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The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth
Open accessAbstract
The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale study of child development that combines neuroimaging, diverse clinical and cognitive phenotypes, and genomics. Data from this rich resource is now publicly available through the Database of Genotypes and Phenotypes (dbGaP). Here we focus on the data from the PNC that is available through dbGaP and describe how users can access this data, which is evolving to be a significant resource for the broader neuroscience community for studies of normal and abnormal neurodevelopment.
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“The sample also completed the Wide Range Achievement Test (WRAT), which is a highly-reliable broad ability measure [67].”
From Global Ancestry and Cognitive Ability (2019) · cites this paper as [67]
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Why do many psychiatric disorders emerge during adolescence?
Paus, T., Keshavan, M., & Giedd, J. N. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2513 · Cited by 3,123 · cited 1×
“The PNC is a resource that responds to a paradigm shift in the field of psychiatric neuroscience, where major mental illnesses are increasingly conceptualized as disorders of development (Insel, 2010; Insel, 2009; Casey et al., 2014; Paus et al., 2008).”
Toward discovery science of human brain function
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“As previously emphasized (Biswal et al., 2010; Gorgolewski et al., 2013; Mennes et al., 2012; Milham, 2012; Nooner et al., 2012; Bis et al., 2012; Stein et al., 2012), large publicly available datasets are a prerequisite for the collaboration necessary to gain traction towards understanding complex…”
Functional impact of global rare copy number variation in autism spectrum disorders
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“Genotyping procedures are not discussed here, but the genotyping pipeline used at CAG has been previously described in detail (14,15) and further information can be accessed at www.”
Self-citationSatterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H. H., Gur, R. C., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,982 · cited 2×
“…assessment, cognitive testing, and imaging procedures is provided in PNC manuscripts that have used these data types (Satterthwaite et al., 2012a; Satterthwaite et al., 2013a; Satterthwaite et al., in press; Satterthwaite et al., 2014b; Satterthwaite et al., 2014c; Roalf et al., 2014;…”
Self-citationInsel, T. R. (2010) · Nature · doi:10.1038/nature09552 · Cited by 1,783 · cited 1×
“The PNC is a resource that responds to a paradigm shift in the field of psychiatric neuroscience, where major mental illnesses are increasingly conceptualized as disorders of development (Insel, 2010; Insel, 2009; Casey et al., 2014; Paus et al., 2008).”
Autism genome-wide copy number variation reveals ubiquitin and neuronal genes
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“Genotyping procedures are not discussed here, but the genotyping pipeline used at CAG has been previously described in detail (Pinto et al., 2010; Glessner et al., 2009) and further information can be accessed at www.”
Self-citationSex differences in the structural connectome of the human brain
Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., & Verma, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316909110 · Cited by 1,223 · cited 2×
“Further information regarding clinical assessment, cognitive testing, and imaging procedures is provided in PNC manuscripts that have used these data types (Satterthwaite et al., 2012a; Satterthwaite et al., 2013a; Satterthwaite et al., in press; Satterthwaite et al., 2014b; Satterthwaite et al., 2014c; Roalf et al.,…”
Self-citationSatterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.063 · Cited by 1,125 · cited 2×
“…information regarding clinical assessment, cognitive testing, and imaging procedures is provided in PNC manuscripts that have used these data types (Satterthwaite et al., 2012a; Satterthwaite et al., 2013a; Satterthwaite et al., in press; Satterthwaite et al., 2014b; Satterthwaite et al.,…”
Self-citationThe NCBI dbGaP database of genotypes and phenotypes
Mailman, M. D., Feolo, M., Jin, Y., Kimura, M., Tryka, K. A., Bagoutdinov, R., Hao, L., Kiang, A., Paschall, J. E., Phan, L. D., et al. (2007) · Nature Genetics · doi:10.1038/ng1007-1181 · Cited by 1,100 · cited 2×
“As described in detail elsewhere (Mailman et al., 2007), dbGaP was created to be a general repository for sharing genetic and phenotypic data from large-scale studies in a secure manner.”
