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Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space
Abstract
OBJECTIVE: In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. MATERIALS AND METHODS: The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. RESULTS: With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. CONCLUSION: We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
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“It has been shown that the automatic stereotaxic transformation is as accurate as the manual procedure but shows higher stability (Collins et al., 1994).”
From Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood (2005) · cites this paper as Collins et al., 1994
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“, 1998) and linear stereotaxic transformation (Collins et al., 1994) into coordinates based on the Talairach atlas (Talairach and Tournoux, 1988).”
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“The native MR volumes were corrected for image intensity non-uniformity (Sled et al., 1998), and linearly and non-linearly transformed into standardizedMNI space using automated featurematching to the ICBM152 template (Collins et al., 1994).”
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“Finally, all of the runs were transformed (Collins et al., 1994) to conform to a standard stereotaxic atlas (Talairach and Tournoux, 1988) with a final isometric voxel size of 2.5 mm3.”
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Self-citationGoldman, A. L., Pezawas, L., Mattay, V. S., Fischl, B. R., Verchinski, B. A., Zoltick, B. J., Weinberger, D. R., & Meyer‐Lindenberg, A. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2007.06.006 · Cited by 148
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“Z-score, correlation coefficient; MNI, Montreal Neurological Institute Atlas coordinates [23].”
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“Preprocessing of the data used SPM5 realignment, reslicing with sinc interpolation, normalisation to the MNI coordinate system (Montreal Neurological Institute) (Collins et al., 1994), and spatial smoothing with a 6-mm full width at half maximum isotropic Gaussian kernel.”
Hua, X., Lee, S., Yanovsky, I., Leow, A. D., Chou, Y.-Y., Ho, A. J., Gutman, B., Toga, A. W., Jack, C. R., Bernstein, M. A., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.07.011 · Cited by 145
“To adjust for linear drifts in position and scale within the same subject, the follow-up scan was linearly registered to its matching baseline scan using a 6-parameter (6P) or 9-parameter (9P) registration, driven by a mutual information (MI) cost function (Collins et al., 1994).”
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Altered cerebral response to noxious heat stimulation in patients with somatoform pain disorder
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“T1 images were registered to the ICBM152 nonlinear sixth generation template with a 12-parameter linear transformation (Collins et al. 1994; Grabner et al. 2006), RF inhomogeneity corrected (Sled et al. 1998), and tissue classiWed (Zijdenbos et al. 1998; Tohka et al. 2004).”
Ischemic injury detected by diffusion imaging 11 minutes after stroke
Hjort, N., Christensen, S., Sølling, C., Ashkanian, M., Wu, O., Røhl, L., Gyldensted, C., Andersen, G., & Østergaard, L. (2005) · Annals of Neurology · doi:10.1002/ana.20595 · Cited by 149
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MethodSegmentation of multiple sclerosis lesions in MR images: a review
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“It includes five main steps: (1) preprocessing including interslice intensity normalization, anisotropic noise reduction, N3 in-homogeneity reduction, stereotaxic registration of one of the image modalities to a streotaxic target [81]; (2) ANN tissue classification includNeuroradiology (2012) 54:299–320 309”
MethodTorres, U. S., Portela-Oliveira, E., Borgwardt, S., & Busatto, G. F. (2013) · BMC Psychiatry · doi:10.1186/1471-244x-13-342 · Cited by 147
“We selected studies considering the following inclusion criteria: a) original research articles; b) quantitative automated whole-brain analyses were performed; c) the VBM method was used [26,63]; c) the samples included subjects with schizophrenia using typical or atypical antipsychotics, and comparisons were performe…”
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Weierich, M. R., Wright, C. I., Negreira, A. M., Dickerson, B. C., & Barrett, L. F. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.047 · Cited by 157
“For Talairach spatial normalization, procedures developed and distributed by Montreal Neurological Institute were used to compute a transformation matrix from the high-resolution MPRAGE volumes (Collins et al., 1994).”
MethodBaumgärtner, U., Iannetti, G. D., Zambreanu, L., Stoeter, P., Treede, R.-D., & Tracey, I. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00253.2010 · Cited by 145
“For the purpose of the group-level analysis, registration of low-resolution functional images to the corresponding high-resolution structural images was performed for each subject (Jenkinson and Smith 2001) and followed by registration to a standard brain (MNI template) (Collins et al. 1994).”
