Cited by 335Functional Brain Connectivity StudiesAutism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

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The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth

Open access

Satterthwaite, Theodore D., Connolly, John J., Ruparel, Kosha, Calkins, Monica E., Jackson, Chad, Elliott, Mark A., Roalf, David R., Hopson, Ryan, Prabhakaran, Karthik, Behr, Meckenzie, Qiu, Haijun, Mentch, Frank D., Chiavacci, Rosetta, Sleiman, Patrick M.A., Gur, Ruben C., Hakonarson, Hakon, Gur, Raquel E.·2016·NeuroImage

DOI 10.1016/j.neuroimage.2015.03.056PMC PMC4591095

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Abstract

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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Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial Reliability and Validity Data
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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…”
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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-citation
An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data
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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;…”
Self-citation
Rethinking schizophrenia
Insel, 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-citation
Sex 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-citation
Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth
Satterthwaite, 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-citation
The 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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BackgroundMethod
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Method
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“The blood oxygenation level-dependent (BOLD) signal was captured utilizing a whole-brain, single-shot, multislice, gradient-echo echoplanar sequence comprising 124 volumes (372 s) [32].”
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“The Philadelphia Neurodevelopmental Cohort (PNC) included 1,422 participants from ages 8 to 24 (Satterthwaite et al., 2016).”
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“For the validation sample, we accessed brain MRI, cognitive, and clinical data from the Philadelphia Neurodevelopmental Cohort (PNC), a large community-based study of brain development in youths aged 8–21 (Satterthwaite et al., 2016).”
Method
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Background
Mapping Complex Brain Torque Components and Their Genetic and Phenomic Architecture in 24,112 healthy individualsPreprint
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“[27] Satterthwaite TD, Connolly JJ, Ruparel K, Calkins ME, Jackson C, Elliott MA, Roalf DR, Hopsona R, Prabhakaran K, Behr M et al.”
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BackgroundSelf-citation
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“The data obtained included information from 9498 participants ages 8 – 21 who underwent a detailed cognitive and psychopathology assessment 35 .”
Influential
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“…of the underlying MR signal rendering the intensity of imaging voxels arbitrary, and the growing availability of conventional structural images across various large public datasets ( Bookheimer et al., 2019; Jack et al., 2008; Miller et al., 2016; Satterthwaite et al., 2016; Snoek et al., 2021).”
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Background
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Self-citation
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“The Philadelphia Neurodevelopmental Cohort[11] (PNC) is a large-scale collaborative work between the Brain Behaviour Laboratory at the University of Pennsylvania and the Children’s Hospital of Philadelphia.”
BackgroundMethodInfluential
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“…neuroimaging datasets: the Healthy Brain Network (HBN, n=1110) dataset 34 , the Adolescent Brain Cognitive Development (ABCD, n=9371) Study 35 , the Human Connectome Project Development (HCPD, n=428) dataset 36 , and the Philadelphia Neurodevelopmental Cohort (PNC, n =1291) dataset 37 .”
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“We first investigated SEV in two publicly available cross-sectional datasets, the Philadelphia Neurodevelopmental Cohort (PNC; 20) and the Healthy Brain Network (HBN; 21).”
Method
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Chan, K.-S., Zwiers, M., Jansen, M. G., Oosterman, J., Norris, D., Beckmann, C., & Marques, J. (2024) · bioRxiv · doi:10.1101/2024.07.17.603922 · Cited by 3
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Background
Individual brain parcellation: Review of methods, validations and applicationsPreprint
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“Participants were recruited from the greater Philadelphia, Pennsylvania, area between November 2009 and December 2011 [32].”
