Abstract:<\/h2>\n
Diffusion<\/span> tensor<\/span> imaging<\/span> (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and subject to random distortions including variance and bias<\/span>. Traditional statistical procedures commonly used for study planning (including power<\/span> analyses and p-value<\/span>\/alpha-rate thresholds) specifically model variability, but neglect potential impacts of bias<\/span>. Herein, we quantitatively investigate the impacts of bias<\/span> in DTI on hypothesis test properties (power<\/span> and alpha-rate) using a two-sided hypothesis testing framework. We present theoretical evaluation of bias<\/span> on hypothesis test properties, evaluate the bias<\/span> estimation technique SIMEX for DTI hypothesis testing using simulated data, and evaluate the impacts of bias<\/span> on spatially varying power<\/span> and alpha rates in an empirical study of 21 subjects. Bias<\/span> is shown to inflame alpha rates, distort the power<\/span> curve, and cause significant power<\/span> loss even in empirical settings where the expected difference in bias<\/span> between groups is zero. These adverse effects can be attenuated by properly accounting for bias<\/span> in the calculation of power<\/span> and p-values. Carolyn B. Lauzon and Bennett A. Landman, \u201cCorrecting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.\u201d Magnetic Resonance Imaging. 2013 Mar 2. pii: S0730-725X(13) PMC23465764 \u2020 Full Text:\u00a0https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=Correcting+Power+and+p-Value+Calculations+for+Bias+in+Diffusion+Tensor+Imaging. Abstract: Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and…<\/p>\n","protected":false},"author":6299,"featured_media":234,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33,4],"tags":[34,11,35],"class_list":["post-202","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diffusion-tensor-imaging","category-neuroimaging","tag-diffusion-tensor-imaging","tag-dti","tag-neuroimaging"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/202","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/6299"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=202"}],"version-history":[{"count":3,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/202\/revisions"}],"predecessor-version":[{"id":293,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/202\/revisions\/293"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/234"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=202"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=202"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=202"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Fig4\"](\"https:\/\/my.vanderbilt.edu\/masi\/wp-content\/uploads\/sites\/2304\n2661\/2016\/10\/nihms531393f4.jpg\")
<\/p>\n","protected":false},"excerpt":{"rendered":"