• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

费城神经发育队列的神经影像学研究。

Neuroimaging of the Philadelphia neurodevelopmental cohort.

机构信息

Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

出版信息

Neuroimage. 2014 Feb 1;86:544-53. doi: 10.1016/j.neuroimage.2013.07.064. Epub 2013 Aug 3.

DOI:10.1016/j.neuroimage.2013.07.064
PMID:23921101
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3947233/
Abstract

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.

摘要

费城神经发育队列(PNC)是一个大规模的、由 NIMH 资助的项目,旨在了解大脑成熟如何介导认知发展和精神疾病易感性,并了解遗传如何影响这一过程。作为该研究的一部分,1445 名 8-21 岁的青少年在入组时接受了多模态神经影像学检查。在这里,我们重点介绍了该研究的概念基础、研究设计以及数据集提供的测量方法。我们关注的神经影像学测量包括 T1 加权结构神经影像学、弥散张量成像、动脉自旋标记灌注神经影像学、工作记忆和情绪识别功能成像任务以及功能连接的静息态成像。此外,我们提供了关于最终获得的样本的特征。最后,我们描述了数据共享的机制,这将使 PNC 成为一个免费的公共资源,以推进我们对正常和病理性大脑发育的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/0e1a5cc1b481/nihms512692f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/124b603f359a/nihms512692f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/540b87f6ee25/nihms512692f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/8ff193997931/nihms512692f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/521d9acef7b0/nihms512692f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/f90d2910e3dc/nihms512692f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/0e1a5cc1b481/nihms512692f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/124b603f359a/nihms512692f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/540b87f6ee25/nihms512692f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/8ff193997931/nihms512692f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/521d9acef7b0/nihms512692f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/f90d2910e3dc/nihms512692f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde1/3947233/0e1a5cc1b481/nihms512692f6.jpg

相似文献

1
Neuroimaging of the Philadelphia neurodevelopmental cohort.费城神经发育队列的神经影像学研究。
Neuroimage. 2014 Feb 1;86:544-53. doi: 10.1016/j.neuroimage.2013.07.064. Epub 2013 Aug 3.
2
The Lifespan Human Connectome Project in Aging: An overview.衰老中的全生命周期人类连接组计划:概述。
Neuroimage. 2019 Jan 15;185:335-348. doi: 10.1016/j.neuroimage.2018.10.009. Epub 2018 Oct 15.
3
A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development.一种用于研究脑连接组发育的多模态多层次神经成像模型。
J Am Stat Assoc. 2022;117(539):1134-1148. doi: 10.1080/01621459.2022.2055559. Epub 2022 Apr 25.
4
The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth.费城神经发育队列研究:一个可公开获取的资源,用于研究青少年正常和异常的大脑发育。
Neuroimage. 2016 Jan 1;124(Pt B):1115-1119. doi: 10.1016/j.neuroimage.2015.03.056. Epub 2015 Mar 31.
5
The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative.费城神经发育队列研究:构建一个深度表型协作项目。
J Child Psychol Psychiatry. 2015 Dec;56(12):1356-1369. doi: 10.1111/jcpp.12416. Epub 2015 Apr 8.
6
Sex differences in brain and behavior in adolescence: Findings from the Philadelphia Neurodevelopmental Cohort.青少年大脑与行为的性别差异:来自费城神经发育队列研究的发现。
Neurosci Biobehav Rev. 2016 Nov;70:159-170. doi: 10.1016/j.neubiorev.2016.07.035. Epub 2016 Aug 3.
7
Neuroimaging young children and associations with neurocognitive development in a South African birth cohort study.对南非出生队列研究中的幼儿进行神经影像学研究及其与神经认知发育的相关性。
Neuroimage. 2020 Oct 1;219:116846. doi: 10.1016/j.neuroimage.2020.116846. Epub 2020 Apr 15.
8
The Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain.发育的时空调控基因组学(Dev-CoG)研究:一项关于发育中大脑的多模态研究。
Neuroimage. 2021 Jan 15;225:117438. doi: 10.1016/j.neuroimage.2020.117438. Epub 2020 Oct 8.
9
Predicting depression risk in early adolescence via multimodal brain imaging.通过多模态脑成像预测青少年早期的抑郁风险。
Neuroimage Clin. 2024;42:103604. doi: 10.1016/j.nicl.2024.103604. Epub 2024 Apr 8.
10
Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial.多模态磁共振成像用于早期糖尿病轻度认知障碍:一项前瞻性诊断试验的研究方案
BMC Med Imaging. 2016 Aug 24;16(1):50. doi: 10.1186/s12880-016-0152-x.