Background
Regression and Alignment for Functional Data and Network Topology
Tu, D., Wrobel, J., Satterthwaite, T., Goldsmith, J., Gur, R., Gur, R., Gertheiss, J., Bassett, D. S., & Shinohara, R. (2023) · bioRxiv · doi:10.1101/2023.07.13.548836 · Cited by 3
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BackgroundMethod
Genome-wide analysis of binge-eating disorder identifies the first three risk loci and implicates iron metabolism
Burstein, D., Griffen, T. C., Therrien, K., Bendl, J., Venkatesh, S., Dong, P., Modabbernia, A., Zeng, B., Mathur, D., Hoffman, G., et al. (2022) · medRxiv · doi:10.1101/2022.04.28.22274437 · Cited by 3
Investigating Neurocognitive Functioning in Youths With Externalizing Disorders From the Philadelphia Neurodevelopmental Cohort
Honrath, P., Kohls, G., Moore, T. M., Dogan, I., Gur, R. C., Habel, U., Konrad, K., Reetz, K., & Gur, R. E. (2020) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2020.10.029 · Cited by 5
Self-citation
Causal inference of brain connectivity from fMRI with ψ-Learning Incorporated Linear non-Gaussian Acyclic Model (ψ-LiNGAM)Preprint
Zhang, A.-Y., Zhang, G.-M., Cai, B., Hu, W.-X., Xiao, L., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2020) · arXiv.org · doi:10.48550/arxiv.2006.09536 · Cited by 3
“The dataset we used is the publicly available Philadelphia Neurodevelopmental Cohort (PNC), which is a large-scale collaborative study of the Brain Behavior Laboratory at the University of Pennsylvania and the Childrens Hospital of Philadelphia [21].”
Single nucleotide polymorphism heritability and differential patterns of genetic overlap between inattention and four neurocognitive factors in youth
Micalizzi, L., Brick, L. A. D., Marraccini, M. E., Benca‐Bachman, C. E., Palmer, R. H. C., & Knopik, V. S. (2020) · Development and Psychopathology · doi:10.1017/s0954579419001573 · Cited by 4
“The participants were children and adolescents ages 8 to 21 years that were enrolled in the Philadelphia Neurodevelopmental Cohort (PNC; Satterthwaite et al., 2016), a large-scale, NIMH-funded collaboration between the Center for Applied Genomics at the Children’s Hospital of Philadelphia and the Brain Behavior Labora…”
BackgroundInfluential
Social Experience and the Developing Brain: Opportunities for Social Epidemiologists in the Era of Population-Based Neuroimaging
LeWinn, K. Z., & Shih, E. (2019) · Current Epidemiology Reports · doi:10.1007/s40471-019-00222-4 · Cited by 3
Generating Templates and Growth Charts for School-Aged Brain Development
Dong, H.-M., Castellanos, F., Yang, N., Zhang, Z., He, Y., Zhang, L., Xu, T., Holmes, A., Yeo, B. T. T., Chen, F.-Y., et al. (2019) · bioRxiv · doi:10.1101/747352 · Cited by 3
Cerebellar grey matter volume is associated with cognitive function and psychopathology in adolescencePreprint
Moberget, T., Alnæs, D., Kaufmann, T., Doan, N. T., Córdova-Palomera, A., Norbom, L., Rokicki, J., van der Meer, D., Andreassen, O., & Westlye, L. (2018) · bioRxiv · doi:10.1101/288134 · Cited by 3
“6 As previously described(17, 36, 37), all MPRAGE T1-weighted images were collected using the same scanner (Siemens Tim Trio 3 Tesla, Erlangen, Germany; 32 channel head coil), using the following parameters; TR 1810 ms, TE 3.”
BackgroundMethodInfluential
Commentary: Leveraging discovery science to advance child and adolescent psychiatric research – a commentary on Zhao and Castellanos 2016
Mennes, M. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12538 · Cited by 3
“In response, increasingly large data sets are being collected (e.g. von Rhein et al., 2015; Satterthwaite et al., 2016), or aggregated through data sharing initiatives; be it within large consortia such as PGC and ENIGMA, or through publically available databases such as ABIDE and ADHD-200.”
Background
Brain age prediction in generalized anxiety disorder using a convolutional neural network
Richier, C. J., Zugman, A., Harrewijn, A., Cardinale, E. M., Khosravi, P., Aghajani, M., Bruin, W., Hilbert, K., Cardoner, N., Porta-Casteràs, D., et al. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04078-3 · Cited by 2
“Structural T1-weighted MRI brain images were non-linearly aligned to a standard brain (MNI152) [46], bias-corrected, subjected to removal of non-brain tissue, segmented into gray and white matter maps, modulated, and smoothed.”
Self-citation
Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers
Adkinson, B. D., Rosenblatt, M., Sun, H., Dadashkarimi, J., Tejavibulya, L., Horien, C. L., Westwater, M. L., Rodriguez, R. X., Noble, S., & Scheinost, D. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02447-y · Cited by 3
“…neuroimaging datasets: the Healthy Brain Network (HBN, n = 1,110) dataset 40 , the Adolescent Brain Cognitive Development (ABCD, n = 9,371) Study 41 , the Human Connectome Project in Development (HCPD, n = 428) dataset 42 and the Philadel-phia Neurodevelopmental Cohort (PNC, n = 1,291) dataset 43 .”
