CASIA OpenIR  > 脑图谱与类脑智能实验室  > 脑网络组研究
Parcellation of the primary cerebral cortices based on local connectivity profiles
Li, Qiaojun1,2; Song, Ming1,2; Fan, Lingzhong1,2; Liu, Yong1,2; Jiang, Tianzi1,2,3,4
发表期刊FRONTIERS IN NEUROANATOMY
2015-04-09
卷号9期号:50页码:1-9
文章类型Article
摘要Connectivity-based parcellation using diffusion MRI has been extensively used to parcellate subcortical areas and the association cortex. Connectivity profiles are vital for connectivity-based parcellation. Two categories of connectivity profiles are generally utilized, including global connectivity profiles, in which the connectivity information is from the seed to the whole brain, and long connectivity profiles, in which the connectivity information is from the seed to other brain regions after excluding the seed. However, whether global or long connectivity profiles should be applied in parcellating the primary cortex utilizing connectivity-based parcellation is unclear. Many sources of evidence have indicated that the primary cerebral cortices are composed of structurally and functionally distinct subregions. Because the primary cerebral cortices are rich in local anatomic hierarchical connections and possess high degree of local functional connectivity profiles, we proposed that local connectivity profiles, that is the connectivity information within a seed region of interest, might be used for parcellating the primary cerebral cortices. In this study, the global, long, and local connectivity profiles were separately used to parcellate the bilateral M1, A1, S1, and V1. We found that results using the three profiles were all quite consistent with reported cytoarchitectonic evidence. More importantly, the results using local connectivity profiles showed less inter-subject variability than the results using the other two, a finding which suggests that local connectivity profiles are superior to global and long connectivity profiles for parcellating the primary cerebral cortices. This also implies that, depending on the characteristics of specific areas of the cerebral cortex, different connectivity profiles may need to be adopted to parcellate different areas.
关键词Primary Cerebral Cortices Connectivity-based Parcellation Local Connectivity Profiles Diffusion Tensor Imaging Tractography
WOS标题词Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]PRIMARY SOMATOSENSORY CORTEX ; PRIMARY AUDITORY-CORTEX ; PRIMARY MOTOR CORTEX ; TRACTOGRAPHY-BASED PARCELLATION ; PRIMARY VISUAL-CORTEX ; NEUROCOGNITIVE NETWORKS ; TONOTOPIC ORGANIZATION ; CINGULATE CORTEX ; MYELIN CONTENT ; AREAS
收录类别SCI
语种英语
WOS研究方向Anatomy & Morphology ; Neurosciences & Neurology
WOS类目Anatomy & Morphology ; Neurosciences
WOS记录号WOS:000352584500001
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8133
专题脑图谱与类脑智能实验室_脑网络组研究
作者单位1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci, Beijing 100190, Peoples R China
4.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia
第一作者单位中国科学院自动化研究所;  模式识别国家重点实验室
推荐引用方式
GB/T 7714
Li, Qiaojun,Song, Ming,Fan, Lingzhong,et al. Parcellation of the primary cerebral cortices based on local connectivity profiles[J]. FRONTIERS IN NEUROANATOMY,2015,9(50):1-9.
APA Li, Qiaojun,Song, Ming,Fan, Lingzhong,Liu, Yong,&Jiang, Tianzi.(2015).Parcellation of the primary cerebral cortices based on local connectivity profiles.FRONTIERS IN NEUROANATOMY,9(50),1-9.
MLA Li, Qiaojun,et al."Parcellation of the primary cerebral cortices based on local connectivity profiles".FRONTIERS IN NEUROANATOMY 9.50(2015):1-9.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
论文1.pdf(3605KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Li, Qiaojun]的文章
[Song, Ming]的文章
[Fan, Lingzhong]的文章
百度学术
百度学术中相似的文章
[Li, Qiaojun]的文章
[Song, Ming]的文章
[Fan, Lingzhong]的文章
必应学术
必应学术中相似的文章
[Li, Qiaojun]的文章
[Song, Ming]的文章
[Fan, Lingzhong]的文章
相关权益政策
暂无数据
收藏/分享
文件名: 论文1.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。