devCCNP

devCCNP generates a longitudinal, multimodal neuroimaging and phenotypic dataset to characterize brain-mind development in Chinese children and adolescents aged 6.0–17.9 years.


Key Features:

  • Longitudinal multimodal dataset: Multicohort longitudinal data collected from 2013 to 2022 encompassing neuroimaging and extensive phenotypic assessments across repeated visits (864 visits, 479 participants).
  • Age range: Enrollment includes typically developing children and adolescents aged 6.0–17.9 years.
  • Phenotypic assessments: Demographic, biophysical, psychological, behavioral, cognitive, affective, and ocular-tracking evaluations are included.
  • Neuroimaging modalities: Magnetic resonance imaging (MRI) measures include brain morphometry, resting-state function, naturalistic viewing function, and diffusion structure.
  • Normative growth references: Resource supports generation of normative brain growth curves for developmental population neuroscience.
  • Study design and validation: Employs multicohort longitudinal framework with rigorous experimental design, sampling strategies, technical validation, and reliability assessment.
  • Data repository: Data are associated with the Chinese Data-sharing Warehouse for In-vivo Imaging Brain within the CCNP Lifespan Brain-Mind Development Data Community hosted at the Science Data Bank (https://ccnp.scidb.cn).

Scientific Applications:

  • Developmental population neuroscience: Characterizing trajectories of brain-mind development in school-age children and adolescents.
  • Phenotype–brain relationships: Investigating associations among demographic, biophysical, psychological, behavioral, cognitive, affective, ocular-tracking measures and neuroimaging metrics.
  • Normative reference generation: Producing normative brain growth curves for comparative and clinical research.

Methodology:

Multicohort longitudinal framework with rigorous experimental design and sampling strategies, and procedures for technical validation and reliability; supports generation of normative brain growth curves.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Added:
11/30/2023
Last Updated:
11/24/2024

Operations

Data Inputs & Outputs

Quantification

Publications

Fan X, Wang Y, Chang D, Yang N, Rong M, Zhang Z, He Y, Hou X, Zhou Q, Gong Z, Cao L, Dong H, Nie J, Chen L, Zhang Q, Zhang J, Zhang L, Li H, Bao M, Chen A, Chen J, Chen X, Ding J, Dong X, Du Y, Feng C, Feng T, Fu X, Ge L, Hong B, Hu X, Huang W, Jiang C, Li L, Li Q, Li S, Liu X, Mo F, Qiu J, Su X, Wei G, Wu Y, Xia H, Yan C, Yan Z, Yang X, Zhang W, Zhao K, Zhu L, Zuo X, Zhu X, Hou X, Wang Y, Wang P, Zhang Y, Sui D, Xu T, Yang Z, Jiang L, Feng T, Chen A, Zhao K, Zhou Y, Zhuo Y, Zuo Z, Ke L, Wang F, Castellanos FX, Milham MP, Zang Y, Adamson C, Adler S, Alexander-Bloch AF, Anagnostou E, Anderson KM, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Bethlehem RAI, Beyer F, Bin Bae J, Blangero J, Cábez MB, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Bullmore ET, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi G, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Jones DT, Jones PB, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim K, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lerch J, Lewis JD, Li J, Liao W, Paz-Linares D, Liston C, Lombardo MV, Lv J, Mallard TT, Mathias SR, Marcelis M, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Misic B, Morgan SE, Mothersill D, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MTM, Paus T, Pausova Z, Binette AP, Pierce K, Qian X, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schöll M, Schultz AP, Seidlitz J, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Vértes PE, Victoria LW, Villeneuve S, Villringer A, Vogel JW, Wagstyl K, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, White SR, Witte AV, Yeo BTT, Yun HJ, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo X. A longitudinal resource for population neuroscience of school-age children and adolescents in China. Scientific Data. 2023;10(1). doi:10.1038/s41597-023-02377-8. PMID:37604823. PMCID:PMC10442366.

PMID: 37604823
Funding: - National Natural Science Foundation of China: 81220108014

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