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Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Open accessAbstract
The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale, NIMH funded initiative to understand how brain maturation mediates cognitive development and vulnerability to psychiatric illness, and understand how genetics impacts this process. As part of this study, 1445 adolescents ages 8-21 at enrollment underwent multimodal neuroimaging. Here, we highlight the conceptual basis for the effort, the study design, and the measures available in the dataset. We focus on neuroimaging measures obtained, including T1-weighted structural neuroimaging, diffusion tensor imaging, perfusion neuroimaging using arterial spin labeling, functional imaging tasks of working memory and emotion identification, and resting state imaging of functional connectivity. Furthermore, we provide characteristics regarding the final sample acquired. Finally, we describe mechanisms in place for data sharing that will allow the PNC to become a freely available public resource to advance our understanding of normal and pathological brain development.
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Cites
(1970) · Cited by 24,983
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.6.593 · Cited by 20,391
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
KAUFMAN, J., BIRMAHER, B., BRENT, D., RAO, U., FLYNN, C., MORECI, P., WILLIAMSON, D., & RYAN, N. (1997) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-199707000-00021 · Cited by 10,148
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067
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Items 201–300 of 503
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
Phillips, O. R., Onopa, A., Hsu, V., Ollila, H. M., Hillary, R. P., Hallmayer, J. F., Gotlib, I. H., Taylor, J., Mackey, L. W., & Singh, M. K. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2018.09.425 · Cited by 27
Gu, S., Xia, C. H., Ćirić, R., Moore, T. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz150 · Cited by 26
Self-citationStojanovski, S., Nazeri, A., Lepage, C., Ameis, S. H., Voineskos, A. N., & Wheeler, A. L. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102102 · Cited by 27
Multi‐scale network regression for brain‐phenotype associations
Xia, C. H., Ma, Z., Cui, Z., Bzdok, D., Thirion, B., Bassett, D. S., Satterthwaite, T. D., Shinohara, R. T., & Witten, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.24982 · Cited by 31
Self-citationPower and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt, M., Tejavibulya, L., Sun, H., Camp, C., Khaitova, M., Adkinson, B. D., Jiang, R., Westwater, M. L., Noble, S., & Scheinost, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01931-7 · Cited by 26
Resample aggregating improves the generalizability of connectome predictive modeling
O’Connor, D. A., Lake, E. M. R., Scheinost, D., & Constable, R. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118044 · Cited by 25
Polygenic risk for psychiatric disorders correlates with executive function in typical development
Schork, A. J., Brown, T. T., Hagler, D. J., Thompson, W. K., Chen, C. ‐., Dale, A. M., Jernigan, T. L., Akshoomoff, N. A., & for the Pediatric Imaging, N. A. G. S. (2018) · Genes Brain & Behavior · doi:10.1111/gbb.12480 · Cited by 28
Jolles, D., Mennigen, E., Gupta, M. W., Hegarty, C. E., Bearden, C. E., & Karlsgodt, K. H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117202 · Cited by 24
Modeling brain, symptom, and behavior in the winds of change
Lydon‐Staley, D. M., Cornblath, E. J., Blevins, A. S., & Bassett, D. S. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00805-6 · Cited by 25
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Orlichenko, A., Qu, G., Zhang, G., Patel, B., Wilson, T. W., Stephen, J. M., Calhoun, V. D., & Wang, Y. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3232964 · Cited by 23
The rise of large-scale imaging studies in psychiatry
