Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI: Motion Bias in Analyses of Structural MRI
Open accessAbstract
While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in-scanner motion on morphological analysis of structural MRI is relatively under-studied. Even among "good quality" structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in-scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend-level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non-overlapping sets of structural MRI scans, convergent evidence showed that in-scanner motion-even at levels which do not manifest in visible motion artifact-can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non-sedated humans. Hum Brain Mapp 37:2385-2397, 2016. © 2016 Wiley Periodicals, Inc.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Motion in the scanner can significantly influence measures of cortical thickness and surface area, DTI parameters, and resting-state networks.
“It is increasingly recognized that motion in the scanner can significantly influence measures of cortical thickness and surface area27, 218, as well as DTI parameters (Box 2)219 and resting-state networks (Box 3)220,219.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [218]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610 · see citing works
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · see citing works
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 6,805 · see citing works
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147 · see citing works
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731 · see citing works
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208 · see citing works
Movement‐Related effects in fMRI time‐series
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350312 · Cited by 3,774 · see citing works
Spatial registration and normalization of images
Friston, K. J., Ashburner, J., Frith, C., Poline, J.-B., Heather, J. D., & Frackowiak, R. S. J. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030303 · Cited by 3,744 · see citing works
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 1× · see citing works
“For the CIVET analysis, images were registered into a standardized stereotaxic space by means of a linear transformation and corrected for intensity non-uniformities [Collins et al., 1994; Sled et al., 1998].”
MethodAdaptive linear step-up procedures that control the false discovery rate
Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006) · Biometrika · doi:10.1093/biomet/93.3.491 · Cited by 3,228 · see citing works
The influence of head motion on intrinsic functional connectivity MRI
Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.044 · Cited by 2,344 · see citing works
A hybrid approach to the skull stripping problem in MRI
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D. H., Hahn, H., & Fischl, B. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.032 · Cited by 2,227 · see citing works
Sequence-independent segmentation of magnetic resonance images
Fischl, B. R., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,117 · see citing works
Fischl, B. R., Liu, A., & Dale, A. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906426 · Cited by 1,755 · see citing works
Jovicich, J., Czanner, S., Greve, D. N., Haley, E., van der Kouwe, A. J. W., Gollub, R. L., Kennedy, D. N., Schmitt, F., Brown, G. G., MacFall, J. R., et al. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.046 · Cited by 1,269 · see citing works
Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.063 · Cited by 1,125 · see citing works
Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops
Ségonne, F., Pacheco, J., & Fischl, B. R. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.887364 · Cited by 995 · see citing works
Kim, J. S., Singh, V., Lee, J. K., Lerch, J. P., Ad-Dab’bagh, Y., MacDonald, D. J., Lee, J. M., Kim, S. I., & Evans, A. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.03.036 · Cited by 816 · see citing works
Adaptive technique for high-definition MR imaging of moving structures.
Ehman, R. L., & Felmlee, J. P. (1989) · Radiology · doi:10.1148/radiology.173.1.2781017 · Cited by 806 · see citing works
Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., Clasen, L., Gogtay, N., & Giedd, J. N. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.0054-11.2011 · Cited by 712 · see citing works
Self-citationFast and robust parameter estimation for statistical partial volume models in brain MRI
Tohka, J., Zijdenbos, A. P., & Evans, A. C. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.05.007 · Cited by 670 · see citing works
Artifacts due to stimulus correlated motion in functional imaging of the brain
Hajnal, J. V., Myers, R., Oatridge, A., Schwieso, J. E., YOUNG, I. R., & Bydder, G. M. (1994) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910310307 · Cited by 486 · see citing works
Head motion during MRI acquisition reduces gray matter volume and thickness estimates
Reuter, M., Tisdall, M. D., Qureshi, A., Buckner, R. L., van der Kouwe, A. J. W., & Fischl, B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.006 · Cited by 457 · see citing works
Brain size and cortical structure in the adult human brain.
Im, K., Lee, J.-M., Lyttelton, O., Kim, S. H., Evans, A. C., & Kim, S. I. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm244 · Cited by 444 · see citing works
Integrated strategy for improving functional connectivity mapping using multiecho fMRI
Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J., Saad, Z. S., Bandettini, P. A., & Bullmore, E. T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301725110 · Cited by 399 · see citing works
Zaitsev, M., Dold, C., Sakas, G., Hennig, J., & Speck, O. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.039 · Cited by 392 · see citing works
PROMO: Real‐time prospective motion correction in MRI using image‐based tracking
White, N. S., Roddey, J. C., Shankaranarayanan, A., Han, E. T., Rettmann, D. W., Santos, J. M., Kuperman, J., & Dale, A. M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22176 · Cited by 377 · see citing works
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series
Patel, A. X., Kundu, P., Rubinov, M., Jones, P. S., Vértes, P. E., Ersche, K. D., John Suckling, A., & Bullmore, E. T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.012 · Cited by 354 · see citing works
What is the best similarity measure for motion correction in fMRI time series?