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MethodSelf-citationTask-induced brain state manipulation improves prediction of individual traits
Greene, A. S., Gao, S., Scheinost, D., & Constable, R. T. (2018) · Nature Communications · doi:10.1038/s41467-018-04920-3 · Cited by 498
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The ABCD study: understanding the development of risk for mental and physical health outcomes
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“org; N= 1029) [29], the National Institutes of Health Pediatric MRI Database (N= 550) [30], IMAGEN (N= 2000) [6], Pediatric Imaging, Neurocognition, and Genetics study (PING) (N= 1493) [31], National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) (N= 831) [4], Human Connectome Project (HCP) (N= 120…”
BackgroundKarlsson Linnér, R., Mallard, T. T., Barr, P. B., Sanchez-Roige, S., Madole, J. W., Driver, M. N., Poore, H. E., de Vlaming, R., Grotzinger, A. D., Tielbeek, J. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00908-3 · Cited by 317
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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-citationData leakage inflates prediction performance in connectome-based machine learning models
Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., & Scheinost, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46150-w · Cited by 236
“…Brain Cognitive Development (ABCD) Study 22 ( N = 7822 – 7969), the Healthy Brain Network (HBN) Dataset 23 ( N = 1024 – 1201), the Human Connectome Project Development (HCPD) Dataset 24 ( N = 424 – 605), and the Philadelphia Neurodeve-lopmental Cohort (PNC) Dataset 25,26 ( N = 1119 – 1126).”
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Shanmugan, S., Wolf, D. H., Calkins, M. E., Moore, T. M., Ruparel, K., Hopson, R. D., Vandekar, S. N., Roalf, D. R., Elliott, M. A., Jackson, C., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15060725 · Cited by 219
“Participants Ashasbeendescribed, thePhiladelphiaNeurodevelopmental Cohort is a collaboration between the Center for Applied Genomics at the Children’s Hospital of Philadelphia and the Brain Behavior Laboratory at the University of Pennsylvania (22, 23).”
BackgroundMethodInfluentialSelf-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 169
Cetin-Karayumak, S., Bouix, S., Ning, L., James, A., Crow, T., Shenton, M., Kubicki, M., & Rathi, Y. (2018) · bioRxiv · doi:10.1016/j.neuroimage.2018.08.073 · Cited by 178
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“[25] Satterthwaite TD, Connolly JJ, Ruparel K, Calkins ME, Jackson C, Elliott MA, Roalf DR, Hopsona R, Prabhakaran K, Behr M et al.”
BackgroundSelf-citationCombining Multiple Connectomes Improves Predictive Modeling of Phenotypic Measures
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Self-citationTemplateFlow: FAIR-sharing of multi-scale, multi-species brain models
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“Pediatric and young adult template created using ANTs diffeomorphic registration and averaging for the Philadelphia Neurodevelopmental Cohort ( Satterthwaite et al., 2016 ).”
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Self-citationKessler, D. A., Angstadt, M., & Sripada, C. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.0088 · Cited by 109
Development of Hippocampal–Prefrontal Cortex Interactions through Adolescence
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“Of the 9498 subjects who participated in the PNC study, 807 participated in a resting state fMRI scan and passed motion and quality checking standards.”
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Baum, G. L., Roalf, D. R., Cook, P. A., Ciric, R., Rosen, A. F. G., Xia, C., Elliott, M. A., Ruparel, K., Verma, R., Tunç, B., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.041 · Cited by 116
Self-citationAlnæs, D., Kaufmann, T., Doan, N. T., Córdova‐Palomera, A., Wang, Y., Bettella, F., Moberget, T., Andreassen, O. A., & Westlye, L. T. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.4277 · Cited by 104
“Satterthwaite TD, Elliott MA, Ruparel K, et al. Neuroimaging of the Philadelphia Neurodevelopmental Cohort.”
MethodInfluentialEvidence for dissociable linkage of dimensions of psychopathology to brain structure in youths
Kaczkurkin, A. N., Park, S. S., Sotiras, A., Moore, T. M., Calkins, M. E., Cieslak, M., Rosen, A. F. G., Ciric, R., Xia, C. H., Cui, Z., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18070835 · Cited by 110
“Data from the PNC can be accessed at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study. cgi?study_id=phs000607.v3.p2.”
