Cited by 302Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

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 access

Gennatas, Efstathios D., Avants, Brian B., Wolf, Daniel H., Satterthwaite, Theodore D., Ruparel, Kosha, Ciric, Rastko, Hakonarson, Hakon, Gur, Raquel E., Gur, Ruben C.·2017·The Journal of Neuroscience

DOI 10.1523/jneurosci.3550-16.2017PMC PMC5444192

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.1523/jneurosci.3550-16.2017

Cites

37 of 37 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain
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
Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
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-citation
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
Avants, 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-citation
Dynamic 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).”
BackgroundMethodResultInfluential
A 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

Show all 37 references

Sorted by citations

Items 101–200 of 301

Maturational Changes in Human Dorsal and Ventral Visual Networks
Ciesielski, K. T. R., Stern, M. E., Diamond, A. D., Khan, S., Busa, E., Goldsmith, T. E., van der Kouwe, A. J. W., Fischl, B. R., & Rosen, B. R. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz053 · Cited by 19
Structural and functional brain correlates of socioeconomic status across the life span: A systematic review
Thanaraju, A., Marzuki, A. A., Chan, J. K., Wong, K. Y., Phon-Amnuaisuk, P., Vafa, S., Chew, J., Chia, Y. C., & Jenkins, M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105716 · Cited by 21
Multicompartmental models and diffusion abnormalities in paediatric mild traumatic brain injury
Mayer, A. R., Ling, J. M., Dodd, A. B., Stephenson, D. D., Reddy, S. P., Robertson‐Benta, C. R., Erhardt, E. B., Harms, R., Meier, T. B., Vakhtin, A. A., et al. (2022) · Brain · doi:10.1093/brain/awac221 · Cited by 18
Altered cortical structure and psychiatric symptom risk in adolescents exposed to maternal stress in utero: A retrospective investigation
McQuaid, G. A., Darcey, V. L., Avalos, M. F., Fishbein, D. H., & VanMeter, J. W. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.112145 · Cited by 23
Dissociating regional gray matter density and gray matter volume in autism spectrum condition
Yankowitz, L. D., Yerys, B. E., Herrington, J. D., Pandey, J., & Schultz, R. T. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102888 · Cited by 18
Region-Dependent Viscoelastic Properties of Human Brain Tissue Under Large Deformations
Sundaresh, S. N., Finan, J. D., Elkin, B. S., Basilio, A. V., McKhann, G. M., & Morrison, B. (2022) · Annals of Biomedical Engineering · doi:10.1007/s10439-022-02910-7 · Cited by 17
Characterization of Hippocampal-Thalamic-Cortical Morphometric Reorganization in Temporal Lobe Epilepsy
Tung, H., Pan, S., Lan, T., Lin, Y.-Y., & Peng, S. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2021.810186 · Cited by 18
Sex differences in brain and behavioral development
Beltz, A. M., Kelly, D. P., & Berenbaum, S. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814411-4.00027-5 · Cited by 21
Developmental trajectories of cortical thickness by functional brain network: The roles of pubertal timing and socioeconomic status
Sanders, A. F. P., Baum, G. L., Harms, M. P., Kandala, S., Bookheimer, S. Y., Dapretto, M., Somerville, L. H., Thomas, K. M., Essen, D. C. V., Yacoub, E. S., et al. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101145 · Cited by 15
Sex-biased neural encoding of threat discrimination in nucleus accumbens afferents drives suppression of reward behavior
Muir, J., Iyer, E. S., Tse, Y. C., Sorensen, J., Wu, S. S., Eid, R. S., Cvetkovska, V., Wassef, K., Gostlin, S. K., Vitaro, P., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01748-7 · Cited by 18
Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample
