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Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion

文献类型：期刊论文

作者Wang, Yu-Wei1,2,3; Chen, Xiao1,2,3; Yan, Chao-Gan1,2,3,4
刊名NEUROIMAGE
出版日期2023-07-01
卷号274页码:22
ISSN号1053-8119
关键词Comparison Harmonization Multi-site pooling Resting-state fMRI
DOI10.1016/j.neuroimage.2023.120089
通讯作者Yan, Chao-Gan(yancg@psych.ac.cn)
英文摘要To embrace big-data neuroimaging, harmonizing the site effect in resting-state functional magnetic resonance imaging (R-fMRI) data fusion is a fundamental challenge. A comprehensive evaluation of potentially effective harmonization strategies, particularly with specifically collected data, has been scarce, especially for R-fMRI metrics. Here, we comprehensively assess harmonization strategies from multiple perspectives, including tests on residual site effect, individual identification, test-retest reliability, and replicability of group-level statisti-cal results, on widely used R-fMRI metrics across various datasets, including data obtained from participants with repetitive measures at different scanners. For individual identifiability (i.e., whether the same subject could be identified across R-fMRI data scanned across different sites), we found that, while most methods decreased site effects, the Subsampling Maximum-mean-distance based distribution shift correction Algorithm (SMA) and parametric unadjusted CovBat outperformed linear regression models, linear mixed models, ComBat series and invariant conditional variational auto-encoder in clustering accuracy. Test-retest reliability was better for SMA and parametric adjusted CovBat than unadjusted ComBat series and parametric unadjusted CovBat in the number of overlapped voxels. At the same time, SMA was superior to the latter in replicability in terms of the Dice coef-ficient and the scale of brain areas showing sex differences reproducibly observed across datasets. Furthermore, SMA better detected reproducible sex differences of ALFF under the site-sex confounded situation. Moreover, we designed experiments to identify the best target site features to optimize SMA identifiability, test-retest reliabil-ity, and stability. We noted both sample size and distribution of the target site matter and introduced a heuristic formula for selecting the target site. In addition to providing practical guidelines, this work can inform continuing improvements and innovations in harmonizing methodologies for big R-fMRI data.
收录类别SCI
WOS关键词RESTING-STATE FMRI ; CONNECTIVITY ; CONNECTOMICS ; REGISTRATION ; PREDICTION ; MOTION ; ROBUST ; POWER
资助项目Sci-Tech Innovation 2030-Major Project of Brain Science and Brain-inspired Intelligence Technology[2021ZD0200600] ; National Key R&D Program of China[2017YFC1309902] ; National Natural Science Foundation of China[82122035] ; National Natural Science Foundation of China[81671774] ; National Natural Science Foundation of China[81630031] ; 13th Five-year Informatization Plan of Chinese Academy of Sciences[XXH13505] ; Key Research Program of the Chinese Academy of Sciences[ZDBS-SSW-JSC006] ; Beijing Nova Program of Science and Technology[Z191100001119104] ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences[E2CX4425YZ]
WOS研究方向Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS记录号WOS:000991075400001
资助机构Sci-Tech Innovation 2030-Major Project of Brain Science and Brain-inspired Intelligence Technology ; National Key R&D Program of China ; National Natural Science Foundation of China ; 13th Five-year Informatization Plan of Chinese Academy of Sciences ; Key Research Program of the Chinese Academy of Sciences ; Beijing Nova Program of Science and Technology ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences
源URL[http://ir.psych.ac.cn/handle/311026/45428]  
专题心理研究所_中国科学院行为科学重点实验室
通讯作者Yan, Chao-Gan
作者单位1.Inst Psychol, CAS Key Lab Behav Sci, 16 Lincui Rd, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Psychol, Int Big Data Ctr Depress Res, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yu-Wei,Chen, Xiao,Yan, Chao-Gan. Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion[J]. NEUROIMAGE,2023,274:22.
APA Wang, Yu-Wei,Chen, Xiao,&Yan, Chao-Gan.(2023).Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion.NEUROIMAGE,274,22.
MLA Wang, Yu-Wei,et al."Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion".NEUROIMAGE 274(2023):22.

入库方式： OAI收割

来源：心理研究所

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