MethodThe effect of template choice on morphometric analysis of pediatric brain data
Yoon, U., Fonov, V., Pérusse, D., & Evans, A. C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.046 · Cited by 142
“…(International Consortium for Brain Mapping), which was generated by averaging anatomical magnetic resonance imaging (MRI) data of 152 healthy normal adults (86 male, 66 female, 18–44 years old) after corrections for overall brain size and orientation (Collins et al., 1994; Grabner et al., 2006).”
InfluentialSelf-citationCerebellum and M1 interaction during early learning of timed motor sequences
Penhune, V. B., & Doyon, J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.041 · Cited by 151
“, 1993) and automatically resampled (Collins et al., 1994) to fit the standardized stereotaxic space of Talairach and Tournoux (1988) as defined by the MNI 152 template.”
MethodSpatial normalization of diffusion tensor fields
Xu, D., Mori, S., Shen, D. G., van Zijl, P. C. M., & Davatzikos, C. A. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10489 · Cited by 144
An Automated Registration Algorithm for Measuring MRI Subcortical Brain Structures
Iosifescu, D. V., Shenton, M. E., Warfield, S. K., Kikinis, R., Dengler, J., Jolesz, F. A., & McCarley, R. W. (1997) · NeuroImage · doi:10.1006/nimg.1997.0274 · Cited by 144
“Also, other research teams (e.g., Andreasen et al., 1996; Collins et al., 1994) have registered MR brain images into standard Talairach coordinates.”
BackgroundFast, Fully Automated Global and Local Magnetic Field Optimization for fMRI of the Human Brain
WILSON, J. L., Jenkinson, M., de Araújo, I., Kringelbach, M. L., Rolls, E. T., & Jezzard, P. (2002) · NeuroImage · doi:10.1006/nimg.2002.1172 · Cited by 144
“Following the olfaction shim, two significant clusters were found with peaks at [x, y, z] [ 6, 66, 10] and [x, y, z] [ 6, 40, 26] in the normalized Montreal Neurological Institute (MNI) mean brain (Collins et al., 1994) and maximal Z scores of 4.”
MethodInfluentialChui, H. T., Christensen, B. K., Zipursky, R. B., Richards, B. A., Hanratty, M. K., Kabani, N. J., Mikulis, D. J., & Katzman, D. K. (2008) · PEDIATRICS · doi:10.1542/peds.2008-0170 · Cited by 142
Arévalo, A. L., Baldo, J. V., & Dronkers, N. F. (2010) · Cortex · doi:10.1016/j.cortex.2010.06.001 · Cited by 145
“The templateswere then digitizedwith in-house software and non-linearly transformed into MNI space (Collins et al., 1994) using SPM5 running on Matlab software (Mathworks, Natick, MA).”
MethodMadsen, S. K., Ho, A. J., Hua, X., Saharan, P., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.002 · Cited by 146
“…all scans were linearly registered to the stereotaxic space defined by the International Consortium for Brain Mapping (ICBM-53) (Mazziotta et al., 2001) with a 9-parameter (9P) transformation (three translations, three rotations, three scales) using the Minctracc algorithm (Collins et al., 1994).”
MR-Based Statistical Atlas of the Göttingen Minipig Brain
Watanabe, H., Andersen, F., Simonsen, C. Z., Evans, S. M., Gjedde, A., & Cumming, P. (2001) · NeuroImage · doi:10.1006/nimg.2001.0910 · Cited by 130
Antel, S. B., Collins, D. L., Bernasconi, N., Andermann, F., Shinghal, R., Kearney, R. E., Arnold, D. L., & Bernasconi, A. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00226-x · Cited by 142
Self-citationCortical Thinning in Vascular Mild Cognitive Impairment and Vascular Dementia of Subcortical Type
Seo, S. W., Ahn, J., Yoon, U., Im, K., Lee, J., Kim, S. T., Ahn, H., Chin, J., Jeong, Y., & Na, D. L. (2009) · Journal of Neuroimaging · doi:10.1111/j.1552-6569.2008.00293.x · Cited by 144
Contrasting Cortical Activity Associated with Category Memory and Recognition MemoryPreprint
Reber, P. J., Stark, C. E. L., & Squire, L. R. (1998) · Learning & Memory · doi:10.1101/lm.5.6.420 · Cited by 143
Mapping the involvement of BA 4a and 4p during Motor Imagery
Sharma, N., Jones, P. S., Carpenter, T. A., & Baron, J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.009 · Cited by 142
“The images were transformed into the standard space of the Montreal Neurological Institute MRI template (Collins et al., 1994).”