Li, B., Zhang, M., Riphagen, J. M., Yochim, K. M., Li, B., Liu, J., & Salat, D. H. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102387 · Cited by 16
Altered Structural Network in Newly Onset Childhood Absence Epilepsy
Kim, E.-H., Shim, W. H., Lee, J. S., Yoon, H. M., Ko, T., & Yum, M. (2020) · Journal of Clinical Neurology · doi:10.3988/jcn.2020.16.4.573 · Cited by 18
Sexual Regional Dimorphism of Post-Adolescent and Middle Age Brain Maturation. A Multi-center 3T MRI Study
Delvecchio, G., Maggioni, E., Pigoni, A., Crespo‐Facorro, B., Nenadić, I., Benedetti, F., Christian Gaser, B., Sauer, H., Roiz‐Santiáñez, R., Poletti, S., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.622054 · Cited by 18
Life Event Stress and Reduced Cortical Thickness in Youth at Clinical High Risk for Psychosis and Healthy Control Subjects
Aberizk, K., Collins, M. A., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Tsuang, M. T., et al. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.04.011 · Cited by 18
Intergenerational transmission of the patterns of functional and structural brain networks
Takagi, Y., Okada, N., Ando, S., Yahata, N., Morita, K., Koshiyama, D., Kawakami, S., Sawada, K., Koike, S., Endo, K., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102708 · Cited by 17
Basal ganglia correlates of wellbeing in early adolescence
Boyes, A., McLoughlin, L. T., Anderson, H. S., Schwenn, P., Shan, Z., Gatt, J. M., Lagopoulos, J., & Hermens, D. F. (2021) · Brain Research · doi:10.1016/j.brainres.2021.147710 · Cited by 17
Comparison of brain structure between pain-susceptible and asymptomatic individuals following experimental induction of low back pain
Boissoneault, J., Penza, C. W., George, S. Z., Robinson, M. E., & Bishop, M. D. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.08.015 · Cited by 17
Sex-specific transcriptional signatures in the medial prefrontal cortex underlying sexually dimorphic behavioural responses to stress in rats
Zhang, Y.-D., Shi, D., Zhang, S., & Wang, Z. (2023) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.220147 · Cited by 17
Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation
Wang, Y., Zhu, D., Zhao, L., Wang, X., Zhang, Z., Hu, B., Wu, D., & Zheng, W. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120660 · Cited by 17
Sleep disturbances are associated with cortical and subcortical atrophy in alcohol use disorder
Zhang, R., Tomasi, D. G., Manza, P., Kojori, E. S., Demiral, Ş. B., Feldman, D. E., Kroll, D. S., Biesecker, C. L., McPherson, K. L., Wang, G., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01534-0 · Cited by 16
Effects of Persistent Binge Drinking on Brain Structure in Emerging Adults: A Longitudinal Study
García, J. M. P., Cadaveira, F., Canales-Rodríguez, E. J., Suárez‐Suárez, S., Holguı́n, S. R., Corral, M., Blanco‐Ramos, J., & Doallo, S. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.935043 · Cited by 15
Association of polygenic risk for bipolar disorder with grey matter structure and white matter integrity in youth
Jiang, X., Zai, C. C. H., Kennedy, K. G., Zou, Y. Z., Nikolova, Y. S., Felsky, D., Young, L. T., MacIntosh, B. J., & Goldstein, B. I. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02607-y · Cited by 16
Modeling sparse longitudinal data in early neurodevelopment
Chen, Y., Dubey, P., Müller, H., Bruchhage, M. M. K., Wang, J.-L., & Deoni, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118079 · Cited by 15
Linking Puberty and the Gut Microbiome to the Pathogenesis of Neurodegenerative Disorders
Esposito, P., & Ismail, N. (2022) · Microorganisms · doi:10.3390/microorganisms10112163 · Cited by 14
Multimodal investigation of the association between shift work and the brain in a population-based sample of older adults
Bittner, N., Korf, H., Stumme, J., Jockwitz, C., Moebus, S., Schmidt, B., Dragano, N., & Caspers, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-05418-1 · Cited by 14
