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 Wide Range Achievement Test is a highly reliable broad ability measure.
“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]
The 'Computerized neurocognitive battery' section describes the Penn CNB as assessing a broad range of cognitive domains, but does not describe the Wide Range Achievement Test. Searches for WRAT and for reliability as a broad ability measure found no passage addressing that claim in our copy; page numbers were not available. (LLM, conf=0.76)
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Cites
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“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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“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.”
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“…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;…”
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“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
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“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.,…”
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“…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
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“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.”
Recent progress and outstanding issues in motion correction in resting state fMRI
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“As part of the PNC, the Penn CNBwas simplified for the pediatric population and shortened to a 1-hour administration time (Gur et al., 2014; Gur et al., 2012; Gur et al., 2010).”
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Neuroimaging of the Philadelphia Neurodevelopmental Cohort
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“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).”
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Psychometric Properties of the Penn Computerized Neurocognitive Battery
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Self-citationMaking data sharing work: The FCP/INDI experience
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Functional maturation of the executive system during adolescence
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Self-citationSatterthwaite, T. D., Wolf, D. H., Ruparel, K., Erus, G., Elliott, M. A., Eickhoff, S. B., Gennatas, E. D., Jackson, C., Prabhakaran, K., Smith, A., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.045 · Cited by 253 · see citing works
Self-citationCommon variants at 12q14 and 12q24 are associated with hippocampal volume
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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…”
Neurocognitive growth charting in psychosis spectrum youths.
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InfluentialSelf-citationThe Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative
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Self-citationImpact of puberty on the evolution of cerebral perfusion during adolescence
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Self-citationImaging Patterns of Brain Development and their Relationship to Cognition
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Self-citationThe “Task B problem” and other considerations in developmental functional neuroimaging
Church, J. A., Petersen, S. E., & Schlaggar, B. L. (2010) · Human Brain Mapping · doi:10.1002/hbm.21036 · Cited by 148 · cited 2× · see citing works
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Comorbidity of Physical and Mental Disorders in the Neurodevelopmental Genomics Cohort Study
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Self-citationOpen neuroscience solutions for the connectome-wide association era.
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Self-citationMaking Data Sharing Count: A Publication-Based Solution
Gorgolewski, K. J., Margulies, D., & Milham, M. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00009 · Cited by 99 · cited 1× · see citing works
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Self-citationIncidental Findings in Youths Volunteering for Brain MRI Research
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Self-citationdbGaP data access requests: A call for greater transparency
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“However, further detail can be found both in published manuscripts (13,43) and also in extensive online guidance on the dbGaP website.”
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Recruiting the ABCD sample: Design considerations and procedures
Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.04.004 · Cited by 1,393 · see citing works
“…UK Biobank (Sudlow et al., 2015), Generation R (Kooijman et al., 2016), GUSTO (Soh et al., 2014), and the Philadelphia Neurodevelopment Cohort (Satterthwaite et al., 2016) or data pooling efforts such as ENIGMA (Thompson et al., 2014) comes the possibility to apply epidemiological principles…”
Using connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen, X.-L., Finn, E., Scheinost, D., Rosenberg, M., Chun, M. M., Papademetris, X., & Constable, R. T. (2017) · Nature Protocols · doi:10.1038/nprot.2016.178 · Cited by 1,088 · see citing works
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042 · see citing works
“The task-free BOLD data used in this study (N=393) were drawn from the Philadelphia Neurodevelopmental Cohort (PNC) (Satterthwaite et al., 2014, 2016) on the basis of age, health, and data quality.”
MethodInfluentialSelf-citationConsidering sex and gender in Alzheimer disease and other dementias
Podcasy, J., & Epperson, C. N. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.4/cepperson · Cited by 623 · see citing works
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Self-citationDevelopment of structure–function coupling in human brain networks during youth
Baum, G. L., Cui, Z., Roalf, D. R., Ciric, R., Betzel, R. F., Larsen, B., Cieslak, M., Cook, P. A., Xia, C. H., Moore, T. M., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912034117 · Cited by 544 · see citing works
“Neuroimaging and behavioral data were originally obtained from 1,601 youth who participated in the Philadelphia Neurodevelopmental Cohort (PNC), a large community-based study of brain development (1, 2).”
