The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
KAUFMAN, J., BIRMAHER, B., BRENT, D., RAO, U., FLYNN, C., MORECI, P., WILLIAMSON, D., & RYAN, N. (1997) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-199707000-00021 · Cited by 10,148 · cited 1×
“GOASSESS was developed from a modified version of the Kiddie-Schedule for Affective Disorders and Schizophrenia (Kaufman et al., 1997).”
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
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970 · cited 1×
“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
Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., Conroy, J., Magalhaes, T. R., Correia, C., Abrahams, B. S., et al. (2010) · Nature · doi:10.1038/nature09146 · Cited by 2,013 · cited 1×
“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
Glessner, J. T., Wang, K., Cai, G., Korvatska, O., Kim, C. E., Wood, S., Zhang, H., Estes, A., Brune, C. W., Bradfield, J. P., et al. (2009) · Nature · doi:10.1038/nature07953 · Cited by 1,421 · cited 2×
“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.”
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 334
The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students
Tsuchida, A., Laurent, A., Crivello, F., Petit, L., Joliot, M., Pepe, A., Beguedou, N., Gueye, M.-F., Verrecchia, V., Nozais, V., et al. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02334-4 · Cited by 40
Linnér, R. K., Mallard, T., Barr, P. B., Sanchez-Roige, S., Madole, J., Driver, M. N., Poore, H., Grotzinger, A., Tielbeek, J., Johnson, E., et al. (2020) · bioRxiv · doi:10.1101/2020.10.16.342501 · Cited by 29
Development of a scale battery for rapid assessment of risk and resilience
Moore, T. M., White, L. K., Barzilay, R., Calkins, M. E., Jones, J. D., Young, J. F., Gur, R. C., & Gur, R. E. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.112996 · Cited by 34
Self-citationUsing human brain imaging studies as a guide toward animal models of schizophrenia
Bolkan, S. S., Poyraz, F. C., & Kellendonk, C. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.05.055 · Cited by 33
“…Philadelphia Neurodevelopmental Cohort – have already begun to yield important findings and publicly available data from such longitudinal cohorts will undoubtedly prove to be a significant resource for future research (Addington et al., 2007; Woods et al., 2009; Satterthwaite et al., 2014, 2015).”
BackgroundHur, J., Kuhn, M., Grogans, S. E., Anderson, A. S., Islam, S., Kim, H. C., Tillman, R. M., Fox, A. S., Smith, J. F., DeYoung, K. A., et al. (2022) · Psychological Science · doi:10.1177/09567976211056635 · Cited by 32
“…many biobank studies, including ABCD (Casey et al., 2018), the Human Connectome Project (HCP) and follow-ons (e.g., Barch et al., 2013; Seok et al., 2020), IMAGEN (Albaugh et al., 2019), the Philadelphia Neurodevelopmental Cohort (Satterthwaite et al., 2016), and UK Biobank (Miller et al., 2016).”
BackgroundElad, D., Karayumak, S. C., Zhang, F., Cho, K. I. K., Lyall, A. E., Seitz‐Holland, J., Ben‐Ari, R., Pearlson, G. D., Tamminga, C. A., Sweeney, J. A., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25574 · Cited by 31
Refined measure of functional connectomes for improved identifiability and prediction
Cai, B., Zhang, G., Hu, W., Zhang, A., Zillé, P., Zhang, Y., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2019) · Human Brain Mapping · doi:10.1002/hbm.24741 · Cited by 30
“PNC is a large-scale study of child development that incorporates rich multimodal neuroimaging, which is well suited to test the ability of the brain FC as a fingerprint across different fMRI protocols (Kaufmann et al., 2017).”
BackgroundMethodInfluentialMulti‐scale network regression for brain‐phenotype associations
Xia, C. H., Ma, Z., Cui, Z., Bzdok, D., Thirion, B., Bassett, D. S., Satterthwaite, T. D., Shinohara, R. T., & Witten, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.24982 · Cited by 31
“Structural and functional imaging data were acquired on a 3T Siemens Tim Trio scanner with a 32-channel head coil (Erlangen, Germany), as previously described (Satterthwaite et al., 2014, 2016).”
MethodSelf-citationAlternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction
Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2884129 · Cited by 33
“, resting-state and fractal n-back task fMRI) from the publicly available Philadelphia Neurodevelopmental Cohort (PNC) data [47], [48] to build a predictor for subjects with different IQs.”
