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Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes
Chen, Xiao1,2; Lu, Bin1,2; Yan, Chao-Gan1,2,3,4
2018
Source PublicationHUMAN BRAIN MAPPING
Correspondent Emailycg.yan@gmail.com
ISSN1065-9471
SubtypeArticle
Volume39Issue:1Pages:300-318
Contribution Rank1,3
Abstract

Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability<0.3 for between-subject sex differences,<0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g.,<80 [40 per group]) not only minimized power (sensitivity<2%), but also decreased the likelihood that significant results reflect true effects (PPV<0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. (c) 2017 Wiley Periodicals, Inc.

Keywordmultiple comparison correction strategies positive predictive value replicability reproducibility resting-state fMRI sample size sensitivity test-retest reliability
DOI10.1002/hbm.23843
Indexed BySCI
Language英语
Funding OrganizationNational Key R&D Program of China(2017YFC1309902) ; National Natural Science Foundation of China(81671774 ; Hundred Talents Program of the Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission(Z161100000216152) ; 81630031)
WOS Research AreaNeurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectNeurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000417282000020
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
WOS KeywordRESTING-STATE FMRI ; DEFAULT-MODE NETWORK ; POSTERIOR CINGULATE CORTEX ; LOW-FREQUENCY FLUCTUATION ; HUMAN BRAIN ; FUNCTIONAL CONNECTIVITY ; SEX-DIFFERENCES ; ACTIVATION PATTERNS ; STATISTICAL MAPS ; BASE-LINE
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Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26011
Collection中国科学院行为科学重点实验室
Affiliation1.CAS Key Lab Behav Sci, Inst Psychol, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing, Peoples R China
4.NYU Langone Med Ctr, Sch Med, Dept Child & Adolescent Psychiat, New York, NY USA
Recommended Citation
GB/T 7714
Chen, Xiao,Lu, Bin,Yan, Chao-Gan. Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes[J]. HUMAN BRAIN MAPPING,2018,39(1):300-318.
APA Chen, Xiao,Lu, Bin,&Yan, Chao-Gan.(2018).Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes.HUMAN BRAIN MAPPING,39(1),300-318.
MLA Chen, Xiao,et al."Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes".HUMAN BRAIN MAPPING 39.1(2018):300-318.
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