The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood
Open accessAbstract
Developmental structural neuroimaging studies in humans have long described decreases in gray matter volume (GMV) and cortical thickness (CT) during adolescence. Gray matter density (GMD), a measure often assumed to be highly related to volume, has not been systematically investigated in development. We used T1 imaging data collected on the Philadelphia Neurodevelopmental Cohort to study age-related effects and sex differences in four regional gray matter measures in 1189 youths ranging in age from 8 to 23 years. Custom T1 segmentation and a novel high-resolution gray matter parcellation were used to extract GMD, GMV, gray matter mass (GMM; defined as GMD × GMV), and CT from 1625 brain regions. Nonlinear models revealed that each modality exhibits unique age-related effects and sex differences. While GMV and CT generally decrease with age, GMD increases and shows the strongest age-related effects, while GMM shows a slight decline overall. Females have lower GMV but higher GMD than males throughout the brain. Our findings suggest that GMD is a prime phenotype for the assessment of brain development and likely cognition and that periadolescent gray matter loss may be less pronounced than previously thought. This work highlights the need for combined quantitative histological MRI studies. SIGNIFICANCE STATEMENT This study demonstrates that different MRI-derived gray matter measures show distinct age and sex effects and should not be considered equivalent but complementary. It is shown for the first time that gray matter density increases from childhood to young adulthood, in contrast with gray matter volume and cortical thickness, and that females, who are known to have lower gray matter volume than males, have higher density throughout the brain. A custom preprocessing pipeline and a novel high-resolution parcellation were created to analyze brain scans of 1189 youths collected as part of the Philadelphia Neurodevelopmental Cohort. A clear understanding of normal structural brain development is essential for the examination of brain–behavior relationships, the study of brain disease, and, ultimately, clinical applications of neuroimaging.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“Women also appear to have lower gray matter volume,50”
From Sex differences in cognitive decline among US adults (2021) · cites this paper as [50]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Wood, S. N. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00749.x · Cited by 7,538
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,413
Self-citationAvants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,924
Self-citationDynamic mapping of human cortical development during childhood through early adulthood
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0402680101 · Cited by 5,604 · cited 3×
“Several developmental neuroimaging studies have described modest decreases in gray matter during adolescence using measures derived from gray matter volume and cortical thickness (Sowell et al., 2003, 2004; Gogtay et al., 2004; Shaw et al., 2008; Brain Development Cooperative Group, 2012).”
BackgroundMethodResultInfluentialA multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288
Neurodegenerative Diseases Target Large-Scale Human Brain Networks
Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.024 · Cited by 2,350
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
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227
Self-citation
Cited by
Sorted by citations
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants
Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., Harris, M. A., Alderson, H. L., Hunter, S., Neilson, E., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy109 · Cited by 855
“2008; note that the insula showing the largest sex difference is consistent with a recent large-scale study of children and adolescents (Gennatas et al. 2017)—it appears this region retains its substantial sex difference into later life), but also areas such as the right fusiform gyrus.”
