The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547
Image matching as a diffusion process: an analogy with maxwell’s demons
Thirion, J.-P. (1998) · Medical Image Analysis · doi:10.1016/s1361-8415(98)80022-4 · Cited by 2,445
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
Brain structural abnormalities in young children with autism spectrum disorder
Sparks, B. F., Friedman, S. D., Shaw, D. W., Aylward, E. H., Echelard, D., Artru, A. A., Maravilla, K. R., Giedd, J. N., Munson, J., Dawson, G., et al. (2002) · Neurology · doi:10.1212/wnl.59.2.184 · Cited by 961
Fox, N. C., Crum, W. R., Scahill, R. I., Stevens, J. M., Janssen, J. C., & Rossor, M. N. (2001) · The Lancet · doi:10.1016/s0140-6736(01)05408-3 · Cited by 455
Variational problems on flows of diffeomorphisms for image matching
Dupuis, P., Grenander, U., & Miller, M. I. (1998) · Quarterly of Applied Mathematics · doi:10.1090/qam/1632326 · Cited by 447
Variational Methods for Multimodal Image Matching
Hermosillo, G., Chefd'Hotel, C., & Faugeras, O. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020830525823 · Cited by 388
Cited by
A reproducible evaluation of ANTs similarity metric performance in brain image registration
Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.025 · Cited by 4,796
Self-citationfMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905
Unbiased average age-appropriate atlases for pediatric studies
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.033 · Cited by 2,290
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227
Self-citationDeformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587
“[80] presented a symmetric LDDMM registration process driven by cross-correlation”
BackgroundA connectome and analysis of the adult Drosophila central brain
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-Y., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., et al. (2020) · eLife · doi:10.7554/elife.57443 · Cited by 1,128
Consolidation alters motor sequence-specific distributed representations
Pinsard, B., Boutin, A., Gabitov, E., Lungu, O., Benali, H., & Doyon, J. (2019) · eLife · doi:10.7554/elife.39324 · Cited by 1,066
The neuronal architecture of the mushroom body provides a logic for associative learning
Aso, Y., Hattori, D., Yu, Y., Johnston, R. M., Iyer, N. A., Ngo, T.-T., Dionne, H., Abbott, L., Axel, R., Tanimoto, H., et al. (2014) · eLife · doi:10.7554/elife.04577 · Cited by 1,051
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886
Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.059 · Cited by 872
Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809
Multi-Atlas Segmentation with Joint Label Fusion
Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.143 · Cited by 796
Neurons for hunger and thirst transmit a negative-valence teaching signal
Betley, J. N., Xu, S., Cao, Z. F. H., Gong, R., Magnus, C. J., Yu, Y., & Sternson, S. M. (2015) · Nature · doi:10.1038/nature14416 · Cited by 704
Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging
Horn, A., Li, N., Dembek, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.068 · Cited by 739
Deep learning in medical image registration: a survey
Haskins, G., Kruger, U., & Yan, P. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01060-x · Cited by 704
The optimal template effect in hippocampus studies of diseased populations
Avants, B. B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., & Gee, J. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.062 · Cited by 640
Self-citationTractSeg - Fast and accurate white matter tract segmentation
Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.070 · Cited by 664
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
Quicksilver: Fast predictive image registration – A deep learning approach
Yang, X., Kwitt, R., Styner, M., & Niethammer, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.008 · Cited by 630
“05 / 204. a similar performance of LO to SyN , for example, is expected as SyN (as used in [33]) is based on a relaxation formulation of LDDMM, whereas LO is based on the shooting formulation of LDDMM. Performance differences may be due to differences in the used regularizer and the image similarity measure.”