BackgroundMethodInfluentialSelf-citationAnxiety and the neurobiology of temporally uncertain threat anticipationPreprint
Hur, J., Smith, J. F., DeYoung, K. A., Anderson, A. S., Kuang, J., Kim, H. C., Tillman, R. M., Kuhn, M., Fox, A. S., & Shackman, A. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.25.964734 · Cited by 35
“1 ABCD (25), Duke Neurogenetics (26), Human Connectome Project (27), IMAGEN (28), Philadelphia Neurodevelopmental Cohort (29), and UK Biobank (30).”
BackgroundMoberget, T., Alnæs, D., Kaufmann, T., Doan, N. T., Córdova‐Palomera, A., Norbom, L. B., Rokicki, J., van der Meer, D., Andreassen, O. A., & Westlye, L. T. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.01.019 · Cited by 114
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“Image acquisition and processing are reported in detail elsewhere (43,44); see Supplemental Methods for details.”
MethodSelf-citationNeuroanatomical morphometric characterization of sex differences in youth using statistical learning
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“…datasets with similar speci fi - cations, such as the publicly available and intensely studied Cam-CAN 69 , Philadelphia Neurodevelopmental Cohort 70 , CENTER-TBI 71 , Harvard Aging Brain Study 72 , Autism Brain Imaging Data Exchange (ABIDE) 73 , and UCLA Neurophenomics 74 datasets among others,…”
BackgroundLarge-scale sparse functional networks from resting state fMRI
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“In the current study, we validate the proposed method with respect to its parameters, based on a simulated dataset, a dataset with both task fMRI (tfMRI) and rsfMRI scans obtained from the Human Connectome Project (HCP) (Glasser et al., 2013), and an rsfMRI dataset obtained from the Philadelphia Neurodevelopmental Coh…”
InfluentialSelf-citationKaczkurkin, A. N., Moore, T. M., Calkins, M. E., Ciric, R., Detre, J. A., Elliott, M. A., Foa, E. B., Garcia de la Garza, A., Roalf, D. R., Rosen, A., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.174 · Cited by 86
Self-citationKaralunas, S. L., & Nigg, J. T. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.11.002 · Cited by 84
“To facilitate mapping across diagnostic boundaries, as well as to address concerns related to low power for validation when sub-profiles are compared, large datasets such as the Adolescent Brain Cognitive Development cohort (89) or the Philadelphia Neurodevelopmental Cohort (90) (or similar large cohorts in other nati…”
BackgroundLarsen, 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-citationDiminished Cortical Thickness Is Associated with Impulsive Choice in Adolescence
Pehlivanova, M., Wolf, D. H., Sotiras, A., Kaczkurkin, A. N., Moore, T. M., Ciric, R., Cook, P. A., Garcia de La Garza, A., Rosen, A. F. G., Ruparel, K., et al. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.2200-17.2018 · Cited by 79
Self-citationThe General Factor of Psychopathology (p): Choosing Among Competing Models and Interpreting p
Caspi, A., Houts, R., Fisher, H. L., Danese, A., & Moffitt, T. E. (2023) · Clinical Psychological Science · doi:10.1177/21677026221147872 · Cited by 88
“The Philadelphia Neurode-velopment Cohort is a good example of a research initiative that has measured broadly (Satterthwaite et al., 2016).”
BackgroundOpen challenges and opportunities in federated foundation models towards biomedical healthcare
Li, X., Peng, L., Wang, Y., & Zhang, W. (2025) · BioData Mining · doi:10.1186/s13040-024-00414-9 · Cited by 84
“Notable datasets like the Philadelphia Neurodevelopmental Cohort (PNC) [199], Autism Brain Imaging Data Exchange (ABIDE) [200], and UK Biobank [201] include both functional and structural brain imaging data.”