BackgroundResultSex differences in cognitive decline among US adults
Levine, D. A., Gross, A. L., Briceño, E. M., Tilton, N., Giordani, B. J., Sussman, J. B., Hayward, R. A., Burke, J. F., Hingtgen, S., Elkind, M. S. V., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.0169 · Cited by 440
Linked dimensions of psychopathology and connectivity in functional brain networks
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05317-y · Cited by 437
Self-citationQuantitative 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
Self-citationSex differences in the developing brain: insights from multimodal neuroimaging
Kaczkurkin, A. N., Raznahan, A., & Satterthwaite, T. D. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-018-0111-z · Cited by 403
Self-citationEliot, L., Ahmed, A., Khan, H., & Patel, J. Y. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.026 · Cited by 379
Zou, L., Zheng, J., Miao, C., McKeown, M. J., & Wang, Z. J. (2017) · IEEE Access · doi:10.1109/access.2017.2762703 · Cited by 313
Gur, R. E., Moore, T. M., Rosen, A. F. G., Barzilay, R., Roalf, D. R., Calkins, M. E., Ruparel, K., Scott, J. C., Almasy, L., Satterthwaite, T. D., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0943 · Cited by 239
Self-citationLiu, S., Seidlitz, J., Blumenthal, J. D., CLASEN, L. S., & Raznahan, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919091117 · Cited by 221
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
Persistent metabolic youth in the aging female brain
Goyal, M. S., Blazey, T., Su, Y., Couture, L. E., Durbin, T. J., Bateman, R. J., Benzinger, T. L. S., Morris, J. C., Raichle, M. E., & Vlassenko, A. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1815917116 · Cited by 181
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 129
Patient-Tailored, Connectivity-Based Forecasts of Spreading Brain Atrophy
Brown, J. A., Deng, J., Neuhaus, J. M., Sible, I. J., Sias, A. C., Lee, S. E., Kornak, J., Marx, G. A., Karydas, A. M., Spina, S., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.037 · Cited by 116
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 119
Self-citationStructural brain correlates of cognitive function in schizophrenia: A meta-analysis
Khalil, M., Hollander, P., Raucher‐Chéné, D., Lepage, M., & Lavigne, K. M. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.11.034 · Cited by 105
Walsh, M. J. M., Wallace, G. L., Gallegos, S. M., & Braden, B. B. (2021) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102719 · Cited by 97
Weighted Stochastic Block Models of the Human Connectome across the Life Span
Faskowitz, J., Yan, X., Zuo, X., & Sporns, O. (2018) · Scientific Reports · doi:10.1038/s41598-018-31202-1 · Cited by 95
Mapping Human Laryngeal Motor Cortex during Vocalization
Eichert, N., Papp, D., Mars, R. B., & Watkins, K. E. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa182 · Cited by 89
Larsen, B., Sydnor, V. J., Keller, A. S., Yeo, B. T. T., & Satterthwaite, T. D. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.07.007 · Cited by 93
Self-citationLarsen, B., Cui, Z., Adebimpe, A., Pines, A., Alexander-Bloch, A., Bertolero, M., Calkins, M. E., Gur, R. E., Gur, R. C., Mahadevan, A. S., et al. (2022) · Science Advances · doi:10.1126/sciadv.abj8750 · Cited by 80
Self-citationCharting brain growth in tandem with brain templates at school age
Dong, H.-M., Castellanos, F. X., Yang, N., Zhang, Z., Zhou, Q., He, Y., Zhang, L., Xu, T., Holmes, A. J., Yeo, B. T. T., et al. (2020) · 中国科学通报：英文版 · doi:10.1016/j.scib.2020.07.027 · Cited by 75
Unbiased age-specific structural brain atlases for Chinese pediatric population
Zhao, T., Liao, X., Fonov, V., Wang, Q., Men, W., Wang, Y., Qin, S., Tan, S., Gao, J., Evans, A. C., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.006 · Cited by 73
Zhang, X., Liang, M., Qin, W., Wan, B., Yu, C., & Ming, D. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00244 · Cited by 71
The Age of Reason: Functional Brain Network Development during Childhood