A novel 3D mouse embryo atlas based on micro-CT
Wong, M. D., Dorr, A. E., Walls, J. R., Lerch, J. P., & Henkelman, R. M. (2012) · Development · doi:10.1242/dev.082016 · Cited by 141
“The image registration was D E V E LO P M E N T executed using MNI AUTOREG tools (Collins et al., 1994).”
MethodGiedd, J. N., CLASEN, L. S., Wallace, G. L., Lenroot, R., Lerch, J. P., Wells, E. M., Blumenthal, J. D., Nelson, J. E., Tossell, J. W., Stayer, C., et al. (2007) · PEDIATRICS · doi:10.1542/peds.2005-2969 · Cited by 141
Self-citationConnectivity-Based Parcellation of the Human Temporal Pole Using Diffusion Tensor Imaging
Fan, L., Wang, J., Zhang, Y., Han, W., Yu, C., & Jiang, T. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht196 · Cited by 144
“Next, the linear transformed T1-weighted images were nonlinearly warped into the MNI template (Collins et al. 1994).”
MethodCortical Thickness Reduction in Individuals at Ultra-High-Risk for Psychosis
Jung, W. H., Kim, J. S., Jang, J. H., Choi, J., Jung, M. H., Park, J., Han, J., Choi, C., Kang, D., Chung, C. K., et al. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbp151 · Cited by 146
“Spatial normalization to a stereotaxic space was performed using a 9-parameter linear registration.(35) The registered and corrected volumes were classified as white matter, GM, cerebrospinal fluid, and background using an advanced neural net classifier.”
MethodThe role of the left Brodmann's areas 44 and 45 in reading words and pseudowords
Heim, S., Alter, K., Ischebeck, A. K., Amunts, K., Eickhoff, S. B., Mohlberg, H., Zilles, K., von Cramon, D. Y., & Friederici, A. D. (2005) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2005.09.022 · Cited by 140
“Then, the individual anatomical 3D data set was normalised to the high-resolution data set of the MNI single subject template [14,23,36] using linear and non-linear transformations [3,4].”
MethodMarangolo, P., Marinelli, C. V., Bonifazi, S., Fiori, V., Ceravolo, M. G., Provinciali, L., & Tomaiuolo, F. (2011) · Behavioural Brain Research · doi:10.1016/j.bbr.2011.08.008 · Cited by 143
Guilt-Specific Processing in the Prefrontal Cortex
Wagner, U., N’Diaye, K., Ethofer, T., & Vuilleumier, P. O. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr016 · Cited by 146
Tensor-Based Cortical Surface Morphometry via Weighted Spherical Harmonic Representation
Chung, M. K., Dalton, K. M., & Davidson, R. J. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.918338 · Cited by 139
“Image intensity nonuniformity was corrected using a nonparametric nonuniform intensity normalization method [42] and then the image was spatially normalized into the Montreal Neurological Institute (MNI), Montreal, QC, Canada, stereotaxic space using a global affine transformation [14].”
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Self-citationStatistical Analysis of Hippocampal Asymmetry in Schizophrenia
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Sex differences in the human brain’s response to hunger and satiation
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“…1) correction of inhomogeneities using N3 (Sled et al., 1998), 2) registration to the stereotaxic space using a linear transform to the ICBM152 template (1x1x1 mm ³ voxel size) (Collins et al., 1994) and 3) cross-normalization of the MRI intensity using the method proposed in Nyul and Udupa (2000).”
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“(Talairach and Tournoux, 1988; Collins et al., 1994) with the structural MRI of a patient with a standard CAH resection.”
BackgroundMethodPositional and surface area asymmetry of the human cerebral cortex
Lyttelton, O. C., Karama, S., Ad-Dab’bagh, Y., Zatorre, R. J., Carbonell, F., Worsley, K. J., & Evans, A. C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.063 · Cited by 138
“The main pipeline processing steps: i) linearly register native MR images to standardized MNI–Talairach space based on the ICBM152 data set (Collins et al., 1994; Mazziotta et al., 1995, 2001; Talairach and Tournoux, 1988).”
MethodSelf-citationMRI Phenotyping of Genetically Altered Mice
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“ca/ software) and ANIMAL registration software (7, 8) (see Note 4).”
