Neuroanatomical Assessment of Biological Maturity
Open accessAbstract
Structural MRI allows unparalleled in vivo study of the anatomy of the developing human brain. For more than two decades, MRI research has revealed many new aspects of this multifaceted maturation process, significantly augmenting scientific knowledge gathered from postmortem studies. Postnatal brain development is notably protracted and involves considerable changes in cerebral cortical, subcortical, and cerebellar structures, as well as significant architectural changes in white matter fiber tracts (see [12]). Although much work has described isolated features of neuroanatomical development, it remains a critical challenge to characterize the multidimensional nature of brain anatomy, capturing different phases of development among individuals. Capitalizing on key advances in multisite, multimodal MRI, and using cross-validated nonlinear modeling, we demonstrate that developmental brain phase can be assessed with much greater precision than has been possible using other biological measures, accounting for more than 92% of the variance in age. Further, our composite metric of morphology, diffusivity, and signal intensity shows that the average difference in phase among children of the same age is only about 1 year, revealing for the first time a latent phenotype in the human brain for which maturation timing is tightly controlled.
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?
Most studies find cortical thickness decreases linearly after age four to five years, although some studies suggest an increase from age six to ten and thinning after ten to twelve.
“Whereas some studies have suggested that cortical thickness increases from age 6 to 10 years and begins to thin after 10–12 years21,20, most studies find that cortical thickness decreases linearly after age 4–5 years25,27,28.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [25]
Association cortices reach peak thickness values later than sensory cortices.
“Notably, association cortices reach peak thickness values later than sensory cortices (Brown et al., 2012, Raznahan et al., 2011, Sowell et al., 2004).”
From Quantifying cortical development in typically developing toddlers and young children, 1–6 years of age (2017) · cites this paper as Brown et al., 2012
Only one prior study had used more than one imaging modality to derive an integrated brain development index.
“Notably, however, only one prior study (Brown et al. 2012) has used more than one imaging modality to derive an integrated index of brain development, which successfully predicted the chronological age of subjects with very high accuracy (subjects aged 3-20, r = 0.96, MAE = 1.03).”
From Imaging Patterns of Brain Development and their Relationship to Cognition (2015) · cites this paper as Brown et al. 2012
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
Brain development during childhood and adolescence: a longitudinal MRI study
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C., & Rapoport, J. L. (1999) · Nature Neuroscience · doi:10.1038/13158 · Cited by 5,552 · see citing works
Mapping cortical change across the human life span
Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003) · Nature Neuroscience · doi:10.1038/nn1008 · Cited by 2,328 · see citing works
Self-citationPrediction of individual brain maturity using fMRI
Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., et al. (2010) · Science · doi:10.1126/science.1194144 · Cited by 2,183 · see citing works
A new system of dental age assessment.
Demirjian, A., Goldstein, H., & Tanner, J. M. (1973) · PubMed · Cited by 2,174 · see citing works
Neurodevelopmental Trajectories of the Human Cerebral Cortex
Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., Greenstein, D., Clasen, L., Evans, A., Rapoport, J. L., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.5309-07.2008 · Cited by 1,644 · see citing works
Alzheimer’s Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W. J., Trojanowski, J. Q., Toga, A. W., & Beckett, L. A. (2007) · Springer eBooks · doi:10.1007/978-0-387-72076-0_18 · Cited by 1,493 · see citing works
Glasser, M. F., & Van Essen, D. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2180-11.2011 · Cited by 1,447 · see citing works
Mapping brain maturation and cognitive development during adolescence
Paus, T. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.12.008 · Cited by 1,348 · see citing works
Structural maturation of neural pathways in children and adolescents: in vivo study
Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., Rapoport, J. L., & Evans, A. C. (1999) · Science · doi:10.1126/science.283.5409.1908 · Cited by 1,322 · see citing works
Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood
Lebel, C., & Beaulieu, C. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5302-10.2011 · Cited by 1,193 · see citing works
Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014821 · Cited by 1,012 · see citing works
Structural MRI of pediatric brain development: what have we learned and where are we going?