引用本文的文献

1
Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach.通过一种新型正则化盲源分离方法揭示动态功能连接组的隐藏来源。
Imaging Neurosci (Camb). 2024 Jul 12;2. doi: 10.1162/imag_a_00220. eCollection 2024.
2
XCP-D: A robust pipeline for the post-processing of fMRI data.XCP-D:一种用于功能磁共振成像(fMRI)数据后处理的强大流程。
Imaging Neurosci (Camb). 2024 Aug 13;2. doi: 10.1162/imag_a_00257. eCollection 2024.
3
A systematic protocol to identify "clinical controls" for pediatric neuroimaging research from clinically acquired brain MRIs.

本文引用的文献

1
Making data sharing count: a publication-based solution.让数据共享有意义:一种基于出版物的解决方案。
Front Neurosci. 2013 Feb 6;7:9. doi: 10.3389/fnins.2013.00009. eCollection 2013.
2
Making data sharing work: the FCP/INDI experience.实现数据共享:FCP/INDI 的经验。
Neuroimage. 2013 Nov 15;82:683-91. doi: 10.1016/j.neuroimage.2012.10.064. Epub 2012 Oct 30.
3
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry.NKI-Rockland 样本:加速精神病学发现科学步伐的模型。
一种从临床获取的脑部磁共振成像中识别儿科神经影像学研究“临床对照”的系统方案。
bioRxiv. 2025 Jul 1:2025.06.25.661530. doi: 10.1101/2025.06.25.661530.
4
Big Data, Small Bias: Harmonizing Diffusion MRI-Based Structural Connectomes to Mitigate Site-Related Bias in Data Integration.大数据,小偏差:协调基于扩散磁共振成像的结构连接组以减轻数据整合中与站点相关的偏差。
Hum Brain Mapp. 2025 Jun 15;46(9):e70256. doi: 10.1002/hbm.70256.
5
Effective workflow from multimodal MRI data to model-based prediction.从多模态磁共振成像数据到基于模型的预测的有效工作流程。
Sci Rep. 2025 Jun 20;15(1):20126. doi: 10.1038/s41598-025-04511-5.
6
Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling.基于个体化生物物理网络模型的青少年皮质兴奋-抑制比率成熟度
Sci Adv. 2025 Jun 6;11(23):eadr8164. doi: 10.1126/sciadv.adr8164. Epub 2025 Jun 4.
7
Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks.利用深度神经网络优化生物物理大规模脑回路模型
bioRxiv. 2025 Apr 7:2025.04.07.647497. doi: 10.1101/2025.04.07.647497.
8
Two Axes of White Matter Development.白质发育的两个轴
bioRxiv. 2025 Mar 20:2025.03.19.644049. doi: 10.1101/2025.03.19.644049.
9
Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health.可重复使用的脑图谱:用于绘制大脑发育及其与心理健康关联的开放数据资源。
bioRxiv. 2025 Feb 26:2025.02.24.639850. doi: 10.1101/2025.02.24.639850.
10
Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents.发育图谱:典型发育青少年功能性脑网络参考图谱。
Dev Cogn Neurosci. 2025 Apr;72:101523. doi: 10.1016/j.dcn.2025.101523. Epub 2025 Feb 7.
Front Neurosci. 2012 Oct 16;6:152. doi: 10.3389/fnins.2012.00152. eCollection 2012.
4
An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.一种改进的框架,用于在静息态功能连接数据预处理中进行混杂回归和滤波,以控制运动伪影。
Neuroimage. 2013 Jan 1;64:240-56. doi: 10.1016/j.neuroimage.2012.08.052. Epub 2012 Aug 25.
5
Neuroanatomical assessment of biological maturity.神经解剖学评估生物成熟度。
Curr Biol. 2012 Sep 25;22(18):1693-8. doi: 10.1016/j.cub.2012.07.002. Epub 2012 Aug 16.
6
The organization of the human striatum estimated by intrinsic functional connectivity.基于内在功能连通性估计的人类纹状体组织。
J Neurophysiol. 2012 Oct;108(8):2242-63. doi: 10.1152/jn.00270.2012. Epub 2012 Jul 25.
7
PyXNAT: XNAT in Python.PyXNAT:Python 版 XNAT。
Front Neuroinform. 2012 May 24;6:12. doi: 10.3389/fninf.2012.00012. eCollection 2012.
8
Common variants at 12q14 and 12q24 are associated with hippocampal volume.12q14 和 12q24 上的常见变异与海马体体积相关。
Nat Genet. 2012 Apr 15;44(5):545-51. doi: 10.1038/ng.2237.
9
Identification of common variants associated with human hippocampal and intracranial volumes.鉴定与人类海马体和颅内体积相关的常见变异。
Nat Genet. 2012 Apr 15;44(5):552-61. doi: 10.1038/ng.2250.
10
Being right is its own reward: load and performance related ventral striatum activation to correct responses during a working memory task in youth.正确本身就是一种回报:在青少年工作记忆任务中,与负荷和表现相关的腹侧纹状体对正确反应的激活。
Neuroimage. 2012 Jul 2;61(3):723-9. doi: 10.1016/j.neuroimage.2012.03.060. Epub 2012 Mar 29.