3dICC is a program that computes whole-brain voxel-wise intraclass correlation (ICC) when each subject has two or more effect estimates (e.g., sessions, scanners, etc. ). All three typical types of ICC are available through proper model specification: ICC(1, 1), ICC(2,1) and ICC(3,1). The latter two types are popular in neuroimaging because ICC(1,1) is usually applicable for scenarios such as twins. The program can be applied to even wider situations (e.g., incorporation of confounding effects or more than two random-effects variables). The modeling approaches are laid out in the following paper:
Chen et al., 2017. Intraclass correlation: Improved modeling approaches and applications for neuroimaging. Human Brain Mapping 39(3): 1187-1206.
Currently it provides in the output the ICC value and the corresponding F-statistic at each voxel. In future, inferences for intercept and covariates may be added.
Input files for 3dICC can be in AFNI, NIfTI, or surface (niml.dset) format. Two input scenarios are considered: 1) effect estimates only, and 2) effect estimates plus their t-statistic values which are used for weighting based on the precision contained in the t-statistic.