Fast robust automated brain extraction

Snakes: Active contour models

Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,330

Cortical Surface-Based Analysis

Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628

A global optimisation method for robust affine registration of brain images

Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018

Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis

Smith, S. M., Zhang, Y., Jenkinson, M., Chen, J. M., Matthews, P. M., Federico, A., & Stefano, N. D. (2002) · NeuroImage · doi:10.1006/nimg.2002.1040 · Cited by 2,013

Normalized Accurate Measurement of Longitudinal Brain Change

Smith, S. M., Stefano, N. D., Jenkinson, M., & Matthews, P. M. (2001) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-200105000-00022 · Cited by 489

Elastic model-based segmentation of 3-D neuroradiological data sets

Kelemen, A., Szekely, G., & Gerig, G. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.811260 · Cited by 434

Fully automatic segmentation of the brain in MRI

Atkins, M. S., & Mackiewich, B. T. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668699 · Cited by 397

Automatic detection of brain contours in MRI data sets

Brummer, M. E., Mersereau, R. M., Eisner, R. L., et al. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.232244 · Cited by 298

3-D segmentation of MR images of the head for 3-D display

Bomans, M., Höhne, Κ. H., Tiede, U., et al. (1990) · IEEE Transactions on Medical Imaging · doi:10.1109/42.56342 · Cited by 282

Perceptual stability reflected in neuronal pattern similarities in human visual cortex

Broday-Dvir, R., Norman, Y., Harel, M., Mehta, A. D., & Malach, R. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112614 · Cited by 28

Traumatic brain injury in a community-based cohort of homeless and vulnerably housed individuals

Schmitt, T., Thornton, A. E., Rawtaer, I., Barr, A. M., Gicas, K. M., Lang, D. J., Vertinsky, A. T., Rauscher, A., Procyshyn, R. M., Buchanan, T., et al. (2017) · Journal of Neurotrauma · doi:10.1089/neu.2017.5076 · Cited by 27

Exercise Training-Related Changes in Cortical Gray Matter Diffusivity and Cognitive Function in Mild Cognitive Impairment and Healthy Older Adults

Callow, D. D., Won, J., Pena, G. S., Jordan, L. S., Arnold-Nedimala, N. A., Kommula, Y., Nielson, K. A., & Smith, J. C. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.645258 · Cited by 27

The APOE ε 4 allele in relation to brain white‐matter microstructure in adulthood and aging

Nyberg, L., & Salami, A. (2014) · Scandinavian Journal of Psychology · doi:10.1111/sjop.12099 · Cited by 28

Working Memory Training Effects on White Matter Integrity in Young and Older Adults

Dziemian, S., Appenzeller, S., von Bastian, C. C., Jäncke, L., & Langer, N. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.605213 · Cited by 26

Comparison of manual and automated fiber quantification tractography in patients with temporal lobe epilepsy

Kreilkamp, B. A. K., Lisanti, L., Glenn, G. R., Wieshmann, U. C., Das, K., Marson, A. G., & Keller, S. S. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.102024 · Cited by 27

Imaging brain function with simultaneous BOLD and viscoelasticity contrast: fMRI/fMRE

Lan, P. S., Glaser, K. J., Ehman, R. L., & Glover, G. H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116592 · Cited by 26

Exploring the neural basis for paternal protection: An investigation of the neural response to infants in danger

van ‘t Veer, A. E., Thijssen, S., Witteman, J., van IJzendoorn, M. H., & Bakermans-Kranenburg, M. J. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz018 · Cited by 25

Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences

Kushki, A., Cardy, R. E., Panahandeh, S., Malihi, M., Hammill, C., Brian, J., Iaboni, A., Taylor, M. J., Schachar, R., Crosbie, J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab142 · Cited by 25

Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study

McMillan, R., Forsyth, A., Campbell, D., Malpas, G., Maxwell, E., Dukart, J., Hipp, J. F., & Muthukumaraswamy, S. (2019) · Journal of Psychopharmacology · doi:10.1177/0269881118822263 · Cited by 21

Interleukin-6-White Matter Network Differences Explained the Susceptibility to Depression after Stressful Life Events

Li, Y., Xie, Y., Xu, Y., Xian, X., Wang, R., Cai, L., Li, G., & Li, Y. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.03.003 · Cited by 22

Inferior frontal and insular cortical thinning is related to dysfunctional brain activation/deactivation during working memory task in schizophrenic patients

Pujol, N., Penadés, R., Rametti, G., Catalán, R., Vidal-Piñeiro, D., Palacios, E., Bargallo, N., Bernardo, M., & Junqué, C. (2013) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2013.06.008 · Cited by 21

Fetal Response to a Maternal Internal Auditory Stimulus

Goldberg, E., McKenzie, C. A., de Vrijer, B., Eagleson, R., & de Ribaupierre, S. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27033 · Cited by 21

Human parietofrontal networks related to action observation detected at rest

Molinari, E., Baraldi, P., Campanella, M., Duzzi, D., Nocetti, L., Pagnoni, G., & Porro, C. A. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhr393 · Cited by 19

