RT Journal Article
SR Electronic
T1 Handling Multiplicity in Neuroimaging through Bayesian Lenses with Hierarchical Modeling
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 238998
DO 10.1101/238998
A1 Gang Chen
A1 Yaqiong Xiao
A1 Paul A. Taylor
A1 Tracy Riggins
A1 Fengji Geng
A1 Elizabeth Redcay
A1 Robert W. Cox
YR 2017
UL http://biorxiv.org/content/early/2017/12/22/238998.abstract
AB In neuroimaging, the multiplicity issue may sneak into data analysis through several channels, affecting expected false positive rates (FPRs; type I errors) in diverse ways. One widely recognized aspect of multiplicity, multiple testing, occurs when the investigator fits a separate model for each voxel in the brain. However, multiplicity also occurs when the investigator conducts multiple comparisons within a model, tests two tails of a t-test separately when prior information is unavailable about the directionality, and branches in the analytic pipelines. The current practice of handling multiple testing through controlling the overall FPR in neuroimaging under the null hypothesis significance testing (NHST) paradigm excessively penalizes the statistical power with inflated type II errors. More fundamentally, the adoption of dichotomous decisions through sharp thresholding under NHST may not be appropriate when the null hypothesis itself is not pragmatically relevant because the effect of interest takes a continuum instead of discrete values and is not expected to be null in most brain regions. When the noise inundates the signal, two different types of error are more relevant than the concept of FPR: incorrect sign (type S) and incorrect magnitude (type M).In light of these considerations, we introduce a different strategy using Bayesian hierarchical modeling (BHM) to achieve two goals: 1) improving modeling efficiency via one integrative (instead of many separate) model and dissolving the multiple testing issue, and 2) turning the focus of conventional NHST on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BHM may simultaneously achieve increased spatial specificity and inference efficiency. The benefits of BHM are illustrated in model performance and quality checking using an experimental dataset. In addition, BHM offers an alternative, confirmatory, or complementary approach to the conventional whole brain analysis under NHST, and promotes results reporting in totality and transparency. The methodology also avoids sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The modeling approach with its auxiliary tools will be available as part of the AFNI suite for general use.