MethodInfluentialAn Unsupervised Learning Model for Deformable Medical Image Registration
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Dalca, A. V., & Guttag, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00964 · Cited by 599
Self-reported sleep and β-amyloid deposition in community-dwelling older adults
Spira, A. P., Gamaldo, A. A., An, Y., Wu, M. N., Simonsick, E. M., Bilgel, M., Zhou, Y., Wong, D. F., Ferrucci, L., & Resnick, S. M. (2013) · JAMA Neurology · doi:10.1001/jamaneurol.2013.4258 · Cited by 570
Advanced Normalization Tools: V1.0
Avants, B., Tustison, N. J., & Song, G. (2009) · The Insight Journal · doi:10.54294/uvnhin · Cited by 559
Self-citationHanson, J. L., Chung, M. K., Avants, B. B., Shirtcliff, E. A., Gee, J. C., Davidson, R. J., & Pollak, S. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.0859-10.2010 · Cited by 455
Self-citationUnsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467
Rojkova, K., Volle, E., Urbanski, M., Humbert, F., Dell’Acqua, F., & Thiebaut de Schotten, M. (2016) · Brain Structure and Function · doi:10.1007/s00429-015-1001-3 · Cited by 361
Closed-loop stimulation of temporal cortex rescues functional networks and improves memory
Ezzyat, Y., Wanda, P. A., Levy, D. F., Kadel, A., Aka, A., Pedisich, I., Sperling, M. R., Sharan, A. D., Lega, B. C., Burks, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02753-0 · Cited by 347
Deoni, S. C. L., Dean, D. C., O'Muircheartaigh, J., Dirks, H., & Jerskey, B. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.037 · Cited by 390
Alves, P. N., Foulon, C., Karolis, V., Bzdok, D., Margulies, D. S., Volle, E., & Thiebaut de Schotten, M. (2019) · Communications Biology · doi:10.1038/s42003-019-0611-3 · Cited by 357
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315
Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., & Davatzikos, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.073 · Cited by 309
“For both methods, the main parameter that regulates the smoothness of the deformation field was sampled at two operational points, specifically g = {0.1, 0.2} for DRAMMS and s = {0.25, 0.5} for ANTS.”
InfluentialGennatas, E. D., Avants, B. B., Wolf, D. H., Satterthwaite, T. D., Ruparel, K., Ciric, R., Hakonarson, H., Gur, R. E., & Gur, R. C. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3550-16.2017 · Cited by 302
Self-citationMaze training in mice induces MRI-detectable brain shape changes specific to the type of learning
Lerch, J. P., Yiu, A. P., Martinez-Canabal, A., Pekar, T., Bohbot, V. D., Frankland, P. W., Henkelman, R. M., Josselyn, S. A., & Sled, J. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.086 · Cited by 300
Birnet: Brain image registration using dual-supervised fully convolutional networks
Fan, J., Cao, X., Yap, P.-T., & Shen, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.006 · Cited by 297
Garyfallidis, E., Côté, M.-A., Rheault, F., Sidhu, J., Hau, J., Petit, L., Fortin, D., Cunanne, S., & Descoteaux, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.015 · Cited by 279
Functional maturation of the executive system during adolescence
Satterthwaite, T. D., Wolf, D. H., Erus, G., Ruparel, K., Elliott, M. A., Gennatas, E. D., Hopson, R., Jackson, C., Prabhakaran, K., Bilker, W. B., et al. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2345-13.2013 · Cited by 276
Dean, D. C., Jerskey, B. A., Chen, K., Protas, H., Thiyyagura, P., Roontiva, A., O'Muircheartaigh, J., Dirks, H., Waskiewicz, N., Lehman, K., et al. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2013.4544 · Cited by 263
Germline Chd8 haploinsufficiency alters brain development in mouse
Gompers, A. L., Su-Feher, L., Ellegood, J., Copping, N. A., Riyadh, M. A., Stradleigh, T. W., Pride, M. C., Schaffler, M. D., Wade, A. A., Catta-Preta, R., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4592 · Cited by 266
Josephs, K. A., Dickson, D. W., Tosakulwong, N., Weigand, S. D., Murray, M. E., Petrucelli, L., Liesinger, A. M., Senjem, M. L., Spychalla, A. J., Knopman, D. S., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30284-3 · Cited by 252
Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition
Solomon, E. A., Kragel, J. E., Sperling, M. R., Sharan, A., Worrell, G., Kucewicz, M., Inman, C. S., Lega, B., Davis, K. A., Stein, J. M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01763-2 · Cited by 221