XCP-D: A Robust Pipeline for the post-processing of fMRI data
Mehta, K., Salo, T., Madison, T. J., Adebimpe, A., Bassett, D. S., Bertolero, M., Cieslak, M., Covitz, S., Houghton, A., Keller, A. S., et al. (2023) · bioRxiv · doi:10.1101/2023.11.20.567926 · Cited by 73
“Specifically, we processed n=200 participants each from the Philadelphia Neurodevelopmental Cohort (PNC; Satterthwaite et al., 2014; Satterthwaite et al., 2016 Notably, prior to post-processing with XCP-D, each of these datasets were pre-processed using different tools.”
MethodSelf-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
“Largescale brain development cohorts are fundamental for charting growth [24-26], but obtaining longitudinal assessments across multiple centers with the same protocols is rare [27,28].”
BackgroundLydon‐Staley, D. M., Ćirić, R., Satterthwaite, T. D., & Bassett, D. S. (2018) · Network Neuroscience · doi:10.1162/netn_a_00071 · Cited by 86
“The rs-fMRI data were drawn from the Philadelphia Neurodevelopmental Cohort (PNC; Satterthwaite et al., 2016; Satterthwaite et al., 2014) on the basis of age, health, and data quality.”
MethodSelf-citationHierarchical Bayesian Regression for Multi-site Normative Modeling of Neuroimaging Data
Kia, S. M., Huijsdens, H., Dinga, R., Wolfers, T., Mennes, M., Andreassen, O. A., Westlye, L. T., Beckmann, C. F., & Marquand, A. F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_68 · Cited by 67
“0 PNC [21] 1514 1 8–23 731/783 6.”
A Network Neuroscience Approach to Typical and Atypical Brain Development
Morgan, S. E., White, S. R., Bullmore, E. T., & Vértes, P. E. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.03.003 · Cited by 69
“Several neuroimaging studies have begun to tackle this question, including the Human Connectome Project Development study [6], the Developing Human Connectome Project [7], the NeuroScience in Psychiatry Network [8] and the Philadelphia Neurodevelopmental Cohort (PNC) [9], amongst others.”
Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression
Kia, S. M., Huijsdens, H., Rutherford, S. E., de Boer, A. A. A., Dinga, R., Wolfers, T., Berthet, P., Mennes, M., Andreassen, O. A., Westlye, L. T., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278776 · Cited by 95
Developmental coupling of cerebral blood flow and fMRI fluctuations in youth
Baller, E. B., Valcarcel, A. M., Adebimpe, A., Alexander-Bloch, A., Cui, Z., Gur, R., Gur, R. E., Larsen, B., Linn, K. A., O'Donnell, C., et al. (2021) · bioRxiv · doi:10.1016/j.celrep.2022.110576 · Cited by 65
Self-citationTielbeek, J., Uffelmann, E., Williams, B., Colodro-Conde, L., Gagnon, É., Mallard, T., Levitt, B., Jansen, P., Johansson, A., Sallis, H., et al. (2021) · bioRxiv · doi:10.1101/2021.10.19.462578 · Cited by 64
Camacho, M. C., Quiñones‐Camacho, L. E., & Perlman, S. B. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116688 · Cited by 65
Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model
Zhang, G., Cai, B., Zhang, A., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2929959 · Cited by 63
“1) Data Acquisition and Preprocessing: The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale collaborative study between the Brain Behaviour Laboratory at the University of Pennsylvania and the Children’s Hospital of Philadelphia [37].”
BackgroundTransdiagnostic neuroimaging markers of psychiatric risk: A narrative review
Vanes, L. D., & Dolan, R. J. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102634 · Cited by 73
“Using data from the Philadelphia Neurodevelopmental Cohort (PNC) (Satterthwaite et al., 2016), a large quasi-epidemiological study of brain development in 8–22 year-old subjects, Kaczkurkin et al. (2019) identified 18 structural covariance networks and assessed their association with a general p-factor.”
