Cortical thickness analysis – The methods
DOI:
https://doi.org/10.7577/radopen.1529Abstract
Over the course of the last century, the cerebral cortex has been of interest for neuroscientists, and the work with mapping and measuring the cortex started in the early 1900s (Brodmann 1909).
The advances in medical imaging over the recent decades has given the opportunity to measure the cortex in vivo, and several algorithms and types of software applications has been developed for this purpose. These software applications can be used to execute complex analysis to determine both cortex thickness and density.
The algorithms and software applications presented in this paper are the ones most utilized to measure cortical thickness today, and include four software applications and two algorithms. The basic principles of these tools will be outlined, as well as their strengths and weaknesses.
References
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https://doi.org/10.1016/j.pscychresns.2015.07.010
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https://doi.org/10.1016/j.pscychresns.2015.07.005
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Lerch, J. P., & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage,24(1), 163-173.
https://doi.org/10.1016/j.neuroimage.2004.07.045
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https://doi.org/10.1016/j.neurobiolaging.2006.09.013
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https://doi.org/10.1016/j.brainres.2011.09.057
Li Y, Wang Y, Wu G, Shi F, Zhou L, Lin W, & Shen D. (2012) Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiology of Aging, 33;(2):427.e15–427.e30
https://doi.org/10.1016/j.neurobiolaging.2010.11.008
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https://doi.org/10.1016/j.neuroimage.2010.12.013
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https://doi.org/10.1080/10867651.1997.10487472
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https://doi.org/10.1093/cercor/bhq095
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https://doi.org/10.1016/j.nicl.2014.08.005
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https://doi.org/10.1016/j.pscychresns.2015.07.010
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https://doi.org/10.1016/j.neuroimage.2010.06.032
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https://doi.org/10.1117/12.131063
Dale, A. M., & Sereno, M. I. (1993). Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. Journal of cognitive neuroscience, 5 (2), 162-176.
https://doi.org/10.1162/jocn.1993.5.2.162
DaSilva AF, Becerra L, Pendse G, Chizh B, Tully S, Borsook D. (2008) Colocalized Structural and Functional Changes in the Cortex of Patients with Trigeminal Neuropathic Pain. PLoS ONE, 3;(10):e3396.1 –e3396.14, 2387-3345:61
https://doi.org/10.1371/journal.pone.0003396
Duncan, J. S., Papademetris, X., Yang, J., Jackowski, M., Zeng, X., & Staib, L. H. (2004). Geometric strategies for neuroanatomic analysis from MRI. Neuroimage, 23, S34-S45.
https://doi.org/10.1016/j.neuroimage.2004.07.027
Erpelding, N., Moayedi, M., & Davis, K. D. (2012). Cortical thickness correlates of pain and temperature sensitivity. PAIN®,153(8), 1602-1609.
https://doi.org/10.1016/j.pain.2012.03.012
Fernández, Jaén, A., López,Martín, S., Albert, J., Fernández, Mayoralas, D. M., Fernández Perrone, A. L., de La Pe-a, M. J.,& Mu-oz Jare-o, N. (2015). Cortical thickness differences in the prefrontal cortex in children and adolescents with ADHD in relation to dopamine transporter (DAT1) genotype. Psychiatry Research: Neuroimaging, 233 (3), 409-417.
https://doi.org/10.1016/j.pscychresns.2015.07.005
Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
https://doi.org/10.1016/j.neuroimage.2012.01.021
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences. 97(20), 11050-11055.
https://doi.org/10.1073/pnas.200033797
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., ... & Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341-355.
https://doi.org/10.1016/S0896-6273(02)00569-X
Fischl, B., Salat, D. H., van der Kouwe, A. J., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004). Sequence-independent segmentation of magnetic resonance images. Neuroimage, 23, S69-S84.
https://doi.org/10.1016/j.neuroimage.2004.07.016
Frasnelli J, Fark T, Lehmann J, Gerber J, Hummel T. (2013) Brain structure is changed in congenital anosmia. NeuroImage Giakoumatos, C. I., Nanda, P., Mathew, I. T., Tandon, N., Shah, J., Bishop, J. R., ... & Keshavan, M. S. (2015). Effects of lithium on cortical thickness and hippocampal subfield volumes in psychotic bipolar disorder. Journal of psychiatric research,61, 180-187.