Turner, J. A. (2014) · GigaScience · doi:10.1186/2047-217x-3-29 · Cited by 25
Kjelkenes, R., Wolfers, T., Alnæs, D., van der Meer, D., Pedersen, M. L., Dahl, A., Voldsbekk, I., Moberget, T., Tamnes, C. K., Andreassen, O. A., et al. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2022.01.007 · Cited by 26
Tong, X., Xie, H., Carlisle, N. B., Fonzo, G. A., Oathes, D. J., Jiang, J., & Zhang, Y. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02134-2 · Cited by 22
Kjelkenes, R., Wolfers, T., Alnæs, D., Norbom, L. B., Voldsbekk, I., Holm, M. C., Dahl, A., Berthet, P., Tamnes, C. K., Marquand, A. F., et al. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101173 · Cited by 22
Rosa, P. S. L., Brooks, T. L., Deych, E., Shands, B., Prior, F., Larson‐Prior, L. J., & Shannon, W. D. (2015) · Statistics in Medicine · doi:10.1002/sim.6757 · Cited by 24
Lynch, K. M., Page, K. A., Shi, Y., Xiang, A. H., Toga, A. W., & Clark, K. A. (2020) · Hippocampus · doi:10.1002/hipo.23280 · Cited by 25
Self-regulation in the pre-adolescent brain
Pas, P., Pol, H. E. H., Raemaekers, M. A. H., & Vink, M. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.101012 · Cited by 34
Development of white matter fiber covariance networks supports executive function in youth
Bagautdinova, J., Bourque, J., Sydnor, V. J., Cieslak, M., Alexander‐Bloch, A. F., Bertolero, M. A., Cook, P. A., Gur, R. E., Gur, R. C., Hu, F., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113487 · Cited by 24
Self-citationShafiei, 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-citationMasked features of task states found in individual brain networks
Porter, A., Nielsen, A. N., Dorn, M., Dworetsky, A., Edmonds, D., & Gratton, C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac247 · Cited by 22
Zhu, X., Li, H., Shen, H. T., Zhang, Z., Ji, Y., & Yong Fan, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.03.006 · Cited by 20
Niu, X., Taylor, A., Shinohara, R. T., Kounios, J., & Zhang, F. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab530 · Cited by 20
Wong, T. Y., Moore, T. M., Seidlitz, J., Yuen, K. S. L., Ruparel, K., Barzilay, R., Calkins, M. E., Alexander‐Bloch, A. F., Satterthwaite, T. D., Gur, R. E., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01908-w · Cited by 20
Self-citationLarsen, B., Baller, E. B., Boucher, A. A., Calkins, M. E., Laney, N. C., Moore, T. M., Roalf, D. R., Ruparel, K., Gur, R. C., Gur, R. E., et al. (2023) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2023.05.005 · Cited by 20
Self-citationDynamic Brain Transformer with Multi-Level Attention for Functional Brain Network Analysis
Kan, X., Gu, A. A. C., Cui, H., Guo, Y., & Yang, C. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313480 · Cited by 21
Qu, G., Orlichenko, A., Wang, J.-Q., Zhang, G.-M., Xiao, L., Zhang, K., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3343365 · Cited by 25
Moore, T. M., Calkins, M. E., Reise, S. P., Gur, R. C., & Gur, R. E. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.02.022 · Cited by 20
Self-citationHarnessing networks and machine learning in neuropsychiatric care
Cornblath, E. J., Lydon‐Staley, D. M., & Bassett, D. S. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.12.010 · Cited by 23
Distance canonical correlation analysis with application to an imaging-genetic study
Hu, W., Zhang, A., Cai, B., Calhoun, V. D., & Wang, Y.-P. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.026501 · Cited by 20
Williams syndrome hemideletion and LIMK1 variation both affect dorsal stream functional connectivity
Gregory, M. D., Mervis, C. Β., Elliott, M. L., Kippenhan, J. S., Nash, T. A., Czarapata, J. B., Prabhakaran, R., Roe, K. V., Eisenberg, D. P., Kohn, P. D., et al. (2019) · Brain · doi:10.1093/brain/awz323 · Cited by 20
Self-citationMachine Learning for Brain Imaging Genomics Methods: A Review
Wang, M., Shao, W., Hao, X., & Zhang, D. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1361-0 · Cited by 20