MethodInfluentialQu, G., Xiao, L., Hu, W., Wang, J., Zhang, K., Calhoun, V. D., & Wang, Y. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3077875 · Cited by 63
Sex differences in Variability of Brain Structure Across the Lifespan
Forde, N., Jeyachandra, J., Joseph, M. J. E., Jacobs, G. R., Dickie, E., Satterthwaite, T., Shinohara, R., Ameis, S., & Voineskos, A. (2019) · bioRxiv · doi:10.1101/842567 · Cited by 59
“Details of T1-weighted magnetic resonance imaging (MRI) acquisition, inclusion/exclusion criteria, and quality assessment can be found in references (Marcus et al. 2007, 2010; Van Essen et al. 2012; Satterthwaite et al. 2016; LaMontagne et al. 2018) and Supplementary Material (see Supplementary Material, Table S1 for…”
BackgroundMethodSelf-citationLund, M. J., Alnæs, D., de Lange, A. G., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102921 · Cited by 54
“MR data was collected by the study team of HBN (Alexander et al., 2017) and PNC (Satterthwaite et al., 2016) and we processed both samples with the same pipeline.”
BackgroundMethodInfluentialBoubela, R., Kalcher, K., Huf, W., Nasel, C., & Moser, E. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00492 · Cited by 56
A network control theory pipeline for studying the dynamics of the structural connectome
Parkes, L., Kim, J. Z., Stiso, J., Brynildsen, J. K., Cieslak, M., Covitz, S. C., Gur, R. E., Gur, R. C., Pasqualetti, F., Shinohara, R. T., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01023-w · Cited by 54
“Here, we present a protocol for applying NCT to two different structural connectomes: one defined using undirected connectivity estimated in the human brain 23,24 and the other using directed connectivity estimated in the mouse brain 25–27 .”
Self-citationNorbom, L. B., Doan, N. T., Alnæs, D., Kaufmann, T., Moberget, T., Rokicki, J., Andreassen, O. A., Westlye, L. T., & Tamnes, C. K. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.09.027 · Cited by 55
Capturing Dynamic Connectivity From Resting State fMRI Using Time-Varying Graphical Lasso
Cai, B., Zhang, G., Zhang, A., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2880428 · Cited by 51
“1) Data Acquisition and Preprocessing: Data used in this work were collected through the Philadelphia Neurodevelopmental Cohort (PNC) project, which was a collaboration between the Brain Behaviour Laboratory at the University of Pennsylvania and the Children’s Hospital of Philadelphia [33].”
InfluentialEstimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI
Cai, B., Zillé, P., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2786553 · Cited by 55
“In particular, we investigate the use of dSCPs on a large fMRI dataset (PNC) to analyze the differences of FC between young adults and children [30].”
BackgroundMethodEsfahlani, F. Z., Byrge, L. A., Tanner, J., Sporns, O., Kennedy, D. P., & Betzel, R. F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119591 · Cited by 44
“…there exist many large, publicly available datasets that include both clinical groups ( Gorgolewski et al., 2017 ) and massive control populations that are accompanied by sub-clinical responses to assessments of different neuropsychiatric disorders ( Essen et al., 2013; Satterthwaite et al., 2016 ).”
Progression from being at-risk to psychosis: next steps
Addington, J., Farris, M. S., Devoe, D. J. A., & Metzak, P. D. (2020) · Schizophrenia · doi:10.1038/s41537-020-00117-0 · Cited by 59
Neurostructural Heterogeneity in Youth with Internalizing Symptoms
Kaczkurkin, A., Sotiras, A., Baller, E. B., Barzilay, R., Calkins, M., Chand, G., Cui, Z., Erus, G., Fan, Y., Gur, R., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.09.005 · Cited by 50
“As part of the Philadelphia Neurodevelopmental Cohort, a large community-based sample of youths, 1601 participants 8 to 23 years of age received multimodal neuroimaging, clinical phenotyping, and cognitive assessment (23,24).”
BackgroundMethodSelf-citationSheynin, J., Duval, E. R., Lokshina, Y., Scott, J. C., Angstadt, M., Kessler, D., Zhang, L., Gur, R. E., Gur, R., & Liberzon, I. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102215 · Cited by 49
“Participants were obtained from the PNC, a large-scale NIMHfunded initiative to examine, among others, the association between brain activation and psychiatric illness in adolescents (Satterthwaite et al., 2016).”
