Here is a list of references to consult about the various algorithms used in CIVET. Some of these references also refer to cortical surface applications and analysis methods. Make sure to include the necessary references in your bibliography when you write your next paper. CIVET also produces, as part of its outputs, a file called References.txt that contain the necessary references to cite.

Image-Processing Pipeline


[1] Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J.-S., Mok, K., Ivanov, O., Vincent, R.D., Lepage, C., Lerch, J., Fombonne, E., and Evans, A.C. (2006). The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping, M. Corbetta, ed. (Florence, Italy, NeuroImage). http://www.bic.mni.mcgill.ca/users/yaddab/Yasser-HBM2006-Poster.pdf

[2] Zijdenbos, A.P., Forghani, R., and Evans, A.C. (2002). Automatic Pipeline Analysis of 3-D MRI Data for Clinical Trials: Application to Multiple Sclerosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 21, pp. 1280–1291. http://www.ncbi.nlm.nih.gov/pubmed/12585710

Non-Uniformity Correction


[1] Sled, J.G., Zijdenbos, A.P., and Evans, A.C. (1998). A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data. IEEE Transactions on Medical Imaging 17, pp. 87–97. http://www.ncbi.nlm.nih.gov/pubmed/9617910

Linear Registration


[1] Collins, D.L., Neelin, P., Peters, T.M., and Evans, A.C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography 18, pp. 192–205. http://www.ncbi.nlm.nih.gov/pubmed/8126267

If the ICBM152 linear model is used, cite [2]:
[2] Mazziotta, J.C., Toga, A.W., Evans, A., Fox, P., and Lancaster, J. (1995). A probabilistic atlas of the human brain: Theory and rationale for its development. The international consortium for brain mapping. NeuroImage 2(2), pp. 89–101. http://www.ncbi.nlm.nih.gov/pubmed/9343592

[3] Talairach, J. and Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain, 3-Dimensional Proportional System: An Approach to Cerebral Imaging (New York: Thieme).

If the ICBM152 non-linear 6th generation symmetric model is used, cite [4]:
[4] Grabner, G., Janke, A.L., Budge, M.M., Smith, D., Pruessner, J., and Collins, D.L. (2006). Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv, vol. 9, pp. 58–66. http://www.ncbi.nlm.nih.gov/pubmed/17354756

If the ICBM152 non-linear 2009 model is used, cite [5]:
[5] Fonov, V.S., Evans, A.C., McKinstry, R.C., Almli, C.R., and Collins, D.L. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, Volume 47, Supplement 1, July 2009, Page S102 Organization for Human Brain Mapping 2009 Annual Meeting. http://www.sciencedirect.com/science/article/pii/S1053811909708845

Non-Linear Registration


[1] Collins, D.L., Neelin, P., Peters, T.M., and Evans, A.C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography 18, pp. 192–205. http://www.ncbi.nlm.nih.gov/pubmed/8126267

[2] Collins, D.L., Holmes, C.J., Peters, T.M., and Evans, A.C. (1995). Automatic 3D Model-Based Neuroanatomical Segmentation. Human Brain Mapping, vol. 3, no. 3, pp. 190–208. http://onlinelibrary.wiley.com/doi/10.1002/hbm.460030304/abstract

[3] Collins, D.L., and Evans, A.C. (1997). ANIMAL: Validation and Applications of Non-Linear Registration-Based Segmentation. International Journal of Pattern Recognition and Artificial Intelligence, vol. 11, pp. 1271–1294. http://www.worldscientific.com/doi/abs/10.1142/S0218001497000597

[4] Robbins, S., Evans, A.C., Collins, D.L., and Whitesides, S. (2004). Tuning and Comparing Spatial Normalization Methods. Med Image Anal, vol. 8, no. 3, pp. 311–323. http://www.ncbi.nlm.nih.gov/pubmed/15450225

Refs [2] and [3] describe the original ANIMAL non-linear registration and segmentation procedure. The registration procedure corresponds to what is publicly available in the mni_autoreg package. The fourth paper describes the optimization of original ANIMAL parameters by Steve Robbins - this optimization resulted in a script nlfit, which has been integrated into CIVET.