Kurth, F., Schijven, D., van den Heuvel, O. A., Hoogman, M., van Rooij, D., Stein, D. J., Buitelaar, J., Bölte, S., Auzias, G., Kushki, A., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26754 · Cited by 17
Self-citationQSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRIPreprint
Cieslak, M., Cook, P. A., He, X., Yeh, F.-C. F., Dhollander, T., Adebimpe, A., Aguirre, G. K., Bassett, D. S., Betzel, R. F., Bourque, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.04.282269 · Cited by 20
Self-citationAlterations in white matter microstructure in individuals at persistent risk for psychosis
Roalf, D., Garza, Á. G. D. L., Rosen, A. F. G., Calkins, M., Moore, T. M., Quarmley, M., Ruparel, K., Xia, C., Rupert, P. E., Satterthwaite, T., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0360-1 · Cited by 19
Self-citationImputation Strategy for Reliable Regional MRI Morphological Measurements
Cruz, S. S., Dinov, I., Herting, M., González-Zacarías, C., Kim, H., Toga, A., & Sepehrband, F. (2019) · Neuroinformatics · doi:10.1007/s12021-019-09426-x · Cited by 14
Zhu, J., & Qiu, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119482 · Cited by 18
Hippocampal anterior- posterior shift in childhood and adolescence
Plachti, A., Latzman, R. D., Balajoo, S. M., Hoffstaedter, F., Madsen, K. S., Baaré, W. F. C., Siebner, H. R., Eickhoff, S. B., & Genon, S. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102447 · Cited by 20
Common variants contribute to intrinsic human brain functional networksPreprint
Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.30.229914 · Cited by 18
Xiao, L., Cai, B., Qu, G., Zhang, G., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y.-P. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3160447 · Cited by 21
Network controllability mediates the relationship between rigid structure and flexible dynamics
Gu, S., Fotiadis, P., Parkes, L., Xia, C. H., Gur, R. C., Gur, R. E., Roalf, D. R., Satterthwaite, T. D., & Bassett, D. S. (2021) · Network Neuroscience · doi:10.1162/netn_a_00225 · Cited by 17
Self-citationFeng, L., Bi, X., & Heping Zhang, D. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1766468 · Cited by 16
Sadaghiani, S., Ng, B., Altmann, A., Poline, J., Banaschewski, T., Bokde, A. L. W., Bromberg, U., Büchel, C., Quinlan, E. B., Conrod, P., et al. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0991-17.2017 · Cited by 19
Scalable Hashing-Based Network Discovery
Safavi, T., Sripada, C., & Koutra, D. (2017) · Proceedings · doi:10.1109/icdm.2017.50 · Cited by 16
Mehta, K. P., Pines, A. R., Adebimpe, A., Larsen, B., Bassett, D. S., Calkins, M. E., Baller, E. B., Gell, M., Patrick, L. M., Shafiei, G., et al. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101265 · Cited by 21
Self-citationWhite matter microstructural deficits in 22q11.2 deletion syndrome
Roalf, D., Schmitt, J. E., Vandekar, S., Satterthwaite, T., Shinohara, R., Ruparel, K., Elliott, M., Prabhakaran, K., McDonald-McGinn, D., Zackai, E., et al. (2017) · Psychiatry research. Neuroimaging · doi:10.1016/j.pscychresns.2017.08.001 · Cited by 17
Self-citationMoore, T. M., Calkins, M. E., Rosen, A. F. G., Butler, E. R., Ruparel, K., Fusar‐Poli, P., Koutsouleris, N., McGuire, P. K., Cannon, T. D., Gur, R. C., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291720005231 · Cited by 16
Self-citationDizaji, A. S., Vieira, B. H., Khodaei, M. R., Ashrafi, M., Parham, E., Hossein‐Zadeh, G., Salmón, C. E. G., & Hamid Soltanian-zadeh, A. (2021) · Basic and Clinical Neuroscience Journal · doi:10.32598/bcn.12.1.574.1 · Cited by 17
Eigenanatomy: Sparse dimensionality reduction for multi-modal medical image analysis
Kandel, B. M., Wang, D. J. J., Gee, J. C., & Avants, B. (2014) · Methods · doi:10.1016/j.ymeth.2014.10.016 · Cited by 15
Corpus callosum shape and morphology in youth across the psychosis Spectrum
Prendergast, D. M., Karlsgodt, K. H., Fales, C. L., Ardekani, B. A., & Szeszko, P. R. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.04.008 · Cited by 16