Converging evidence for the neuroanatomic basis of combinatorial semantics in the angular gyrus
Price, A. R., Bonner, M. F., Peelle, J. E., & Grossman, M. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.3446-14.2015 · Cited by 262
Self-citationEvaluation of volume-based and surface-based brain image registration methods
Klein, A., Ghosh, S. S., Avants, B., Yeo, B. T. T., Fischl, B., Ardekani, B., Gee, J. C., Mann, J. J., & Parsey, R. V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.091 · Cited by 255
Self-citationSatterthwaite, T. D., Wolf, D. H., Ruparel, K., Erus, G., Elliott, M. A., Eickhoff, S. B., Gennatas, E. D., Jackson, C., Prabhakaran, K., Smith, A., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.045 · Cited by 253
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M., Castro, D. C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01442 · Cited by 227
Wang, H., & Yushkevich, P. A. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00027 · Cited by 218
Shanmugan, S., Wolf, D. H., Calkins, M. E., Moore, T. M., Ruparel, K., Hopson, R. D., Vandekar, S. N., Roalf, D. R., Elliott, M. A., Jackson, C., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15060725 · Cited by 219
Elliott, C., Wolinsky, J. S., Hauser, S. L., Kappos, L., Barkhof, F., Bernasconi, C., Wei, W., Belachew, S., & Arnold, D. L. (2019) · Multiple Sclerosis Journal · doi:10.1177/1352458518814117 · Cited by 217
A survey of methods for 3D histology reconstruction
Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207
Billiet, T., Vandenbulcke, M., Mädler, B., Peeters, R., Dhollander, T., Zhang, H., Deprez, S., Van den Bergh, B. R. H., Sunaert, S., & Emsell, L. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.02.029 · Cited by 203
Koster, R., Chadwick, M. J., Chen, Y., Berron, D., Banino, A., Düzel, E., Hassabis, D., & Kumaran, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.009 · Cited by 191
Layer-dependent activity in human prefrontal cortex during working memory
Finn, E. S., Huber, L., Jangraw, D. C., Molfese, P. J., & Bandettini, P. A. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0487-z · Cited by 192
Latremoliere, A., Cheng, L., DeLisle, M., Wu, C., Chew, S., Hutchinson, E. B., Sheridan, A., Alexandre, C., Latremoliere, F., Sheu, S.-H., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.029 · Cited by 183
Schilling, K. G., Rheault, F., Petit, L., Hansen, C. B., Nath, V., Yeh, F.-C., Girard, G., Barakovic, M., Rafael-Patino, J., Yu, T., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118502 · Cited by 182
Neural circuits for evidence accumulation and decision making in larval zebrafish
Bahl, A., & Engert, F. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0534-9 · Cited by 166
Wang, H., Das, S. R., Suh, J. W., Altinay, M., Pluta, J., Craige, C., Avants, B., & Yushkevich, P. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.006 · Cited by 177
Self-citationDifferentiating primary progressive aphasias in a brief sample of connected speech
Ash, S., Evans, E., O'Shea, J., Powers, J., Boller, A., Weinberg, D., Haley, J., McMillan, C., Irwin, D. J., Rascovsky, K., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e31829c5d0e · Cited by 171
Self-citationDeep conservation of the enhancer regulatory code in animals
Wong, E. S., Zheng, D., Tan, S. Z., Bower, N. I., Garside, V., Vanwalleghem, G., Gaiti, F., Scott, E., Hogan, B. M., Kikuchi, K., et al. (2020) · Science · doi:10.1126/science.aax8137 · Cited by 176
Characterizing longitudinal white matter development during early childhood
Dean, D. C., O’Muircheartaigh, J., Dirks, H., Waskiewicz, N., Walker, L., Doernberg, E., Piryatinsky, I., & Deoni, S. C. L. (2015) · Brain Structure and Function · doi:10.1007/s00429-014-0763-3 · Cited by 174
Modeling healthy male white matter and myelin development: 3 through 60months of age
Dean, D. C., O'Muircheartaigh, J., Dirks, H., Waskiewicz, N., Lehman, K., Walker, L., Han, M., & Deoni, S. C. L. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.058 · Cited by 163
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
Alexander, L., Gaskin, P. L. R., Sawiak, S. J., Fryer, T. D., Hong, Y. T., Cockcroft, G. J., Clarke, H. F., & Roberts, A. C. (2019) · Neuron · doi:10.1016/j.neuron.2018.11.021 · Cited by 120
Calcium imaging of sleep-wake related neuronal activity in the dorsal pons
Cox, J., Pinto, L., & Dan, Y. (2016) · Nature Communications · doi:10.1038/ncomms10763 · Cited by 133