MethodInfluentialSelf-citationA Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms
Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2921207 · Cited by 54
“extensive experiments on the publicly available Philadelphia Neurodevelopmental Cohort (PNC) dataset [32], [33] Here we predict the continuous-value intelligence quotient (IQ) scores of subjects by using fMRI data in two task conditions (working memory and emotion), with the goal of this study to investigate which com…”
BackgroundMethodThe Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain
Stephen, J. M., Solis, I., Janowich, J. R., Stern, M. E., Frenzel, M. R., Eastman, J. A., Mills, M. S., Embury, C. M., Coolidge, N. M., Heinrichs‐Graham, E., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117438 · Cited by 48
Fusar‐Poli, P., Micheli, A. D., Signorini, L., Baldwin, H., de Pablo, G. S., & McGuire, P. K. (2020) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100578 · Cited by 47
“This finding is relevant to inform ongoing large-scale international consortia (e.g. PSYSCAN [48, 49], PRONIA [50], NAPLS [51], PNC [52], HARMONY) that are developing and validating risk prediction models in this group.”
BackgroundTruelove‐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
“To investigate the generalizability of our multimodal NMI, we first derived separate models for both structural and functional data from participants in the Philadelphia Neurodevelopmental Cohort (PNC; Satterthwaite et al., 2016).”
MethodSelf-citationDevelopment of a computerized adaptive screening tool for overall psychopathology (“p”)
Moore, T. M., Calkins, M. E., Satterthwaite, T. D., Roalf, D. R., Rosen, A. F. G., Gur, R. C., & Gur, R. E. (2019) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2019.05.028 · Cited by 44
“Briefly, 1601 youth who completed clinical and cognitive assessment as part of the PNC also completed multi-modal neuroimaging (Satterthwaite et al., 2014a,b; Satterthwaite et al., 2016a,b).”
BackgroundMethodInfluentialSelf-citationSydnor, V. J., Bagautdinova, J., Larsen, B., Arcaro, M. J., Barch, D. M., Bassett, D. S., Alexander‐Bloch, A. F., Cook, P. A., Covitz, S. C., Franco, A. R., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01991-6 · Cited by 57
Self-citationAdebimpe, A., Bertolero, M., Dolui, S., Cieslak, M., Murtha, K., Baller, E. B., Boeve, B., Boxer, A., Butler, E. R., Cook, P. A., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01458-7 · Cited by 43
“These datasets included four ASL sequences collected on Siemens scanners using pseudo-continuous labeling (PCASL), but different encoding schemes: two-dimensional (2D) spin-echo PCASL images from the Philadelphia Neurodevelopmental Cohort (PNC) ( n = 1,527, 797 females, mean age 15.00 years, s.”
MethodInfluentialSelf-citationKaczkurkin, A. N., Moore, T. M., Sotiras, A., Xia, C. H., Shinohara, R. T., & Satterthwaite, T. D. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.12.015 · Cited by 42
Self-citationElvsåshagen, T., Bahrami, S., van der Meer, D., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., Bertolino, A., Beyer, M. K., Blasi, G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17376-1 · Cited by 47
Hippocampal Shape Maturation in Childhood and Adolescence
Lynch, K. M., Shi, Y., Toga, A. W., Clark, K. A., & Pediatric Imaging, N. A. G. S. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy244 · Cited by 50
“These databases include the Cincinnati MR Imaging of NeuroDevelopment (C-MIND) data repository, the Philadelphia Neurodevelopmental Cohort (PNC) research initiative, and the Pediatric Imaging, Neurocognition and Genetics (PING) Study database.”
MethodInfluentialPsychosis Risk and Development: What Do We Know From Population-Based Studies?