Giedd, J. N., & Rapoport, J. L. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.040 · Cited by 836 · see citing works
Nagy, Z., Westerberg, H., & Klingberg, T. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/0898929041920441 · Cited by 794 · see citing works
Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) and Volumetry
Westlye, L. T., Walhovd, K. B., Dale, A. M., Bjornerud, A., Due-Tonnessen, P., Engvig, A., Grydeland, H., Tamnes, C. K., Ostby, Y., & Fjell, A. M. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp280 · Cited by 765 · see citing works
Self-citationBrain growth across the life span in autism: Age-specific changes in anatomical pathology
Courchesne, E., Campbell, K., & Solso, S. (2011) · Brain Research · doi:10.1016/j.brainres.2010.09.101 · Cited by 711 · see citing works
Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder
Sowell, E. R., Thompson, P. M., Welcome, S. E., Henkenius, A. L., Toga, A. W., & Peterson, B. S. (2003) · The Lancet · doi:10.1016/s0140-6736(03)14842-8 · Cited by 567 · see citing works
Self-citationThe Human Brain Age 7–11 Years: A Volumetric Analysis Based on Magnetic Resonance Images
Caviness, V. S., Kennedy, D. N., Richelme, C., Rademacher, J., & Filipek, P. A. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.5.726 · Cited by 509 · see citing works
Self-citationØstby, Y., Tamnes, C. K., Fjell, A. M., Westlye, L. T., Due-Tønnessen, P., & Walhovd, K. B. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.1242-09.2009 · Cited by 480 · see citing works
Holland, D., Kuperman, J. M., & Dale, A. M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.044 · Cited by 471 · see citing works
Self-citationCerebellum development during childhood and adolescence: A longitudinal morphometric MRI study
Tiemeier, H. W., Lenroot, R., GREENSTEIN, D. K., Tran, L., Pierson, R. K., & Giedd, J. N. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.016 · Cited by 463 · see citing works
MATURATION OF HUMAN CEREBRUM OBSERVED <i>IN VIVO</i> DURING ADOLESCENCE
JERNIGAN, T. L., TRAUNER, D. A., HESSELINK, J. R., & TALLAL, P. A. (1991) · Brain · doi:10.1093/brain/114.5.2037 · Cited by 444 · see citing works
Self-citationMukherjee, P., Miller, J. H., Shimony, J. S., Conturo, T. E., Lee, B. C. P., Almli, C. R., & McKinstry, R. C. (2001) · Radiology · doi:10.1148/radiol.2212001702 · Cited by 426 · see citing works
MRI analysis of an inherited speech and language disorder: structural brain abnormalities
Watkins, K. E., Vargha‐Khadem, F., Ashburner, J., Passingham, R. E., Connelly, A., Friston, K. J., Frackowiak, R. S. J., Mishkin, M., & Gadian, D. G. (2002) · Brain · doi:10.1093/brain/awf057 · Cited by 345 · see citing works
Cerebellar development and clinical outcome in attention deficit hyperactivity disorder
Mackie, S., Shaw, P., Lenroot, R., Pierson, R., Greenstein, D. K., Nugent, T. F., Sharp, W. S., Giedd, J. N., & Rapoport, J. L. (2007) · American Journal of Psychiatry · doi:10.1176/appi.ajp.164.4.647 · Cited by 333 · see citing works
Cerebellar Development and Clinical Outcome in Attention Deficit Hyperactivity Disorder
Mackie, S. E., Shaw, P., Lenroot, R., Pierson, R. K., GREENSTEIN, D. K., Nugent, T. F., Sharp, W. S., Giedd, J. N., & Rapoport, J. L. (2007) · American Journal of Psychiatry · doi:10.1176/ajp.2007.164.4.647 · Cited by 332 · see citing works
Hagler, D. J., Ahmadi, M. E., Kuperman, J. M., Holland, D., McDonald, C. R., Halgren, E., & Dale, A. M. (2008) · Human Brain Mapping · doi:10.1002/hbm.20619 · Cited by 286 · see citing works
Self-citationNeuroanatomical Markers for Dyslexia: A Review of Dyslexia Structural Imaging Studies
Eckert, M. A. (2004) · The Neuroscientist · doi:10.1177/1073858404263596 · Cited by 193 · see citing works
Normal deposition of brain iron in childhood and adolescence: MR imaging at 1.5 T.