Brain-Masking


[1] Smith, S.M. (2002). Fast robust automated brain extraction, Human Brain Mapping, 17(3), pp. 143–155. http://www.ncbi.nlm.nih.gov/pubmed/12391568

Tissue Classification


[1] Zijdenbos, A., Forghani, R., and Evans, A. (1998). Automatic Quantification of MS Lesions in 3D MRI Brain Data Sets: Validation of INSECT. In Medical Image Computing and Computer-Assisted Interventation (MICCAI98), W.M. Wells, A. Colchester, and S. Delp, eds. (Cambridge, MA, Springer-Verlag Berlin Heidelberg), pp. 439–448. http://link.springer.com/chapter/10.1007/BFb0056229

[2] Tohka, J., Zijdenbos, A., and Evans, A.C. (2004). Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage, 23(1), pp. 84–97. http://www.ncbi.nlm.nih.gov/pubmed/15325355

ANIMAL Segmentation (available from CIVET-2.0.0 onwards)


If ANIMAL Segmentation is used, cite [1]:
[1] Collins, D.L., Zijdenbos, A.P., Baaré, W.F.C., and Evans, A.C. (1999). ANIMAL+INSECT: Improved Cortical Structure Segmentation. In Proc. of the Annual Symposium on Information Processing in Medical Imaging (A. Kuba, M. Samal, A. Todd-Pokropek, eds.), vol. 1613 of LNCS, Springer, pp. 210–223. http://link.springer.com/chapter/10.1007%2F3-540-48714-X_16

Extra references [2]-[7]:
[2] Evans, A.C., Kamber, M., Collins, D., and Macdonald, D. (1994). An MRI-based probabilistic atlas of neuroanatomy. In: S. Shorvon, D. Fish, F. Andermann, G. Bydder and H. Stefan, Editors, Magnetic Resonance Scanning and Epilepsy, Plenum, New York, pp. 263–274. http://link.springer.com/chapter/10.1007%2F978-1-4615-2546-2_48

[3] Evans, A., Collins, D., and Holmes, C. (1996). Computational approaches to quantifying human neuroanatomical variability. In J. Mazziotta and A. Toga, editors, Brain Mapping: The Methods, Academic Press, pp. 343–361.

[4] Mazziotta, J., Toga, A., Evans, A., Fox, P., and Lancaster, J. (1995). A probabilistic atlas of the human brain: Theory and rationale for its development. The international consortium for brain mapping. NeuroImage 2(2), pp. 89–101. http://www.ncbi.nlm.nih.gov/pubmed/9343592

[5] Collins, D.L., Holmes, C.J., Peters, T.M., and Evans, A.C. (1995). Automatic 3D Model-Based Neuroanatomical Segmentation. Human Brain Mapping, vol. 3, no. 3, pp. 190–208. http://onlinelibrary.wiley.com/doi/10.1002/hbm.460030304/abstract

[6] Evans, A., Collins, D., and Holmes, C. (1995). Automatic 3D regional MRI segmentation and statistical probability anatomy maps. In Quantification of Brain Function: Tracer kinetics and image analysis in brain PET (T. Jones, ed.), pp. 123–130.

[7] Collins, D.L., and Evans, A.C. (1997). ANIMAL: Validation and Applications of Non-Linear Registration-Based Segmentation. International Journal of Pattern Recognition and Artificial Intelligence, vol. 11, pp. 1271–1294. http://www.worldscientific.com/doi/abs/10.1142/S0218001497000597

Cortical Surface Extraction


[1] Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., and 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, pp. 210–221. http://www.ncbi.nlm.nih.gov/pubmed/15896981

[2] Kabani, N., Goualher, G.L., MacDonald, D., and Evans, A.C. (2001). Measurement of Cortical Thickness Using an Automated 3-D Algorithm: A Validation Study. NeuroImage 13, pp. 375–380. http://www.ncbi.nlm.nih.gov/pubmed/11162277

[3] MacDonald, D., Kabani, N., Avis, D., and Evans, A.C. (2000). Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI. NeuroImage 12, pp. 340–356. http://www.ncbi.nlm.nih.gov/pubmed/10944416

Cortical Thickness


[1] Lerch, J.P., and Evans, A.C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. NeuroImage 24, pp. 163–173. http://www.ncbi.nlm.nih.gov/pubmed/15588607

[2] Ad-Dab’bagh, Y., Singh, V., Robbins, S., Lerch, J., Lyttelton, O., Fombonne, E., and Evans, A.C. (2005). Native space cortical thickness measurement and the absence of correlation to cerebral volume. In Proceedings of the 11th Annual Meeting of the Organization for Human Brain Mapping, K. Zilles, ed. (Toronto, NeuroImage). http://www.oocities.org/yaddab/HBM2005_nativeCT.pdf

Surface Registration


[1] Lyttelton, O., Boucher, M., Robbins, S., and Evans, A. (2007). An unbiased iterative group registration template for cortical surface analysis. NeuroImage 34, pp. 1535–1544. http://www.ncbi.nlm.nih.gov/pubmed/17188895

[2] Robbins, S.M. (2004). Anatomical Standardization of the Human Brain in Euclidean 3-Space and on the Cortical 2-Manifold. Ph.D. Thesis, School of Computer Science (Montreal, McGill University). http://www.sumost.ca/steve/publications/phd.pdf