Goebel, R. (2012). BrainVoyager—past, present, future. Neuroimage, 62(2), 748-756.
https://doi.org/10.1016/j.neuroimage.2012.01.083
Goebel, R. Brainvoyager QX Users Guide[website],Maastricht; [last updated 2014, quoted on the 05.10.2015]. Available from: http://www.brainvoyager.com/bvqx/doc/UsersGuide/BrainVoyagerQXUsersGuide.html
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https://doi.org/10.1097/00004728-199809000-00030
Green, G. (2008). An Essay on the Application of mathematical Analysis to the theories of Electricity and Magnetism. arXiv preprint arXiv:0807.0088
Han, X., Pham, D. L., Tosun, D., Rettmann, M. E., Xu, C., & Prince, J. L. (2004). CRUISE: cortical reconstruction using implicit surface evolution. NeuroImage, 23(3), 997-1012.
https://doi.org/10.1016/j.neuroimage.2004.06.043
Hawking, SW. & Ellis, G. F. R. (1973). The large scale structure of space-time (Vol. 1). Cambridge university press. Jones, S. E., Buchbinder, B. R., & Aharon, I. (2000). Three dimensional mapping of cortical thickness using Laplace's Equation. Human brain mapping, 11(1), 12-32.
https://doi.org/10.1017/CBO9780511524646
Kaufman, Z. Freesurfer [website],Charlestown: Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging ; [last updated 24.08.2015, quoted on the 30.09.2015]. Available from: http://freesurfer.net/fswiki/FsTutorial/OutputData_freeview
Kapellou, O., Counsell, S. J., Kennea, N., Dyet, L., Saeed, N., Stark, J & Edwards, A. D. (2006). Abnormal cortical development after premature birth shown by altered allometric scaling of brain growth. PLoS Med, 3(8), e265.
https://doi.org/10.1371/journal.pmed.0030265
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International journal of computer vision, 1(4), 321-331.
https://doi.org/10.1007/BF00133570
Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dab'bagh, Y., MacDonald, D., ... & Evans, A. C. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage, 27(1), 210-221.
https://doi.org/10.1016/j.neuroimage.2005.03.036
Lerch, J. P., & Evans, A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage,24(1), 163-173.
https://doi.org/10.1016/j.neuroimage.2004.07.045
Lerch, J. P., Pruessner, J., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H., & Evans, A. C. (2008). Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiology of aging, 29(1), 23-30.
https://doi.org/10.1016/j.neurobiolaging.2006.09.013
Li J, Li W, Xian J, Li Y, Liu Z & Liu S et.al. (2012) Cortical thickness analysis and optimized voxel based morphometry in children and adolescents with prelingually profound sensorineural hearing loss. Brain Research, 1430: 35 –42
https://doi.org/10.1016/j.brainres.2011.09.057
Li Y, Wang Y, Wu G, Shi F, Zhou L, Lin W, & Shen D. (2012) Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiology of Aging, 33;(2):427.e15–427.e30
https://doi.org/10.1016/j.neurobiolaging.2010.11.008
Lorensen, W. E., & Cline, H. E. (1987, August). Marching cubes: A high resolution 3D surface construction algorithm. In ACM siggraph computer graphics (Vol. 21, No. 4, pp. 163-169). ACM.
https://doi.org/10.1145/37402.37422
MacDonald, D., Kabani, N., Avis, D., & Evans, A. C. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage,12(3), 340-356.