Tooley, U. A., Park, A. T., Leonard, J., Boroshok, A. L., McDermott, C. L., Tisdall, M. D., Bassett, D. S., & Mackey, A. P. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0511-22.2022 · Cited by 82
Obstructive sleep apnea and cortical thickness in females and males
Macey, P. M., Haris, N., Kumar, R., Thomas, M. A., Woo, M. A., & Harper, R. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193854 · Cited by 70
What has sex got to do with it? The role of hormones in the transgender brain
Nguyen, H. B., Loughead, J., Lipner, E., Hantsoo, L. V., Kornfield, S. L., & Epperson, C. N. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0140-7 · Cited by 78
Zhou, X., Wu, R., Zeng, Y., Qi, Z., Ferraro, S., Xu, L., Zheng, X., Li, J., Fu, M., Yao, S., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03880-1 · Cited by 66
Cumplido‐Mayoral, I., García-Prat, M., Operto, G., Falcón, C., Shekari, M., Cacciaglia, R., Milà‐Alomà, M., Lorenzini, L., Ingala, S., Wink, A. M., et al. (2023) · eLife · doi:10.7554/elife.81067 · Cited by 74
The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined
Brouwer, R. M., Schutte, J., Janssen, R. J., Boomsma, D. I., Pol, H. E. H., & Schnack, H. G. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa296 · Cited by 63
Ryali, S., Zhang, Y., Angeles, C. D. L., Supekar, K., & Menon, V. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310012121 · Cited by 71
Sex differences in the neurochemistry of frontal cortex: Impact of early life stress
Perry, C. J., Campbell, E. J., Drummond, K. D., Lum, J. S., & Kim, J. H. (2020) · Journal of Neurochemistry · doi:10.1111/jnc.15208 · Cited by 61
Individual variation underlying brain age estimates in typical development
Ball, G., Kelly, C. E., Beare, R. J., & Seal, M. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118036 · Cited by 62
Popovic, D., Ruef, A., Dwyer, D., Antonucci, L. A., Eder, J., Sanfelici, R., Kambeitz‐Ilankovic, L., Oztuerk, O. F., Dong, M. S., Paul, R., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.05.020 · Cited by 61
Ge, R., Torres, I. J., Brown, J. J., Gregory, E., McLellan, E., Downar, J. H., Blumberger, D. M., Daskalakis, Z. J., Lam, R. W. M., & Vila‐Rodriguez, F. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.04.096 · Cited by 58
Volumetric gray matter measures of amygdala and accumbens in childhood overweight/obesity
Perlaki, G., Molnár, D., Smeets, P. A. M., Ahrens, W., Wolters, M., Eiben, G., Lissner, L., Erhard, P., van Meer, F., Herrmann, M., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205331 · Cited by 55
García, J. M. P., Suárez‐Suárez, S., Doallo, S., & Cadaveira, F. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104637 · Cited by 51
Bas‐Hoogendam, J. M., van Steenbergen, H., Tissier, R., Houwing‐Duistermaat, J. J., Westenberg, P. M., & van der Wee, N. J. A. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.08.048 · Cited by 50
Lafta, M. S., Mwinyi, J., Affatato, O., Rukh, G., Dang, J., Andersson, G., & Schiöth, H. B. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1340108 · Cited by 58
Truelove‐Hill, M., Erus, G., Bashyam, V., Varol, E., Sako, C., Gur, R. C., Gur, R. E., Koutsouleris, N., Zhuo, C., Yong Fan, A., et al. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2092-19.2019 · Cited by 50
Self-citationLi, Y., Sethi, S. K., Zhang, C., Miao, Y., Yerramsetty, K. K., Palutla, V. K., Gharabaghi, S., Wang, C., He, N., Cheng, J., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.607705 · Cited by 48
Modabbernia, A., Reichenberg, A., Ing, A., Moser, D. A., Doucet, G. E., Artiges, É., Banaschewski, T., Barker, G. J., Becker, A., Bokde, A. L. W., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0757-x · Cited by 47
Sato, M. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107404 · Cited by 52
Assessment of conditioned fear extinction in male and female adolescent rats
Perry, C. J., Ganella, D. E., Nguyen, L. D., Du, X., Drummond, K. D., Whittle, S., Pang, T. Y., & Kim, J. H. (2020) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2020.104670 · Cited by 47