Freire, L., Roche, A., & Mangin, J. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.1009383 · Cited by 353 · see citing works
Motion correction and the use of motion covariates in multiple‐subject fMRI analysis
Johnstone, T., Walsh, K. S. O., Greischar, L. L., Alexander, A. L., Fox, A. S., Davidson, R. J., & Oakes, T. R. (2006) · Human Brain Mapping · doi:10.1002/hbm.20219 · Cited by 320 · see citing works
MR image artifacts from periodic motion
Wood, M. L., & Henkelman, R. M. (1985) · Medical Physics · doi:10.1118/1.595782 · Cited by 306 · see citing works
Ducharme, S., Albaugh, M. D., Nguyen, T.-V., Hudziak, J. J., Mateos-Pérez, J. M., Labbe, A., Evans, A. C., & Karama, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.010 · Cited by 304 · see citing works
Gender difference analysis of cortical thickness in healthy young adults with surface-based methods
Im, K., Lee, M.-J., Lee, J., Shin, Y.-W., Kim, I. Y., Kwon, J. M., & Kim, S. I. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.11.042 · Cited by 294 · see citing works
Atkinson, D., Hill, D., Stoyle, P. N. R., Summers, P. E., & Keevil, S. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.650886 · Cited by 282 · see citing works
Jo, H. J., Gotts, S. J., Reynolds, R. C., Bandettini, P. A., Martin, A. D., Cox, R. W., & Saad, Z. S. (2013) · Journal of Applied Mathematics · doi:10.1155/2013/935154 · Cited by 277 · see citing works
Characterizing white matter with magnetization transfer andT2
Stanisz, G. J., Kecojević, A., Bronskill, M. J., & Henkelman, R. M. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199912)42:6 · Cited by 249 · see citing works
Real‐time rigid body motion correction and shimming using cloverleaf navigators
van der Kouwe, A. J. W., Benner, T., & Dale, A. M. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21038 · Cited by 231 · see citing works
Spherical navigator echoes for full 3D rigid body motion measurement in MRI
Welch, E. B., Manduca, A., Grimm, R. C., Ward, H. A., & Jack, C. R. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10012 · Cited by 213 · see citing works
Comparison of fMRI motion correction software tools
Oakes, T., Johnstone, T., Walsh, K. S. O., Greischar, L., Alexander, A., Fox, A., & Davidson, R. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.05.058 · Cited by 207 · see citing works
Measurement of Signal-to-Noise and Contrast-to-Noise in the fBIRN Multicenter Imaging Study
Magnotta, V. A., Friedman, L., & BIRN, F. (2006) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-006-0264-x · Cited by 194 · see citing works
Automatic quality assessment in structural brain magnetic resonance imaging
Mortamet, B., Bernstein, M. A., Jack, C. R., Gunter, J. L., Ward, C. P., Britson, P. J., Meuli, R. A., Thiran, J., & Krüger, G. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21992 · Cited by 171 · see citing works
An Image-based Approach to Understanding the Physics of MR Artifacts
Morelli, J. N., Runge, V. M., Ai, F., Attenberger, U. I., Vu, L., Schmeets, S. H., Nitz, W. R., & KIRSCH, J. E. (2011) · Radiographics · doi:10.1148/rg.313105115 · Cited by 171 · see citing works
Prospective real‐time correction for arbitrary head motion using active markers
Ooi, M. B., Krueger, S., Thomas, W. J., Swaminathan, S. V., & Brown, T. R. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22082 · Cited by 166 · see citing works
Qin, L., van Gelderen, P., Derbyshire, J. A., Jin, F., Lee, J., de Zwart, J. A., Tao, Y., & Duyn, J. H. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22076 · Cited by 152 · see citing works
From genes to folds: a review of cortical gyrification theory
Ronan, L., & Fletcher, P. C. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0961-z · Cited by 145 · see citing works
Self-citationMotion Artifact in Magnetic Resonance Imaging: Implications for Automated Analysis
Blumenthal, J. D., Zijdenbos, A. P., Molloy, E. A., & Giedd, J. N. (2002) · NeuroImage · doi:10.1006/nimg.2002.1076 · Cited by 137 · see citing works
Self-citationAnisotropic diffusion in kidney: Apparent diffusion coefficient measurements for clinical use
Fukuda, Y., Ohashi, I., Hanafusa, K., Nakagawa, T., Ohtani, S., Annaka, Y., Hayashi, T., & Shibuya, H. (2000) · Journal of Magnetic Resonance Imaging · doi:10.1002/(sici)1522-2586(200002)11:2 · Cited by 128 · see citing works
Dantendorfer, K., Amering, M., Bankier, A. A., Helbich, T. H., Prayer, D., Youssefzadeh, S., Alexandrowicz, R. W., Imhof, H., & Katschnig, H. (1997) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(96)00385-2 · Cited by 95 · see citing works
Adaptive technique for three-dimensional MR imaging of moving structures.
