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Multiple Bases of Human Intelligence Revealed by Cortical Thickness and Neural Activation

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dc.contributor.authorChoi, Yu Yong-
dc.contributor.authorShamosh, Noah A.-
dc.contributor.authorCho, Sun Hee-
dc.contributor.authorDeYoung, Colin G.-
dc.contributor.authorLee, Min Joo-
dc.contributor.authorLee, Jong-Min-
dc.contributor.authorKim, Sun I.-
dc.contributor.authorCho, Zang-Hee-
dc.contributor.authorKim, Kyungjin-
dc.contributor.authorGray, Jeremy R.-
dc.contributor.authorLee, Kun Ho-
dc.date.accessioned2022-12-21T01:10:35Z-
dc.date.available2022-12-21T01:10:35Z-
dc.date.created2022-08-26-
dc.date.issued2008-10-
dc.identifier.issn0270-6474-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/177865-
dc.description.abstractWehypothesized that individual differences in intelligence (Spearman's g) are supported by multiple brain regions, and in particular that fluid (gF) and crystallized (gC) components of intelligence are related to brain function and structure with a distinct profile of association across brain regions. In 225 healthy young adults scanned with structural and functional magnetic resonance imaging sequences, regions of interest (ROIs) were defined on the basis of a correlation between g and either brain structure or brain function. In these ROIs, gC was more strongly related to structure (cortical thickness) than function, whereas gF was more strongly related to function (blood oxygenation level-dependent signal during reasoning) than structure. We further validated this finding by generating a neurometric prediction model of intelligence quotient (IQ) that explained 50% of variance in IQ in an independent sample. The data compel a nuanced view of the neurobiology of intelligence, providing the most persuasive evidence to date for theories emphasizing multiple distributed brain regions differing in function.-
dc.language영어-
dc.language.isoen-
dc.publisherSOC NEUROSCIENCE-
dc.titleMultiple Bases of Human Intelligence Revealed by Cortical Thickness and Neural Activation-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, Jong-Min-
dc.identifier.doi10.1523/JNEUROSCI.3259-08.2008-
dc.identifier.scopusid2-s2.0-55249113236-
dc.identifier.wosid000259912400014-
dc.identifier.bibliographicCitationJOURNAL OF NEUROSCIENCE, v.28, no.41, pp.10323 - 10329-
dc.relation.isPartOfJOURNAL OF NEUROSCIENCE-
dc.citation.titleJOURNAL OF NEUROSCIENCE-
dc.citation.volume28-
dc.citation.number41-
dc.citation.startPage10323-
dc.citation.endPage10329-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaNeurosciences & Neurology-
dc.relation.journalWebOfScienceCategoryNeurosciences-
dc.subject.keywordPlusVOXEL-BASED MORPHOMETRY-
dc.subject.keywordPlusFLUID INTELLIGENCE-
dc.subject.keywordPlusPREFRONTAL CORTEX-
dc.subject.keywordPlusBRAIN-
dc.subject.keywordPlusMEMORY-
dc.subject.keywordPlusLOBE-
dc.subject.keywordPlusMECHANISMS-
dc.subject.keywordPlusDEMENTIA-
dc.subject.keywordPlusIQ-
dc.subject.keywordAuthorgeneral intelligence-
dc.subject.keywordAuthorcrystallized intelligence-
dc.subject.keywordAuthorfluid intelligence-
dc.subject.keywordAuthorIQ-
dc.subject.keywordAuthorcortical thickness-
dc.subject.keywordAuthorfMRI-
dc.identifier.urlhttps://www.jneurosci.org/content/28/41/10323-
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