| Brain Anatomical Network and Intelligence | |
Li, Yonghui1; Liu, Yong1 ; Li, Jun1,2; Qin, Wen3; Li, Kuncheng3; Yu, Chunshui3; Jiang, Tianzi1; Jiang T
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| 发表期刊 | PLOS COMPUTATIONAL BIOLOGY
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| 2009-05-01 | |
| 卷号 | 5期号:5页码:2016 |
| 文章类型 | Article |
| 摘要 | Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence. |
| 关键词 | Network |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 关键词[WOS] | WHITE-MATTER TRACTOGRAPHY ; DIFFUSION-WEIGHTED MRI ; SMALL-WORLD ; GENERAL INTELLIGENCE ; COMPLEX NETWORKS ; CEREBRAL-CORTEX ; GRAY-MATTER ; FUNCTIONAL CONNECTIVITY ; AXONAL PROJECTIONS ; FLUID INTELLIGENCE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| WOS类目 | Biochemical Research Methods ; Mathematical & Computational Biology |
| WOS记录号 | WOS:000267081300008 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/3106 |
| 专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
| 通讯作者 | Jiang T |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, LIAMA Ctr Computat Med, Beijing, Peoples R China 2.Beijing Normal Univ, Natl Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China 3.Capital Med Univ, Xuanwu Hosp, Dept Radiol, Beijing, Peoples R China |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Li, Yonghui,Liu, Yong,Li, Jun,et al. Brain Anatomical Network and Intelligence[J]. PLOS COMPUTATIONAL BIOLOGY,2009,5(5):2016. |
| APA | Li, Yonghui.,Liu, Yong.,Li, Jun.,Qin, Wen.,Li, Kuncheng.,...&Jiang T.(2009).Brain Anatomical Network and Intelligence.PLOS COMPUTATIONAL BIOLOGY,5(5),2016. |
| MLA | Li, Yonghui,et al."Brain Anatomical Network and Intelligence".PLOS COMPUTATIONAL BIOLOGY 5.5(2009):2016. |
| 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 | ||
| Li_PLosCB.pdf（1636KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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