Faster family-wise error control for neuroimaging with a parametric bootstrap.
In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussia...
| Publicado en: | Biostatistics Vol. 19; no. 4; pp. 497 - 514 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132363245&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132363245 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Oct2018 vid: 19 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 132363245 132363245 NLM29059370 132363245 10.1093/biostatistics/kxx051 NLM29059370 132363245 ppf: 497 ppct: 17 formats: tig: atl: Faster family-wise error control for neuroimaging with a parametric bootstrap. aug: au: Vandekar, Simon N Satterthwaite, Theodore D Rosen, Adon Ciric, Rastko Roalf, David R Ruparel, Kosha Gur, Ruben C Gur, Raquel E Shinohara, Russell T affil: Department of Biostatistics, Epidemiology, and Informatics, 423 Guardian Dr., University of Pennsylvania, Philadelphia PA, USA sug: subj: Brain Data Analysis, Statistical Models, Statistical Cerebrovascular Circulation Neuroradiography Methods Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates (FWER) that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted p-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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