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Brain structure-function associations identified in large-scale neuroimaging data
Yang, Zhi1,2,3; Qiu, Jiang4; Wang, Peipei5,6; Liu, Rui1,2,7; Zuo, Xi-Nian1,2,4,8; Zhi Yang; Xi-Nian Zuo
First AuthorZhi Yang
2016-12-01
Source PublicationBRAIN STRUCTURE & FUNCTION
Correspondent Emailyangz@psych.ac.cn ; zuoxn@psych.ac.cn
ISSN1863-2653
SubtypeArticle
Volume221Issue:9Pages:4459-4474
QuartileQ1
Contribution Rank1
AbstractThe relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure-function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.
KeywordStructure-function Association Independent Component Analysis Data Mining Connectomics Multi-modal Integration
Subject Area生理心理学/生物心理学
DOI10.1007/s00429-015-1177-6
Indexed BySCI ; SSCI
Language英语
Funding OrganizationNational Basic Research Program (973 Program)(2015CB351702) ; Natural Science Foundation of China(81571756 ; Foundation of Beijing Key Laboratory of Mental Disorders(2014JSJB03) ; Chinese Academy of Sciences(KSZD-EW-TZ-002) ; Beijing Nova Program for Science and Technology(XXJH2015B079) ; Institute of Psychology, Chinese Academy of Sciences ; 81270023 ; 81278412 ; 81471740 ; 81220108014)
Project Intro.This study was supported through funding from the National Basic Research Program (973 Program: 2015CB351702 to XNZ), the Natural Science Foundation of China (81571756 and 81270023 to ZY, 81278412 to JQ, 81471740 and 81220108014 to XNZ), the Foundation of Beijing Key Laboratory of Mental Disorders (2014JSJB03 to ZY), the Key Research Program (KSZD-EW-TZ-002) and the Hundred Talents Program of the Chinese Academy of Sciences (to XNZ), the Beijing Nova Program for Science and Technology (XXJH2015B079 to ZY), and the Outstanding Young Investigator Award of Institute of Psychology, Chinese Academy of Sciences (to ZY).
WOS Research AreaAnatomy & Morphology ; Neurosciences & Neurology
WOS SubjectAnatomy & Morphology ; Neurosciences
WOS IDWOS:000387657200011
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
WOS KeywordRESTING-STATE FMRI ; HUMAN CEREBRAL-CORTEX ; INDEPENDENT COMPONENT ANALYSIS ; LOW-FREQUENCY FLUCTUATION ; SURFACE-BASED ANALYSIS ; CORTICAL THICKNESS ; 1ST-EPISODE SCHIZOPHRENIA ; CORRELATIONAL SELECTION ; PHENOTYPIC CORRELATIONS ; CONNECTIVITY NETWORKS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/20744
Collection中国科学院行为科学重点实验室
Corresponding AuthorZhi Yang; Xi-Nian Zuo
Affiliation1.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Lab Funct Connectome & Dev, 16 Lincui Rd, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, 16 Lincui Rd, Beijing 100101, Peoples R China
3.Shanghai Jiao Tong Univ, Sch Med, Shanghai Key Lab Psychot Disorders, Shanghai Mental Hlth Ctr, Beijing 100101, Peoples R China
4.Southwest Univ, Fac Psychol, Chongqing 400715, Peoples R China
5.Capital Med Univ, Beijing Inst Brain Disorders, Beijing 100069, Peoples R China
6.Capital Med Univ, Sch Basic Med Sci, Ctr Higher Brain Funct Res, Beijing 100069, Peoples R China
7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
8.Guangxi Teachers Educ Univ, Sch Educ Sci, Dept Psychol, Nanning 530001, Guangxi, Peoples R China
Recommended Citation
GB/T 7714
Yang, Zhi,Qiu, Jiang,Wang, Peipei,et al. Brain structure-function associations identified in large-scale neuroimaging data[J]. BRAIN STRUCTURE & FUNCTION,2016,221(9):4459-4474.
APA Yang, Zhi.,Qiu, Jiang.,Wang, Peipei.,Liu, Rui.,Zuo, Xi-Nian.,...&Xi-Nian Zuo.(2016).Brain structure-function associations identified in large-scale neuroimaging data.BRAIN STRUCTURE & FUNCTION,221(9),4459-4474.
MLA Yang, Zhi,et al."Brain structure-function associations identified in large-scale neuroimaging data".BRAIN STRUCTURE & FUNCTION 221.9(2016):4459-4474.
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