MethodExamining overlap and homogeneity in ASD, ADHD, and OCD: a data-driven, diagnosis-agnostic approach
Kushki, A., Anagnostou, E. A., Hammill, C. F., Duez, P., Brian, J. A., Iaboni, A., Schachar, R. J., Crosbie, J., Arnold, P. D., & Lerch, J. P. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0631-2 · Cited by 160
van der Werf, Y. D., & Paus, T. (2006) · Experimental Brain Research · doi:10.1007/s00221-006-0551-2 · Cited by 139
“Scans were transformed from native space to standardized stereotaxic space (Talairach and Tournoux 1988) using an automated feature-matching algorithm (Collins et al. 1994).”
MethodPaus, T., Nawaz-Khan, I., Leonard, G. T., Perron, M., Pike, G. B., Pitiot, A., Richer, L., Susman, E. J., Veillette, S., & Pausová, Z. (2009) · Hormones and Behavior · doi:10.1016/j.yhbeh.2009.08.004 · Cited by 140
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Westman, E., Simmons, A., Zhang, Y., Muehlboeck, J., Tunnard, C., Liu, Y., Collins, D. L., Evans, A. C., Mecocci, P., Vellas, B. J., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.044 · Cited by 141
“Anatomical labels are defined in the new volume by interpolation from the original labels, via a 3D deformation field (Collins et al., 1994).”
BackgroundSelf-citationla Fougère, C. J., Grant, S., Kostikov, A. P., Schirrmacher, R., Gravel, P., Schipper, H. M., Reader, A. J., Evans, A. C., & Thiel, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.015 · Cited by 138
“, 2001) using a twelve parameter linear transformation (Collins et al., 1994), an intensity correction for signal intensity nonuniformity (Sled et al.”
Self-citationCornelissen, P. L., Kringelbach, M. L., Ellis, A. W., Whitney, C., Holliday, I. E., & Hansen, P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005359 · Cited by 138
“Group-level statistical maps for each condition, for each time window were generated by first registering the individual participant t statistic maps to MNI standard space [87], then combining these transformed maps across participants for each time window and frequency band.”
MethodStriatal Dopamine Responses to Intranasal Cocaine Self-Administration in Humans
Cox, S. M. L., Benkelfat, C., Dagher, A., Delaney, J. S., Durand, F., McKenzie, S. A., Kolivakis, T., Casey, K. F., & Leyton, M. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.01.021 · Cited by 142
Admon, R., Leykin, D., Lubin, G., Engert, V., Andrews, J., Pruessner, J., & Hendler, T. (2013) · Human Brain Mapping · doi:10.1002/hbm.22100 · Cited by 138
“, 1998], linear stereotaxic transformation [Collins et al., 1994] into coordinates based on the Talairach atlas [Talairach and Tournoux, 1988], and resampling onto a 1-mm rectangular voxel grid using a linear interpolation kernel.”
MethodJärnum, H., Eskildsen, S. F., Steffensen, E. G., Lundbye‐Christensen, S., Simonsen, C. W., Thomsen, I. S., Fründ, T., Théberge, J., & Larsson, E. (2011) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2011.01766.x · Cited by 142
“The 3D T1-weighted MRI data were registered to the ICBM152 model (31) using an automatic iterative multiresolution approach (32, 33).”
MethodOverwalle, F. V., Ma, Q.-Y., & Heleven, E. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa124 · Cited by 123
Narr, K. L., Thompson, P. M., Sharma, T., Moussai, J., Blanton, R. E., Anvar, B., Edris, A., Krupp, R., Rayman, J., Khaledy, M., et al. (2001) · Biological Psychiatry · doi:10.1016/s0006-3223(00)01120-3 · Cited by 137
“…complicates the issue of using an average anatomical template; 3) linear AC-PC scaling can be statistically removed from the data; 4) results are easily interpreted; and 5) widely used digital atlases rely on linear scaling to map composite MR data into stereotaxic space (Collins et al 1994).”
Effects of chewing in working memory processing
Hirano, Y., Obata, T., Kashikura, K., Nonaka, H., Tachibana, A., Ikehira, H., & Onozuka, M. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2008.03.033 · Cited by 138
“Four hundred successive functional images obtained from each subject were normalized to the MNI template [7], and spatially smoothed by 8-mm Gaussian kernel.”
MethodBiophysical model for integrating neuronal activity, EEG, fMRI and metabolism
Sotero, R. C., & Trujillo‐Barreto, N. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.001 · Cited by 136
“In the simulations presented in this paper, 71 different brain areas were defined based on the Probabilistic MRI Atlas (PMA) produced by the Montreal Neurological Institute (Collins et al., 1994).”