Aoki, S., Okada, Y., Nishimura, K., Barkovich, A. J., Kjos, B. O., Brasch, R. C., & Norman, D. M. (1989) · Radiology · doi:10.1148/radiology.172.2.2748819 · Cited by 172 · see citing works
Nonlinear registration of longitudinal images and measurement of change in regions of interest
Holland, D., & Dale, A. M. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.02.005 · Cited by 114 · see citing works
Self-citationTHE REGULATION OF HUMAN GROWTH1
Tanner, J. M. (1963) · Child Development · doi:10.1111/j.1467-8624.1963.tb05970.x · Cited by 102 · see citing works
The Regulation of Human Growth
Tanner, J. M. (1963) · Child Development · doi:10.2307/1126530 · Cited by 74 · see citing works
Hagmann, C. F., Vita, E. D., Bainbridge, A. P., Gunny, R., Kapetanakis, A. B., Chong, W. K., Cady, E. B., Gadian, D. G., & Robertson, N. J. (2009) · Radiology · doi:10.1148/radiol.2522080589 · Cited by 50 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Image processing and analysis methods for the Adolescent Brain Cognitive Development Study
Hagler, D. J., Hatton, S. N., Cornejo, M. D., Makowski, C., Fair, D. A., Dick, A. S., Sutherland, M. T., Casey, B. J., Barch, D. M., Harms, M. P., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116091 · Cited by 1,060 · see citing works
Self-citationImaging 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
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
Cole, J. H., & Franke, K. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.10.001 · Cited by 912 · see citing works
Tamnes, 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-citationTen Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?
Franke, K., & Christian Gaser, B. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00789 · Cited by 615 · see citing works
A Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559 · see citing works
Predicting 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
Accurate brain age prediction with lightweight deep neural networks
Peng, H., Gong, W., Beckmann, C. F., Vedaldi, A., & Smith, S. M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101871 · Cited by 510 · see citing works
Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.064 · Cited by 504 · see citing works
Prediction of brain age suggests accelerated atrophy after traumatic brain injury
Cole, J. H., Leech, R., Sharp, D. J., & Initiative, A. D. N. (2015) · Annals of Neurology · doi:10.1002/ana.24367 · Cited by 439 · see citing works
Brain Development During the Preschool Years
Brown, T. T., & Jernigan, T. L. (2012) · Neuropsychology Review · doi:10.1007/s11065-012-9214-1 · Cited by 463 · see citing works
Self-citationKoutsouleris, N., Davatzikos, C., Borgwardt, S., Gaser, C., Bottlender, R., Frodl, T., Falkai, P., Riecher-Rossler, A., Moller, H.-J., Reiser, M., et al. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbt142 · Cited by 448 · see citing works
Brain age and other bodily ‘ages’: implications for neuropsychiatry
Cole, J. H., Marioni, R. E., Harris, S. E., & Deary, I. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0098-1 · Cited by 435 · see citing works
Dynamic Development of Regional Cortical Thickness and Surface Area in Early Childhood
Lyall, A. E., Shi, F., Geng, X., Woolson, S., Li, G., Wang, L., Hamer, R. M., Shen, D., & Gilmore, J. H. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu027 · Cited by 376 · see citing works
Cole, J. H. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.03.014 · Cited by 379 · see citing works
Bashyam, V., Erus, G., Doshi, J. J., Habes, M., Nasrallah, I. M., Truelove‐Hill, M., Srinivasan, D., Mamourian, L., Pomponio, R., Yong Fan, A., et al. (2020) · Brain · doi:10.1093/brain/awaa160 · Cited by 372 · see citing works