Ravindranath, O., Ordaz, S. J., Padmanabhan, A., Foran, W., Jalbrzikowski, M., Calabro, F. J., & Luna, B. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100836 · Cited by 14
A Comprehensive Analysis of Cerebellar Volumes in the 22q11.2 Deletion Syndrome
Schmitt, J. E., DeBevits, J. J., Roalf, D. R., Ruparel, K., Gallagher, R. S., Gur, R. C., Alexander‐Bloch, A. F., Eom, T.-Y., Alam, S., Steinberg, J., et al. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.11.008 · Cited by 16
Self-citationNetwork enrichment significance testing in brain–phenotype association studies
Weinstein, S. M., Vandekar, S., Li, B., Alexander-Bloch, A., Raznahan, A., Li, M.-Y., Gur, R. E., Gur, R., Roalf, D., Park, M. T. M., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26714 · Cited by 9
Self-citationSaberi, A., Wischnewski, K. J., Jung, K., Lotter, L. D., Schaare, H. L., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Desrivières, S., Flor, H., et al. (2025) · Science Advances · doi:10.1126/sciadv.adr8164 · Cited by 19
Sahoo, D., Satterthwaite, T. D., & Davatzikos, C. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3042873 · Cited by 15
Self-citationCiric, R., Wolf, D., Power, J. D., Roalf, D., Baum, G. L., Ruparel, K., Shinohara, R., Elliott, M., Eickhoff, S., Davatzikos, C., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.03616 · Cited by 28
Self-citationApplications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
Hu, M., Nardi, C., Zhang, H., & Ang, K. K. (2023) · Applied Sciences · doi:10.3390/app13042302 · Cited by 14
The effect of seed location on functional connectivity: evidence from an image-based meta-analysis
Li, M.-T., Sun, J.-W., Zhan, L., Antwi, C. O., Lv, Y., Jia, X., & Ren, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1120741 · Cited by 14
Zhang, A., Cai, B., Hu, W., Jia, B., Liang, F., Wilson, T. W., Stephen, J. M., Calhoun, V. D., & Wang, Y. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2926667 · Cited by 14
Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data
Xiao, L., Zhang, A., Cai, B., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3022335 · Cited by 14
Qiao, C., Yang, L., Calhoun, V. D., Xu, Z.-B., & Wang, Y. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.12.007 · Cited by 14
Integration of network topological features and graph Fourier transform for fMRI data analysis
Wang, J., Calhoun, V. D., Stephen, J. M., Wilson, T. W., & Wang, Y. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363530 · Cited by 14
Córdova‐Palomera, A., Kaufmann, T., Bettella, F., Wang, Y., Doan, N. T., van der Meer, D., Alnæs, D., Rokicki, J., Moberget, T., Sønderby, I. E., et al. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-018-0134-2 · Cited by 13
Warren, D. E., Rangel, A., Christopher‐Hayes, N. J., Eastman, J. A., Frenzel, M. R., Stephen, J. M., Calhoun, V. D., Wang, Y., & Wilson, T. W. (2021) · Human Brain Mapping · doi:10.1002/hbm.25458 · Cited by 14
Shared grey matter correlates of reading and attention
Lee, M. M., Drury, B. C., McGrath, L. M., & Stoodley, C. J. (2023) · Brain and Language · doi:10.1016/j.bandl.2023.105230 · Cited by 12
Wang, Y., Qiao, C., Qu, G., Calhoun, V. D., Stephen, J. M., Wilson, T. W., & Wang, Y. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3423803 · Cited by 13
Development and Maturation of the Human Brain, from Infancy to Adolescence
Paus, T. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_514 · Cited by 13
Pan, G., Xiao, L., Bai, Y., Wilson, T. W., Stephen, J. M., Calhoun, V. D., & Wang, Y. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3048594 · Cited by 14
Voineskos, A. N., Felsky, D., Wheeler, A. L., Rotenberg, D. J., Lévesque, M. L., Patel, S., Szeszko, P. R., Kennedy, J. L., Lencz, T., & Malhotra, A. K. (2015) · Schizophrenia Bulletin · doi:10.1093/schbul/sbv180 · Cited by 14
A method for integrating neuroimaging into genetic models of learning performance
Mehta, C. M., Gruen, J. R., & Zhang, H. (2016) · Genetic Epidemiology · doi:10.1002/gepi.22025 · Cited by 12