An unbiased template of the Drosophila brain and ventral nerve cord
Bogovic, J. A., Otsuna, H., Heinrich, L., Ito, M., Jeter, J., Meissner, G., Nern, A., Colonell, J., Malkesman, O., Ito, K., et al. (2020) · PLOS ONE · doi:10.1371/journal.pone.0236495 · Cited by 131
Memory reactivation in healthy aging: evidence of stimulus-specific dedifferentiation
St-Laurent, M., Abdi, H., Bondad, A., & Buchsbaum, B. R. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3054-13.2014 · Cited by 126
Eden, A. S., Schreiber, J., Anwander, A., Keuper, K., Laeger, I., Zwanzger, P., Zwitserlood, P., Kugel, H., & Dobel, C. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.3659-14.2015 · Cited by 123
Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., & Shen, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00919-9_7 · Cited by 123
Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging
Dean, D. C., O'Muircheartaigh, J., Dirks, H., Travers, B. G., Adluru, N., Alexander, A. L., & Deoni, S. C. L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.040 · Cited by 121
Cross-sectional associations of tau-PET signal with cognition in cognitively unimpaired adults
Lowe, V. J., Bruinsma, T. J., Wiste, H. J., Min, H.-K., Weigand, S. D., Fang, P., Senjem, M. L., Therneau, T. M., Boeve, B. F., Josephs, K. A., et al. (2019) · Neurology · doi:10.1212/wnl.0000000000007728 · Cited by 107
Neurite orientation dispersion and density imaging (NODDI) and free‐water imaging in Parkinsonism
Mitchell, T., Archer, D. B., Chu, W. T., Coombes, S. A., Lai, S., Wilkes, B. J., McFarland, N. R., Okun, M. S., Black, M. L., Herschel, E., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24760 · Cited by 118
Deep brain stimulation of symptom-specific networks in Parkinson’s disease
Rajamani, N., Friedrich, H., Butenko, K., Dembek, T., Lange, F., Navrátil, P., Zvarova, P., Hollunder, B., de Bie, R. M. A., Odekerken, V. J. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48731-1 · Cited by 118
Dodero, L., Damiano, M., Galbusera, A., Bifone, A., Tsaftsaris, S. A., Scattoni, M. L., & Gozzi, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076655 · Cited by 116
Alexander, L., Wood, C. M., Gaskin, P. L. R., Sawiak, S. J., Fryer, T. D., Hong, Y. T., McIver, L., Clarke, H. F., & Roberts, A. C. (2020) · Nature Communications · doi:10.1038/s41467-020-19167-0 · Cited by 100
White matter maturation is associated with the emergence of Theory of Mind in early childhood
Grosse Wiesmann, C., Schreiber, J., Singer, T., Steinbeis, N., & Friederici, A. D. (2017) · Nature Communications · doi:10.1038/ncomms14692 · Cited by 114
A multidimensional magnetic resonance histology atlas of the Wistar rat brain
Johnson, G. A., Calabrese, E., Badea, A., Paxinos, G., & Watson, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.041 · Cited by 113
Distributed neural representations of phonological features during speech perception
Arsenault, J. S., & Buchsbaum, B. R. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2454-14.2015 · Cited by 111
Pluta, J., Yushkevich, P., Das, S., & Wolk, D. (2012) · Journal of Alzheimer's Disease · doi:10.3233/jad-2012-111931 · Cited by 107
Deep Learning in Medical Image Registration
Chen, X., Diaz-Pinto, A., Ravikumar, N., & Frangi, A. (2020) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/abd37c · Cited by 106
Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse
Lerch, J. P., Gazdzinski, L., Germann, J., Sled, J. G., Henkelman, R. M., & Nieman, B. J. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00006 · Cited by 104
Billiet, T., Mädler, B., D'Arco, F., Peeters, R., Deprez, S., Plasschaert, E., Leemans, A., Zhang, H., den Bergh, B. V., Vandenbulcke, M., et al. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.04.005 · Cited by 101
Liebrand, L. C., Caan, M. W. A., Schuurman, P. R., van den Munckhof, P., Figee, M., Denys, D., & van Wingen, G. A. (2019) · Brain Stimulation · doi:10.1016/j.brs.2018.11.014 · Cited by 98
Latimer, C. S., Shively, C. A., Keene, C. D., Jorgensen, M. J., Andrews, R. N., Register, T. C., Montine, T. J., Wilson, A. M., Neth, B. J., Mintz, A., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.06.3057 · Cited by 98
Corre, C., Friedel, M., Vousden, D. A., Metcalf, A., Spring, S., Qiu, L. R., Lerch, J. P., & Palmert, M. R. (2016) · Brain Structure and Function · doi:10.1007/s00429-014-0952-0 · Cited by 98