No Association Between Loneliness, Episodic Memory and Hippocampal Volume Change in Young and Healthy Older Adults: A Longitudinal European Multicenter Study
Solé‐Padullés, C., Macià, D., Andersson, M., Stiernstedt, M., Pudas, S., Düzel, S., Zsoldos, E., Ebmeier, K. P., Binnewies, J., Drevon, C. A., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.795764 · Cited by 13
Longitudinal Sex-at-Birth and Age Analyses of Cortical Structure in the ABCD Study
Marshall, A. T., Adise, S., Kan, E., & Sowell, E. R. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1091-24.2025 · Cited by 13
Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution
Liu, G., Cao, Z., Xu, Q., Zhang, Q., Yang, F., Xie, X., Hao, J., Shi, Y., Bernhardt, B. C., He, Y., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118687 · Cited by 14
Phytopharmaceuticals and Herbal Approaches to Target Neurodegenerative Disorders
Islam, A., Mishra, A., Ahsan, R., & Fareha, S. (2023) · Drug Research · doi:10.1055/a-2076-7939 · Cited by 13
Uncovering sex/gender differences of arithmetic in the human brain: Insights from fMRI studies
Chang, T., Chen, N.-F., & Fan, Y. (2022) · Brain and Behavior · doi:10.1002/brb3.2775 · Cited by 16
Regional Gray Matter Density Associated With Fast-Paced Walking in Older Adults: A Voxel-Based Morphometry Study
Chen, N., Rosano, C., Karim, H. T., Studenski, S. A., & Rosso, A. L. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa091 · Cited by 12
Trajectories of brain volumes in young children are associated with maternal education
Zhu, C., Chen, Y., Müller, H., Wang, J., O’Muircheartaigh, J., Bruchhage, M. M. K., & Deoni, S. (2023) · Human Brain Mapping · doi:10.1002/hbm.26271 · Cited by 10
Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study
Kambeitz‐Ilankovic, L., Haas, S. S., Meisenzahl, E. M., Dwyer, D., Weiske, J., Peters, H., Möller, H., Falkai, P., & Koutsouleris, N. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.101624 · Cited by 13
Association of Lower Rostral Anterior Cingulate GABA+ and Dysregulated Cortisol Stress Response With Altered Functional Connectivity in Young Adults With Lifetime Depression: A Multimodal Imaging Investigation of Trait and State Effects
Ironside, M., Duda, J. M., Moser, A. D., Holsen, L. M., Zuo, C. S., Du, F., Perlo, S., Richards, C. E., Chen, X., Nickerson, L. D. H., et al. (2024) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20230382 · Cited by 12
Correspondence between patterns of cerebral blood flow and structure in adolescents with and without bipolar disorder
Luciw, N. J., Toma, S., Goldstein, B. I., & MacIntosh, B. J. (2021) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x21989246 · Cited by 12
Region-Dependent Mechanical Properties of Human Brain Tissue Under Large Deformations Using Inverse Finite Element Modeling
Basilio, A. V., Zeng, D., Pichay, L. A., Maas, S. A., Sundaresh, S. N., Finan, J. D., Elkin, B. S., McKhann, G. M., Ateshian, G. A., & Morrison, B. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03407-7 · Cited by 10
Age-related decline in thickness and surface area in the cortical surface and hippocampus: lifespan trajectories and decade-by-decade analyses
Yu, J. (2024) · GeroScience · doi:10.1007/s11357-024-01220-1 · Cited by 13
Gray-Matter Morphometry of Internalizing-Symptom Dimensions During Adolescence
Smolker, H. R., Snyder, H. R., Hankin, B. L., & Banich, M. T. (2022) · Clinical Psychological Science · doi:10.1177/21677026211071091 · Cited by 15
Sensorimotor performance after high-definition transcranial direct current stimulation over the primary somatosensory or motor cortices in men versus women
Swissa, Y., Hacohen, S., Friedman, J., & Frenkel‐Toledo, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-15226-2 · Cited by 11
Individual variation in longitudinal postnatal development of the primate brain
Ball, G., & Seal, M. (2018) · Brain Structure and Function · doi:10.1007/s00429-019-01829-5 · Cited by 12
Factors Associated with Cognitive Outcomes After First-Ever Ischemic Stroke: The Impact of Small Vessel Disease Burden and Neurodegeneration
