Statistical testing and power analysis for brain-wide association study
文献类型：期刊论文
| 作者 | Gong, Weikang3,4; Grunewald, Stefan3,4; Wan, Lin4; Ma, Liang4; Wan, Lin5; Lu, Wenlian2,6,7; Feng, Jianfeng2,6,7; Cheng, Fan2,7; Cheng, Wei2; Ma, Liang8 |
| 刊名 | MEDICAL IMAGE ANALYSIS
 |
| 出版日期 | 2018 |
| 卷号 | 47期号:-页码:15-30 |
| 关键词 | Brain-wide association study Random field theory Functional connectivity Statistical power |
| ISSN号 | 1361-8415 |
| DOI | 10.1016/j.media.2018.03.014 |
| 文献子类 | Article |
| 英文摘要 | The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. (C) 2018 Elsevier B.V. All rights reserved. |
| 学科主题 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS关键词 | FALSE DISCOVERY RATE ; GAUSSIAN RANDOM-FIELDS ; ORBITOFRONTAL CORTEX ; FUNCTIONAL MRI ; SAMPLE-SIZE ; FMRI ; CONNECTIVITY ; MODEL ; INFERENCE ; NETWORKS |
| 语种 | 英语 |
| WOS记录号 | WOS:000437390100002 |
| 出版者 | ELSEVIER SCIENCE BV |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/730]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England, 2.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China; 3.Chinese Acad Sci, CAS MPG Partner Inst Computat Biol, Shanghai Inst Biol Sci, Key Lab Computat Biol, Shanghai 200031, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Acad Math & Syst Sci, LSC, NCMIS, Beijing 100190, Peoples R China; 6.Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China; 7.Fudan Univ, Shanghai Ctr Math Sci, Shanghai 200433, Peoples R China; 8.Chinese Acad Sci, Beijing Inst Genom, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Gong, Weikang,Grunewald, Stefan,Wan, Lin,et al. Statistical testing and power analysis for brain-wide association study[J]. MEDICAL IMAGE ANALYSIS,2018,47(-):15-30. |
| APA | Gong, Weikang.,Grunewald, Stefan.,Wan, Lin.,Ma, Liang.,Wan, Lin.,...&,.(2018).Statistical testing and power analysis for brain-wide association study.MEDICAL IMAGE ANALYSIS,47(-),15-30. |
| MLA | Gong, Weikang,et al."Statistical testing and power analysis for brain-wide association study".MEDICAL IMAGE ANALYSIS 47.-(2018):15-30. |
入库方式： OAI收割
来源：上海营养与健康研究所
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