Somerville, L. H., Bookheimer, S. Y., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Dapretto, M., Elam, J. S., Gaffrey, M. S., Harms, M. P., Hodge, C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.050 · Cited by 356 · see citing works
Unraveling the Miswired Connectome: A Developmental Perspective
Martino, A. D., Fair, D. A., Kelly, C. A. M., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., Craddock, R. C., Luna, B., LEVENTHAL, B. L., Zuo, X., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.050 · Cited by 346 · see citing works
The Pediatric Imaging, Neurocognition, and Genetics (PING) Data Repository
Jernigan, T. L., Brown, T. T., Hagler, D. J., Akshoomoff, N. A., Bartsch, H., Newman, E. R., Thompson, W. K., Bloss, C. S., Murray, S. S., Schork, N. J., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.057 · Cited by 281 · see citing works
Self-citationCortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years
Frangou, S., Modabbernia, A., Williams, S., Papachristou, E., Doucet, G. E., Agartz, I., Aghajani, M., Akudjedu, T. N., Albajes‐Eizagirre, A., Alnæs, D., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25364 · Cited by 283 · see citing works
Self-citationBrain development and aging: Overlapping and unique patterns of change
Tamnes, C. K., Walhovd, K. B., Dale, A. M., Østby, Y., Grydeland, H., Richardson, G., Westlye, L. T., Roddey, J. C., Hagler, D. J., Due-Tønnessen, P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.039 · Cited by 287 · see citing works
Self-citationDevelopment and aging of cortical thickness correspond to genetic organization patterns
Fjell, A. M., Grydeland, H., Krogsrud, S. K., Amlien, I. K., Rohani, D. A., Ferschmann, L., Storsve, A. B., Tamnes, C. K., Sala‐Llonch, R., Due‐Tønnessen, P., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508831112 · Cited by 275 · see citing works
Self-citationFunctional 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 · see citing works
Methods and considerations for longitudinal structural brain imaging analysis across development
Mills, K., & Tamnes, C. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2014.04.004 · Cited by 278 · see citing works
Satterthwaite, 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 · see citing works
Walhovd, K. B., Fjell, A. M., Giedd, J. N., Dale, A. M., & Brown, T. T. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv301 · Cited by 233 · see citing works
Self-citationLebel, C., Walton, M., Letourneau, N., Giesbrecht, G. F., Kaplan, B. J., & Dewey, D. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.004 · Cited by 237 · see citing works
Connectivity in Autism: A Review of MRI Connectivity Studies
Rane, P., Cochran, D., Hodge, S. M., Haselgrove, C., Kennedy, D. N., & Frazier, J. A. (2015) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000072 · Cited by 234 · see citing works
Self-citationNeurocognitive growth charting in psychosis spectrum youths.
Gur, R., Calkins, M., Satterthwaite, T., Ruparel, K., Bilker, W., Moore, T. M., Savitt, A. P., Hakonarson, H., & Gur, R. E. (2014) · JAMA psychiatry · doi:10.1001/jamapsychiatry.2013.4190 · Cited by 247 · see citing works
Imaging Patterns of Brain Development and their Relationship to Cognition
Erus, G., Battapady, H., Satterthwaite, T. D., Hakonarson, H., Gur, R. E., Davatzikos, C., & Gur, R. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht425 · Cited by 226 · see citing works
Human Connectomics across the Life Span
Zuo, X., He, Y., Betzel, R. F., Colcombe, S. J., Sporns, O., & Milham, M. P. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.10.005 · Cited by 211 · see citing works
The NIH Toolbox Cognition Battery: Results from a large normative developmental sample (PING).