BackgroundMethodBDNF-Dependent Effects on Amygdala–Cortical Circuitry and Depression Risk in Children and Youth
Wheeler, A. L., Felsky, D., Viviano, J. D., Stojanovski, S., Ameis, S. H., Szatmári, P., Lerch, J. P., Chakravarty, M. M., & Voineskos, A. N. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx086 · Cited by 28
Ciric, R., Wolf, D., Power, J. D., Roalf, D., Baum, G. L., Ruparel, K., Shinohara, R., Elliott, M., Eickhoff, S., Davatzikos, C., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.03616 · Cited by 28
Self-citationSubject-level measurement of local cortical coupling
Vandekar, S. N., Shinohara, R. T., Raznahan, A., Hopson, R. D., Roalf, D. R., Ruparel, K., Gur, R. C., Gur, R. E., & Satterthwaite, T. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.002 · Cited by 27
Self-citationJirsaraie, R. J., Kaczkurkin, A. N., Rush, S., Piiwia, K., Adebimpe, A., Bassett, D. S., Bourque, J., Calkins, M. E., Cieslak, M., Ćirić, R., et al. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0508-3 · Cited by 31
“Image acquisition The baseline structural images were collected as part of the Philadelphia Neurodevelopment Cohort [31, 32].”
MethodInfluentialSelf-citationFuture Directions for Examination of Brain Networks in Neurodevelopmental Disorders
Uddin, L. Q., & Karlsgodt, K. H. (2018) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2018.1443461 · Cited by 31
“The Philadelphia Neurodevelopmental Cohort (http://www. med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort. html) is a research initiative funded by National Institute of Mental Health that focuses on characterizing brain and behavior interaction with genetics (Satterthwaite et al., 2016).”
BackgroundInfluentialChinese Color Nest Project: Growing up in China
Yang, N., He, Y., Zhang, Z., Dong, H.-M., Zhang, L., Zhu, X.-T., Hou, X., Wang, Y., Zhou, Q., Gong, Z.-Q., et al. (2017) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972017-00362 · Cited by 29
Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework
Zillé, P., Calhoun, V. D., & Wang, Y. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2721301 · Cited by 32
“1) Data acquisition: The Philadelphia Neurodevelopmental Cohort[26] (PNC) is a large-scale collaborative study between the Brain Behaviour Laboratory at the University of Pennsylvania and the Children’s Hospital of Philadelphia.”
MethodZhao, L., Matloff, W. J., Shi, Y., Cabeen, R. P., & Toga, A. W. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.11.002 · Cited by 32
Qu, G., Orlichenko, A., Wang, J.-Q., Zhang, G.-M., Xiao, L., Zhang, K., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3343365 · Cited by 25
“The identification of the visual network may also be attributed to the data collection context [22], [53], as subjects were instructed to concentrate on a crosshair during fMRI data collection, potentially engaging visual networks.”
BackgroundHe, Q., & Li, J. J. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13243 · Cited by 31
Phillips, O. R., Onopa, A., Hsu, V., Ollila, H. M., Hillary, R. P., Hallmayer, J. F., Gotlib, I. H., Taylor, J., Mackey, L. W., & Singh, M. K. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2018.09.425 · Cited by 27
“Psychopathology data were extracted from 252 individual item-level responses to a semi-structured interview from dbGaP).(24) We conducted a factor analysis to dimensionalize the psychopathology data using R’s Psych Package(25) (see Supplement 1: Factor Methods Details, available online).”
MethodKjelkenes, R., Wolfers, T., Alnæs, D., van der Meer, D., Pedersen, M. L., Dahl, A., Voldsbekk, I., Moberget, T., Tamnes, C. K., Andreassen, O. A., et al. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2022.01.007 · Cited by 26
Fang, J., Xu, C., Zille, P., Lin, D., Deng, H.-W., Calhoun, V., & Wang, Y.-P. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2783244 · Cited by 23
“It is available in the dbGaP database [35], and contains fMRI and SNP for children aged from 8 to 22 years.”
The potential of precision psychiatry: what is in reach?
Kambeitz‐Ilankovic, L., Koutsouleris, N., & Upthegrove, R. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2022.23 · Cited by 33
“A broad population-based study, the Philadelphia Neurodevelopmental Cohort (PNC), 12 pursued clinical and neurobehavioural characterisation of genotyped youths for the prospective emergence of psychiatric illness.”
BackgroundLoohuis, L. M. O., Mennigen, E., Ori, A. P. S., Perkins, D. O., Robinson, E., Addington, J., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01203-2 · Cited by 29
Self-citationSex differences in estimated brain metabolism in relation to body growth through adolescence
Vandekar, S., Shou, H., Satterthwaite, T. D., Shinohara, R. T., Merikangas, A. K., Roalf, D. R., Ruparel, K., Rosen, A. F. G., Gennatas, E. D., Elliott, M. A., et al. (2017) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x17737692 · Cited by 36
Self-citationHoy, N., Lynch, S. J., Waszczuk, M. A., Reppermund, S., & Mewton, L. R. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105431 · Cited by 31
“Prominent examples include the Adolescent Brain and Cognitive Development (ABCD) Study (Satterthwaite et al., 2016), the Philadelphia Neurodevelopmental Cohort (PNC) (Satterthwaite et al., 2016), and the UK Biobank (Sudlow et al., 2015).”
BackgroundInfluentialKjelkenes, R., Wolfers, T., Alnæs, D., Norbom, L. B., Voldsbekk, I., Holm, M. C., Dahl, A., Berthet, P., Tamnes, C. K., Marquand, A. F., et al. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101173 · Cited by 22
Sullivan, E., Brumback, T., Tapert, S., Prouty, D., Fama, R., Thompson, W. K., Brown, S. A., Cummins, K., Colrain, I., Baker, F., et al. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.01.003 · Cited by 21
“Satterthwaite, T.D., Connolly, J.J., Ruparel, K., Calkins, M.E., Jackson, C., Elliott, M.A., Roalf, D.R., Ryan Hopsona, K.P., Behr, M., Qiu, H., Mentch, F.D., Chiavacci, R., Sleiman, P.M., Gur, R.C., Hakonarson, H., Gur, R.E., 2016.”
The Human Connectome Project of adolescent anxiety and depression dataset
Hubbard, N. A., Bauer, C. C. C., Siless, V., Auerbach, R. P., Elam, J. S., Frosch, I. R., Henin, A., Hofmann, S. G., Hodge, M. R., Jones, R. J., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03629-x · Cited by 19
Lynch, K. M., Page, K. A., Shi, Y., Xiang, A. H., Toga, A. W., & Clark, K. A. (2020) · Hippocampus · doi:10.1002/hipo.23280 · Cited by 25
“Participants in the PNC were administered an abbreviated version of the Penn computerized neurocognitive battery (Penn CNB) (Satterthwaite et al., 2016) that consists of 14 tests that evaluate multiple cognitive domains, including executive function, complex cognitive processes, social cognition, motor speed, and epis…”
MethodInfluentialMulti-paradigm fMRI fusion via sparse tensor decomposition in brain functional connectivity study
Zhang, Y., Xiao, L., Zhang, G.-M., Cai, B., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2020) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2020.3019421 · Cited by 20
“Multi-paradigm neuroimaging data and multiple genetic factors for nearly 900 adolescents aged from 8 to 21 years are obtained from the PNC dataset, which are available in the dbGaP database [30].”
MethodDisentangling common from specific processing across tasks using task potency
Chauvin, R. J., Mennes, M., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.059 · Cited by 19
“Similar projects are translating these efforts to the clinical domain (e.g., NeuroIMAGE (von Rhein et al., 2015a), PNC (Satterthwaite et al., 2016), ABCD (Bjork et al., 2017)).”
BackgroundLifespan normative modeling of brain microstructure
Villalón‐Reina, J. E., Zhu, A. H., Nabulsi, L., Thomopoulos, S. I., Moreau, C. A., Feng, Y., Chattopadhyay, T., Benavidez, S. M., Kushan, L., John, J. P., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72875-x · Cited by 15
“…(PedsDTI) 57 , the Pediatric Imaging, Neu-rocognition, and Genetics data repository (PING) 58 , the Philadelphia Neurodevelopmental Cohort (PNC) 59 , the Queensland Twin Adolescent Brain (QTAB) 60 , the Queensland Twin Imaging dataset (QTIM) 61 , the Southwest University Longitudinal Imaging…”
Alterations in white matter microstructure in individuals at persistent risk for psychosis
Roalf, D., Garza, Á. G. D. L., Rosen, A. F. G., Calkins, M., Moore, T. M., Quarmley, M., Ruparel, K., Xia, C., Rupert, P. E., Satterthwaite, T., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0360-1 · Cited by 19
Self-citationVoineskos, A. N., Felsky, D., Wheeler, A. L., Rotenberg, D. J., Lévesque, M. L., Patel, S., Szeszko, P. R., Kennedy, J. L., Lencz, T., & Malhotra, A. K. (2015) · Schizophrenia Bulletin · doi:10.1093/schbul/sbv180 · Cited by 14
Connectome-based machine learning models are vulnerable to subtle data manipulations
Rosenblatt, M., Rodriguez, R. X., Westwater, M. L., Dai, W., Horien, C. L., Greene, A. S., Constable, R. T., Noble, S., & Scheinost, D. (2023) · Patterns · doi:10.1016/j.patter.2023.100756 · Cited by 18
Zhu, J., & Qiu, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119482 · Cited by 18
“These neurocogniive categories were adapted from the design of the PNC study reported n Gur et al. (2012) .”
MethodInfluentialNeuroimaging brain growth charts: A road to mental health
Chen, L.-Z., Holmes, A. J., Zuo, X., & Dong, Q. (2021) · Psychoradiology · doi:10.1093/psyrad/kkab022 · Cited by 18
Educational fMRI: From the Lab to the Classroom
Seghier, M. L., Fahim, M. A., & Habak, C. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02769 · Cited by 22
Multimodal Sparse Classifier for Adolescent Brain Age Prediction
Kassani, P. H., Gossmann, A., & Wang, Y.-P. (2019) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2019.2925710 · Cited by 18
“The data preprocessing is borrowed from [24].”
Rethinking functional brain connectome analysis: do graph deep learning models Help
Han, K., Su, Y., He, L., Zhan, L., Plis, S., Calhoun, V. D., & Yang, C. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00067-x · Cited by 12
Hong, J., Hwang, J., & Lee, J. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.12.037 · Cited by 17
White matter microstructural deficits in 22q11.2 deletion syndrome
Roalf, D., Schmitt, J. E., Vandekar, S., Satterthwaite, T., Shinohara, R., Ruparel, K., Elliott, M., Prabhakaran, K., McDonald-McGinn, D., Zackai, E., et al. (2017) · Psychiatry research. Neuroimaging · doi:10.1016/j.pscychresns.2017.08.001 · Cited by 17
Self-citationAzzam, M., Xu, Z., Liu, R., Li, L., Soh, K. M., Challagundla, K. B., Wan, S., & Wang, J. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae042 · Cited by 18
The future of neuroscience in developmental psychopathology
Hyde, L. W., Bezek, J. L., & Michael, C. (2024) · Development and Psychopathology · doi:10.1017/s0954579424000233 · Cited by 14
Nakua, H., Hawco, C. S., Forde, N. J., Joseph, M. J. E., Grillet, M., Johnson, D., Jacobs, G. R., Hill, S., Voineskos, A. N., Wheeler, A. L., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120119 · Cited by 16
“Several prior reports have shown a link between higher head motion and younger age at scanning (Alexander et al., 2017; Pardoe et al., 2016; Satterthwaite et al., 2012; Siegel et al., 2017).”
Power and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt, M., Tejavibulya, L., Camp, C. C., Jiang, R., Westwater, M., Noble, S., & Scheinost, D. (2023) · bioRxiv · doi:10.1101/2023.10.25.563971 · Cited by 15
Accurate sex prediction of cisgender and transgender individuals without brain size bias
Wiersch, L., Hamdan, S., Hoffstaedter, F., Votinov, M., Habel, U., Clemens, B., Derntl, B., Eickhoff, S. B., Patil, K. R., & Weis, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-37508-z · Cited by 25
Leveraging big data to map neurodevelopmental trajectories in pediatric anxiety
Zacharek, S. J., Kribakaran, S., Kitt, E. R., & Gee, D. G. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.100974 · Cited by 18
“…to anxiety (Alexander et al., 2017; Casey et al., 2018; Evans, 2006; Hubbard et al., 2020; Jaddoe et al., 2006; Jernigan et al., 2016; Nooner et al., 2012; Pausova et al., 2017; Satterthwaite et al., 2016; Salum et al., 2015; Schumann et al., 2010; Somerville et al., 2018; Volkow et al., 2018).”
BackgroundCornblath, E. J., Ashourvan, A., Kim, J. Z., Betzel, R. F., Ćirić, R., Baum, G. L., He, X., Ruparel, K., Moore, T. M., Gur, R. C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/412429 · Cited by 10
“[21] Satterthwaite TD, Connolly JJ, Ruparel K, Calkins ME, Jackson C, Elliott MA, Roalf DR, Hopsona R, Prabhakaran K, Behr M et al.”
BackgroundSelf-citationTwo axes of white matter development
Luo, A. C., Meisler, S. L., Sydnor, V. J., Alexander‐Bloch, A. F., Bagautdinova, J., Barch, D. M., Bassett, D. S., Davatzikos, C. A., Franco, A. R., Goldsmith, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68714-8 · Cited by 11
“Minimal initial exclusion criteria were applied to the PNC and included medical conditions that could impact brain function 63 .”
Self-citationUsing network control theory to study the dynamics of the structural connectomePreprint
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. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.23.554519 · Cited by 12
Self-citationCommon functional brain networks between attention deficit and disruptive behaviors in youth
Wong, T., Zhang, H., White, T., Xu, L., & Qiu, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118732 · Cited by 14
Functional network estimation using multigraph learning with application to brain maturation study
Wang, J.-Q., Xiao, L., Hu, W.-X., Qu, G., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2021) · Human Brain Mapping · doi:10.1002/hbm.25410 · Cited by 14
“Finally, we apply the proposed model to neuroimaging data collected from the Philadelphia Neu-rodevelopmental Cohort (PNC) (Satterthwaite et al., 2014).”
MethodInfluentialLog-sum enhanced sparse deep neural network
Qiao, C., Shi, Y. H., Diao, Y.-X., Calhoun, V. D., & Wang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.118 · Cited by 15
A GICA-TVGL Framework to Study Sex Differences in Resting State fMRI Dynamic Connectivity
Cai, B., Zhang, G.-M., Zhang, A.-Y., Hu, W.-X., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108531 · Cited by 14
Imputation Strategy for Reliable Regional MRI Morphological Measurements
Cruz, S. S., Dinov, I., Herting, M., González-Zacarías, C., Kim, H., Toga, A., & Sepehrband, F. (2019) · Neuroinformatics · doi:10.1007/s12021-019-09426-x · Cited by 14
Sadaghiani, S., Ng, B., Altmann, A., Poline, J., Banaschewski, T., Bokde, A. L. W., Bromberg, U., Büchel, C., Quinlan, E. B., Conrod, P., et al. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0991-17.2017 · Cited by 19
Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents
Doucet, G. E., Goldsmith, C., Myers, K., Rice, D. L., Ende, G. C., Pavelka, D. J., Joliot, M., Calhoun, V. D., Wilson, T. W., & Uddin, L. Q. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101523 · Cited by 13
“…typically-developing children and adolescent participants from three large developmental projects: the Philadelphia Neurodevelopmental Cohort (PNC) (Satterthwaite et al., 2016; Satterthwaite et al., 2014), the Pediatric Imaging, Neurocognition, and Genetics (PING) study (Jernigan et al., 2016),…”
Kraft, D., Alnæs, D., & Kaufmann, T. (2023) · Nature Communications · doi:10.1038/s41467-023-41839-w · Cited by 14
“To validate our framework and to test its applicability to other datasets, we trained our model in a sample ofsubjects spanning a wide agerangefromthePNCcohort 13 withsimultaneoussuperviseddomain adaptationandtesteditontwoindependentvalidationsamples,thatis longitudinal data from the ABCD Study 14 and a clinical popul…”
BackgroundMethodInfluentialLei, H. C., Li, L., Chen, M., Xu, J., Jiang, M., Wang, Y., Jiang, L., Chen, X., Qiu, J., Tan, S., et al. (2021) · Neuroscience Bulletin · doi:10.1007/s12264-021-00650-7 · Cited by 19
Dizaji, A. S., Vieira, B. H., Khodaei, M. R., Ashrafi, M., Parham, E., Hossein‐Zadeh, G., Salmón, C. E. G., & Hamid Soltanian-zadeh, A. (2021) · Basic and Clinical Neuroscience Journal · doi:10.32598/bcn.12.1.574.1 · Cited by 17
“Through the use of CPM, Greene, Gao, Scheinost, and Constable (2018) could explain around 20% of the variance of fluid intelligence with task fMRI in contrast to only 6% with resting-state fMRI, with data from the HCP and PNC (Philadelphia Neurodevelopmental Cohort) (Satterthwaite et al., 2016, 2014) cohorts.”
MethodData-driven 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) · bioRxiv · doi:10.1101/125161 · Cited by 13
“Importantly, several recent reports described significant relationships between age and in-scanner head motion in a variety of MRI protocols (Power et al., 2012; Roalf et al., 2016; Satterthwaite et al., 2016).”
Self-citationBrain age has limited utility as a biomarker for capturing fluid cognition in older individuals
Tetereva, A., & Pat, N. (2024) · eLife · doi:10.7554/elife.87297 · Cited by 12
“Next, Butler et al., 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 ).”
BackgroundRai, B., Naylor, P. E., Sanchez, M. S., Wintermark, M., Raman, M. M., Jo, B., Reiss, A. L., & Green, T. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02504-4 · Cited by 27
“2 deletion syndrome in the Philadelphia Neurodevelopmental Cohort (PNC) datasets [46], are warranted for the RASopathies.”
BackgroundWarren, D. E., Rangel, A., Christopher‐Hayes, N. J., Eastman, J. A., Frenzel, M. R., Stephen, J. M., Calhoun, V. D., Wang, Y., & Wilson, T. W. (2021) · Human Brain Mapping · doi:10.1002/hbm.25458 · Cited by 14
“Spatial similarity of neocortical rs-FC maps for different ROIs (e.g., anterior vs. posterior hippocampus) or different datasets (Dev-CoG vs. Philadelphia Neurodevelopmental Cohort [PNC]) was measured using spatial correlation.”
MethodInfluentialFaster family‐wise error control for neuroimaging with a parametric bootstrap
Vandekar, S., Satterthwaite, T., Rosen, A. F. G., Ciric, R., Roalf, D., Ruparel, K., Gur, R., Gur, R. E., & Shinohara, R. (2017) · Biostatistics · doi:10.1093/biostatistics/kxx051 · Cited by 12
“While the processed data are not publicly available, unprocessed data are available for download through the Database of Genotypes and Phenotypes (dbGaP) (Satterthwaite et al., 2016).”
MethodSelf-citationParcellation of individual brains: From group level atlas to precise mapping.
Li, C., Yu, S., & Cui, Y. (2025) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106172 · Cited by 11
Adkinson, B. D., Rosenblatt, M., Dadashkarimi, J., Tejavibulya, L., Jiang, R., Noble, S., & Scheinost, D. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101464 · Cited by 11
“PNC participants were 1291 individuals ages 8 – 21 recruited from the greater Philadelphia, Pennsylvania area (Satterthwaite et al., 2016).”
Mehta, K. P., Pines, A. R., Adebimpe, A., Larsen, B., Bassett, D. S., Calkins, M. E., Baller, E. B., Gell, M., Patrick, L. M., Shafiei, G., et al. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101265 · Cited by 21
Self-citationShafiei, G., Esper, N. B., Hoffmann, M. S., Ai, L., Chen, A. A., Cluce, J., Covitz, S. C., Giavasis, S., Lane, C., Mehta, K. P., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.026 · Cited by 32
Self-citationAdolescent brain maturation associated with environmental factors: a multivariate analysis
Ray, B., Jensen, D., Suresh, P., Thapaliya, B., Sapkota, R., Farahdel, B., Fu, Z., Chen, J., Calhoun, V. D., & Liu, J. (2024) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2024.1390409 · Cited by 8
Power and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt, M., Tejavibulya, L., Sun, H., Camp, C., Khaitova, M., Adkinson, B. D., Jiang, R., Westwater, M. L., Noble, S., & Scheinost, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01931-7 · Cited by 26
“26–28).”
InfluentialReplication and Refinement of Brain Age Model for Adolescent Development
Ray, B., Chen, J., Fu, Z., Suresh, P., Thapaliya, B., Farahdel, B., Calhoun, V. D., & Liu, J. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635532 · Cited by 11
Rai, B., Naylor, P. E., Sanchez, M. S., Wintermark, M., Raman, M. M., Jo, B., Reiss, A. R., & Green, T. (2023) · Research Square · doi:10.21203/rs.3.rs-2580911/v1 · Cited by 10
Sato, J. R., Biazoli, C. E., Zugman, A., Pan, P. M., Bueno, A. P. A., Moura, L. M., Gadelha, A., Picon, F. A., Amaro, E., Salum, G. A., et al. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15435 · Cited by 17
Mollon, J., Knowles, E. E. M., Mathias, S. R., Rodrigue, A., Moore, T. M., Calkins, M. E., Gur, R. C., Peralta, J. M., Weiner, D. J., Robinson, E., et al. (2021) · European Psychiatry · doi:10.1192/j.eurpsy.2021.21 · Cited by 11
Self-citationYudien, M. A., Moore, T. M., Port, A. M., Ruparel, K., Gur, R., & Gur, R. (2019) · Psychological Assessment · doi:10.1037/pas0000738 · Cited by 10
“…et al., 2015), clinical psychopathology was assessed using a modified version of the KIDDIE-SADS described in Calkins et al. (2014), census-based measures of SES are described in Moore et al. (2016), and neuroimaging in this cohort has been described extensively (Satterthwaite et al., 2014, 2016).”
MethodSelf-citationHegarty, C. E., Jolles, D., Mennigen, E., Jalbrzikowski, M., Bearden, C., & Karlsgodt, K. (2018) · bioRxiv · doi:10.1101/423574 · Cited by 10
“The PNC protocol divides a 64-direction diffusion-weighted imaging set into two independent 32-direction sequences (b value: 1000 s/mm2) [see Supplement and (36) for Hegarty et al.”
BackgroundMethodInfluentialErus, G., Habes, M., & Davatzikos, C. A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00021-1 · Cited by 11
MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY
Royer, J., Kebets, V., Piguet, C., Chen, J.-Z., Ooi, L., Kirschner, M., Siffredi, V., Mišić, B., Yeo, B. T. T., & Bernhardt, B. (2024) · bioRxiv · doi:10.1101/2023.03.02.530821 · Cited by 9
“Despite increasing availability of multimodal datasets (Casey et al., 2018; Jernigan et al., 2016; Miller et al., 2016; Royer et al., 2022; Satterthwaite et al., 2016; Thompson et al., 2014; Van Essen et al., 2013), combined assessments of structural and functional substrates of psychopathology…”
BackgroundA sensorimotor-association axis of thalamocortical connection developmentPreprint
Sydnor, V., Bagautdinova, J., Larsen, B., Arcaro, M., Barch, D., Bassett, D. S., Alexander-Bloch, A., Cook, P. A., Covitz, S., Franco, A., et al. (2024) · bioRxiv · doi:10.1101/2024.06.13.598749 · Cited by 9
“Initial exclusion criteria for the PNC were minimal and included inability to engage in psychiatric and cognitive phenotyping, impaired vision or hearing, and medical problems that could impact brain function (described in detail in Satterthwaite et al., 2016).”
BackgroundSelf-citationNetwork enrichment significance testing in brain–phenotype association studies
Weinstein, S. M., Vandekar, S., Li, B., Alexander-Bloch, A., Raznahan, A., Li, M.-Y., Gur, R. E., Gur, R., Roalf, D., Park, M. T. M., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26714 · Cited by 9
“…the Philadel-phia Neurodevelopmental Cohort (PNC), a large-scale study of children, adolescents, and young adults between 8 and 21 years of age (Satterthwaite et al., 2016 summarized in Appendix A. Utilizing the same data from our earlier work (Weinstein et al., 2021), data were processed…”
MethodSelf-citationGottschewsky, N., Kraft, D., & Kaufmann, T. (2024) · Biology of Sex Differences · doi:10.1186/s13293-024-00604-4 · Cited by 15
“PNC: We used data from N = 786 female participants (mean age = 15.25 years, SD = 3.65) of the Philadelphia Neurodevelopmental Cohort (PNC; [29]) as an independent training sample to derive an age prediction model.”
MethodInfluentialNetwork Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction
He, L., Wang, S., Chen, C., Wang, Y., Fan, Q., Chu, C., Fan, L., & Xu, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70239 · Cited by 9
“…2018), the enhanced Nathan Kline Institute (NKI; Nooner et al. 2012), Consortium for Reliability and Reproducibility (CoRR), the UK Biobank (UBK; https:// www. ukbio bank. ac. uk/ ), the Philadelphia Neurodevelopmental Cohort (PNC; Satterthwaite et al. 2016), and the 973 dataset (in-house data).”
Rodriguez, R. X., Noble, S., Tejavibulya, L., & Scheinost, D. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119742 · Cited by 10
Reliability and Validity of Bifactor Models of Dimensional Psychopathology in Youth
Hoffmann, M., Moore, T. M., Axelrud, L. K., Tottenham, N., Zuo, X.-L., Rohde, L. A., Milham, M., Satterthwaite, T., & Salum, G. (2021) · medRxiv · doi:10.1101/2021.06.27.21259601 · Cited by 9
“The studies composing the RBC are the Philadelphia Neurodevelopmental Cohort (PNC) (Satterthwaite et al., 2016), the Brazilian High-Risk Cohort Study for Mental Conditions (BHRCS) (Salum et al., 2015), the Healthy Brain Network (HBN) (Alexander et al., 2017), the Nathan Kline Institute-Rockland…”
Self-citationAshraf, G. M., Ebada, M. A., Suhail, M., Ali, A., Uddin, M. S., Bilgrami, A. L., Perveen, A., Husain, A., Tarique, M., Hafeez, A., et al. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/4572471 · Cited by 11
“A Philadelphia Neurodevelopment Cohort study based on brain imaging between 8 and 22 years has explained developmental sex differences [31].”
Automated Analysis of Low-Field Brain MRI in Cerebral Malaria
Tu, D., Goyal, M., Dworkin, J. D., Kampondeni, S., Vidal, L., Biondo-Savin, E., Juvvadi, S., Raghavan, P., Nicholas, J. M., Chetcuti, K., et al. (2020) · bioRxiv · doi:10.1111/biom.13708 · Cited by 9
“Our atlas set comprises a sample of 12 participants who were imaged in a high-field 3T scanner as part of the study-specific atlas in the Philadelphia Neurodevelopmental Cohort (Satterthwaite et al., 2016).”
Self-citationExamining Brain Maturation during Adolescence Using Graph Laplacian Learning Based Fourier Transform
Wang, J.-Q., Xiao, L., Wilson, T., Stephen, J., Calhoun, V., & Wang, Y.-P. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108649 · Cited by 9
“In our work, we applied our method on two types of task-based fMRI data from PNC [26] to understand how brain functional networks activated by cognitive tasks mature across development.”
MethodInfluentialResolution-based Spectral Clustering for Brain Parcellation using Functional MRI
Dillon, K., & Wang, Y.-P. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108628 · Cited by 9
“For more details on the imaging acquisition refer to Table 1 in [42].”
BackgroundMethodInfluentialJalbrzikowski, M., Liu, F., Foran, W., Roeder, K., Devlin, B., & Luna, B. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz053 · Cited by 9
Network Enrichment Significance Testing in Brain-Phenotype Association Studies
Weinstein, S. M., Vandekar, S., Alexander-Bloch, A., Raznahan, A., Li, M.-Y., Gur, R. E., Gur, R., Roalf, D., Park, M. T. M., Chakravarty, M., et al. (2023) · bioRxiv · doi:10.1101/2023.11.10.566593 · Cited by 8
“We evaluate the performance of NEST using data from the Philadelphia Neurodevelopmental Cohort (PNC), a large-scale study of children, adolescents, and young adults between 8 and 21 years of age (Satterthwaite et al. 2016).”
MethodSelf-citationRescuing missing data in connectome-based predictive modeling
Liang, Q.-H., Jiang, R.-T., Adkinson, B., Rosenblatt, M., Mehta, S., Foster, M. L., Dong, S.-Y., You, C., Negahban, S. N., Zhou, H. H., et al. (2023) · bioRxiv · doi:10.1101/2023.06.09.544392 · Cited by 8
“Robust matrix completion, which represents the original high-dimensional data using low-rank approximation, ranks highest among all tested methods in PNC and second in HCP and CNP for predicting fluid intelligence.”
BackgroundInfluentialSpatially-enhanced clusterwise inference for testing and localizing intermodal correspondence
Weinstein, S. M., Vandekar, S., Baller, E. B., Tu, D., Adebimpe, A., Tapera, T. M., Gur, R. C., Gur, R. E., Detre, J. A., Raznahan, A., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119712 · Cited by 6
Self-citationĆirić, R., Lorenz, R., Thompson, W. H., Goncalves, M., MacNicol, E., Markiewicz, C. J., Halchenko, Y. O., Ghosh, S., Gorgolewski, K. J., Poldrack, R. A., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-264855/v1 · Cited by 3
Control of brain network dynamics across diverse scales of space and time
Tang, E., Ju, H., Baum, G. L., Roalf, D. R., Satterthwaite, T. D., Pasqualetti, F., & Bassett, D. S. (2020) · Physical review. E · doi:10.1103/physreve.101.062301 · Cited by 20
Self-citationFlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI
Wang, M., Ye, W.-H., Xia, J.-F., Xu, M., Wen, H.-K., & Liu, Q.-Y. (2026) · European Conference on Computer Vision · doi:10.48550/arxiv.2606.11500 · Cited by 7
“Mainstream public datasets, for instance, exhibit spatial resolutions varying significantly from 2 mm to 4 mm , with temporal sampling rates ranging drastically from 720 ms to over 3000 ms [6, 8, 23,27].”
Kuenzel, E., Al‐Saoud, S., Fang, M., & Duerden, E. G. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02890-z · Cited by 8
“Similarly, the ventral striatum, which plays a key role in reward processing and motivation, can be affected by early adversity, potentially leading to impaired reward responsiveness and increased risk for childhood psychopa - thologies such as ADHD (Marsh et al. 2009; Satterthwaite et al. 2016 ).”
Dragendorf, E., Bültmann, E., & Wolff, D. (2024) · Frontiers Neuroinformatics · doi:10.3389/fninf.2024.1496143 · Cited by 7
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