Chen, D., Strang, J. F., Kolbuck, V. D., Rosenthal, S. M., Wallen, K., Waber, D. P., Steinberg, L. D., Sisk, C. L., ROSS, J. L., Paus, T., et al. (2020) · Transgender Health · doi:10.1089/trgh.2020.0006 · Cited by 47
Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework
Kuo, C.-Y., Tai, T., Lee, P., Tseng, C.-W., Chen, C.-Y., Chen, L., Lee, C.-K., Chou, K., See, S. C. W., & Lin, C. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.626677 · Cited by 46
Tapia‐Serrano, M. Á., García‐Hermoso, A., Sevil‐Serrano, J., Sánchez‐Oliva, D., & Miguel, P. A. S. (2021) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2021.09.005 · Cited by 47
Cannabis use in youth is associated with limited alterations in brain structure
Scott, J. C., Rosen, A. F. G., Moore, T. M., Roalf, D. R., Satterthwaite, T. D., Calkins, M. E., Ruparel, K., Gur, R. E., & Gur, R. C. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0347-2 · Cited by 44
Self-citationLiloia, D., Zamfira, D. A., Tanaka, M., Manuello, J., Crocetta, A., Keller, R., Cozzolino, M., Duca, S., Cauda, F., & Costa, T. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105791 · Cited by 48
Dynamic gray matter volume changes in pediatric multiple sclerosis
Meo, E. D., Meani, A., Moiola, L., Ghezzi, A., Veggiotti, P., Filippi, M., & Rocca, M. A. (2019) · Neurology · doi:10.1212/wnl.0000000000007267 · Cited by 46
Qiu, L., Yan, H., Zhu, R., Yan, J., Yuan, H., Han, Y., Yue, W., Tian, L., & Zhang, D. (2018) · BMC Psychiatry · doi:10.1186/s12888-018-1806-8 · Cited by 38
León, J. J., Sánchez-Kuhn, A., Fernández‐Martín, P., Páez-Pérez, M. A., Thomas, C., Datta, A., Sánchez-Santed, F., & Flores, P. (2020) · Behavioural Brain Research · doi:10.1016/j.bbr.2020.112485 · Cited by 44
Belov, V., Erwin-Grabner, T., Aghajani, M., Aleman, A., Amod, A. R., Başgöze, Z., Benedetti, F., Besteher, B., Bülow, R., Ching, C. R. K., et al. (2024) · Scientific Reports · doi:10.1038/s41598-023-47934-8 · Cited by 44
Chow, H. M., Garnett, E. O., Li, H., Etchell, A. C., Sepulcre, J., Drayna, D. T., Chugani, D. C., & Chang, S. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00017 · Cited by 38
The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students
Tsuchida, A., Laurent, A., Crivello, F., Petit, L., Joliot, M., Pepe, A., Beguedou, N., Gueye, M.-F., Verrecchia, V., Nozais, V., et al. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02334-4 · Cited by 40
Qu, G., Zhou, Z., Calhoun, V. D., Zhang, A., & Wang, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103570 · Cited by 35
Thomas, C., Ghodratitoostani, I., Delbem, A. C. B., Ali, A., & Datta, A. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856898 · Cited by 37
Morphometrical Brain Markers of Sex Difference
Brennan, D. J., Wu, T., & Fan, J. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab037 · Cited by 32
Canales-Rodríguez, E. J., Alonso-Lana, S., Verdolini, N., Sarró, S., Feria‐Raposo, I., Montoro, I., Garcia‐Ruiz, B., Jiménez, E., Varo, C., Albacete, À., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.06.002 · Cited by 34
Walter, A. E., Wilkes, J. R., Arnett, P. A., Miller, S. J., Sebastianelli, W. J., Seidenberg, P. H., & Slobounov, S. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00489-6 · Cited by 35
Wadsley, M. G., & Ihssen, N. (2023) · Brain Sciences · doi:10.3390/brainsci13050787 · Cited by 40
Burrowes, S. A. B., Rhodes, C. S., Meeker, T. J., Greenspan, J. D., Gullapalli, R. P., & Seminowicz, D. A. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00095-7 · Cited by 33
Statsenko, Y. A., Habuza, T., Smetanina, D., Simiyu, G. L., Uzianbaeva, L., Gorkom, K., Zaki, N., Charykova, I., Koteesh, J. A., Almansoori, T. M., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.713680 · Cited by 31
A systematic comparison of VBM pipelines and their application to age prediction
Antonopoulos, G., More, S., Raimondo, F., Eickhoff, S. B., Hoffstaedter, F., & Patil, K. R. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120292 · Cited by 30
How does hormonal contraception affect the developing human adolescent brain?