Korin, H. W., Felmlee, J. P., Ehman, R. L., & Riederer, S. J. (1990) · Radiology · doi:10.1148/radiology.177.1.2399320 · Cited by 78 · see citing works
Autocorrection in MR Imaging: Adaptive Motion Correction without Navigator Echoes
Manduca, A., McGee, K. P., Welch, E. B., Felmlee, J. P., Grimm, R. C., & Ehman, R. L. (2000) · Radiology · doi:10.1148/radiology.215.3.r00jn19904 · Cited by 61 · see citing works
Prospective motion correction improves diagnostic utility of pediatric MRI scans
Kuperman, J. M., Brown, T. T., Golsefidi, M. A., Erhart, M. J., White, N. S., Roddey, J. C., Shankaranarayanan, A., Han, E. T., Rettmann, D. W., & Dale, A. M. (2011) · Pediatric Radiology · doi:10.1007/s00247-011-2205-1 · Cited by 57 · see citing works
Kundu, P., Santin, M. D., Bandettini, P. A., Bullmore, E. T., & Petiet, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.025 · Cited by 50 · see citing works
Norris, D. G., & Driesel, W. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1099 · Cited by 47 · see citing works
Preboske, G. M., Gunter, J. L., Ward, C. P., & Jack, C. R. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.10.049 · Cited by 44 · see citing works
Online motion correction for diffusion-weighted segmented-EPI and FLASH imaging
Weih, K., Driesel, W., von Mengershausen, M., & Norris, D. G. (2004) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-004-0036-x · Cited by 23 · see citing works
Simulation of Acquisition Artefacts in MR Scans: Effects on Automatic Measures of Brain Atrophy
Cámara, Ó., Sneller, B., Ridgway, G. R., Garde, E., Fox, N. C., & Hill, D. (2006) · Lecture notes in computer science · doi:10.1007/11866565_34 · Cited by 9 · see citing works
Buckley (1999) · doi:10.1002/(sici)1522-2594(199901)41:1
Flagged first, then by citations
Cited by
Sorted by citations
Imaging structural and functional brain development in early childhood
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2018.1 · Cited by 1,013 · see citing works
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920 · see citing works
On testing for spatial correspondence between maps of human brain structure and function
Alexander‐Bloch, A. F., Shou, H., Liu, S., Satterthwaite, T. D., Glahn, D. C., Shinohara, R. T., Vandekar, S., & Raznahan, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.070 · Cited by 733 · see citing works
Self-citationTamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R., Blakemore, S.-J., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., Crone, E. A., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3302-16.2017 · Cited by 685 · see citing works
Self-citationPredicting brain-age from multimodal imaging data captures cognitive impairment
Liem, F., Varoquaux, G. P., Kynast, J., Beyer, F., Masouleh, S. K., Huntenburg, J. M., Lampe, L., Rahim, M., Abraham, A., Craddock, R. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.005 · Cited by 502 · see citing works
Quantitative Assessment of Structural Image Quality
Rosen, A. F. G., Roalf, D., Ruparel, K., Blake, J., Seelaus, K., Villa, L., Ciric, R., Cook, P. A., Davatzikos, C., Elliott, M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.059 · Cited by 443 · see citing works
Adolescent Tuning of Association Cortex in Human Structural Brain Networks
Váša, F., Seidlitz, J., Romero-García, R., Whitaker, K. J., Rosenthal, G., Vértes, P. E., Shinn, M., Alexander‐Bloch, A. F., Fonagy, P., Dolan, R. J., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx249 · Cited by 345 · see citing works
Self-citationStudying neuroanatomy using MRI
Lerch, J. P., van der Kouwe, A. J. W., Raznahan, A., Paus, T., Johansen‐Berg, H., Miller, K. L., Smith, S. M., Fischl, B. R., & Sotiropoulos, S. N. (2017) · Nature Neuroscience · doi:10.1038/nn.4501 · Cited by 313 · see citing works
Self-citationValk, S. L., Bernhardt, B. C., Trautwein, F.-M., Böckler, A., Kanske, P., Guizard, N., Collins, D. L., & Singer, T. (2017) · Science Advances · doi:10.1126/sciadv.1700489 · Cited by 264 · see citing works
Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change
Oschwald, J., Guye, S., Liem, F., Rast, P., Willis, S. L., Röcke, C., Jäncke, L., Martin, M., & Mérillat, S. (2019) · Reviews in the Neurosciences · doi:10.1515/revneuro-2018-0096 · Cited by 251 · see citing works
Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data