MethodIntegration of fuzzy spatial relations in deformable models—Application to brain MRI segmentation
Colliot, O., Cámara, Ó., & Isabelle Bloch, A. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.02.022 · Cited by 135
“Images are first linearly registered into a common stereotaxic space [46], which allows roughly normalizing the position and size of structures that are used to construct the initialization.”
MethodImage registration: an essential tool for nuclear medicine
Hutton, B. F., Braun, M., Thurfjell, L., & Lau, D. Y. (2002) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-001-0700-6 · Cited by 136
Establishing a Normative Atlas of the Human Lung
Li, B., Christensen, G. E., Hoffman, E. A., McLennan, G., & Reinhardt, J. M. (2003) · Academic Radiology · doi:10.1016/s1076-6332(03)80099-5 · Cited by 137
Neuroimaging Study Designs, Computational Analyses and Data Provenance Using the LONI Pipeline
Dinov, I. D., Lozev, K., Petrosyan, P., Liu, Z., Eggert, P., Pierce, J., Zamanyan, A., Chakrapani, S., Horn, J. D. V., Parker, D. S., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013070 · Cited by 136
Köhler, S., Paus, T., Buckner, R., & Milner, B. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/089892904322984490 · Cited by 144
The neural and cognitive time course of theory of mind
McCleery, J. P., Surtees, A. D. R., Graham, K. A., Richards, J. E., & Apperly, I. A. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.1392-11.2011 · Cited by 134
“The average MRI was rotated so that its orientation was approximate to that of the ICBM-152 template, which is oriented to MNI space (Collins et al., 1994, MNI-305 defined; Mazziotta et al., 2001, ICBM-152 defined; Joshi et al., 2004).”
Method3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects
Apostolova, L. G., Hwang, K., Andrawis, J. P., Green, A. E., Babakchanian, S., Morra, J. H., Cummings, J. L., Toga, A. W., Trojanowski, J. Q., Shaw, L. M. J., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.003 · Cited by 144
Orbitofrontal cortex: A key prefrontal region for encoding information.
Frey, S., & Petrides, M. (2000) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.140543497 · Cited by 134
VANDERWEE, N., Ramsey, N. F., Jansma, J. M., Denys, D. A. J. P., van Megen, H. J. G. M., Westenberg, H. G. M., & Kahn, R. S. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.05.001 · Cited by 139
Reproducibility of primary motor cortex somatotopy under controlled conditions.
Alkadhi, H., Crelier, G. R., Boendermaker, S. H., Golay, X., Hepp‐Reymond, M., & Kollias, S. (2002) · PubMed Central · Cited by 136
Ellegood, J., Babineau, B. A., Henkelman, R. M., Lerch, J. P., & Crawley, J. N. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.029 · Cited by 135
“All registrations were performed using a combination of the mni_autoreg tools (Collins et al., 1994) and ANTS (Avants et al.”
MethodSimmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Wahlund, L., Soininen, H. S., Lovestone, S., et al. (2010) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2491 · Cited by 134
“Anatomical labels are defined in the new volume by interpolation from the original labels, via the 3D deformation field (Collins et al., 1994, 1995).”
Self-citationPointing with the eyes: The role of gaze in communicating danger
Hadjikhani, N. K., Hoge, R. D., Snyder, J., & de Gelder, B. (2008) · Brain and Cognition · doi:10.1016/j.bandc.2008.01.008 · Cited by 140
“Volumes containing the estimated effect size and associated standard error for the primary contrast (gaze diversion vs. direct gaze) at each voxel were then registered to a standard space based on the MNI Talairach template ( Collins, Neelin, Peters, & Evans, 1994 ).”
MethodArchitecture of cognitive flexibility revealed by lesion mapping
Barbey, A. K., Colom, R., & Grafman, J. H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.087 · Cited by 141
“This template was created by spatial normalization of a neurologically healthy individual's CT brain scan to MNI space (Collins et al., 1994) using the Automated Image Registration program (Woods et al., 1993).”
MethodGiving speech a hand: Gesture modulates activity in auditory cortex during speech perception
Hubbard, A. L., Wilson, S. M., Callan, D. E., & Dapretto, M. (2008) · Human Brain Mapping · doi:10.1002/hbm.20565 · Cited by 143
“Functional images for each participant were realigned to correct for head motion, normalized into MNI space [Collins et al., 1994; Mazziotta et al., 2001] and smoothed with a 7 mm Gaussian kernel.”