Akshoomoff, N. A., Newman, E. R., Thompson, W. K., McCabe, C. J., Bloss, C. S., Chang, L., Amaral, D. G., Casey, B. J., Ernst, T. M., Frazier, J. A., et al. (2013) · Neuropsychology · doi:10.1037/neu0000001 · Cited by 203 · see citing works
Self-citationNeurodevelopmental origins of lifespan changes in brain and cognition
Walhovd, K. B., Krogsrud, S. K., Amlien, I. K., Bartsch, H., Bjørnerud, A., Due‐Tønnessen, P., Grydeland, H., Hagler, D. J., Haberg, A. K., Kremen, W. S., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1524259113 · Cited by 196 · see citing works
Self-citationBedford, 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
Smith, S. M., Elliott, L. T., Alfaro‐Almagro, F., McCarthy, P., Nichols, T. E., Douaud, G., & Miller, K. L. (2020) · eLife · doi:10.7554/elife.52677 · Cited by 194 · see citing works
Amlien, I. K., Fjell, A. M., Tamnes, C. K., Grydeland, H., Krogsrud, S. K., Chaplin, T. A., Rosa, M. G. P., & Walhovd, K. B. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu214 · Cited by 188 · see citing works
Long-term influence of normal variation in neonatal characteristics on human brain development
Walhovd, K. B., Fjell, A. M., Brown, T. T., Kuperman, J. M., Chung, Y., Hagler, D. J., Roddey, J. C., Erhart, M., McCabe, C., Akshoomoff, N., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208180109 · Cited by 187 · see citing works
Self-citationPrediction complements explanation in understanding the developing brain
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-02887-9 · Cited by 167 · see citing works
Self-citationQuantifying performance of machine learning methods for neuroimaging data
Jollans, L., Boyle, R. T., Eric Artiges, A., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J., Paus, T., Smolka, M. N., Walter, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.082 · Cited by 165 · see citing works
Pfefferbaum, A., Torsten Rohlfing, B., Pohl, K. M., Lane, B., Chu, W., Kwon, D., Nichols, B. N., Brown, S. A., Tapert, S. F., Cummins, K. M., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv205 · Cited by 167 · see citing works
Self-citationLi, G., Lin, W., Gilmore, J. H., & Shen, D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4107-14.2015 · Cited by 164 · see citing works
Evolution of deep gray matter volume across the human lifespan
Narvacan, K., Treit, S., Camicioli, R. M., Martin, W. R. W., & Beaulieu, C. (2017) · Human Brain Mapping · doi:10.1002/hbm.23604 · Cited by 153 · see citing works
Updated report on tools to measure outcomes of clinical trials in fragile X syndrome
Budimirovic, D. B., Berry‐Kravis, E., Erickson, C. A., Hall, S. S., Hessl, D. R., Reiss, A. L., King, M. K., Abbeduto, L., & Kaufmann, W. E. (2017) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-017-9193-x · Cited by 157 · see citing works
Self-citationWalhovd, K. B., Johansen-Berg, H., & Káradóttir, R. T. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.06.058 · Cited by 156 · see citing works
Remer, J., Croteau-Chonka, E., Dean, D. C., D’Arpino, S., Dirks, H., Whiley, D., & Deoni, S. C. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.010 · Cited by 152 · see citing works
Age-Related Differences in Cortical Thickness Vary by Socioeconomic Status
Piccolo, L. D. R., Merz, E. C., He, X., Sowell, E. R., Noble, K. G., & Pediatric Imaging, N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162511 · Cited by 153 · see citing works
Self-citationChung, Y., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., Tsuang, M. T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.1543 · Cited by 150 · see citing works
Self-citationTamnes, C. K., Walhovd, K. B., Grydeland, H., Holland, D., Østby, Y., Dale, A. M., & Fjell, A. M. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00434 · Cited by 147 · see citing works
Self-citationWierenga, L. M., Sexton, J. A., Laake, P., Giedd, J. N., Tamnes, C. K., & the Pediatric Imaging, N. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx154 · Cited by 144 · see citing works
Socioeconomic Status, Amygdala Volume, and Internalizing Symptoms in Children and Adolescents
Merz, E. C., Tottenham, N., & Noble, K. G. (2017) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2017.1326122 · Cited by 144 · see citing works
Bjork, J. M., & Pardini, D. A. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2014.07.008 · Cited by 143 · see citing works