Klapwijk, E. T., van de Kamp, F., van der Meulen, M., Peters, S., & Wierenga, L. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.014 · Cited by 192 · see citing works
Bedford, S. A., Park, M. T. M., Devenyi, G. A., Tullo, S., Germann, J., Patel, R., Anagnostou, E. A., Baron‐Cohen, S., Bullmore, E. T., Chura, L. R., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0420-6 · Cited by 181 · see citing works
Self-citationChildhood Obesity, Cortical Structure, and Executive Function in Healthy Children
Ronan, L., Alexander‐Bloch, A. F., & Fletcher, P. C. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz257 · Cited by 190 · see citing works
Self-citationHerting, M. M., Johnson, C., Mills, K. L., Vijayakumar, N., Dennison, M., Liu, C., Goddings, A.-L., Dahl, R. E., Sowell, E. R., Whittle, S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.020 · Cited by 185 · see citing works
Savalia, N. K., Agres, P. F., Chan, M. Y., Feczko, E., Kennedy, K. M., & Wig, G. S. (2016) · Human Brain Mapping · doi:10.1002/hbm.23397 · Cited by 167 · see citing works
Grydeland, H., Vértes, P. E., Váša, F., Romero-García, R., Whitaker, K. J., Alexander‐Bloch, A. F., Bjørnerud, A., Patel, A. X., Sederevičius, D., Tamnes, C. K., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy330 · Cited by 166 · see citing works
Self-citationBackhausen, L. L., Herting, M. M., Buse, J., Roessner, V., Smolka, M. N., & Vetter, N. C. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00558 · Cited by 147 · see citing works
Merz, E. C., Maskus, E. A., Melvin, S. A., He, X., & Noble, K. G. (2019) · Child Development · doi:10.1111/cdev.13239 · Cited by 140 · see citing works
Norbom, L. B., Ferschmann, L., Parker, N., Agartz, I., Andreassen, O. A., Paus, T., Westlye, L. T., & Tamnes, C. K. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102109 · Cited by 122 · see citing works
Tomasi, D. G., & Volkow, N. D. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01130-0 · Cited by 126 · see citing works
Structural brain development: A review of methodological approaches and best practices
(2017) · doi:10.1016/j.dcn.2017.11.008 · Cited by 124 · see citing works
Self-citationHedges, E. P., Dimitrov, M., Zahid, U., Vega, B. B., Si, S., Dickson, H., McGuire, P. K., Williams, S., Barker, G. J., & Kempton, M. J. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118751 · Cited by 118 · see citing works
Test–retest reliability of brain morphology estimates
Madan, C. R., & Kensinger, E. A. (2017) · Brain Informatics · doi:10.1007/s40708-016-0060-4 · Cited by 120 · see citing works
The impact of in-scanner head motion on structural connectivity derived from diffusion MRI
Baum, G. L., Roalf, D. R., Cook, P. A., Ciric, R., Rosen, A. F. G., Xia, C., Elliott, M. A., Ruparel, K., Verma, R., Tunç, B., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.041 · Cited by 116 · see citing works
Brain structure in pediatric Tourette syndrome
and The Tourette Association of America Neuroimaging Consortium, Greene, D. J., Williams III, A. C., Koller, J. M., Schlaggar, B. L., & Black, K. J. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.194 · Cited by 89 · see citing works
Predicting age from cortical structure across the lifespan
Madan, C. R., & Kensinger, E. A. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13835 · Cited by 91 · see citing works
Opportunities for increased reproducibility and replicability of developmental neuroimaging
Klapwijk, E. T., van den Bos, W., Tamnes, C. K., Raschle, N. M., & Mills, K. L. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100902 · Cited by 90 · see citing works
Variations in structural MRI quality significantly impact commonly used measures of brain anatomy
Gilmore, A. D., Buser, N. J., & Hanson, J. L. (2021) · Brain Informatics · doi:10.1186/s40708-021-00128-2 · Cited by 86 · see citing works
Dimitrova, R., Pietsch, M., Ciarrusta, J., Fitzgibbon, S. P., Williams, L. Z. J., Christiaens, D., Cordero‐Grande, L., Batallé, D., Makropoulos, A., Schuh, A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118488 · Cited by 87 · see citing works
Merz, E. C., Desai, P. M., Maskus, E. A., Melvin, S. A., Rehman, R., Torres, S. D., Meyer, J. S., He, X., & Noble, K. G. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.05.024 · Cited by 86 · see citing works
