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The contributions of brain structural and functional variance in predicting age | |
Ning-Xuan Chen1,2,3,4![]() ![]() ![]() ![]() | |
第一作者 | Ning-Xuan Chen |
通讯作者邮箱 | michael.milham@childmind.org (m.p. milham) ; lusuwcums@hotmail.com (s. lui) ; ycg.yan@gmail.com (c.-g. yan) |
心理所单位排序 | 1 |
摘要 | Structural and functional neuroimaging have been widely used to track and predict demographic and clinical variables, including treatment outcomes. However, it is challenging to establish and compare the respective weights and contributions of brain structure and function in prediction studies. The present study aimed to directly investigate the respective roles of brain structural and functional indices, along with their contributions to the prediction of demographic variables (age/sex) and clinical changes in schizophrenia patients. The present study enrolled 492 healthy people from the Southwest University Adult Lifespan Dataset (SALD) for demographic variable analysis and 39 patients with schizophrenia from the West China Hospital for treatment analysis. We conducted a model fit test with two variables (one voxel-based structural metric and another voxel-based functional metric) and then performed variance partitioning on the voxels that could be predicted sufficiently. Permutation tests were applied to compare the difference in contribution between each pair of structural and functional measurements. We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. Therefore, through variance partitioning, we could clearly and directly explore and compare the voxel-based structural and functional indices with respect to particular variables. In sum, for the variables reflecting long-term changes (age) and constant biological features (sex), the voxel-based structural metrics would contribute more than voxelbased functional metrics, but for the variable reflecting short-term changes (schizophrenia treatment), the functional metrics could contribute more. |
2021 | |
语种 | 英语 |
DOI | ttps:10.1016/j.ynirp.2021.100024 |
发表期刊 | Neuroimage: Reports
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期刊论文类型 | 实证研究 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/40713 |
专题 | 中国科学院行为科学重点实验室 |
作者单位 | 1.CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China 2.Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China 3.International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China 4.Department of Psychology, University of Chinese Academy of Sciences, Beijing, China e Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer 5.Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China 6.Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, 100083, China 7.MATTER Lab, Child Mind Institute, New York, NY, 10022, USA 8.Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA 9.Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China |
第一作者单位 | 中国科学院行为科学重点实验室; 中国科学院心理研究所 |
推荐引用方式 GB/T 7714 | Ning-Xuan Chen,Gui Fu,Xiao Chen,et al. The contributions of brain structural and functional variance in predicting age[J]. Neuroimage: Reports,2021. |
APA | Ning-Xuan Chen.,Gui Fu.,Xiao Chen.,Le Li.,Michael P. Milham.,...&Chao-Gan Yan.(2021).The contributions of brain structural and functional variance in predicting age.Neuroimage: Reports. |
MLA | Ning-Xuan Chen,et al."The contributions of brain structural and functional variance in predicting age".Neuroimage: Reports (2021). |
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