Prediction of brain maturity in infants using machine-learning algorithms
Smyser, C. D., Dosenbach, N. U. F., Smyser, T. A., Snyder, A. Z., Rogers, C. E., Inder, T. E., Schlaggar, B. L., & Neil, J. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.029 · Cited by 140 · see citing works
Improved prediction of brain age using multimodal neuroimaging data
Niu, X., Zhang, F., Kounios, J., & Liang, H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24899 · Cited by 132 · see citing works
Tamnes, C. K., Walhovd, K. B., Torstveit, M., Sells, V. T., & Fjell, A. M. (2013) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.05.001 · Cited by 131 · see citing works
Age prediction on the basis of brain anatomical measures
Valizadeh, S. A., Hänggi, J., Mérillat, S., & Jäncke, L. (2016) · Human Brain Mapping · doi:10.1002/hbm.23434 · Cited by 124 · see citing works
Hippocampal maturity promotes memory distinctiveness in childhood and adolescence
Keresztes, A., Bender, A. R., Bodammer, N., Lindenberger, U., Shing, Y. L., & Werkle‐Bergner, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710654114 · Cited by 126 · see citing works
Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
de Lange, A. G., Anatürk, M., Suri, S., Kaufmann, T., Cole, J. H., Griffanti, L., Zsoldos, E., Jensen, D. E. A., Filippini, N., Singh‐Manoux, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117292 · Cited by 125 · see citing works
The Human Cerebral Cortex Flattens during Adolescence
Alemán‐Gómez, Y., Janssen, J., Schnack, H. G., Balaban, E., Pina‐Camacho, L., Alfaro‐Almagro, F., Castro‐Fornieles, J., Otero, S., Baeza, I., Moreno, D. M., et al. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1459-13.2013 · Cited by 121 · see citing works
Rokicki, J., Wolfers, T., Nordhøy, W., Tesli, N., Quintana, D. S., Alnæs, D., Richard, G., de Lange, A. G., Lund, M. J., Norbom, L. B., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25323 · Cited by 124 · see citing works
Cortical thickness asymmetry from childhood to older adulthood
Zhou, D., Lebel, C. A., Evans, A. C., & Beaulieu, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.073 · Cited by 106 · see citing works
Longitudinal development of hippocampal subregions from childhood to adulthood
Tamnes, C. K., Bos, M. G. N., van de Kamp, F. C., Peters, S., & Crone, E. A. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.009 · Cited by 113 · see citing works
Anxiety, depression, impulsivity, and brain structure in children and adolescents
Merz, E. C., He, X., & Noble, K. G. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.07.020 · Cited by 108 · see citing works
Vuoksimaa, E., Panizzon, M. S., Chen, C., Fiecas, M. J. A., Eyler, L. T., Fennema‐Notestine, C., Hagler, D. J., Fischl, B. R., Franz, C. E., Jak, A. J., et al. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu018 · Cited by 106 · see citing works
Self-citationGenome‐wide association study of shared components of reading disability and language impairment
Eicher, J. D., Powers, N. R., Miller, L. L., Akshoomoff, N. A., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Darst, B. F., Casey, B. J., et al. (2013) · Genes Brain & Behavior · doi:10.1111/gbb.12085 · Cited by 108 · see citing works
Self-citationMwangi, B., Hasan, K. M., & Soares, J. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.055 · Cited by 106 · see citing works
Accelerated longitudinal cortical thinning in adolescence
Zhou, D., Lebel, C. A., Treit, S., Evans, A. C., & Beaulieu, C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.005 · Cited by 106 · see citing works
Pitfalls in brain age analyses
Butler, E. R., Chen, A. A., Ramadan, R., Le, T. T., Ruparel, K., Moore, T. M., Satterthwaite, T. D., Zhang, F., Shou, H., Gur, R. C., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25533 · Cited by 117 · see citing works
Gee, D. G., Fetcho, R. N., Jing, D., Li, A., Glatt, C. E., Drysdale, A. T., Cohen, A. O., Dellarco, D. V., Yang, R. R., Dale, A. M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600013113 · Cited by 98 · see citing works
Self-citationGur, R. E., Yi, J. J., McDonald‐McGinn, D. M., Tang, S. X., Calkins, M. E., Whinna, D. A., Souders, M. C., Savitt, A. P., Zackai, E. H., Moberg, P. J., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.189 · Cited by 96 · see citing works