Associations Between Youth Marijuana and Alcohol Use, Neurocognitive Performance, and Triple-Network Resting-State Connectivity
Blyth, S., Hill, L. D., Huang, A. S., Woodward, N. D., Rogers, B. P., Vandekar, S. N., & Ward, H. (2026) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.12.014 · Cited by 2
VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data
Deshpande, G., Lu, B., Huynh, N., & Rangaprakash, D. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1553035 · Cited by 3
Enhancing the discriminatory power of polygenic scores for ADHD and autism in clinical and non-clinical samples
Li, J. J., He, Q., Dorn, S., Wang, Z., & Lu, Q. (2025) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-025-09620-w · Cited by 4
“Additional information of this study are detailed elsewhere [27].”
Gyral peak variations between HCP and CHCP: functional and structural implications
Cao, G., Zhang, S.-Y., He, Z.-B., Wang, Z., Guo, L., Yan, Z.-Q., Han, J.-F., Jiang, X., & Zhang, T. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02894-9 · Cited by 2
“Building on previous studies (Montembeault et al. (2012); Satterthwaite et al. (2016)), we employed an approach that compares clusters formed by all participants, rather than restricting the analysis to clusters directly shared between groups.”
Mapping Individual Differences in Intermodal Coupling in Neurodevelopment
Pan, R.-Y., Weinstein, S. M., Tu, D., Hu, F.-L., Tanrıverdi, B., Zhang, R.-Q., Vandekar, S., Baller, E. B., Gur, R., Gur, R. E., et al. (2024) · bioRxiv · doi:10.1101/2024.06.26.600817 · Cited by 2
“We apply our proposed method to multimodal neuroimaging data from the Philadelphia Neurodevel-opmental Cohort (Satterthwaite et al. 2016), mapping age and sex differences in intermodal coupling between (i) cortical thickness (CT) and sulcal depth (SD) and (ii) cerebral blood flow (CBF) and amplitude of low-frequency f…”
Self-citation
Unraveling Multimodal Brain Signatures: Deciphering Transdiagnostic Dimensions of Psychopathology in Adolescents
Xia, J., Chen, N., & Qiu, A. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300577 · Cited by 2
An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets
Bartnik, A., Singh, S., Sum, C., Smith, M., Bergsland, N., Zivadinov, R., & Dwyer, M. G. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09659-5 · Cited by 2
“…Disor - der (ADHD)-200 Consortium (Milham et al., 2012), Cam-bridge Centre for Ageing and Neuroscience (Cam-CAN) data repository (Taylor et al., 2017 ), the Philadelphia Neu-rodevelopmental Cohort (PNC) (Satterthwaite et al., 2016), and several datasets publicly available on openneuro.org.”
Converging on consistent functional connectomicsPreprint
Luppi, A., Gellersen, H., Liu, Z.-Q., Peattie, A. R. D., Manktelow, A., Adapa, R., Owen, A., Naci, L., Menon, D. K., Dimitriadis, S., et al. (2023) · bioRxiv · doi:10.1101/2023.06.23.546329 · Cited by 2
Transdiagnostic symptom networks in relation to mental health service use in community youth
Imperiale, M. N., Lieb, R., Calkins, M. E., & Meinlschmidt, G. (2022) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.2782 · Cited by 1
“The institutional review boards of the University of Pennsylvania as well as the Children's Hospital of Philadelphia approved all study procedures (protocol number 810336) (Satterthwaite et al., 2016).”
BackgroundSelf-citation
Multimodal graph isomorphism network to study fMRI connectivity in developmental stages of adolescence
Patel, B., Orlichenko, A., & Wang, Y.-P. (2022) · Medical Imaging · doi:10.1117/12.2611190 · Cited by 2
“The automated anatomical labeling (AAL) template is used to divide the brain into 264 regions of interest (ROIs) based on the Power coordinates.(4) A total number of 624 subjects were in this study.”