State-Dependent Functional Dysconnectivity in Youth With Psychosis Spectrum Symptoms
Mennigen, E., Jolles, D., Hegarty, C. E., Gupta, M. W., Jalbrzikowski, M., Loohuis, L. M. O., Ophoff, R. A., Karlsgodt, K. H., & Bearden, C. E. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz052 · Cited by 13
Fong, A. K., Allen, M. D., Waltzman, D., Sarmiento, K., Yeates, K. O., Suskauer, S. J., Wintermark, M., Lindberg, D. M., Tate, D. F., Wilde, E. A., et al. (2020) · Journal of Neurotrauma · doi:10.1089/neu.2020.7100 · Cited by 15
Two axes of white matter development
Luo, A. C., Meisler, S. L., Sydnor, V. J., Alexander‐Bloch, A. F., Bagautdinova, J., Barch, D. M., Bassett, D. S., Davatzikos, C. A., Franco, A. R., Goldsmith, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68714-8 · Cited by 11
Self-citationDev-Atlas: A reference atlas of functional brain networks for typically developing adolescents
Doucet, G. E., Goldsmith, C., Myers, K., Rice, D. L., Ende, G. C., Pavelka, D. J., Joliot, M., Calhoun, V. D., Wilson, T. W., & Uddin, L. Q. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101523 · Cited by 13
Quantity and quality: Normative open-access neuroimaging databases
Isherwood, S. J., Bazin, P., Alkemade, A., & Forstmann, B. U. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248341 · Cited by 12
Li, J., Shi, Y., & Toga, A. W. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_10 · Cited by 12
Honnorat, N., Satterthwaite, T. D., Gur, R. E., Gur, R. C., & Davatzikos, C. A. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.11.014 · Cited by 12
Self-citationGenome wide association study of incomplete hippocampal inversion in adolescents
Cury, C., Scelsi, M. A., Toro, R., Frouin, V., Artiges, É., Grigis, A., Heinz, A., Lemaître, H., Martinot, J., Poline, J., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227355 · Cited by 13
Common functional brain networks between attention deficit and disruptive behaviors in youth
Wong, T., Zhang, H., White, T., Xu, L., & Qiu, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118732 · Cited by 14
Using network control theory to study the dynamics of the structural connectomePreprint
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White, T. J. H. (2015) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2015.07.011 · Cited by 11
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Metadata
- Authors
- Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., Mentch, F. D., Sleiman, P., Verma, R., Davatzikos, C., Hakonarson, H., Gur, R. C., & Gur, R. E.
- Affiliations
- University of Pennsylvania (US)
- Children's Hospital of Philadelphia (US)
- Philadelphia VA Medical Center (US)
- Corresponding author
- Satterthwaite, Theodore D.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 86
- Pages
- 544-553
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3947233
- OpenAlex
- W2069088601
- Cited by
- 504
- References
- 64
- Citations by year
- 2026: 21 · 2025: 28 · 2024: 39 · 2023: 50 · 2022: 49 · 2021: 51 · 2020: 49 · 2019: 58 · 2018: 43 · 2017: 44 · 2016: 24 · 2015: 27 · 2014: 19
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Funders
- Brain and Behavior Research Foundation (K08MH79364)
- Children's Hospital of Philadelphia
- National Institute of Mental Health (MH089924, T32 MH019112, MH089983, K23MH098130)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- Philadelphia Neurodevelopmental Cohort, brain development, brain maturation, cognitive development, psychiatric vulnerability, adolescents, T1-weighted structural MRI, diffusion tensor imaging, arterial spin labeling, perfusion imaging, working memory, emotion recognition, resting-state functional connectivity, multimodal neuroimaging, data sharing
- MeSH terms
- Adolescent, Adult, Brain, Child, Cognition, Female, Humans, Research Design, Philadelphia, Cohort Studies, Young Adult, Neuroimaging, Multimodal Imaging
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