Subcortical brain volumes in young infants exposed to antenatal maternal depression: Findings from a South African birth cohort

Groenewold, N. A., Wedderburn, C. J., Pellowski, J. A., Fouché, J.-P., Michalak, L., Roos, A., Woods, R. P., Narr, K. L., Zar, H. J., Donald, K. A., et al. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103206 · Cited by 22

Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data

Sobotka, D., Ebner, M., Schwartz, E., Nenning, K.-H., Taymourtash, A., Vercauteren, T., Ourselin, S., Kasprian, G., Prayer, D., Langs, G., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119213 · Cited by 20

A fetal fMRI specific motion correction algorithm using 2nd order edge features

Scheinost, D., Onofrey, J. A., Kwon, S. H., Cross, S. N., Sze, G., Ment, L. R., & Papademetris, X. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363807 · Cited by 19

Prenatal maternal depressive symptoms are associated with neonatal left amygdala microstructure in a sex‐dependent way

Hashempour, N., Tuulari, J. J., Merisaari, H., Acosta, H., Lewis, J. D., Pelto, J., Scheinin, N. M., Fonov, V. S., Collins, D. L., Lehtola, S. J., et al. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.15989 · Cited by 20

Comparative quantification of dietary supplemented neural creatine concentrations with 1H-MRS peak fitting and basis spectrum methods

Turner, C. E., Russell, B. R., & Gant, N. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.06.018 · Cited by 19

Differential hemispheric predilection of microstructural white matter and functional connectivity abnormalities between respectively semantic and behavioral variant frontotemporal dementia

Meijboom, R., Steketee, R. M. E., Ham, L. S., van der Lugt, A., van Swieten, J. C., & Smits, M. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160564 · Cited by 17

NeuN+ neuronal nuclei in non-human primate prefrontal cortex and subcortical white matter after clozapine exposure

Halene, T. B., Kozlenkov, A., Jiang, Y., Mitchell, A. C., Javidfar, B., Dincer, A., Park, R., Wiseman, J., Croxson, P. L., Giannaris, E. L., et al. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2015.12.016 · Cited by 18

Increased white matter connectivity in traumatized children with attention deficit hyperactivity disorder

Park, S., Lee, J.-M., Kim, J.-W., Kwon, H., Cho, S.-C., Han, D. H., Cheong, J. H., & Kim, B.-N. (2016) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2015.09.012 · Cited by 17

Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction

Mahmoodi, A., Luo, S., Harbison, C., Piray, P., & Rushworth, M. F. S. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.001 · Cited by 17

Impact of COVID-19 Related Maternal Stress on Fetal Brain Development: A Multimodal MRI Study

Rajagopalan, V., Reynolds, W. T., Zepeda, J., Lopez, J., Ponrartana, S., Wood, J., Ceschin, R., & Panigrahy, A. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226635 · Cited by 20

Bupropion administration increases resting-state functional connectivity in dorso-medial prefrontal cortex

Rzepa, E., Dean, Z., & McCabe, C. (2017) · International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyx016 · Cited by 15

Cerebrospinal Fluid 7-Ketocholesterol Level is Associated with Amyloid-β42 and White Matter Microstructure in Cognitively Healthy Adults

Iriondo, A., García-Sebastian, M., Arrospide, A., Arriba, M., Aurtenetxe, S., Barandiaran, M., Clerigue, M., Ecay-Torres, M., Estanga, A., Gabilondo, A., et al. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200105 · Cited by 16

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence

Azadeh, S., Hobbs, B. P., Ma, L., Nielsen, D. A., Gerard Moeller, F., & Baladandayuthapani, V. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.033 · Cited by 12

Evaluation of SWI in Children with Sickle Cell Disease

Winchell, A. M., Taylor, B. A., Song, R., Loeffler, R. B., Grundlehner, P., Hankins, J. S., Wang, W. C., Ogg, R. J., Hillenbrand, C. M., & Helton, K. J. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3794 · Cited by 13

Abnormal white matter microstructure among early adulthood smokers: a tract-based spatial statistics study

Wang, S., Zuo, L., Jiang, T., Peng, P., Chu, S., & Xiao, D. (2017) · Neurological Research · doi:10.1080/01616412.2017.1379277 · Cited by 12

Feasibility of studying brain morphology in major depressive disorder with structural magnetic resonance imaging and clinical data from the electronic medical record: A pilot study

Hoogenboom, W. S., Perlis, R. H., Smoller, J. W., Zeng-Treitler, Q., Gainer, V. S., Murphy, S. N., Churchill, S. E., Kohane, I. S., Shenton, M. E., & Iosifescu, D. V. (2013) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2012.07.007 · Cited by 13

An investigation of twenty/20 vision in reading

Hellyer, P. J., Woodhead, Z. V. J., Leech, R., & Wise, R. J. S. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2740-11.2011 · Cited by 13