MethodCortical thickness measured from MRI in the YAC128 mouse model of Huntington's disease
Lerch, J. P., Carroll, J. B., Dorr, A. E., Spring, S., Evans, A. C., Hayden, M. R., Sled, J. G., & Henkelman, R. M. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.019 · Cited by 134
“All registrations are performed using the mni_autoreg tools (Collins et al., 1995, 1994), which use an elastic registration algorithm.”
MethodSelf-citationA PET activation study of brush-evoked allodynia in patientswith nerve injury pain
Witting, N., Kupers, R. C., Svensson, P. J., & Jensen, T. S. (2005) · Pain · doi:10.1016/j.pain.2005.10.034 · Cited by 135
“The MINCTRACC algorithm (Collins et al., 1994) or automated image coregistration software system (AIR) (Woods et al., 1992) were used for MRI–PET and PET–PET coregistration to correct for motion artifacts.”
MethodT2 Relaxometry Can Lateralize Mesial Temporal Lobe Epilepsy in Patients with Normal MRI
Bernasconi, A., Bernasconi, N., Caramanos, Z., et al. (2000) · NeuroImage · doi:10.1006/nimg.2000.0724 · Cited by 134
“Images were automatically registered into stereotaxic space (Talairach and Tournoux, 1988) to adjust for differences in total brain volume and brain orientation, and to facilitate the identification of boundaries by minimizing variability in slice orientation (Collins et al., 1994).”
Palomero‐Gallagher, N., Eickhoff, S. B., Hoffstaedter, F., Schleicher, A., Mohlberg, H., Vogt, B. A., Amunts, K., & Zilles, K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.053 · Cited by 136
“hemispheres of the single-subject T1-weighted MNI reference brain (Collins et al., 1994; Evans et al., 2012) in anatomical MNI space (Amunts et al.”
Surface-based TBM boosts power to detect disease effects on the brain: An N=804 ADNI study
Wang, Y., Song, Y., Rajagopalan, P., An, T., Liu, K., Chou, Y., Gutman, B. A., Toga, A. W., & Thompson, P. M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.040 · Cited by 129
“These images were then spatially normalized to the ICBM space with a 9-parameter (3 translations, 3 rotations and 3-scales) linear transformation using Minctracc algorithm (Collins et al., 1994) for the correction of head tilt and alignment.”
Yang, D. Y. ‐., Beam, D. E., Pelphrey, K. A., Abdullahi, S. M., & Jou, R. J. (2016) · Molecular Autism · doi:10.1186/s13229-016-0076-x · Cited by 137
“In one of the first stages, FreeSurfer registers the individual scans to the MNI305 atlas [66].”
BackgroundChen, J.-I., Ha, B., Bushnell, M. C., Pike, G. B., & Duncan, G. H. (2002) · Journal of Neurophysiology · doi:10.1152/jn.2002.88.1.464 · Cited by 132
“All images were resampled into stereotaxic space using an automated registration method based on a multiscale, three-dimensional crosscorrelation with the average of 305 normal MR scans registered into Talairach space (Collins et al. 1994).”
MethodCorbo, V., Clément, M.-H., Armony, J. L., Pruessner, J. C., & Brunet, A. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.02.032 · Cited by 134
Raemaekers, M. A. H., Jansma, J. M., Cahn, W., van der Geest, J. N., van der Linden, J. A., Kahn, R. S., & Ramsey, N. F. (2002) · Archives of General Psychiatry · doi:10.1001/archpsyc.59.4.313 · Cited by 132
“Finally, all the now coregistered t volumes and structural scans were spatially normalized in Talairach orientation to enable groupwise comparisons.(48) To test for specific effects of inhibition and of illness on inhibition in the brain activity maps, a multivariate repeated-measures analysis was applied.”
MethodNeural Correlates of Successful Encoding Identified Using Functional Magnetic Resonance Imaging
Reber, P. J., Siwiec, R. M., Gitleman, D. R., Parrish, T. B., Mesulam, M. M., & Paller, K. A. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09541.2002 · Cited by 133
“Each of the runs was then transformed (Collins et al., 1994 The five runs of f unctional data were concatenated into a single time series for each participant .”