Structural covariance networks across the life span, from 6 to 94 years of age
DuPré, E., & Spreng, R. N. (2017) · Network Neuroscience · doi:10.1162/netn_a_00016 · Cited by 87 · see citing works
Structural MRI of brain similarity networks
Sebenius, I., Dorfschmidt, L., Seidlitz, J., Alexander‐Bloch, A. F., Morgan, S. E., & Bullmore, E. T. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00882-2 · Cited by 81 · see citing works
Self-citationCrowdsourced MRI quality metrics and expert quality annotations for training of humans and machines
Estéban, O., Blair, R. W., Nielson, D. M., Varada, J. C., Marrett, S., Thomas, A. G., Poldrack, R. A., & Gorgolewski, K. J. (2019) · Scientific Data · doi:10.1038/s41597-019-0035-4 · Cited by 79 · see citing works
Head motion: the dirty little secret of neuroimaging in psychiatry
Makowski, C., Lepage, M., & Evans, A. C. (2018) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.180022 · Cited by 77 · see citing works
Patterns of sociocognitive stratification and perinatal risk in the child brain
Alnæs, D., Kaufmann, T., Marquand, A. F., Smith, S. M., & Westlye, L. T. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001517117 · Cited by 73 · see citing works
Less head motion during MRI under task than resting-state conditions
Huijbers, W., Dijk, K. R. A. V., Boenniger, M. M., Stirnberg, R., & Breteler, M. M. B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.002 · Cited by 70 · see citing works
Markerless high‐frequency prospective motion correction for neuroanatomical MRI
Frost, R., Wighton, P., Karahanoğlu, F. I., Robertson, R. L., Grant, P. E., Fischl, B. R., Tisdall, M. D., & van der Kouwe, A. J. W. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27705 · Cited by 70 · see citing works
Sujit, S. J., Coronado, I., Kamali, A., Narayana, P. A., & Gabr, R. E. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26693 · Cited by 68 · see citing works
Phan, T. V., Smeets, D., Talcott, J. B., & Vandermosten, M. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.08.009 · Cited by 67 · see citing works
Atypical Functional Connectivity in Tourette Syndrome Differs Between Children and Adults
Nielsen, A. N., Gratton, C., Church, J. A., Dosenbach, N. U. F., BLACK, K. J., Petersen, S. E., Schlaggar, B. L., & Greene, D. J. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.06.021 · Cited by 65 · see citing works
A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
Torbati, M. E., Minhas, D. S., Ahmad, G., O’Connor, E. E., Muschelli, J., Laymon, C. M., Yang, Z., Cohen, A. D., Aizenstein, H. J., Klunk, W. E., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118703 · Cited by 64 · see citing works
White, T. J. H., Jansen, P. R., Muetzel, R. L., Sudre, G. P., Marroun, H. E., Tiemeier, H. W., Qiu, A., Shaw, P., Michael, A. M., & Verhulst, F. C. (2017) · Human Brain Mapping · doi:10.1002/hbm.23911 · Cited by 63 · see citing works
Age differences in head motion and estimates of cortical morphology
Madan, C. R. (2018) · PeerJ · doi:10.7717/peerj.5176 · Cited by 63 · see citing works
Functional Parcellation of the Cerebral Cortex Across the Human Adult Lifespan
Han, L., Savalia, N. K., Chan, M. Y., Agres, P. F., Nair, A., & Wig, G. S. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy218 · Cited by 59 · see citing works
Bedford, S. A., Lai, M., Lombardo, M. V., Chakrabarti, B., Ruigrok, A. N. V., John Suckling, A., Anagnostou, E. A., Lerch, J. P., Taylor, M. J., Nicolson, R., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.07.024 · Cited by 62 · see citing works
Self-citationNárai, Á., Hermann, P., Auer, T., Kemenczky, P., Szalma, J., Homolya, I., Somogyi, E., Vakli, P., Weiss, B., & Vidnyánszky, Z. (2022) · Scientific Data · doi:10.1038/s41597-022-01694-8 · Cited by 55 · see citing works
Sex-biased trajectories of amygdalo-hippocampal morphology change over human development
Fish, A. M., Nadig, A., Seidlitz, J., Reardon, P. K., Mankiw, C., McDermott, C. L., Blumenthal, J. D., CLASEN, L. S., Lalonde, F. M., Lerch, J. P., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116122 · Cited by 53 · see citing works
Self-citationInvestigating the impact of motion in the scanner on brain age predictions
Moqadam, R., Dadar, M., & Zeighami, Y. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00079 · Cited by 49 · see citing works