Richard, G., Kolskår, K. K., Sanders, A., Kaufmann, T., Petersen, A., Doan, N. T., Sánchez, J. M., Alnæs, D., Ulrichsen, K. M., Dørum, E. S., et al. (2018) · PeerJ · doi:10.7717/peerj.5908 · Cited by 102 · see citing works
Schultz, V., Stern, R. A., Tripodis, Y., Stamm, J. M., Wróbel, P., Lepage, C., Weir, I. R., Guenette, J. P., Chua, A. S., Alosco, M. L., et al. (2017) · Journal of Neurotrauma · doi:10.1089/neu.2017.5145 · Cited by 98 · see citing works
A Life-Long Approach to Physical Activity for Brain Health
Macpherson, H., Teo, W.-P., Schneider, L. A., & Smith, A. E. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00147 · Cited by 103 · see citing works
Thinning of the lateral prefrontal cortex during adolescence predicts emotion regulation in females
Vijayakumar, N., Whittle, S., Yücel, M., Dennison, M., Simmons, J. G., & Allen, N. B. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst183 · Cited by 97 · see citing works
Prediction of brain maturity based on cortical thickness at different spatial resolutions
Khundrakpam, B. S., Tohka, J., & Evans, A. C. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.046 · Cited by 90 · 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
Brain Imaging During the Transition from Psychosis Prodrome to Schizophrenia
Chung, Y., & Cannon, T. D. (2015) · The Journal of Nervous and Mental Disease · doi:10.1097/nmd.0000000000000286 · Cited by 92 · see citing works
Self-citationOn the Origins of Schizophrenia
Kahn, R. S. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20020147 · Cited by 94 · see citing works
T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance
Lewis, J. D., Evans, A. C., & Tohka, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.050 · Cited by 87 · see citing works
Nielsen, A. N., Greene, D. J., Gratton, C., Dosenbach, N. U. F., Petersen, S. E., & Schlaggar, B. L. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy117 · Cited by 89 · see citing works
Cui, Z., Stiso, J., Baum, G. L., Kim, J. Z., Roalf, D. R., Betzel, R. F., Gu, S., Lu, Z., Xia, C. H., He, X., et al. (2020) · eLife · doi:10.7554/elife.53060 · Cited by 87 · see citing works
Ferrer, E. E., Whitaker, K. J., Steele, J. S., Green, C. T., Wendelken, C., & Bunge, S. A. (2013) · Developmental Science · doi:10.1111/desc.12088 · Cited by 86 · see citing works
Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.061 · Cited by 85 · see citing works
Shi, W., Yan, G., Li, Y., Li, H., Liu, T., Sun, C., Wang, G., Zhang, Y., Zou, Y., & Wu, D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117316 · Cited by 83 · see citing works
Regional Hippocampal Volumes and Development Predict Learning and Memory
Tamnes, C. K., Walhovd, K. B., Engvig, A., Grydeland, H., Krogsrud, S. K., Østby, Y., Holland, D., Dale, A. M., & Fjell, A. M. (2014) · Developmental Neuroscience · doi:10.1159/000362445 · Cited by 80 · see citing works
Self-citationPremature brain aging in humans exposed to maternal nutrient restriction during early gestation
Franke, K., Christian Gaser, B., Roseboom, T. J., Schwab, M. G., & de Rooij, S. R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.047 · Cited by 79 · see citing works
Wierenga, L. M., Bos, M. G. N., van Rossenberg, F., & Crone, E. A. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01375 · Cited by 78 · see citing works
Age Prediction Based on Brain MRI Image: A Survey
Sajedi, H., & Pardakhti, N. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1401-7 · Cited by 77 · see citing works
Cognitive insights from tertiary sulci in prefrontal cortex
Voorhies, W. I., Miller, J. A., Yao, J. K., Bunge, S. A., & Weiner, K. S. (2021) · Nature Communications · doi:10.1038/s41467-021-25162-w · Cited by 77 · see citing works
Rudolph, M. D., Miranda-Domínguez, Ó., Cohen, A. O., Breiner, K., Steinberg, L. D., Bonnie, R. J., Scott, E. S., Taylor‐Thompson, K., Chein, J. M., Fettich, K. C., et al. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.01.010 · Cited by 76 · see citing works
Self-citationUnbiased age‐specific structural brain atlases for Chinese pediatric population
Zhao, T.-D., Liao, X.-H., Fonov, V. S., Wang, Q., Men, W.-W., Wang, Y.-P., Qin, S., Tan, S.-P., Gao, J.-H., Evans, A. C., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.006 · Cited by 71 · see citing works