Mennigen, E., & Bearden, C. E. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.12.014 · Cited by 40
Hubbard, N. A., Siless, V., Frosch, I. R., Goncalves, M., Lo, N., Wang, J., Bauer, C. C. C., Conroy, K., Cosby, E. A., Hay, A. C., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102240 · Cited by 36
Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain
Madan, C. R. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09519-6 · Cited by 48
Development of Network Topology and Functional Connectivity of the Prefrontal Cortex
Lopez, K. C., Kandala, S., Marek, S. A., & Barch, D. M. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz255 · Cited by 47
Predictors of transition in patients with clinical high risk for psychosis: an umbrella review
Andreou, C., Eickhoff, S., Heide, M., de Bock, R., Obleser, J., & Borgwardt, S. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02586-0 · Cited by 50
“Large samples and harmonization of methods are necessary to overcome the limitations of a currently still very heterogeneous fi eld of research.”
BackgroundAsymmetric signaling across the hierarchy of cytoarchitecture within the human connectome
Parkes, L., Kim, J. Z., Stiso, J., Calkins, M. E., Cieslak, M., Gur, R. E., Gur, R. C., Moore, T. M., Ouellet, M., Roalf, D. R., et al. (2022) · Science Advances · doi:10.1126/sciadv.add2185 · Cited by 34
“All DWI and T1-weighted images underwent rigorous quality control by highly trained image analysts [see ( 78 ) and ( 77 ) for details on DWI and T1-weighted imaging, respectively].”
Self-citationBurstein, D., Griffen, T. C., Therrien, K., Bendl, J. M., Venkatesh, S., Dong, P., Modabbernia, A., Zeng, B., Mathur, D. D., Hoffman, G. E., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01464-1 · Cited by 49
Stojanovski, S., Felsky, D., Viviano, J. D., Shahab, S., Bangali, R., Burton, C. L., Devenyi, G. A., O’Donnell, L. J., Szatmari, P., Chakravarty, M. M., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.06.024 · Cited by 35
“012), but negative association with ADHD symptom score in youth with TBI (t(31)=−3.”
BackgroundMethodSiless, V., Hubbard, N. A., Jones, R. J., Wang, J., Lo, N., Bauer, C. C. C., Goncalves, M., Frosch, I. R., Norton, D. J., Vergara, G. A., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102242 · Cited by 34
XCP-D: A robust pipeline for the post-processing of fMRI data
Mehta, K. P., Salo, T., Madison, T. J., Adebimpe, A., Bassett, D. S., Bertolero, M. A., Cieslak, M., Covitz, S. C., Houghton, A., Keller, A. S., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00257 · Cited by 104
“The following command was used to process the data (via the CIFTI surface-based workflow, with the anatomical workflow enabled): PNC: singularity run – cleanenv –”
InfluentialSelf-citationTejavibulya, L., Peterson, H., Greene, A. S., Gao, S., Rolison, M. J., Noble, S., & Scheinost, D. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119040 · Cited by 37
Genetics of brain age suggest an overlap with common brain disorders
Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D., Baur-Streubel, R., Bertolino, A., et al. (2018) · bioRxiv · doi:10.1101/303164 · Cited by 34
Global Ancestry and Cognitive Ability
Lasker, J., Pesta, B. J., Fuerst, J. G. R., & Kirkegaard, E. O. W. (2019) · Psych · doi:10.3390/psych1010034 · Cited by 32
“The sample also completed the Wide Range Achievement Test (WRAT), which is a highly-reliable broad ability measure [67].”
BackgroundMethodFederated Multi-Site Normative Modeling using Hierarchical Bayesian RegressionPreprint
Kia, S. M., Huijsdens, H., Rutherford, S., Dinga, R., Wolfers, T., Mennes, M., Andreassen, O., Westlye, L., Beckmann, C., & Marquand, A. (2021) · bioRxiv · doi:10.1101/2021.05.28.446120 · Cited by 32
Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge
Higgins, I. A., Kundu, S., & Guo, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.015 · Cited by 29
“These fi ndings are supported by previous work and has been linked to variations in emotional identi fi cation and spatial cognitive tasks (Satterthwaite et al., 2016).”