Ji, C., Patil, K. R., Yeo, B. T. T., & Eickhoff, S. B. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.07.025 · Cited by 43 · see citing works
A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal
Shaw, R., Sudre, C. H., Varsavsky, T., Sebastien Ourselin, A., & Cardoso, M. J. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2972547 · Cited by 45 · see citing works
Associations between handedness and brain functional connectivity patterns in children
Tomasi, D. G., & Volkow, N. D. (2024) · Nature Communications · doi:10.1038/s41467-024-46690-1 · Cited by 42 · see citing works
Advances in Studying Brain Morphology: The Benefits of Open-Access Data
Madan, C. R. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00405 · Cited by 42 · see citing works
Frew, S., Samara, A., Shearer, H., Eilbott, J. A., & Vanderwal, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265112 · Cited by 39 · see citing works
The YOUth cohort study: MRI protocol and test-retest reliability in adults
Buimer, E. E. L., Pas, P., Brouwer, R. M., Froeling, M., Hoogduin, H. M., Leemans, A., Luijten, P. R., van Nierop, B. J., Raemaekers, M. A. H., Schnack, H. G., et al. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100816 · Cited by 38 · see citing works
Latent Clinical-Anatomical Dimensions of Schizophrenia
Kirschner, M., Shafiei, G., Markello, R. D., Makowski, C., Talpalaru, A., Hodzic-Santor, B. H., Devenyi, G. A., Paquola, C., Bernhardt, B. C., Lepage, M., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa097 · Cited by 40 · see citing works
Khundrakpam, B. S., Lewis, J. D., Jeon, S., Kostopoulos, P., Medina, Y. I., Chouinard-Decorte, F., & Evans, A. C. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx317 · Cited by 35 · see citing works
Makowski, C., Bodnar, M. D., MALLA, A. K., Joober, R., & Lepage, M. (2016) · Schizophrenia · doi:10.1038/npjschz.2016.29 · Cited by 38 · see citing works
Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders
Bednarz, H., & Kana, R. (2018) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.03.025 · Cited by 28 · see citing works
Restoring statistical validity in group analyses of motion‐corrupted MRI data
Lutti, A., Corbin, N., Ashburner, J., Ziegler, G., Draganski, B., Phillips, C. L. M., Kherif, F., Callaghan, M. F., & Domenicantonio, G. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25767 · Cited by 35 · see citing works
Wen, J., Samper‐Gonzàlez, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Epelbaum, S., Bertrand, A., et al. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09469-5 · Cited by 34 · see citing works
Decline Variability of Cortical and Subcortical Regions in Aging: A Longitudinal Study
Sele, S., Liem, F., Mérillat, S., & Jäncke, L. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00363 · Cited by 33 · see citing works
Hippocampal shape across the healthy lifespan and its relationship with cognition
Bussy, A., Patel, R., Plitman, E., Tullo, S., Salaciak, A., Bedford, S. A., Farzin, S., Béland, M., Valiquette, V., Kazazian, C., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.03.018 · Cited by 30 · see citing works
NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction
Pardoe, H., & Kuzniecky, R. (2017) · Neuroinformatics · doi:10.1007/s12021-017-9346-9 · Cited by 33 · see citing works
Ferschmann, L., Fjell, A. M., Vollrath, M. E., Grydeland, H., Walhovd, K. B., & Tamnes, C. K. (2018) · Child Development · doi:10.1111/cdev.13016 · Cited by 32 · see citing works
Pulli, E. P., Silver, E. A., Kumpulainen, V., Copeland, A., Merisaari, H., Saunavaara, J. P., Parkkola, R. K., Lähdesmäki, T., Saukko, E., Nolvi, S., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.874062 · Cited by 32 · see citing works
Weight loss reduces head motion: Revisiting a major confound in neuroimaging
Beyer, F., Prehn, K., Wüsten, K. A., Villringer, A., Ordemann, J., Flöel, A., & Witte, A. V. (2020) · Human Brain Mapping · doi:10.1002/hbm.24959 · Cited by 31 · see citing works
MR‐PET head motion correction based on co‐registration of multicontrast MR images
Chen, Z., Sforazzini, F., Baran, J., Close, T., Shah, N. J., & Egan, G. F. (2019) · Human Brain Mapping · doi:10.1002/hbm.24497 · Cited by 30 · see citing works