Method
Enhancing the Discriminatory Power of ADHD and Autism Spectrum Disorder Polygenic Scores in Clinical and Non-Clinical SamplesPreprint
Li, J. J., He, Q.-F., Wang, Z.-H., & Lu, Q.-S. (2022) · medRxiv · doi:10.1101/2022.02.09.22270697 · Cited by 2
“Additional information of this study are detailed elsewhere (27).”
Background
Sex‐specific associations between traumatic experiences and resting‐state functional connectivity in the Philadelphia Neurodevelopmental Cohort
Wang, S., Malins, J. G., Zhang, H., & Gruen, J. R. (2021) · JCPP Advances · doi:10.1002/jcv2.12049 · Cited by 3
Brain Maturation Study during Adolescence Using Graph Laplacian Learning Based Fourier Transform.Preprint
Wang, J.-Q., Xiao, L., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07211 · Cited by 2
“Result: We first evaluated our method on the synthetic data and further applied the method to resting and task state fMRI imaging data from Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to 22.”
BackgroundInfluential
Spectral Resolution Clustering for Brain ParcellationPreprint
Dillon, K., & Wang, Y.-P. (2018) · arXiv.org · doi:10.48550/arxiv.1810.04026 · Cited by 2
Probing brain developmental patterns of myelination and associations with psychopathology in youth using gray/white matter contrast
Norbom, L., Doan, N. T., Alnæs, D., Kaufmann, T., Moberget, T., Rokicki, J., Andreassen, O., Westlye, L., & Tamnes, C. (2018) · bioRxiv · doi:10.1101/305995 · Cited by 2
And the best task is …? Using Task potency to infer task specificityPreprint
Chauvin, R. J., Mennes, M., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/111187
“Similar projects are translating these efforts to the clinical domain (e.g., NeuroIMAGE (Rhein et al., 2015), PNC (Satterthwaite et al., 2016), ABCD (Bjork et al., 2017)).”
The uniqueness of the individual functional connectome
Horien, C. L., Scheinost, D., & Constable, R. T. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813838-0.00004-2 · Cited by 3
Untangling relationships between cognitive development and child and adolescent mental health: Findings from the ABCD Study
Chen, Y. D., Agrawal, S., Anokhin, A. P., Luciana, M. M., Gee, D. G., & Cho, Y. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101772 · Cited by 1
“…Genetics through Meta Analysis (ENIGMA) Consortium (n dependent on image modality and group of interest) (Thompson et al., 2014), the Philadelphia Neu-rodevelopmental Cohort (n ≈ 9500) (Satterthwaite et al., 2015), the Orygen Adolescent Development Study (n = 245) (Cotton et al., 2016), and others.”
Automated quality assurance of segmentation techniques with statistically‑based failure evaluation (SAFE)
Bartz, K. M., Remedios, S. W., Dewey, B., Hays, S. P., Prince, J. L., & Carass, A. (2026) · Medical Imaging · doi:10.1117/12.3085826 · Cited by 1
Integrating Functional Transdiagnostic Dimensions of Psychopathology with Cortical OrganizationPreprint
Monaghan, A., Mišić, B., Shafiei, G., Tsvetanov, K., Astle, D., & Bethlehem, R. (2026) · bioRxiv · doi:10.64898/2026.02.02.702560 · Cited by 1
Spatial heterogeneity and subtypes of functional connectivity development in youth
Li, H., Cui, Z.-X., Cieslak, M., Salo, T., Moore, T. M., Gur, R., Gur, R. C., Shinohara, R., Oathes, D., Davatzikos, C., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68707-7 · Cited by 1
Self-citation
Concurrence: A dependence criterion for time series, applied to biological data
Sariyanidi, E., Herrington, J., Yankowitz, L., Chaudhari, P., Satterthwaite, T., Zampella, C., Morris, J., Gunning, E., Schultz, R., Shinohara, R., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2512.16001 · Cited by 1
“…the connectivity matrices obtained with Pearson’s r (i.e., correlation matrix) and the concurrence coefficient (i.e., concurrence matrix) on a version of the Philadelphia Neurodevelopmental Cohort dataset (Baum et al., 2020) that was pre-processed as in prior work (Satterthwaite et al., 2016).”