Sung, P., Lee, K., Lin, P., Su, H., Yu, R., Tsai, K., Lin, S., & Chen, C. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210587 · Cited by 11
Statistical Pitfalls in Brain Age AnalysesPreprint
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. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.21.163741 · Cited by 11
Self-citation
Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging
Bao, S., Liao, C., Xu, N., Deng, A., Luo, Y., Ouyang, Z., Guo, X., Liu, Y., Ke, T., & Yang, J. J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.963668 · Cited by 10
Changes in the Auditory Association Cortex in Dementing Illnesses
Aylward, A., Auduong, P., Anderson, J. S., Zielinski, B. A., Wang, A. Y., Weng, C., Foster, N. L., & Gurgel, R. K. (2020) · Otology & Neurotology · doi:10.1097/mao.0000000000002786 · Cited by 10
Sex differences in regional gray matter density in pre-adolescent binge eating disorder: a voxel-based morphometry study
Murray, S. B., Diaz‐Fong, J. P., Duval, C. J., Balkchyan, A. A., Nagata, J. M., Lee, D. J., Ganson, K. T., Toga, A. W., Siegel, S. J., & Jann, K. (2022) · Psychological Medicine · doi:10.1017/s0033291722003269 · Cited by 10
Significant age by childhood trauma interactions on grey matter volumes: A whole brain VBM analysis
Malhi, G. S., Das, P., Outhred, T., Bell, E., Gessler, D., Bryant, R. A., & Mannie, Z. N. (2023) · Bipolar Disorders · doi:10.1111/bdi.13286 · Cited by 9
Genetic scores for adult subcortical volumes associate with subcortical volumes during infancy and childhood
Lamballais, S., Jansen, P. R., Labrecque, J. A., Ikram, M. A., & White, T. J. H. (2021) · Human Brain Mapping · doi:10.1002/hbm.25292 · Cited by 9
Regulation of the Cerebral Circulation During Development
Koehler, R. C. (2021) · Comprehensive physiology · doi:10.1002/cphy.c200028 · Cited by 18
Recent tobacco use has widespread associations with adolescent white matter microstructure
Thayer, R. E., Hansen, N. S., Prashad, S., Karoly, H. C., Filbey, F. M., Bryan, A. D., & Ewing, S. W. F. (2019) · Addictive Behaviors · doi:10.1016/j.addbeh.2019.106152 · Cited by 9
Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume
Sun, J., Tu, Z., Meng, D., Gong, Y., Zhang, M., & Xu, J. (2022) · Brain Sciences · doi:10.3390/brainsci12111517 · Cited by 9
Can gray matter loss in early adolescence be explained by white matter growth?
Chad, J. A., & Lebel, C. A. (2024) · Human Brain Mapping · doi:10.1002/hbm.26758 · Cited by 10
Neuroanatomical Differences Among Sexual Offenders: A Targeted Review with Limitations and Implications for Future Directions
Kirk‐Provencher, K. T., Nelson, R. J., & Spillane, N. S. (2020) · Violence and Gender · doi:10.1089/vio.2019.0051 · Cited by 9
Recent Advances and Future Directions in Brain MR Imaging Studies in Schizophrenia: Toward Elucidating Brain Pathology and Developing Clinical Tools
Koike, S., Uematsu, A., Sasabayashi, D., Maikusa, N., Takahashi, T., Ohi, K., Nakajima, S., Noda, Y., & Hirano, Y. (2021) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2021-0050 · Cited by 10
Linking temporal-parietal junction to internet addiction tendency: Moderating effect of critical thinking
Liu, D., Ma, Y., Zhuang, K., Chen, Q., Shi, B., & Qiu, J. (2021) · Journal of Behavioral Addictions · doi:10.1556/2006.2021.00049 · Cited by 9
Role of Family Risk and of Pre-Reading Auditory and Neurostructural Measures in Predicting Reading Outcome
Blockmans, L., Golestani, N., da Rocha, J. L. D., Wouters, J. M., Ghesquière, P., & Vandermosten, M. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00111 · Cited by 12
Effect of Healthy Aging and Gender on Syntactic Input Processing: A P600 Event-Related Potential Study
Dorme, A., Oudenhove, B. V., Criel, Y., Depuydt, E., Groote, E. D., Stalpaert, J., Huysman, E., van Mierlo, P., & Letter, M. D. (2023) · Journal of Speech Language and Hearing Research · doi:10.1044/2023_jslhr-22-00633 · Cited by 9