MethodSelf-citationTask-induced brain state manipulation improves prediction of individual traits
Greene, A., Gao, S., Scheinost, D., & Constable, R. T. (2018) · Nature Communications · doi:10.1038/s41467-018-04920-3 · Cited by 525 · see citing works
Quantitative Assessment of Structural Image Quality
Rosen, A. F. G., Roalf, D., Ruparel, K., Blake, J., Seelaus, K., Villa, L., Ciric, R., Cook, P. A., Davatzikos, C., Elliott, M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.059 · Cited by 443 · see citing works
Self-citationSex differences in the developing brain: insights from multimodal neuroimaging
Kaczkurkin, A. N., Raznahan, A., & Satterthwaite, T. D. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-018-0111-z · Cited by 403 · see citing works
Self-citationSomerville, L. H., Bookheimer, S. Y., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Dapretto, M., Elam, J. S., Gaffrey, M. S., Harms, M. P., Hodge, C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.050 · Cited by 356 · see citing works
The ABCD study: understanding the development of risk for mental and physical health outcomes
Karcher, N. R., & Barch, D. M. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0736-6 · Cited by 351 · see citing works
“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 · see citing works
The Biology of Human Resilience: Opportunities for Enhancing Resilience Across the Life Span
Feder, A., Fred-Torres, S., Southwick, S. M., & Charney, D. S. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.07.012 · Cited by 269 · see citing works
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 · see citing works
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 · see citing works
“…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).”
Kaufmann, T., Alnæs, D., Doan, N. T., Brandt, C. L., Andreassen, O., & Westlye, L. (2017) · Nature Neuroscience · doi:10.1038/nn.4511 · Cited by 234 · see citing works
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 · see citing works
“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 167 · see citing works
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 · see citing works
Cetin-Karayumak, S., Biase, M. D. D., Chunga, N., Reid, B. E., Somes, N., Lyall, A., Kelly, S., Solgun, B., Pasternak, O., Vangel, M., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0509-y · Cited by 171 · see citing works
Cornblath, E., Ashourvan, A., Kim, J. Z., Betzel, R. F., Ciric, R., Adebimpe, A., Baum, G. L., He, X.-S., Ruparel, K., Moore, T. M., et al. (2018) · Communications Biology · doi:10.1038/s42003-020-0961-x · Cited by 160 · see citing works
“[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
Gao, S., Greene, A., Constable, R. T., & Scheinost, D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116038 · Cited by 149 · see citing works
Gee, D. G. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2021.21090944 · Cited by 148 · see citing works
Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.23665 · Cited by 137 · see citing works
Self-citationTemplateFlow: FAIR-sharing of multi-scale, multi-species brain models
Ciric, R., Thompson, W. H., Lorenz, R., Goncalves, M., MacNicol, E., Markiewicz, C. J., Halchenko, Y. O., Ghosh, S. S., Gorgolewski, K. J., Poldrack, R., et al. (2021) · bioRxiv · doi:10.1038/s41592-022-01681-2 · Cited by 134 · see citing works
“Pediatric and young adult template created using ANTs diffeomorphic registration and averaging for the Philadelphia Neurodevelopmental Cohort ( Satterthwaite et al., 2016 ).”
Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms
Satterthwaite, T. D., Wolf, D. H., Calkins, M. E., Vandekar, S. N., Erus, G., Ruparel, K., Roalf, D. R., Linn, K. A., Elliott, M. A., Moore, T. M., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.3463 · Cited by 132 · see citing works
Self-citationWarped Bayesian Linear Regression for Normative Modelling of Big Data
Fraza, C., Dinga, R., Beckmann, C., & Marquand, A. (2021) · bioRxiv · doi:10.1101/2021.04.05.438429 · Cited by 120
Associations between Neighborhood SES and Functional Brain Network Development
Tooley, U. A., Mackey, A. P., Ćirić, R., Ruparel, K., Moore, T. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz066 · Cited by 134 · see citing works
Self-citationKessler, D. A., Angstadt, M., & Sripada, C. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.0088 · Cited by 109 · see citing works
Development of Hippocampal-Prefrontal Cortex Interactions through Adolescence.