[3] Boucher, M., Whitesides, S. and Evans, A. (2009). Depth potential function for folding pattern representation, registration and analysis. Medical Image Analysis 13 (2), pp. 203–214. http://www.ncbi.nlm.nih.gov/pubmed/18996043

Surface Parcellation


If the AAL surface parcellation is used, cite [1]:
[1] Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., and Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, pp. 273–289. http://www.sciencedirect.com/science/article/pii/S1053811901909784

If the DKT-40 surface parcellation is used, cite [2]:
[2] Klein, A. and Tourville, J. (2012). 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Front Neurosic. 6 (171). http://journal.frontiersin.org/Journal/10.3389/fnins.2012.00171/full

Surface Diffusion Smoothing


[1] Boucher, M., Whitesides, S. and Evans, A. (2009). Depth potential function for folding pattern representation, registration and analysis. Medical Image Analysis 13 (2), pp. 203–214. http://www.ncbi.nlm.nih.gov/pubmed/18996043

Analysis Methods and Tools


[1] Ihaka, R., and Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics 5, pp. 299–314. http://www.jstor.org/stable/1390807

[2] Benjamini, Y., and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B (Methodological) 57, pp. 289–300. http://www.jstor.org/stable/2346101

[3] Genovese, C.R., Lazar, N.A., and Nichols, T. (2002). Thresholding of statistical maps in Functional Neuroimaging Using the False Discovery Rate. NeuroImage 15, pp. 870–878. http://www.ncbi.nlm.nih.gov/pubmed/11906227

[4] Ashburner, J., and Friston, K.J. (2000). Voxel-based morphometry: The methods. NeuroImage 11, pp. 805–821. http://www.ncbi.nlm.nih.gov/pubmed/10860804

[5] Wright, I.C., McGuire, P.K., Poline, J.B., Travere, J.M., Murray, R.M., Frith, C.D., Frackowiak, R.S., and Friston, K.J. (1995). A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage 2, pp. 244–252. http://www.ncbi.nlm.nih.gov/pubmed/9343609

[6] Luders, E., Gaser, C., Jancke, L., and Schlaug, G. (2004). A voxel-based approach to gray matter asymmetries. Neuroimage 22, pp. 656–664. http://www.ncbi.nlm.nih.gov/pubmed/15193594

[7] Davatzikos, C. (2004). Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 23(1), pp. 17–20. http://www.ncbi.nlm.nih.gov/pubmed/15325347

[8] Good, C.D., Scahill, R.I., Fox, N.C., Ashburner, J., Friston, K.J., Chan, D., Crum, W.R., Rossor, M.N., and Frackowiak, R.S. (2002). Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias. NeuroImage 17 (1), pp. 29–46. http://www.ncbi.nlm.nih.gov/pubmed/12482066

[9] Worsley, K.J., Marrett, S., Neelin, P., Vandal, A.C., Friston, K.J., and Evans, A.C. (1996). A unified statistical approach for determining significant voxels in images of cerebral activation. Human Brain Mapping 4, pp. 58–73. http://www.ncbi.nlm.nih.gov/pubmed/20408186 http://onlinelibrary.wiley.com/doi/10.1002/1097-01934:1<58::AID-HBM4>3.0.CO;2-O/abstract

[10] Worsley, K.J., Andermann, M., Koulis, T., MacDonald, D., and Evans, A.C. (1999). Detecting changes in non-isotropic images. Human Brain Mapping 8, pp. 98–101. http://www.ncbi.nlm.nih.gov/pubmed/10524599

[11] Ashburner, J., Hutton, C., Frackowiak, R.S.J., Johnsrude, I., Price, C., and Friston, K.J. (1998). Identifying global anatomical differences: Deformation-based morphometry. Human Brain Mapping 6, pp. 348–357. http://www.ncbi.nlm.nih.gov/pubmed/9788071

[12] Chung, M.K., Worsley, K.J., Paus, T., Cherif, C., Collins, D.L., Giedd, J.N., Rapoport, J.L., and Evans, A.C. (2001). A unified statistical approach to deformation-based morphometry. NeuroImage 14(3), pp. 595–606. http://www.ncbi.nlm.nih.gov/pubmed/11506533

[13] Le Goualher, G., Argenti, A.M., Duyme, M., Baaré, W.F., Hulshoff, Pol, H.E., Boomsma, D.I., Zouaoui, A., Barillot, C., and Evans, A.C. (2000). Statistical sulcal shape comparisons: application to the detection of genetic encoding of the central sulcus shape. Neuroimage 11(5 Pt 1), pp. 564–574. http://www.ncbi.nlm.nih.gov/pubmed/10806042


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