Predicting brain age during typical and atypical development based on structural and functional neuroimaging
文献类型：期刊论文
| 作者 | Wang, Qi1,2,3 ; Hu, Ke1,2,3; Wang, Meng1,2,3; Zhao, Yuxin1,2,3; Liu, Yong4; Fan, Lingzhong1,2,3,5; Liu, Bing6,7
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| 刊名 | HUMAN BRAIN MAPPING
 |
| 出版日期 | 2021-09-14 |
| 页码 | 13 |
| 关键词 | autism spectrum disorder brain age prediction developmental heterogeneity multimodal magnetic resonance imaging predictor weights analysis |
| ISSN号 | 1065-9471 |
| DOI | 10.1002/hbm.25660 |
| 通讯作者 | Liu, Bing(bing.liu@bnu.edu.cn) |
| 英文摘要 | Exploring typical and atypical brain developmental trajectories is very important for understanding the normal pace of brain development and the mechanisms by which mental disorders deviate from normal development. A precise and sex-specific brain age prediction model is desirable for investigating the systematic deviation and individual heterogeneity of disorders associated with atypical brain development, such as autism spectrum disorders. In this study, we used partial least squares regression and the stacking algorithm to establish a sex-specific brain age prediction model based on T1-weighted structural magnetic resonance imaging and resting-state functional magnetic resonance imaging. The model showed good generalization and high robustness on four independent datasets with different ethnic information and age ranges. A predictor weights analysis showed the differences and similarities in changes in structure and function during brain development. At the group level, the brain age gap estimation for autistic patients was significantly smaller than that for healthy controls in both the ABIDE dataset and the healthy brain network dataset, which suggested that autistic patients as a whole exhibited the characteristics of delayed development. However, within the ABIDE dataset, the premature development group had significantly higher Autism Diagnostic Observation Schedule (ADOS) scores than those of the delayed development group, implying that individuals with premature development had greater severity. Using these findings, we built an accurate typical brain development trajectory and developed a method of atypical trajectory analysis that considers sex differences and individual heterogeneity. This strategy may provide valuable clues for understanding the relationship between brain development and mental disorders. |
| WOS关键词 | PARAHIPPOCAMPAL ; ORGANIZATION ; PREVALENCE ; DISORDERS ; NETWORK ; DISEASE |
| 资助项目 | National Natural Science Foundation of China[81771451] ; Beijing Normal University |
| WOS研究方向 | Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| WOS记录号 | WOS:000695613600001 |
| 出版者 | WILEY |
| 资助机构 | National Natural Science Foundation of China ; Beijing Normal University |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46010]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 通讯作者 | Liu, Bing |
| 作者单位 | 1.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing, Peoples R China 5.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China 6.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China 7.Chinese Inst Brain Res, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Qi,Hu, Ke,Wang, Meng,et al. Predicting brain age during typical and atypical development based on structural and functional neuroimaging[J]. HUMAN BRAIN MAPPING,2021:13. |
| APA | Wang, Qi.,Hu, Ke.,Wang, Meng.,Zhao, Yuxin.,Liu, Yong.,...&Liu, Bing.(2021).Predicting brain age during typical and atypical development based on structural and functional neuroimaging.HUMAN BRAIN MAPPING,13. |
| MLA | Wang, Qi,et al."Predicting brain age during typical and atypical development based on structural and functional neuroimaging".HUMAN BRAIN MAPPING (2021):13. |
入库方式： OAI收割
来源：自动化研究所
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