Lesion correlates of conversational speech production deficits
Borovsky, A., Saygın, A. P., Bates, E. A., & Dronkers, N. F. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.03.023 · Cited by 138
A Framework for Callosal Fiber Distribution Analysis
Xu, D., Mori, S., Solaiyappan, M., van Zijl, P. C. M., & Davatzikos, C. A. (2002) · NeuroImage · doi:10.1006/nimg.2002.1285 · Cited by 133
“match image intensities across subjects (Gee et al., 1993; Collins et al., 1994; Friston et al., 1995a; Christensen et al., 1996; Thirion, 1998; Freeborough and Fox, 1998; Rueckert et al., 1999), and feature-based, which extract features such as structure boundaries, sulci, and gyri from MR images and subsequently det…”
BackgroundTjandra, T., Brooks, J. C. W., Figueiredo, P., Wise, R. G., Matthews, P. M., & Tracey, I. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.04.021 · Cited by 132
“Registration to individual high-resolution structural images and the MNI (Montreal Neurological Institute) standard brain (Collins et al., 1994) was carried out using FLIRT (FMRIB’s Linear Image Registration Tool) (Jenkinson and Smith, 2002) using 9 and 12 degrees of freedom (DOF) respectively.”
MethodRay, N. J., Miyasaki, J. M., Zurowski, M., Ko, J. H., Cho, S. S., Pellecchia, G., Antonelli, F., Houle, S., Lang, A. E., & Strafella, A. P. (2012) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.06.021 · Cited by 131
“Images were normalised to an MNI template using the transformation parameters of the T1-weighted MRIs, and averaged across all patients.”
InfluentialSpatial patterns of water diffusion along white matter tracts in temporal lobe epilepsy
Concha, L., Kim, H., Bernasconi, A., Bernhardt, B. C., & Bernasconi, N. (2012) · Neurology · doi:10.1212/wnl.0b013e31826170b6 · Cited by 127
A phenotype of early infancy predicts reactivity of the amygdala in male adults
Schwartz, C., Kunwar, P. S., Greve, D. N., Kagan, J., Snidman, N. C., & Bloch, R. B. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.96 · Cited by 132
Ethofer, T., Kreifelts, B., Wiethoff, S., Wolf, J., Grodd, W., Vuilleumier, P. O., & Wildgruber, D. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21099 · Cited by 131
“For both sessions, preprocessing steps comprised realignment to the first volume of the time series, unwarping by use of a static field map (Andersson, Hutton, Ashburner, Turner, & Friston, 2001 ), normalization into MNI space (Montreal Neurological Institute, Collins, Neelin, Peters, & Evans, 1994 , resampled voxel s…”
MethodIndividual voxel-based analysis of gray matter in focal cortical dysplasia
Colliot, O., Bernasconi, N., Khalili, N., Antel, S. B., Naessens, V., & Bernasconi, N. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.07.021 · Cited by 132
“Images were automatically registered into a standard stereotaxic space to adjust for differences in total brain volume and brain orientation (Collins et al., 1994).”
Magnetic resonance spectroscopy and imaging of the thalamus in idiopathic generalized epilepsy
Bernasconi, A. (2003) · Brain · doi:10.1093/brain/awg249 · Cited by 132
“Images were automatically registered in a standard, stereotaxic space (Talairach and Tournoux, 1988) to adjust for differences in total brain volume and brain orientation, and to facilitate the identi®cation of boundaries by minimizing variability in slice orientation (Collins et al., 1994).”
Metadata
- Authors
- Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- Venue
- Journal of Computer Assisted Tomography
- Published
- 1994-03
- Volume
- 18
- Issue
- 2
- Pages
- 192-205
- ISSN
- 0363-8715 (ISSN-L)
- OpenAlex
- W2141796362
- Cited by
- 3,431
- References
- 0
- Citations by year
- 2026: 57 · 2025: 76 · 2024: 100 · 2023: 76 · 2022: 85 · 2021: 134 · 2020: 103 · 2019: 110 · 2018: 108 · 2017: 102 · 2016: 114 · 2015: 154 · 2014: 157 · 2013: 160 · 2012: 160
- FWCI
- 37.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Tomography (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- 3D registration, automatic image registration, interobserver variability
- MeSH terms
- Algorithms, Basal Ganglia, Brain, Brain Mapping, Cerebellum, Cerebral Cortex, Cerebral Ventricles, Corpus Callosum, Humans, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Structural, Stereotaxic Techniques
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