https://doi.org/10.1006/nimg.1999.0534
MacDonald, J. D. (1997). A method for identifying geometrically simple surfaces from three
MacDonald, D., Avis, D., & Evans, A. C. (1994, September). Multiple surface identification and matching in magnetic resonance images. In Visualization in Biomedical Computing 1994 (pp. 160-169). International Society for Optics and Photonics.
https://doi.org/10.1117/12.185176
Madsen, S. K., Zai, A., Pirnia, T., Arienzo, D., Zhan, L., Moody, T. D., ... & Feusner, J. D. (2015). Cortical thickness and brain volumetric analysis in body dysmorphic disorder. Psychiatry Research: Neuroimaging, 232(1), 115-122.
https://doi.org/10.1016/j.pscychresns.2015.02.003
Martinussen, M., Fischl, B., Larsson, H. B., Skranes, J., Kulseng, S., Vangberg, T. R., ... & Dale, A. M. (2005). Cerebral cortex thickness in 15-year-old adolescents with low birth weight measured by an automated MRI- based method. Brain, 128(11), 2588-2596.
https://doi.org/10.1093/brain/awh610
McConnel Brain Imaging Center, Montreal Neurological Institute. CIVET [website],
Moayedi, M., Weissman-Fogel, I., Crawley, A. P., Goldberg, M. B., Freeman, B. V., Tenenbaum, H. C., & Davis, K. D. (2011). Contribution of chronic pain and neuroticism to abnormal forebrain gray matter in patients with temporomandibular disorder. Neuroimage, 55(1), 277-286.
https://doi.org/10.1016/j.neuroimage.2010.12.013
Möller, T. (1997). A fast triangle-triangle intersection test. Journal of graphics tools, 2(2), 25-30.
https://doi.org/10.1080/10867651.1997.10487472
Nagy, Z., Lagercrantz, H., &Hutton, C. (2011). Effects of preterm birth on cortical thickness measured in adolescence. Cerebral Cortex, 21(2), 300-306.
https://doi.org/10.1093/cercor/bhq095
Nosarti, C., Nam, K. W., Walshe, M., Murray, R. M., Cuddy, M., Rifkin, L., & Allin, M. P. (2014). Preterm birth and structural brain alterations in early adulthood. NeuroImage: Clinical, 6, 180-191.
https://doi.org/10.1016/j.nicl.2014.08.005
Oertel-Knöchel, V., Reuter, J., Reinke, B., Marbach, K., Feddern, R., Alves, G., ... & Knöchel, C. (2015). Association between age of disease-onset, cognitive performance and cortical thickness in bipolar disorders. Journal of affective disorders,174, 627-635.
https://doi.org/10.1016/j.jad.2014.10.060
Querbes, O., Aubry, F., Pariente, J., Lotterie, J. A., Démonet, J. F., Duret, V., ... &Alzheimer's Disease Neuroimaging Initiative. (2009). Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain, 132(8), 2036-2047.
https://doi.org/10.1093/brain/awp105
Richter, J., Poustka, L., Vomstein, K., Haffner, J., Parzer, P., Stieltjes, B., & Henze, R. (2015).Volumetric alterations in the heteromodal association cortex in children with autism spectrum disorder. European Psychiatry,30(2), 214-220.
https://doi.org/10.1016/j.eurpsy.2014.11.005
Rudra, A. K., Sen, M., Chowdhury, A. S., Elnakib, A., &El-Baz, A. (2011). 3D Graph cut with new edge weights for cerebral white matter segmentation. Pattern Recognition Letters, 32(7), 941-947.
https://doi.org/10.1016/j.patrec.2010.12.013
Salat, DH, Buckner RL, Snyder AZ, Greve DN, Desikan RSR, & Busa E et al. Thinning of the Cerebral Cortex in Aging. Cerebral Cortex, 2004, mar, 14;(7): 721 –730
https://doi.org/10.1093/cercor/bhh032
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2015-11-30
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Gransjøen, A. M. (2015). Cortical thickness analysis – The methods. Radiography Open, 2(1), 52–64. https://doi.org/10.7577/radopen.1529
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