• 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

基于 UNC/UMN 婴儿连接组计划 (BCP) 队列的 4D 婴儿脑容量图谱。

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort.

机构信息

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.

出版信息

Neuroimage. 2022 Jun;253:119097. doi: 10.1016/j.neuroimage.2022.119097. Epub 2022 Mar 14.

DOI:10.1016/j.neuroimage.2022.119097
PMID:35301130
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9155180/
Abstract

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

摘要

时空(四维)婴儿专用脑图谱对于早期动态脑发育的神经影像学分析至关重要。然而,由于婴儿脑磁共振成像采集和处理方面存在重大技术挑战,密集覆盖婴儿期动态脑发育的 4D 图谱仍然稀缺。现有的少数图谱通常存在组织对比度模糊和时空分辨率低的问题,导致基于图谱的归一化和后续分析的准确性降低。为了解决这个问题,在本文中,我们基于 UNC/UMN 婴儿连接组计划(BCP)数据集构建了一个用于婴儿大脑的 4D 结构磁共振成像图谱,该图谱具有高空间分辨率、广泛的年龄范围覆盖和密集采样的时间点。具体来说,我们利用了 240 名正常发育婴儿的 542 个纵向 T1w 和 T2w 扫描,年龄范围从出生到 26 个月。为了提高婴儿脑图像的配准精度,我们采用了最先进的配准方法,并利用我们生成的可靠的脑组织概率图,除了强度图像外,还提高了个体图像的对齐度。为了在婴儿和成人脑图像上实现一致的区域标记,以便在不同年龄进行基于区域的分析,我们通过按年龄递减的映射方式将广泛使用的 Desikan 皮质分区映射到我们的图谱上。同时,手动勾勒出典型的皮质下结构,以促进与皮质下相关的研究。与现有的婴儿脑图谱相比,我们的 4D 图谱具有更高的时空分辨率,保留了更多的结构细节,因此可以提高婴儿期神经发育分析的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/081fa562d093/nihms-1809167-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/a886ad01f2ce/nihms-1809167-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/ab5587ebc9e8/nihms-1809167-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/936e4cdd80e3/nihms-1809167-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/9c10f5bc7dc2/nihms-1809167-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/4f154e332ee1/nihms-1809167-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/e8e5291a5dc0/nihms-1809167-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/e8bd5d12bd1d/nihms-1809167-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/f7f16a16c046/nihms-1809167-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/081fa562d093/nihms-1809167-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/a886ad01f2ce/nihms-1809167-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/ab5587ebc9e8/nihms-1809167-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/936e4cdd80e3/nihms-1809167-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/9c10f5bc7dc2/nihms-1809167-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/4f154e332ee1/nihms-1809167-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/e8e5291a5dc0/nihms-1809167-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/e8bd5d12bd1d/nihms-1809167-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/f7f16a16c046/nihms-1809167-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/9155180/081fa562d093/nihms-1809167-f0010.jpg

相似文献

1
A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort.基于 UNC/UMN 婴儿连接组计划 (BCP) 队列的 4D 婴儿脑容量图谱。
Neuroimage. 2022 Jun;253:119097. doi: 10.1016/j.neuroimage.2022.119097. Epub 2022 Mar 14.
2
Construction of 4D high-definition cortical surface atlases of infants: Methods and applications.婴儿4D高清皮质表面图谱的构建:方法与应用
Med Image Anal. 2015 Oct;25(1):22-36. doi: 10.1016/j.media.2015.04.005. Epub 2015 Apr 17.
3
Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains.用于发育中婴儿大脑的一致时空纵向图谱构建
IEEE Trans Med Imaging. 2016 Dec;35(12):2568-2577. doi: 10.1109/TMI.2016.2587628. Epub 2016 Jul 7.
4
Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation.基于球面补丁的组稀疏表示构建具有锐利折叠模式的 4D 婴儿皮质表面图谱。
Hum Brain Mapp. 2019 Sep;40(13):3860-3880. doi: 10.1002/hbm.24636. Epub 2019 May 21.
5
The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.北卡罗来纳大学教堂山分校/明尼苏达大学双城分校婴儿连接组计划(BCP):研究设计和方案制定概述。
Neuroimage. 2019 Jan 15;185:891-905. doi: 10.1016/j.neuroimage.2018.03.049. Epub 2018 Mar 22.
6
HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease.HybraPD 图谱:使用多模态医学图像对帕金森病患者的皮质下核进行精确分割。
Hum Brain Mapp. 2021 Sep;42(13):4399-4421. doi: 10.1002/hbm.25556. Epub 2021 Jun 8.
7
Stereotaxic Magnetic Resonance Imaging Brain Atlases for Infants from 3 to 12 Months.3至12个月婴儿的立体定向磁共振成像脑图谱
Dev Neurosci. 2015;37(6):515-32. doi: 10.1159/000438749. Epub 2015 Oct 7.
8
Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.使用无监督球面网络学习4D婴儿皮质表面图谱。
Med Image Comput Comput Assist Interv. 2021 Sep-Oct;12902:262-272. doi: 10.1007/978-3-030-87196-3_25. Epub 2021 Sep 21.
9
Detail-preserving construction of neonatal brain atlases in space-frequency domain.在空间频率域中进行保留细节的新生儿脑图谱构建。
Hum Brain Mapp. 2016 Jun;37(6):2133-50. doi: 10.1002/hbm.23160. Epub 2016 Mar 14.
10
Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.利用谱功能网络学习构建精细时空新生儿功能图谱。
Hum Brain Mapp. 2024 Jun 1;45(8):e26718. doi: 10.1002/hbm.26718.