Regional gray matter volume mediates the relationship between neuroticism and depressed emotion
Yang, J., Huangfu, X., Tong, D., & He, A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.993694 · Cited by 8
Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD)
Baboli, R., Cao, M., Martin, E., Halperin, J. M., Wu, K., & Li, X. (2024) · Cortex · doi:10.1016/j.cortex.2024.06.019 · Cited by 8
Identification of multimodal brain imaging biomarkers in first-episode drugs-naive major depressive disorder through a multi-site large-scale MRI consortium data
Dai, P., Shi, Y., Zhou, X., Xiong, T., Luo, J., Chen, Q., Liao, S., Huang, Z., & Yi, X. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.10.006 · Cited by 8
Assessing neonatal brain glymphatic system development using diffusion tensor imaging along the perivascular space and choroid plexus volume
Peng, T., Lin, Y., Xu, X., Li, J., Liu, M., Zhang, C., Liao, X., Ji, X., Xiong, Z., Gu, Z., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01673-6 · Cited by 8
Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction
He, L., Wang, S., Chen, C., Wang, Y., Fan, Q., Chu, C., Fan, L., & Xu, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70239 · Cited by 9
Different Electrophysiological Responses to Informative Value of Feedback Between Children and Adults
Du, B., Cao, B., He, W., & Li, F. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00346 · Cited by 9
First use of cannabis compared to first use of alcohol and tobacco: Associations with single and poly-substance use behavior.
Cohn, A. M., & Elmasry, H. (2023) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2023.109904 · Cited by 8
Sex Differences and Behavioral Associations with Typically Developing Pediatric Regional Cerebellar Gray Matter Volume
Rice, L. C., Rochowiak, R., Plotkin, M. R., Rosch, K. S., Mostofsky, S. H., & Crocetti, D. (2023) · The Cerebellum · doi:10.1007/s12311-023-01569-7 · Cited by 8
Development of brain behavior integration systems related to criminal culpability from childhood to young adulthood: Does it stop at 18 years?
Gur, R. C. (2021) · Journal of Pediatric Neuropsychology · doi:10.1007/s40817-021-00101-1 · Cited by 9
Self-citation
Social functioning and brain imaging in individuals at clinical high-risk for psychosis: A systematic review
Metzak, P. D., Farris, M. S., Placsko, T. J., Braun, A., Bonneville, D., Brummitt, K., Chu, M. P., & Addington, J. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.04.013 · Cited by 8
Bipolar spectrum disorders are associated with increased gray matter volume in the medial orbitofrontal cortex and nucleus accumbens
Damme, K. S. F., Alloy, L. B., Kelley, N. J., Carroll, A., Young, C. B., Chein, J. M., Ng, T. H., Titone, M. K., Bart, C. P., & Nusslock, R. (2022) · JCPP Advances · doi:10.1002/jcv2.12068 · Cited by 8
Right Fusiform Gray Matter Volume in Children with Long-Term Abacus Training Positively Correlates with Arithmetic Ability
Zhou, H., Yao, Y., Geng, F., Chen, F., & Hu, Y. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.11.006 · Cited by 10
Improved gray matter surface based spatial statistics in neuroimaging studies
Parvathaneni, P., Lyu, I., Huo, Y., Rogers, B. P., Schilling, K. G., Nath, V., Blaber, J. A., Hainline, A. E., Anderson, A. W., Woodward, N. D., et al. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.016 · Cited by 8
A preliminary examination of brain morphometry in youth with Down syndrome with and without parent-reported sleep difficulties
Lee, N., Perez, M., Hamner, T., Adeyemi, E. I., & CLASEN, L. S. (2020) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2020.103575 · Cited by 8
Reducing Fear During Childhood to Prevent Anxiety Disorders Later: Insights From Developmental Psychobiology
Kim, J. H. (2017) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/2372732217719544 · Cited by 7
Is Adolescence a Sensitive Period for the Development of Incentive-Reward Motivation?