Self-citation
Cognition varies across the calendar year in multiple large-scale datasets
Keller, A. S., Shetty, A., Barzilay, R., Calkins, M. E., Chong, Y. S., Dave, N., Fair, D. A., Gluckman, P. D., Gur, R. E., Gur, R. C., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2506054122 · Cited by 1
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First establishment of population-specific brain volume reference standards for Chinese children and adolescents: an AI-based segmentation study
Qian, H., Liao, J., & Cheng, J. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1723455 · Cited by 1
Developmental Connectomics from a “Big Data” Perspective
Xu, Y., Zhao, T., Xia, M., & Liao, X. (2025) · Journal of Pacific Rim Psychology · doi:10.1177/18344909251392757 · Cited by 1
MJNMF-GAT: Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network for Understanding Adolescent Neurodevelopment
Patel, B., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2025) · IEEE Access · doi:10.1109/access.2025.3601649 · Cited by 1
“The scanning session lasted for a total of 10.5 minutes, with the BOLD signal captured using a whole-brain, single-shot, multislice, gradient-echo echoplanar sequence consisting of 124 volumes [28].”
Influential
Longitudinal Development of Neurocognitive Functioning and Gray Matter Volume in Youths With Recurrent Psychosis Spectrum Symptoms
Wong, T. Y., Moore, T. M., Hillman, N., Calkins, M. E., Shahriar, S. S., Dietterich, T. E., Ruparel, K., Roalf, D. R., Wolf, D. H., Satterthwaite, T. D., et al. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf049 · Cited by 2
Self-citation
Mediating Role of Trauma Connecting Psychiatric Family History and Adolescent Mental Health
Chaiyachati, B. H., Catalano, J. L., Schultz, L. M., Almasy, L. A., Visoki, E., Seidlitz, J., Moore, T. M., Taylor, J. H., Calkins, M. E., Gur, R. E., et al. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100525 · Cited by 3
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Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning
Li, H., Zhuo, C., Cui, Z.-X., Cieslak, M., Salo, T., Gur, R. E., Gur, R., Shinohara, R., Oathes, D., Davatzikos, C., et al. (2025) · bioRxiv · doi:10.1101/2025.04.07.647618 · Cited by 1
“We applied the model built on the HCP training cohort to fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) [50], consisting of 969 individuals (ages from 8 to 23 years).”
Self-citation
Configural and Metric Invariance Across Full-Form and Computerized Adaptive Versions of the Penn Computerized Neurocognitive Battery (CAT-CCNB)
Moore, T. M., Lopez, K. C., Scott, J., Lennon, J., Sandro, A. D., Zoupou, E., Gorgone, A., Calkins, M., Wolf, D., Kable, J. W., et al. (2024) · Journal of Psychoeducational Assessment · doi:10.1177/07342829241288746 · Cited by 1
“…by using tasks validated with functional neuro-imaging (Ragland et al., 1997; Roalf et al., 2014) and examining correlations with brain parameters (Cui et al., 2020; Roalf et al., 2014; Satterthwaite et al., 2016) and genetics (Greenwood et al., 2011; Gur et al., 2007; Swagerman et al., 2016).”
Self-citation
Regression and alignment for functional data and network topology
Tu, D., Wrobel, J., Satterthwaite, T. D., Goldsmith, J., Gur, R. C., Gur, R. E., Gertheiss, J., Bassett, D. S., & Shinohara, R. T. (2024) · Biostatistics · doi:10.1093/biostatistics/kxae026 · Cited by 2
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Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach
Ran, J.-L., Wang, Y.-K., & Guo, Y. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00220 · Cited by 1
“We apply dyna-LOCUS to analyze the resting-state fMRI (rs-fMRI) data from the Philadelphia Neurodevelopmental Cohort (PNC) project ( Satterthwaite et al., 2014, 2016).”
MethodInfluential
The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older IndividualsPreprint
Tetereva, A., & Pat, N. (2024) · bioRxiv · doi:10.1101/2022.12.31.522374 · Cited by 1
“Next, Butler and colleagues (2021) studied the utility of Brain Age on cognitive functioning of another group of youths aged 8-22 years old from the Philadelphia Neurodevelopmental Cohort (PNC) (Satterthwaite et al., 2016).”
Background
Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
Qu, G., Orlichenko, A., Wang, J.-Q., Zhang, G.-M., Xiao, L., Zhang, A.-Y., Ding, Z.-M., & Wang, Y.-P. (2024) · arXiv.org · doi:10.48550/arxiv.2401.10348 · Cited by 1
“Without prior clustering knowledge, several brain functional networks (BFN)[19] can be identified from the heatmap, including the auditory network (AUD)[20], visual network (VIS)[21], and memory retrieval network (MEM)[22] in a purely data-driven way.”