Cahill, L. F. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.06.015 · Cited by 31
Ibrahim, K., Kalvin, C. B., Li, F., He, G. S., Pelphrey, K. A., McCarthy, G., & Sukhodolsky, D. G. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100884 · Cited by 33
Raine, P. J., & Rao, H. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00659-0 · Cited by 30
Ma, Q., Wang, H., Rolls, E. T., Xiang, S., Li, J., Li, Y., Zhou, Q., Cheng, W., & Li, F. R. (2022) · BMC Medicine · doi:10.1186/s12916-022-02627-3 · Cited by 33
Wang, S., Yang, C.-M., Zhao, Y.-J., Lai, H., Zhang, L., & Gong, Q. (2019) · European Child and Adolescent Psychiatry · doi:10.1007/s00787-019-01421-6 · Cited by 30
Gur, R. C., Butler, E. R., Moore, T. M., Rosen, A. F. G., Ruparel, K., Satterthwaite, T. D., Roalf, D. R., Gennatas, E. D., Bilker, W. B., Shinohara, R. T., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa282 · Cited by 27
Self-citationNormative cerebral cortical thickness for human visual areas
Alvarez, I., Parker, A. J., & Bridge, H. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116057 · Cited by 30
Shifting brain circuits in pain chronicity
Youssef, A. M., Azqueta‐Gavaldon, M., Silva, K. E., Barakat, N., Lopez, N., Mahmud, F., Lebel, A., Sethna, N. F., Zurakowski, D., Simons, L. E., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24709 · Cited by 27
Lv, Q., Yan, M., Shen, X., Wu, J., Yu, W., Yan, S., Yang, F., Zeljic, K., Shi, Y., Zhou, Z., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa229 · Cited by 28
Sex and Sleep Disruption as Contributing Factors in Alzheimer’s Disease
Johnson, C. E., Duncan, M. J., & Murphy, M. P. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230527 · Cited by 34
Yang, D., Ha, S. G., Ryoo, E., Choi, J. Y., & Kim, H. J. (2019) · Resuscitation · doi:10.1016/j.resuscitation.2019.01.033 · Cited by 27
Cortical morphometry in anorexia nervosa: An out‐of‐sample replication study
Leppänen, J., Sedgewick, F., Cardi, V., Treasure, J. L., & Tchanturia, K. (2019) · European Eating Disorders Review · doi:10.1002/erv.2686 · Cited by 25
Dennis, E. L., Caeyenberghs, K., Asarnow, R. F., Babikian, T., Bartnik‐Olson, B., Bigler, E. D., Figaji, A., Giza, C. C., Goodrich‐Hunsaker, N. J., Hodges, C. B., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00363-x · Cited by 26
Brain structure changes associated with sexual orientation
Votinov, M., Goerlich, K. S., Puiu, A. A., Smith, E. S., Nickl‐Jockschat, T., Derntl, B., & Habel, U. (2021) · Scientific Reports · doi:10.1038/s41598-021-84496-z · Cited by 32
Prenatal Programming of Neurodevelopment: Structural and Functional Changes
Demers, C. H., Aran, Ö., Glynn, L. M., & Davis, E. P. (2021) · Springer eBooks · doi:10.1007/978-3-030-60159-1_9 · Cited by 25
Relationship between Lipoprotein (a) and cognitive function – Results from the Berlin Aging Study II
Rohr, F., Bucholtz, N., Toepfer, S., Norman, K., Spira, D., Steinhagen‐Thiessen, E., Lill, C. M., Bertram, L., Demuth, I., Buchmann, N., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66783-3 · Cited by 26
Skorska, M. N., Chavez, S., Devenyi, G. A., Patel, R., Thurston, L. T., Lai, M., Zucker, K. J., Chakravarty, M. M., Lobaugh, N. J., & VanderLaan, D. P. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10020345 · Cited by 25
Sexual Dimorphism in Brain Development: Influence on Affective Disorders
López-Ojeda, W., & Hurley, R. A. (2021) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.20100269 · Cited by 24
Lin, H.-M., Chang, Y.-T., Chen, M.-H., Liu, S.-T., Chen, B.-S., Li, L., Lee, C.-Y., Sue, Y.-R., Sung, T.-M., Sun, C.-K., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316277 · Cited by 26
Measures of Brain Connectivity and Cognition by Sex in US Children
Tomasi, D. G., & Volkow, N. D. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.0157 · Cited by 27
Complexity of Sex Differences and Their Impact on Alzheimer’s Disease
Kadlecová, M., Freude, K., & Haukedal, H. (2023) · Biomedicines · doi:10.3390/biomedicines11051261 · Cited by 27