Contribution of oxytocin receptor polymorphisms to amygdala activation in schizophrenia spectrum disorders

Haram, M., Bettella, F., Brandt, C. L., Quintana, D. S., Nerhus, M., Bjella, T., Djurovic, S., Westlye, L. T., Andreassen, O. A., Melle, I., et al. (2016) · BJPsych Open · doi:10.1192/bjpo.bp.116.003376 · Cited by 13

Resting state connectivity and cognitive performance in adults with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy

Cullen, B., Moreton, F. C., Stringer, M. S., Krishnadas, R., Kalladka, D., López-González, M. R., Santosh, C., Schwarzbauer, C., & Muir, K. W. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x16636395 · Cited by 12

Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data

Momeni, A., Thibault, M., & Gevaert, O. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_41 · Cited by 12

Neural correlates of fat preference in frontotemporal dementia: translating insights from the obesity literature

Ahmed, R. M., Tse, N. Y., Chen, Y., Henning, E., Hodges, J. R., Kiernan, M. C., Irish, M., Farooqi, I. S., & Piguet, O. (2021) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51369 · Cited by 12

Lower fractional anisotropy of the corticothalamic tract and increased response time variability in adult patients with ADHD

Kölle, M., Mackert, S., Heckel, K., Philipsen, A., Ulrich, M., & Grön, G. (2022) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.210135 · Cited by 11

Grey and white matter brain network changes in frontotemporal dementia subtypes

Nguyen, T., Bertoux, M., O’Callaghan, C., Ahmed, S., Hodges, J., & Hornberger, M. (2013) · Translational Neuroscience · doi:10.2478/s13380-013-0141-2 · Cited by 9

Machine Learning Based Analysis of Structural MRI for Epilepsy Diagnosis

Sahebzamani, G., Saffar, M., & Soltanian-Zadeh, H. (2019) · 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) · doi:10.1109/pria.2019.8785985 · Cited by 8

Associations between connectivity in functional brain networks and gait speed in older adults with and without multiple sclerosis

Nayak, S., Wagshul, M. E., Foley, F. W., Motl, R. W., & Holtzer, R. (2025) · Journal of Neurology · doi:10.1007/s00415-025-12955-y · Cited by 6

Scattered slice shard reconstruction for motion correction in multi-shell diffusion MRI

Christiaens, D., Cordero-Grande, L., Pietsch, M., Hutter, J., Price, A. N., Hughes, E. J., Vecchiato, K., Deprez, M., Edwards, A. D., Hajnal, J. V., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117437 · Cited by 4

Exploring the neural basis of reaction time variability in ADHD: The importance of examining data at the trial level

Tamm, L., Dudley, J. A., Karalunas, S. L., Simon, J. O., Maloney, T. C., Atluri, G., & Epstein, J. N. (2025) · NeuroImage: Reports · doi:10.1016/j.ynirp.2025.100263 · Cited by 4

Activity in human dorsal raphe nucleus signals changes in behavioural policy

Priestley, L., Mahmoodi, A., Reith, W. D., Khalighinejad, N., & Rushworth, M. F. S. (2026) · Nature Communications · doi:10.1038/s41467-026-68349-9 · Cited by 3

White matter microstructural abnormality precedes cortical volumetric decline in Alzheimer’s disease: evidence from data-driven disease progression modelling

Parker, C., Weston, P., Zhang, H., Oxtoby, N., & Initiative, T. A. D. N. (2022) · doi:10.1101/2022.07.12.499784 · Cited by 2

Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis

Al-Amin, M. M., Grandjean, J., Klohs, J., & Kim, J. (2020) · doi:10.1101/2020.04.27.064634 · Cited by 2

Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning

Liu, Y., Spiering, L., Luo, S., Kingston, N., Grohn, J., & Rushworth, M. F. S. (2026) · Neuron · doi:10.1016/j.neuron.2026.07.029 · Cited by 1

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering

D’Ambrogio, S., Grohn, J., Khalighinejad, N., Mattar, M. G., Hunt, L., & Rushworth, M. F. S. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02342-9 · Cited by 1

['Gold standard', not 'golden standard'].

Claassen, J. A. H. R. (2005) · PubMed · Cited by 9

‘Cure’ as the gold standard for likelihood ratio assessment: theoretical considerations

Rutten, A. L. B., Stolper, C. F., Lugten, R. F. G., et al. (2004) · Homeopathy · doi:10.1016/j.homp.2004.03.001 · Cited by 18

A Study on Intercategorial Polysemy of Head

Min, H. (2020) · STUDIES IN HUMANITIES · doi:10.33252/sih.2020.9.66.117

Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard

Karch, A., Koch, A., Zapf, A., Zerr, I., & Karch, A. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.03.022 · Cited by 23

Head-to-Head Polymers. XXVIII. Attempted Syntheses of Linear Head-to-Head Polyisobutylene

Malanga, M., & Vogl, O. (1985) · Journal of Macromolecular Science Part A - Chemistry · doi:10.1080/00222338508063361 · Cited by 4