Calabro, F., Murty, V. P., Jalbrzikowski, M., Tervo-Clemmens, B., & Luna, B. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz186 · Cited by 119 · see citing works
“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.”
MethodInfluentialThe impact of in-scanner head motion on structural connectivity derived from diffusion MRI
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 · see citing works
Self-citationAlnæs, D., Kaufmann, T., Doan, N. T., Córdova-Palomera, A., Wang, Y.-P., Bettella, F., Moberget, T., Andreassen, O., & Westlye, L. (2018) · JAMA psychiatry · doi:10.1001/jamapsychiatry.2017.4277 · Cited by 112 · see citing works
“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 · see citing works
“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., Anderson, A. S., Kuang, J., Kim, H. C., Tillman, R., Kuhn, M., Fox, A., & Shackman, A. (2020) · Journal of Neuroscience · doi:10.1101/2020.02.25.964734 · Cited by 101 · see citing works
“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., Rokicki, J., van der Meer, D., Andreassen, O., & Westlye, L. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.01.019 · Cited by 99 · see citing works
Elevated Amygdala Perfusion Mediates Developmental Sex Differences in Trait Anxiety
Kaczkurkin, A. N., Moore, T. M., Ruparel, K., Ćirić, R., Calkins, M. E., Shinohara, R. T., Elliott, M. A., Hopson, R. D., Roalf, D. R., Vandekar, S., et al. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.04.021 · Cited by 96 · see citing works
“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
Sepehrband, F., Lynch, K. M., Cabeen, R. P., González-Zacarías, C., Zhao, L., D'Arcy, M., Kesselman, C., Herting, M., Dinov, I., Toga, A., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.065 · Cited by 90 · see citing works
Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics
Luppi, A., Gellersen, H., Liu, Z.-Q., Peattie, A. R. D., Manktelow, A., Adapa, R., Owen, A., Naci, L., Menon, D., Dimitriadis, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48781-5 · Cited by 89 · see citing works
“…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
Li, H.-M., Satterthwaite, T., & Fan, Y. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.004 · Cited by 87 · see citing works
“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 · see citing works
Self-citationKaralunas, S. L., & Nigg, J. T. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.11.002 · Cited by 84 · see citing works
“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 · see citing works
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 · see citing works
Self-citationThe general factor of psychopathology (p): Choosing among competing models and interpreting p
Caspi, A., Houts, R., Fisher, H., Danese, A., & Moffitt, T. (2023) · Clinical psychological science : a journal of the Association for Psychological Science · doi:10.1177/21677026221147872 · Cited by 79 · see citing works
“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 71 · see citing works
“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., Yeo, B. T. T., et al. (2020) · Science Bulletin · doi:10.1016/j.scib.2020.07.027 · Cited by 72 · see citing works
“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., Ciric, R., Satterthwaite, T., & Bassett, D. (2019) · Network Neuroscience · doi:10.1162/netn_a_00071 · Cited by 71 · see citing works
“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., Westlye, L., Beckmann, C., & Marquand, A. (2020) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-59728-3_68 · Cited by 69 · see citing works
“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 63 · see citing works
“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., de Boer, A. A. A., Dinga, R., Wolfers, T., Berthet, P., Mennes, M., Andreassen, O., Westlye, L., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278776 · Cited by 65 · see citing works
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 · see citing works
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 · see citing works
Camacho, M. C., Quiñones‐Camacho, L. E., & Perlman, S. B. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116688 · Cited by 59 · see citing works
Estimating dynamic functional brain connectivity with a sparse hidden Markov model
Zhang, G.-M., Cai, B., Zhang, A.-Y., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2929959 · Cited by 64 · see citing works
“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. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102634 · Cited by 61 · see citing works
“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.-X., Wang, J.-Q., Zhang, K., Calhoun, V., & Wang, Y.-P. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3077875 · Cited by 60 · see citing works
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.-M. G., Andreassen, O., Westlye, L., & Kaufmann, T. (2021) · medRxiv · doi:10.1016/j.nicl.2021.102921 · Cited by 57 · see citing works
“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 · see citing works
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 53 · see citing works
“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 53 · see citing works
Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso
Cai, B., Zhang, G.-M., Zhang, A.-Y., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2880428 · Cited by 52 · see citing works
“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., Zille, P., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2786553 · Cited by 52 · see citing works
“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., Tanner, J. C., Sporns, O., Kennedy, D. P., & Betzel, R. F. (2021) · bioRxiv · doi:10.1016/j.neuroimage.2022.119591 · Cited by 50 · see citing works
“…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., Devoe, D., & Metzak, P. D. (2020) · npj Schizophrenia · doi:10.1038/s41537-020-00117-0 · Cited by 50 · see citing works
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 · see citing works
“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 · see citing works
“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 53 · see citing works
“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., Solis, I., Janowich, J., Stern, M., Frenzel, Eastman, J. A., Mills, M. S., Embury, C. M., Coolidge, N., Heinrichs-Graham, E., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117438 · Cited by 46 · see citing works
Fusar-Poli, P., de Micheli, A., Signorini, L., Baldwin, H., de Pablo, G. S., & McGuire, P. (2020) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100578 · Cited by 45 · see citing works
“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. M., Varol, E., Sako, C., Gur, R., Gur, R. E., Koutsouleris, N., Zhuo, C., Fan, Y., et al. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2092-19.2019 · Cited by 45 · see citing works
“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 · see citing works
“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 55 · see citing works
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 · see citing works
“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., Moore, T. M., Sotiras, A., Xia, C., Shinohara, R., & Satterthwaite, T. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.12.015 · Cited by 43 · see citing works
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 46 · see citing works
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 49 · see citing works
“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 · see citing works
Hubbard, N., Siless, V., Frosch, I. R., Goncalves, M., Lo, N., Wang, J., Bauer, C. C., Conroy, K., Cosby, E., Hay, A., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102240 · Cited by 39 · see citing works
Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain
Madan, C. (2020) · Neuroinformatics · doi:10.1007/s12021-021-09519-6 · Cited by 38 · see citing works
Development of Network Topology and Functional Connectivity of the Prefrontal Cortex.
Lopez, K. C., Kandala, S., Marek, S. A., & Barch, D. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz255 · Cited by 38 · see citing works
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 37 · see citing works
“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 · see citing works
“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., Therrien, K., Bendl, J., Venkatesh, S., Dong, P., Modabbernia, A., Zeng, B., Mathur, D., Hoffman, G., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01464-1 · Cited by 36 · see citing works
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 · see citing works
“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., Goncalves, M., Frosch, I., Norton, D. J., Vergara, G., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102242 · Cited by 35 · see citing works
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. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00257 · Cited by 34 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge
Higgins, I., Kundu, S., & Guo, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.015 · Cited by 32 · see citing works
“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).”
MethodResultZille, P., Calhoun, V., Stephen, J., Wilson, T., & Wang, Y.-P. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2721640 · Cited by 32 · see citing works
“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 29 · see citing works
“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 · see citing works
A Review of Fusion Methods for Omics and Imaging Data
Huang, W., Tan, K., Zhang, Z., Hu, J., & Dong, S. (2022) · IEEE/ACM Transactions on Computational Biology & Bioinformatics · doi:10.1109/tcbb.2022.3143900 · Cited by 30 · see citing works
“…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., Gur, R., Raznahan, A., Satterthwaite, T., Alexander-Bloch, A., et al. (2020) · bioRxiv · doi:10.1002/hbm.25577 · Cited by 30 · see citing works
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 · see citing works
Self-citationYip, S. W., & Potenza, M. N. (2016) · Clinical Psychology Review · doi:10.1016/j.cpr.2016.11.003 · Cited by 28 · see citing works
“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.”
- 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 · 2015: 2
- 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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