Pujala, A., & Koyama, M. (2019) · eLife · doi:10.7554/elife.42135 · Cited by 80
Whole-body somatotopic maps in the cerebellum revealed with 7T fMRI
Boillat, Y., Bazin, P.-L., & van der Zwaag, W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116624 · Cited by 85
Synaptic Loss in Primary Tauopathies Revealed by [ 11 C ] UCB‐J Positron Emission Tomography
Holland, N., Jones, P. S., Savulich, G., Wiggins, J. K., Hong, Y. T., Fryer, T. D., Manavaki, R., Sephton, S. M., Boros, I., Malpetti, M., et al. (2020) · Movement Disorders · doi:10.1002/mds.28188 · Cited by 93
Amyloid-β deposition and regional grey matter atrophy rates in dementia with Lewy bodies
Sarro, L., Senjem, M. L., Lundt, E. S., Przybelski, S. A., Lesnick, T. G., Graff-Radford, J., Boeve, B. F., Lowe, V. J., Ferman, T. J., Knopman, D. S., et al. (2016) · Brain · doi:10.1093/brain/aww193 · Cited by 89
Robust and efficient linear registration of white-matter fascicles in the space of streamlines
Garyfallidis, E., Ocegueda, O., Wassermann, D., & Descoteaux, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.016 · Cited by 84
The human thalamus orchestrates neocortical oscillations during NREM sleep
Schreiner, T., Kaufmann, E., Noachtar, S., Mehrkens, J.-H., & Staudigl, T. (2022) · Nature Communications · doi:10.1038/s41467-022-32840-w · Cited by 77
Larsen, B., Cui, Z., Adebimpe, A., Pines, A., Alexander-Bloch, A., Bertolero, M., Calkins, M. E., Gur, R. E., Gur, R. C., Mahadevan, A. S., et al. (2022) · Science Advances · doi:10.1126/sciadv.abj8750 · Cited by 80
Suspected non-AD pathology in mild cognitive impairment
Wisse, L. E. M., Butala, N., Das, S. R., Davatzikos, C., Dickerson, B. C., Vaishnavi, S. N., Yushkevich, P. A., & Wolk, D. A. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.08.029 · Cited by 78
Rollins, C. P. E., Gallino, D., Kong, V., Ayranci, G., Devenyi, G. A., Germann, J., & Chakravarty, M. M. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.11.016 · Cited by 79
Neuronal Architecture of a Visual Center that Processes Optic Flow
Kramer, A., Wu, Y., Baier, H., & Kubo, F. (2019) · Neuron · doi:10.1016/j.neuron.2019.04.018 · Cited by 78
Tetzloff, K. A., Graff-Radford, J., Martin, P. R., Tosakulwong, N., Machulda, M. M., Duffy, J. R., Clark, H. M., Senjem, M. L., Schwarz, C. G., Spychalla, A. J., et al. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-170740 · Cited by 78
The neural basis of vivid memory is patterned on perception
Buchsbaum, B. R., Lemire-Rodger, S., Fang, C., & Abdi, H. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00253 · Cited by 74
Howell, B. R., Grand, A. P., McCormack, K. M., Shi, Y., LaPrarie, J. L., Maestripieri, D., Styner, M. A., & Sanchez, M. M. (2014) · Developmental Psychobiology · doi:10.1002/dev.21237 · Cited by 72
Structural Abnormalities in Bipolar Euthymia: A Multicontrast Molecular Diffusion Imaging Study
Canales-Rodríguez, E. J., Pomarol-Clotet, E., Radua, J., Sarró, S., Alonso-Lana, S., Del Mar Bonnín, C., Goikolea, J. M., Maristany, T., García-Álvarez, R., Vieta, E., et al. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.09.027 · Cited by 72
Assessing atrophy measurement techniques in dementia: results from the MIRIAD atrophy challenge
Cash, D. M., Frost, C., Iheme, L. O., Ünay, D., Kandemir, M., Fripp, J., Salvado, O., Bourgeat, P., Reuter, M., Fischl, B., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.087 · Cited by 72
Stress Impairs Episodic Retrieval by Disrupting Hippocampal and Cortical Mechanisms of Remembering
Gagnon, S. A., Waskom, M. L., Brown, T. I., & Wagner, A. D. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy162 · Cited by 69
Pagani, M., Damiano, M., Galbusera, A., Tsaftaris, S. A., & Gozzi, A. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.04.007 · Cited by 60
Bassoon contributes to tau-seed propagation and neurotoxicity
Martinez, P., Patel, H., You, Y., Jury, N., Perkins, A., Lee-Gosselin, A., Taylor, X., You, Y., Viana Di Prisco, G., Huang, X., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01191-6 · Cited by 68
Manera, A. L., Dadar, M., Collins, D. L., & Ducharme, S. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.102079 · Cited by 65
Metadata
- Authors
- AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 12
- Issue
- 1
- Pages
- 26-41
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W1970928383
- Cited by
- 5,669
- References
- 9
- FWCI
- 27.49
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.