Wu, J., & Tang, X. (2019) · Neuroinformatics · doi:10.1007/s12021-019-09438-7 · Cited by 25
Haapala, E. A., Haapala, H. L., Syväoja, H., Tammelin, T. H., Finni, T., & Kiuru, N. (2020) · Journal of Sport and Health Science · doi:10.1016/j.jshs.2019.07.003 · Cited by 24
Sung, D., Park, B., Kim, S.-Y., Kim, B.-N., Park, S., Jung, K.-I., Kim, J., & Park, M.-H. (2020) · Scientific Reports · doi:10.1038/s41598-020-60692-1 · Cited by 24
Prospective Study of Gray Matter Atrophy Following Pediatric Mild Traumatic Brain Injury
Mayer, A. R., Meier, T. B., Dodd, A. B., Stephenson, D. D., Robertson‐Benta, C. R., Ling, J. M., Reddy, S. P., Zotev, V. S., Vakamudi, K., Campbell, R. A., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201470 · Cited by 23
Kim, J., Jung, Y., Barcus, R. A., Bachevalier, J., Sánchez, M. M., Nader, M. A., & Whitlow, C. T. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa015 · Cited by 23
Sex effects on cortical morphological networks in healthy young adults
Ge, R., Liu, X., Long, D., Frangou, S., & Vila‐Rodriguez, F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117945 · Cited by 20
Chen, Y., Chaudhary, S., Wang, W., & Li, C. R. (2021) · Addiction Neuroscience · doi:10.1016/j.addicn.2021.100003 · Cited by 22
Linli, Z., Rolls, E. T., Zhao, W. D., Kang, J., Feng, J., & Guo, S. (2022) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2022.110698 · Cited by 21
Pan, N., Yang, C., Suo, X., Shekara, A., Hu, S., Gong, Q., & Wang, S. (2023) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-023-02231-7 · Cited by 24
Relating sex-bias in human cortical and hippocampal microstructure to sex hormones
Küchenhoff, S., Bayrak, Ş., Zsido, R. G., Saberi, A., Bernhardt, B. C., Weis, S., Schaare, H. L., Sacher, J., Eickhoff, S. B., & Valk, S. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51459-7 · Cited by 31
Shafiei, G., Esper, N. B., Hoffmann, M. S., Ai, L., Chen, A. A., Cluce, J., Covitz, S. C., Giavasis, S., Lane, C., Mehta, K. P., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.026 · Cited by 32
Self-citationBrain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks
Abdolmaleki, S., & Abadeh, M. S. (2020) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip49855.2020.9116877 · Cited by 24
Rosales, E. G., Baumgärtner, T., & Knoch, D. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119664 · Cited by 21
Ross, M. C., Dvorak, D., Sartin‐Tarm, A., Botsford, C., Cogswell, I. E., Hoffstetter, A., Putnam, O., Schomaker, C. M., Smith, P., Stalsberg, A., et al. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111297 · Cited by 21
Yang, D., Ryoo, E., & Kim, H. J. (2019) · Frontiers in Pediatrics · doi:10.3389/fped.2019.00223 · Cited by 20
Metadata
- Authors
- Gennatas, E. D., Avants, B. B., Wolf, D. H., Satterthwaite, T. D., Ruparel, K., Ciric, R., Hakonarson, H., Gur, R. E., & Gur, R. C.
- Affiliations
- University of Pennsylvania (US)
- Children's Hospital of Philadelphia (US)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2017-04-21
- Volume
- 37
- Issue
- 20
- Pages
- 5065-5073
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC5444192
- OpenAlex
- W2605712228
- Cited by
- 302
- References
- 37
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 34 · 2023: 37 · 2022: 38 · 2021: 51 · 2020: 38 · 2019: 35 · 2018: 14 · 2017: 4
- FWCI
- 19.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01mh107235-03, MH-089983, MH-096891, MH107235)
- National Institute of Mental Health (RC2MH089983, P50MH096891, R01-MH-107235)
- 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
- gray matter density, gray matter volume, cortical thickness, brain development, sex differences, age-related changes, adolescence, Philadelphia Neurodevelopmental Cohort, structural MRI
- MeSH terms
- Adolescent, Aging, Brain, Child, Female, Humans, Magnetic Resonance Imaging, Male, Organ Size, Sensitivity and Specificity, Sex Characteristics, Reproducibility of Results, Young Adult, Connectome, 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