引用本文的文献

1
Pushing the boundaries of MEG based on optically pumped magnetometers towards early human life.基于光泵磁力计的脑磁图技术在早期人类生命研究领域的突破。
Imaging Neurosci (Camb). 2025 Mar 13;3. doi: 10.1162/imag_a_00489. eCollection 2025.
2
Assessing the Early Lateralization of White Matter in the Infant Language Network.评估婴儿语言网络中白质的早期偏侧化
Hum Brain Mapp. 2025 Aug 1;46(11):e70286. doi: 10.1002/hbm.70286.
3
Groupwise registration of infant brain diffusion tensor images using intermediate subgroup templates.

本文引用的文献

1
Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins.基于皮质折叠特征的发育中 singleton 婴儿和双胞胎的个体识别和个体变异性分析。
Hum Brain Mapp. 2020 Jun 1;41(8):1985-2003. doi: 10.1002/hbm.24924. Epub 2020 Jan 12.
2
FRNET: FLATTENED RESIDUAL NETWORK FOR INFANT MRI SKULL STRIPPING.FRNET:用于婴儿MRI颅骨剥离的扁平残差网络。
Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:999-1002. doi: 10.1109/ISBI.2019.8759167. Epub 2019 Jul 11.
3
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces.
使用中间子组模板对婴儿脑扩散张量图像进行逐组配准。
PLoS One. 2025 Jun 26;20(6):e0325844. doi: 10.1371/journal.pone.0325844. eCollection 2025.
4
Surface Expansion Regionalization of the Hippocampus in Early Brain Development.早期脑发育中海马体的表面扩展区域划分
bioRxiv. 2025 Feb 24:2025.02.22.639699. doi: 10.1101/2025.02.22.639699.
5
Deciphering Multiway Multiscale Brain Network Connectivity: Insights from Birth to 6 Months.解读多向多尺度脑网络连通性:从出生到6个月的见解
bioRxiv. 2025 Jan 27:2025.01.24.634772. doi: 10.1101/2025.01.24.634772.
6
Beyond motor learning: Insights from infant magnetic resonance imaging on the critical role of the cerebellum in behavioral development.超越运动学习:婴儿磁共振成像对小脑在行为发展中的关键作用的见解。
Dev Cogn Neurosci. 2025 Apr;72:101514. doi: 10.1016/j.dcn.2025.101514. Epub 2025 Jan 27.
7
A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases.一种适用于磁共振图像的、可推广至整个生命周期的颅骨剥离模型,该模型利用了脑图谱中的先验知识。
Nat Biomed Eng. 2025 May;9(5):700-715. doi: 10.1038/s41551-024-01337-w. Epub 2025 Jan 8.
8
A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images.一种用于对齐纵向婴儿脑张量图像的新型配准框架。
bioRxiv. 2024 Jul 16:2024.07.12.603305. doi: 10.1101/2024.07.12.603305.
9
Brain volume in infants with metopic synostosis: Less white matter volume with an accelerated growth pattern in early life.额缝早闭婴儿的脑容量:生命早期白质体积减少,生长模式加快。
J Anat. 2024 Dec;245(6):894-902. doi: 10.1111/joa.14028. Epub 2024 Feb 28.
10
Parenting Influences on Frontal Lobe Gray Matter and Preterm Toddlers' Problem-Solving Skills.养育方式对额叶灰质及早产幼儿解决问题能力的影响。
Children (Basel). 2024 Feb 6;11(2):206. doi: 10.3390/children11020206.
图像和曲面的概率微分同胚配准的无监督学习
Med Image Anal. 2019 Oct;57:226-236. doi: 10.1016/j.media.2019.07.006. Epub 2019 Jul 12.
4
Developmental topography of cortical thickness during infancy.婴儿期皮质厚度的发育拓扑
Proc Natl Acad Sci U S A. 2019 Aug 6;116(32):15855-15860. doi: 10.1073/pnas.1821523116. Epub 2019 Jul 22.
5
Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis.基于体积分析6个月大婴儿脑部MRI以识别自闭症生物标志物并进行早期诊断。
Med Image Comput Comput Assist Interv. 2018 Sep;11072:411-419. doi: 10.1007/978-3-030-00931-1_47. Epub 2018 Sep 13.
6
The impact of traditional neuroimaging methods on the spatial localization of cortical areas.传统神经影像学方法对皮质区空间定位的影响。
Proc Natl Acad Sci U S A. 2018 Jul 3;115(27):E6356-E6365. doi: 10.1073/pnas.1801582115. Epub 2018 Jun 20.
7
Population-averaged atlas of the macroscale human structural connectome and its network topology.人群平均的宏观人类结构连接组图谱及其网络拓扑。
Neuroimage. 2018 Sep;178:57-68. doi: 10.1016/j.neuroimage.2018.05.027. Epub 2018 May 24.
8
Baby brain atlases.婴儿脑图谱。
Neuroimage. 2019 Jan 15;185:865-880. doi: 10.1016/j.neuroimage.2018.04.003. Epub 2018 Apr 3.
9
The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.北卡罗来纳大学教堂山分校/明尼苏达大学双城分校婴儿连接组计划(BCP):研究设计和方案制定概述。
Neuroimage. 2019 Jan 15;185:891-905. doi: 10.1016/j.neuroimage.2018.03.049. Epub 2018 Mar 22.
10
Computational neuroanatomy of baby brains: A review.婴儿大脑的计算神经解剖学:综述。
Neuroimage. 2019 Jan 15;185:906-925. doi: 10.1016/j.neuroimage.2018.03.042. Epub 2018 Mar 21.