Nassar, R., Kaczkurkin, A., Xia, C., Sotiras, A., Pehlivanova, M., Moore, T. M., de la Garza, A. G., Roalf, D., Rosen, A. F. G., Lorch, S., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy091 · Cited by 30 · see citing works
Yu, M., Cullen, N. C., Linn, K. A., Oathes, D. J., Seok, D., Cook, P. A., Duprat, R., Aselcioglu, I., Moore, T. M., Davatzikos, C. A., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01039-8 · Cited by 29 · see citing works
Ai, L., Craddock, R. C., Tottenham, N., Dyke, J. P., Lim, R. W.-S., Colcombe, S. J., Milham, M. P., & Franco, A. R. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117585 · Cited by 28 · see citing works
Quality control in functional MRI studies with MRIQC and fMRIPrep
Provins, C., MacNicol, E., Seeley, S. H., Hagmann, P., & Estéban, O. (2023) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.1073734 · Cited by 28 · see citing works
Ferschmann, L., Vijayakumar, N., Grydeland, H., Øverbye, K., Mills, K. L., Fjell, A. M., Walhovd, K. B., Pfeifer, J. H., & Tamnes, C. K. (2021) · Cortex · doi:10.1016/j.cortex.2020.11.022 · Cited by 29 · see citing works
Yin, G., Li, T., Jin, S., Wang, N., Li, J., Wu, C., He, H., & Wang, J. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad178 · Cited by 26 · see citing works
Yao, N., Winkler, A. M., Barrett, J., Book, G. A., Beetham, T., Horseman, R., Leach, O., Hodgson, K., Knowles, E. E. M., Mathias, S. R., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23612 · Cited by 25 · see citing works
Involvement of the habenula in the pathophysiology of autism spectrum disorder
Germann, J., Gouveia, F. V., Brentani, H., Bedford, S. A., Tullo, S., Chakravarty, M. M., & Devenyi, G. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-00603-0 · Cited by 25 · see citing works
MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data
Torbati, M. E., Minhas, D. S., Laymon, C. M., Maillard, P., Wilson, J. D., Chen, C., Crainiceanu, C. M., DeCarli, C. S., Hwang, S. J., & Tudorascu, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102926 · Cited by 24 · see citing works
Beelen, C., Phan, T. V., Wouters, J. M., Ghesquière, P., & Vandermosten, M. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00143 · Cited by 23 · see citing works
Major depressive disorder on a neuromorphic continuum
Li, J., Long, Z., Ji, G., Han, S., Chen, Y., Yao, G., Xu, Y. J., Zhang, K., Zhang, Y., Cheng, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57682-0 · Cited by 22 · see citing works
Brain morphometry in older adults with and without dementia using extremely rapid structural scans
Elliott, M. L., Hanford, L. C., Hamadeh, A., Hilbert, T., Kober, T., Dickerson, B. C., Mair, R. W., Eldaief, M. C., & Buckner, R. L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120173 · Cited by 21 · see citing works
Mareva, S., Akarca, D., The CALM team, & Holmes, J. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13685 · Cited by 20 · see citing works
McDonough, I. M., & Madan, C. R. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.10.023 · Cited by 20 · see citing works
Examining the reliability of brain age algorithms under varying degrees of participant motion
Hanson, J. L., Adkins, D. J., Bacas, E., & Zhou, P. (2024) · Brain Informatics · doi:10.1186/s40708-024-00223-0 · Cited by 22 · see citing works
FetMRQC: A robust quality control system for multi-centric fetal brain MRI
Sanchez, T., Estéban, O., Gomez, Y., Pron, A., Koob, M., Dunet, V., Girard, N. J., András, J., Eixarch, E., Auzias, G., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103282 · Cited by 19 · see citing works
Ume, C. O., Onah, O. G., Okpukpara, B., Ume, N. N. C., Ukwuaba, I. C., Omeje, E. E., Chiemela, C. J., Ibe, J. C., & ORAZULIKE, O. F. (2023) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2023.1173043 · Cited by 18 · see citing works
Kecskemeti, S., Freeman, A., Travers, B. G., & Alexander, A. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118447 · Cited by 18 · see citing works
Addressing artifactual bias in large, automated MRI analyses of brain development
Elyounssi, S., Kunitoki, K., Clauss, J. A., Laurent, E., Kane, K. A., Hughes, D., Hopkinson, C. E., Bazer, O. M., Sussman, R. F., Doyle, A. E., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01990-7 · Cited by 17 · see citing works
Loizillon, S., Bottani, S., Maire, A., Ströer, S., Dormont, D., Colliot, O., & Burgos, N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103073 · Cited by 17 · see citing works