Luciana, M., & Collins, P. F. (2021) · Current Topics in Behavioral Neurosciences · doi:10.1007/7854_2021_275 · Cited by 7
Age-related changes in the primary auditory cortex of newborn, adults and aging bottlenose dolphins (Tursiops truncatus) are located in the upper cortical layers
Graïc, J., Corain, L., Finos, L., Vadori, V., Grisan, E., Gerussi, T., Orekhova, K., Centelleghe, C., Cozzi, B., & Peruffo, A. (2024) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2023.1330384 · Cited by 9
Association of handgrip strength weakness and asymmetry with cognitive impairment and depressive symptoms in older Chinese adults
Abudukelimu, N., Zhang, P., Du, J., Li, M., Shen, Y., Mao, Y., Wang, D., & Zhu, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-93573-6 · Cited by 7
Impact of artificial light at night and night shift work on brain functions and metabolism
Korf, H., Bittner, N., Caspers, S., & von Gall, C. (2025) · General and Comparative Endocrinology · doi:10.1016/j.ygcen.2025.114822 · Cited by 11
Sex differences in global metrics of brain size across the lifespan
Vucic, S. N., Georges, B., Frangou, S., Sadeghi, N., & White, T. W. T. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1646144 · Cited by 7
Association of markers of inflammation on attention and neurobehavioral outcomes in survivors of childhood acute lymphoblastic leukemia
Cheung, Y. T., To, K. K. W., Hua, R., Lee, C. P., Chan, A. S.-Y., & Li, C. K. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1117096 · Cited by 8
Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi‐site MRI data
Hao, Y., Xu, H., Xia, M., Yan, C., Zhang, Y., Zhou, D., Kärkkäinen, T., Nickerson, L. D. H., Li, H., & Cong, F. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16120 · Cited by 7
Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children
Tsai, M., Hsieh, K. L., Liu, Y., Yang, Y.-S., Chang, H. C., Wong, T., & Peng, S. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00881-1 · Cited by 7
Sex differences in the programming of stress resilience
Morrison, K. E., Epperson, C. N., & Bale, T. L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813983-7.00006-9 · Cited by 7
Adolescent development and schooling in the digital age
Kreniske, P., Spindler, E., & Santelli, J. S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817319-0.00001-3 · Cited by 8
Structural Brain Patterns Associated with Traumatic Stress Resilience and Susceptibility to Mood and Anxiety Symptoms in Youths
Barzilay, R., Rosen, A. F. G., Moore, T. M., Roalf, D. R., Satterthwaite, T. D., Calkins, M. E., Ruparel, K., Patrick, A., Scott, J. C., Wolf, D. H., et al. (2020) · Adversity and Resilience Science · doi:10.1007/s42844-020-00014-6 · Cited by 10
Self-citation
Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy
Sungura, R., Mpolya, E. A., Spitsbergen, J., Onyambu, C. K., Sauli, E., & Vianney, J.-M. (2020) · European Journal of Radiology Open · doi:10.1016/j.ejro.2020.100281 · Cited by 7
Neurostructural differences associated with self‐harm in youth bipolar disorder
Dimick, M. K., Kennedy, K. G., Mitchell, R. H. B., Sinyor, M., MacIntosh, B. J., & Goldstein, B. I. (2021) · Bipolar Disorders · doi:10.1111/bdi.13137 · Cited by 7
Volumetric changes in gray matter after radiotherapy detected with longitudinal magnetic resonance imaging in glioma patients.