MethodResultZillé, P., Calhoun, V. D., Stephen, J. M., Wilson, T. W., & Wang, Y. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2721640 · Cited by 32
“1) Data Acquisition and Preprocessing: The Philadelphia Neurodevelopmental Cohort [21] (PNC) is a large-scale col-”
BackgroundInfluentialNeurocognitive and functional heterogeneity in depressed youth
Baller, E. B., Kaczkurkin, A. N., Sotiras, A., Adebimpe, A., Bassett, D. S., Calkins, M. E., Chand, G. B., Cui, Z., Gur, R. E., Gur, R. C., et al. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00871-w · Cited by 31
“METHODS Participants A total of 9,498 participants aged 8-22 years received cognitive assessment and clinical phenotyping as part of the Philadelphia Neurodevelopmental Cohort (PNC), a large communitybased sample of youths (14,15).”
MethodSelf-citationGenome-wide association analysis identifies common variants influencing infant brain volumes
Xia, K., Zhang, J., Ahn, M., Jha, S., Crowley, J. J., Szatkiewicz, J., Li, T., Zou, F., Zhu, H., Hibar, D., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.159 · Cited by 30
A Review of Fusion Methods for Omics and Imaging Data
Huang, W., Tan, K., Zhang, Z., Hu, J., & Dong, S. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3143900 · Cited by 38
“…Annotations [21] The Mayo Clinic [175] Lung adenocarcinoma RNA sequencing, histopathology annotations, and survival information [132] The The Philadelphia Neurodevelopmental Cohort (PNC) [176] Brain SNPs, fMRI, computerized neurocognitive battery (CNB) [57] [177] input of a Cox regression model.”
MethodA simple permutation‐based test of intermodal correspondence
Weinstein, S. M., Vandekar, S., Adebimpe, A., Tapera, T. M., Robert‐Fitzgerald, T., Gur, R. C., Gur, R. E., Raznahan, A., Satterthwaite, T. D., Alexander‐Bloch, A. F., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25577 · Cited by 36
Self-citationNassar, R., Kaczkurkin, A. N., Xia, C. H., Sotiras, A., Pehlivanova, M., Moore, T. M., de la Garza, Á. G., Roalf, D. R., Rosen, A. F. G., Lorch, S. A., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy091 · Cited by 27
Self-citationYip, S. W., & Potenza, M. N. (2016) · Clinical Psychology Review · doi:10.1016/j.cpr.2016.11.003 · Cited by 29
“Publicly available largescale neuroimaging datasets represent a powerful resource for future studies in this area; e.g., the Philadelphia Neurodevelopmental Cohort PDN (Satterthwaite et al., 2015) and Pediatric Imaging, Neurocognition, and Genetics (PING) (Jernigan et al., 2016) databases.”
Metadata
- Authors
- Satterthwaite, T. D., Connolly, J. J., Ruparel, K., Calkins, M. E., Jackson, C., Elliott, M. A., Roalf, D. R., Hopson, R., Prabhakaran, K., Behr, M., Qiu, H., Mentch, F. D., Chiavacci, R., Sleiman, P. M. A., Gur, R. C., Hakonarson, H., & 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
- 2016-01
- Volume
- 124
- Issue
- Pt B
- Pages
- 1115-1119
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4591095
- OpenAlex
- W2030561475
- Cited by
- 335
- References
- 44
- Citations by year
- 2026: 23 · 2025: 27 · 2024: 36 · 2023: 22 · 2022: 33 · 2021: 46 · 2020: 37 · 2019: 40 · 2018: 31 · 2017: 26 · 2016: 11
- FWCI
- 14.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (MH089983, MH089924, MH019112, U01HG006830, K23MH098130, K01MH102609, K08MH79364)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- Philadelphia Neurodevelopmental Cohort, brain development, child development, neuroimaging, genomics, clinical phenotype, cognitive phenotypes, public data, dbGaP, data access
- MeSH terms
- Adolescent, Brain, Child, Child Development, Developmental Disabilities, Cognition, Female, Humans, Male, Nervous System, Internet, Genomics, Information Dissemination, Neuroimaging
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