Pan, X., Zhang, M., Tian, A., Chen, L., Sun, Z., Wang, L., & Chen, P. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102870 · Cited by 16 · see citing works
Nakua, H., Hawco, C., Forde, N. J., Joseph, M. J. E., Grillet, M., Johnson, D., Jacobs, G. R., Hill, S. L., Voineskos, A., Wheeler, A. L., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120119 · Cited by 16 · see citing works
FreeSurfer 7 quality control: Key problem areas and importance of manual corrections
Vahermaa, V., Aydogan, D. B., Raij, T. T., Armio, R.-L., Laurikainen, H., Saramäki, J., & Suvisaari, J. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120306 · Cited by 16 · see citing works
Alexander‐Bloch, A. F., Mathias, S. R., Fox, P. T., Olvera, R. L., Göring, H. H. H., Duggirala, R., Curran, J. E., Blangero, J., & Glahn, D. C. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx309 · Cited by 16 · see citing works
Self-citationPhan, T. V., Sima, D. M., Beelen, C., Vanderauwera, J., Smeets, D., & Vandermosten, M. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.05.030 · Cited by 16 · see citing works
Hippocampal Volume in Provisional Tic Disorder Predicts Tic Severity at 12-Month Follow-up
Kim, S., Greene, D. J., D’Andrea, C. B., Bihun, E. C., Koller, J. M., O’Reilly, B., Schlaggar, B. L., & BLACK, K. J. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061715 · Cited by 16 · see citing works
Plitman, E., Bussy, A., Valiquette, V., Salaciak, A., Patel, R., Cupo, L., Béland, M., Tullo, S., Tardif, C. L., Rajah, M. N., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118172 · Cited by 16 · see citing works
Bray, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23526 · Cited by 15 · see citing works
Olafson, E. R., Bedford, S. A., Devenyi, G. A., Patel, R., Tullo, S., Park, M. T. M., Parent, O., Anagnostou, E. A., Baron‐Cohen, S., Bullmore, E. T., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab015 · Cited by 15 · see citing works
Self-citationMorphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application
Li, J., Jin, S., Li, Z., Zeng, X., Yang, Y., Luo, Z., Xu, X., Cui, Z., Liu, Y., & Wang, J. (2024) · Advanced Science · doi:10.1002/advs.202400061 · Cited by 13 · see citing works
Uncovering and mitigating bias in large, automated MRI analyses of brain developmentPreprint
Elyounssi, S., Kunitoki, K., Clauss, J. A., Laurent, E., Kane, K. A., Hughes, D., Hopkinson, C. E., Bazer, O. M., Sussman, R. F., Doyle, A. E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.28.530498 · Cited by 14 · see citing works
Osadebey, M. E., Pedersen, M., Arnold, D. L., & Wendel-Mitoraj, K. E. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2855213 · Cited by 13 · see citing works
Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets
Ding, Y., Suffren, S., Bellec, P., & Lodygensky, G. A. (2018) · Human Brain Mapping · doi:10.1002/hbm.24449 · Cited by 13 · see citing works
- Authors
- Alexander-Bloch, A., Clasen, L., Stockman, M., Ronan, L., Lalonde, F., Giedd, J., & Raznahan, A.
- Affiliations
- Developmental Neurogenomics Unit; Child Psychiatry Branch; National Institute of Mental Health; Bethesda Maryland
- National Institutes of Health (US)
- University of Cambridge (GB)
- University of California San Diego (US)
- Corresponding author
- Raznahan, Armin
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2016-03-23
- Volume
- 37
- Issue
- 7
- Pages
- 2385-2397
- ISSN
- 1065-9471 (ISSN-L)
- PMC
- PMC5110234
- OpenAlex
- W2310662295
- Cited by
- 178
- References
- 62
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 18 · 2023: 20 · 2022: 10 · 2021: 21 · 2020: 20 · 2019: 16 · 2018: 22 · 2017: 18 · 2016: 6
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (095692/Z/11/Z)
- National Institutes of Health (Z99 MH999999)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- structural MRI, brain morphometry, cortical gray matter volume, cortical thickness, mean curvature, automated brain segmentation, motion estimation, pediatric neuroimaging, convergent validity
- MeSH terms
- Adolescent, Adult, Brain, Child, Child, Preschool, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Organ Size, Pattern Recognition, Automated, Artifacts, Young Adult, Gray Matter
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