Lee, H. I., Kang, M. K., Hwang, K., Kim, C.-Y., Kim, Y. J., Suh, K. J., Choi, B. S., Choe, G., Kim, I. A., & Jang, B. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.09.022 · Cited by 8
Testing relationships between multimodal modes of brain structural variation and age, sex and polygenic scores for neuroticism in children and adolescents
Norbom, L. B., Rokicki, J., van der Meer, D., Alnæs, D., Doan, N. T., Moberget, T., Kaufmann, T., Andreassen, O. A., Westlye, L. T., & Tamnes, C. K. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00931-1 · Cited by 7
A Primer on Alcohol and Adolescent Brain Development: Implications for Prevention
White, A. M., & Hingson, R. (2019) · Advances in prevention science · doi:10.1007/978-3-030-00627-3_1 · Cited by 7
Structural MRI correlates of cognitive and neuropsychiatric symptoms in Long COVID: a pilot study
Joshi, S. H., Siddarth, P., & Lavretsky, H. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1412020 · Cited by 6
Development and Validation of a Brief Age-Normed Screening Tool for Subthreshold Psychosis Symptoms in Youth
Calkins, M. E., Ered, A., Moore, T. M., White, L. K., Taylor, J. H., Moxam, A., Ruparel, K., Wolf, D. H., Satterthwaite, T. D., Kohler, C. G., et al. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbae224 · Cited by 6
Self-citation
Age‐ and Sex‐Specific Patterns in Adult Brain Network Segregation
Sidhu, A. S., Duarte, K. T. N., Shahid, T. H., Sharkey, R. J., Lauzon, M. L., Salluzzi, M., McCreary, C. R., Protzner, A. B., Goodyear, B. G., & Frayne, R. (2025) · Human Brain Mapping · doi:10.1002/hbm.70169 · Cited by 6
Neuroenhancement and vulnerability in adolescence
Erhardt, J., & Zagorac, I. (2019) · JAHR · doi:10.21860/j.10.1.8 · Cited by 6
Development of cardiorespiratory fitness standards for working memory using receiver operating curves in 15-year-old adolescents
Weber, V. M. R., Fernandes, D. Z., Volpato, L. A., Bueno, M. R. D. O., Romanzini, M., Castro‐Piñero, J., & Ronque, Ê. R. V. (2021) · BMC Pediatrics · doi:10.1186/s12887-021-02681-5 · Cited by 6
Dispositional Negative Emotionality in Childhood and Adolescence Predicts Structural Variation in the Amygdala and Caudal Anterior Cingulate During Early Adulthood: Theoretically and Empirically Based Tests
Lahey, B. B., Hinton, K. E., Burgess, L. L., Meyer, F. C., Landman, B. A., Villalta‐Gil, V., Yang, X., Rathouz, P. J., Applegate, B. E., & Zald, D. H. (2021) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-021-00811-2 · Cited by 5
Major gender differences in relations between life stressor frequency and gray matter in adolescence and emerging adulthood.
Fassett-Carman, A. N., Smolker, H. R., Hankin, B. L., Snyder, H. R., & Banich, M. T. (2022) · Developmental Psychology · doi:10.1037/dev0001489 · Cited by 6
Atypical language organization following perinatal infarctions of the left hemisphere is associated with structural changes in right‐hemispheric grey matter
Schnaufer, L., Gschaidmeier, A., Heimgärtner, M., Driever, P. H., Hauser, T., Wilke, M., Lidzba, K., & Staudt, M. (2023) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15751 · Cited by 6
Sex differences in anhedonia in bipolar depression: a resting-state fMRI study
Wang, X., Xia, Y. X., Yan, R., Sun, H., Huang, Y., Xia, Q., Sheng, J., You, W., Hua, L., Tang, H., et al. (2024) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-024-01765-4 · Cited by 7
SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging
Long, Z. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.946156 · Cited by 6
Structural and Functional Trajectories of Middle Temporal Gyrus Sub-Regions During Life Span: A Potential Biomarker of Brain Development and Aging
Xu, J., Zhang, J., Li, J., Wang, H., Chen, J., Lyu, H., & Hu, Q. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.799260 · Cited by 6
Cholinergic-estrogen interaction is associated with the effect of education on attenuating cognitive sex differences in a Thai healthy population
Chen, C., Khanthiyong, B., Charoenlappanit, S., Roytrakul, S., Reynolds, G. P., Thanoi, S., & Nudmamud‐Thanoi, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278080 · Cited by 6
A structural MRI study of differential neuromorphometric characteristics of binge and heavy drinking
Maksimovskiy, A. L., Fortier, C. B., Milberg, W., & McGlinchey, R. E. (2019) · Addictive Behaviors Reports · doi:10.1016/j.abrep.2019.100168 · Cited by 6

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: Gennatas, Efstathios D.
Avants, Brian B.
Wolf, Daniel H.
Satterthwaite, Theodore D.
Ruparel, Kosha
Hakonarson, Hakon
Gur, Raquel E.
Gur, Ruben C.
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
DOI: 10.1523/jneurosci.3550-16.2017
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: cc-by-nc-sa
creativecommons.org/licenses/by-nc-sa/4.0/ (vor, 210 d)
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

Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks