Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Open accessAbstract
The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale, NIMH funded initiative to understand how brain maturation mediates cognitive development and vulnerability to psychiatric illness, and understand how genetics impacts this process. As part of this study, 1445 adolescents ages 8-21 at enrollment underwent multimodal neuroimaging. Here, we highlight the conceptual basis for the effort, the study design, and the measures available in the dataset. We focus on neuroimaging measures obtained, including T1-weighted structural neuroimaging, diffusion tensor imaging, perfusion neuroimaging using arterial spin labeling, functional imaging tasks of working memory and emotion identification, and resting state imaging of functional connectivity. Furthermore, we provide characteristics regarding the final sample acquired. Finally, we describe mechanisms in place for data sharing that will allow the PNC to become a freely available public resource to advance our understanding of normal and pathological brain development.
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“In order to evaluate the multi-site segmentation performance of our method we created a large multi-study dataset of clinically normal subjects by pooling data from studies including BLSA (Resnick et al., 2000, 2003), ADNI-1 (Jack et al., 2008), PNC (Satterthwaite et al., 2014), BBL-NC 7 , and NiCK (Hartung et al., 20…”
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Self-citationFunctional maturation of the executive system during adolescence
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Self-citationIndividual Variation in Functional Topography of Association Networks in Youth
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Self-citationNormative brain size variation and brain shape diversity in humans
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Self-citationImaging Patterns of Brain Development and their Relationship to Cognition
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Self-citationCommon genetic variation influencing human white matter microstructure
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Impact of puberty on the evolution of cerebral perfusion during adolescence
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Self-citationData leakage inflates prediction performance in connectome-based machine learning models
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“Examples of re-identification, such as highly accurate identification via facial reconstruction (Schwarz et al., 2019) and machine learning identification from generative models (Rocher, Hendrickx, & de Montjoye, 2019) challenge the technical and legal adequacy of the de-identification…”
MethodBrain Imaging Genomics: Integrated Analysis and Machine Learning
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Self-citationCommon variants contribute to intrinsic human brain functional networks
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The modular organization of human anatomical brain networks: Accounting for the cost of wiring
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Self-citationEstablishing a link between sex-related differences in the structural connectome and behaviour
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Self-citationAssociations between Neighborhood SES and Functional Brain Network Development
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Self-citationStructural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms
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Self-citationModeling and interpreting mesoscale network dynamics
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Development of Hippocampal-Prefrontal Cortex Interactions through Adolescence.
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Self-citationEvidence for dissociable linkage of dimensions of psychopathology to brain structure in youths
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Self-citationExecutive Function Capacities, Negative Driving Behavior and Crashes in Young Drivers
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Self-citationMulti-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data
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Pitfalls in brain age analyses
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Self-citationRegional Variations in Brain Gyrification Are Associated with General Cognitive Ability in Humans
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Self-citationIdentifying Sparse Connectivity Patterns in the brain using resting-state fMRI
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Self-citationFunctional Neuroimaging Abnormalities in Youth With Psychosis Spectrum Symptoms
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Self-citationEvolution of brain network dynamics in neurodevelopment
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Self-citationCortical Thickness and Subcortical Gray Matter Volume in Pediatric Anxiety Disorders
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Self-citationSex Differences in the Effect of Puberty on Hippocampal Morphology
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Self-citationElevated Amygdala Perfusion Mediates Developmental Sex Differences in Trait Anxiety
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Self-citationXCP-D: A robust pipeline for the post-processing of fMRI data
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Self-citationSleep Spindles and Intelligence: Evidence for a Sexual Dimorphism
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Neuroanatomical morphometric characterization of sex differences in youth using statistical learning
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Age effects on the default mode and control networks in typically developing children
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The Dual Mechanisms of Cognitive Control Project
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Self-citationSex differences in network controllability as a predictor of executive function in youth
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Self-citationLydon-Staley, D., Ciric, R., Satterthwaite, T., & Bassett, D. (2019) · Network Neuroscience · doi:10.1162/netn_a_00071 · Cited by 71 · see citing works
Self-citationDissociable multi-scale patterns of development in personalized brain networks
Pines, A. R., Larsen, B., Cui, Z., Sydnor, V. J., Bertolero, M. A., Adebimpe, A., Alexander‐Bloch, A. F., Davatzikos, C. A., Fair, D. A., Gur, R. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30244-4 · Cited by 79 · see citing works
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Self-citation
- Authors
- Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., Mentch, F. D., Sleiman, P., Verma, R., Davatzikos, C., Hakonarson, H., Gur, R. C., & Gur, R. E.
- Affiliations
- University of Pennsylvania (US)
- Children's Hospital of Philadelphia (US)
- Philadelphia VA Medical Center (US)
- Corresponding author
- Satterthwaite, Theodore D.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 86
- Pages
- 544-553
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3947233
- OpenAlex
- W2069088601
- Cited by
- 504
- References
- 64
- Citations by year
- 2026: 21 · 2025: 28 · 2024: 39 · 2023: 50 · 2022: 49 · 2021: 51 · 2020: 49 · 2019: 58 · 2018: 43 · 2017: 44 · 2016: 24 · 2015: 27 · 2014: 19 · 2013: 2
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Funders
- Brain and Behavior Research Foundation (K08MH79364)
- Children's Hospital of Philadelphia
- National Institute of Mental Health (MH089924, T32 MH019112, MH089983, K23MH098130)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- Philadelphia Neurodevelopmental Cohort, brain development, brain maturation, cognitive development, psychiatric vulnerability, adolescents, T1-weighted structural MRI, diffusion tensor imaging, arterial spin labeling, perfusion imaging, working memory, emotion recognition, resting-state functional connectivity, multimodal neuroimaging, data sharing
- MeSH terms
- Adolescent, Adult, Brain, Child, Cognition, Female, Humans, Research Design, Philadelphia, Cohort Studies, Young Adult, Neuroimaging, Multimodal Imaging
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