Background
Signatures of pubertal brain development and health revealed through domain adapted brain network fusionPreprint
Kraft, D., Alnæs, D., & Kaufmann, T. (2023) · medRxiv · doi:10.1101/2023.01.26.23285055 · Cited by 1
Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in YouthPreprint
Mehta, K., Pines, A., Adebimpe, A., Larsen, B., Bassett, D., Calkins, M., Baller, E. B., Gell, M., Patrick, L. M., Gur, R. E., et al. (2023) · bioRxiv · doi:10.1101/2023.01.25.525577 · Cited by 1
Self-citation
Intelligence-associated Polygenic Scores Predict g, Independent of Ancestry, Parental Educational Levels, and Color among Hispanics in comparison to European, European- African, and African AmericansPreprint
Pesta, B. J., Fuerst, J. G. R., Piffer, D., & Kirkegaard, E. O. W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.24.312074 · Cited by 1
A Manifold Regularized Multi-Task Learning Model for IQ Prediction from Multiple fMRI ParadigmsPreprint
Li, X., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.05913 · Cited by 1
“To validate the efficiency and effectiveness of our proposed model, we perform extensive experiments on the publicly available Philadelphia Neurodevelopmental Cohort (PNC) dataset [31], [32] Here we predict the continuous-value intelligence quotient (IQ) scores of subjects by using fMRI data in two task conditions (wo…”
Method
Robust Spatial Extent Inference with a Semiparametric Bootstrap Joint Testing ProcedurePreprint
Vandekar, S., Satterthwaite, T., Xia, C., Ruparel, K., Gur, R., Gur, R., & Shinohara, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07449 · Cited by 1
Self-citation
Heritable cognitive and psychopathology factors in youth are predicted by brain fronto-temporal white matter pathwayPreprint
Alnæs, D., Kaufmann, T., Doan, N. T., Córdova-Palomera, A., Wang, Y.-P., Bettella, F., Moberget, T., Andreassen, O., & Westlye, L. (2017) · bioRxiv · doi:10.1101/172080 · Cited by 1
Development of an Itemwise Efficiency Scoring Method: Concurrent, Convergent, Discriminant, and Neuroimaging-Based Predictive Validity Assessed in a Large Community Sample
Moore, T. M., Reise, S., Roalf, D., Satterthwaite, T., Davatzikos, C., Bilker, W. B., Port, A. M., Jackson, C. T., Ruparel, K., Savitt, A. P., et al. (2016) · Psychological Assessment · doi:10.1037/pas0000284 · Cited by 1
“The neuroimaging procedures have been described in detail (Satterthwaite et al., 2015, 2014).”
Self-citation
Special considerations for acquisition of pediatric MRI of high spatial and temporal resolution
Roberts, T. P. L., Zhu, T., & Huang, H. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00019-3 · Cited by 1
To Collapse or not to Collapse Multiple Measures of the Same Psychological Construct: A Demonstration Across 14 Independent Studies of Autistic and Non-Autistic Children
Reckner, E., Thourani, N., Parker, D., Herrington, J. D., Roberts, T. P. L., Schultz, R. T., Travers, B. G., Vaidya, C. J., Verma, R., Tunç, B., et al. (2026) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-026-01512-4
“…et al., 2018) or a very large single-site study like the Philadelphia Neurodevelopmental Cohort (Calkins et al., 2015; Satterthwaite et al., 2014; Satterthwaite et al., 2016) that collect cognitive and mental health phenotype data and magnetic resonance imaging (MRI) scans at a scale that is not…”
Differentiation of frontolimbic functional connectivity from birth to early adulthood links with adversity exposure and cognition.
Kim, W., Whittle, S., Zalesky, A., & Tian, Y. (2026) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2600520123

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: Satterthwaite, Theodore D.
Connolly, John J.
Ruparel, Kosha
Calkins, Monica E.
Jackson, Chad
Elliott, Mark A.
Roalf, David R.
Mentch, Frank D.
Gur, Ruben C.
Hakonarson, Hakon
Gur, Raquel E.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2016-01
Volume: 124
Issue: Pt B
Pages: 1115-1119
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2015.03.056
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth

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