.0396 CrossRefPubMed

58.

Fischl B, Sereno MI, Tootell RB, Dale AM (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8(4):272–284. doi:10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4 CrossRefPubMed

59.

Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355. doi:S089662730200569X CrossRefPubMed

60.

Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14(1):11–22CrossRefPubMed

Title: Computational analysis of cerebral cortex
Authors: Hidemasa Takao
Osamu Abe
Kuni Ohtomo
Publication date: 01-08-2010
Publisher: Springer-Verlag
Published in: Neuroradiology / Issue 8/2010
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-010-0715-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Computational analysis of cerebral cortex

Abstract

Introduction

Discussion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Introduction

Discussion

Please log in to get access to this content

Other articles of this Issue 8/2010

The regional distribution of T2-relaxation times in MR images of the substantia nigra and crus cerebri

Diffusion tensor tract-specific analysis of the uncinate fasciculus in patients with amyotrophic lateral sclerosis

Voxel-based analysis of the diffusion tensor

The efficacy of a voxel-based morphometry on the analysis of imaging in schizophrenia, temporal lobe epilepsy, and Alzheimer's disease/mild cognitive impairment: a review

European Society of Neuroradiology (ESNR)

Correlation of quantitative sensorimotor tractography with clinical grade of cerebral palsy