PT  - JOURNAL ARTICLE
AU  - Robert W. Cox
TI  - Equitable Thresholding and Clustering (ETAC): A novel method for FMRI clustering in AFNI
AID  - 10.1101/295931
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 295931
4099  - http://biorxiv.org/content/early/2019/05/21/295931.short
4100  - http://biorxiv.org/content/early/2019/05/21/295931.full
AB  - This paper describes a hybrid method to threshold FMRI group statistical maps derived from voxelwise second-level statistical analyses. The proposed “Equitable Thresholding and Clustering” (ETAC) approach seeks to reduce the dependence of clustering results on arbitrary parameter values by using multiple sub-tests, each equivalent to a standard FMRI clustering analysis, to make decisions about which groups of voxels are potentially significant. The union of these sub-test results decides which voxels are accepted. The approach adjusts the cluster-thresholding parameter of each sub-test in an equitable way, so that the individual false positive rates (FPRs) are balanced across sub-tests to achieve a desired final FPR (e.g., 5%). ETAC utilizes resampling methods to estimate the FPR, and thus does not rely on parametric assumptions about the spatial correlation of FMRI noise. The approach was validated with pseudo-task timings in resting state brain data. Additionally, a task FMRI data collection was used to compare ETAC’s true positive detection power vs. a standard cluster detection method, demonstrating that ETAC is able to detect true results and control false positives while reducing reliance on arbitrary analysis parameters.ACFAutocorrelation FunctionAFNIAnalysis of Functional NeuroImagesBOLDBlood Oxygenation Level DependentEPIEcho Planar Imaging (or Image)ETACEquitable Thresholding and ClusteringFMRIFunctional Magnetic Resonance Imaging (or Image)FOMFigure of MeritFPRFalse Positive RateFWHMFull Width at Half MaximumHRFHemodynamic Response FunctionSPMStatistical Parametric Mapping