Koenis, M. M. G., Brouwer, R. M., Swagerman, S. C., van Soelen, I. L. C., Boomsma, D. I., & Pol, H. E. H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23885 · Cited by 68 · see citing works
Vijayakumar, N., Whittle, S., Dennison, M., Yücel, M., Simmons, J., & Allen, N. B. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.12.002 · Cited by 71 · see citing works
Clausen, A. N., Fercho, K. A., Monsour, M., Disner, S. G., Salminen, L. E., Haswell, C. C., Clarke‐Rubright, E. K., Watts, A. A., Buckley, M. N., Maron‐Katz, A., et al. (2021) · Brain and Behavior · doi:10.1002/brb3.2413 · Cited by 71 · see citing works
Predictors of Transition to Psychosis in Individuals at Clinical High Risk
Addington, J., Farris, M. S., Stowkowy, J., Santesteban‐Echarri, O., Metzak, P. D., & Kalathil, M. S. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1027-y · Cited by 69 · see citing works
Age estimation using cortical surface pattern combining thickness with curvatures
Wang, J., Li, W., Miao, W., Dai, D., Hua, J., & He, H. (2014) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-013-1131-9 · Cited by 67 · 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
Cortical abnormalities in youth at clinical high-risk for psychosis: Findings from the NAPLS2 cohort
Chung, Y., Allswede, D. M., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101862 · Cited by 67 · see citing works
Self-citationPredicting brain age with complex networks: From adolescence to adulthood
Bellantuono, L., Marzano, L., Rocca, M. L., Duncan, D., Lombardi, A., Maggipinto, T., Monaco, A., Tangaro, S., Amoroso, N., & Bellotti, R. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117458 · 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
Galdi, P., Blesa, M., Stoye, D. Q., Sullivan, G., Lamb, G. J., Quigley, A. J., Thrippleton, M. J., Bastin, M. E., & Boardman, J. P. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102195 · Cited by 65 · see citing works
- Authors
- Brown, T. T., Kuperman, J. M., Chung, Y., Erhart, M., McCabe, C., Hagler, D. J., Venkatraman, V. K., Akshoomoff, N., Amaral, D. G., Bloss, C. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kaufmann, W. E., Kenet, T., Kennedy, D. N., Murray, S. S., Sowell, E. R., Jernigan, T. L., & Dale, A. M.
- Affiliations
- University of California San Diego (US)
- University of California, Davis (US)
- Scripps Health (US)
- Weill Cornell Medicine (US)
- University of Hawaiʻi at Mānoa (US)
- University of Massachusetts Chan Medical School (US)
- Yale University (US)
- Kennedy Krieger Institute (US)
- Massachusetts General Hospital (US)
- University of Southern California (US)
- Corresponding author
- Brown, Timothy T.
- ORCID
- Brown, Timothy T.
- Kuperman, Joshua M.
- Chung, Yoonho
- McCabe, Connor
- Hagler, Donald J.
- Venkatraman, Vijay K.
- Amaral, David G.
- Bloss, Cinnamon S.
- Casey, B.J.
- Chang, Linda
- Frazier, Jean A.
- Gruen, Jeffrey R.
- Kaufmann, Walter E.
- Kenet, Tal
- Kennedy, David N.
- Murray, Sarah S.
- Sowell, Elizabeth R.
- Jernigan, Terry L.
- Dale, Anders M.
- Venue
- Current Biology
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 22
- Issue
- 18
- Pages
- 1693-1698
- ISSN
- 0960-9822 (ISSN-L)
- PMC
- PMC3461087
- OpenAlex
- W1969129137
- Cited by
- 332
- References
- 31
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 16 · 2023: 17 · 2022: 23 · 2021: 25 · 2020: 34 · 2019: 27 · 2018: 26 · 2017: 32 · 2016: 25 · 2015: 33 · 2014: 27 · 2013: 23 · 2012: 5
- FWCI
- 21.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Current Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship@UMassChan (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH083320)
- National Institute on Drug Abuse (RC2DA029475)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD061414, P30HD004147)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- biological maturity, brain maturation, human brain development, structural MRI, white matter tracts, cortical development, cerebellar development, diffusion MRI, developmental timing, children
- MeSH terms
- Adolescent, Aging, Brain, Brain Mapping, Cerebral Cortex, Child, Child, Preschool, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Fibers, Myelinated, Neural Pathways, Biomarkers, Young Adult
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
