# Statistical inference

### From FMRI Methods Wiki

**Inference on Statistic Image (thresholding)**

NB: These points refer primarily to classical inference; an additional set of guidelines is necessary for Bayesian inference.

- Type of search region considered, and the volume in voxels or CC.
- If not whole brain, how region was found; method for constructing region should be independent of present statistic image.
- If threshold used for inference and threshold used for visualization in figures is different, clearly state so and list each.

- Explicitly state if inferences are corrected for multiple comparisons, and if so, what method and over what region.
- If correction is limited to a small volume, the method for selecting the region should be stated explicitly.
- If no formal multiple comparisons method is used, the inference must be explicitly labeled "uncorrected".

- Voxel-wise significance? Corrected for Familywise Error (FWE) or False Discovery Rate (FDR).
- If FWE found by random field theory (e.g. with SPM) list the smoothness in mm FWHM and the RESEL count.
- If FWE found by simulation (e.g., AFNI AlphaSim), provide details of parameters for simulation
- If not a standard method, specify the method for finding significance (e.g. "Custom in-lab software was used to construct statistic maps and thresholded at FDR<0.05 (Benjamini & Hochberg 1995)".

- Cluster-wise significance?
- State cluster-defining threshold (e.g. P=0.01)
- State the corrected cluster significance level
- E.g. "Statistic images were assessed for cluster-wise significance using a cluster-defining threshold of P=0.01; the 0.05 FWE-corrected critical cluster size was 103."

- If significance determined with random field theory, then smoothness and RESEL count must be supplied.

- Correction for multiple planned comparisons within each voxel?
- False Negative Discussion
- Any discussion of failure to reject the null hypothesis (e.g., lack of activation in a particular region) should be accompanied by an estimate of the actually observed effect (allows reader to infer power to estimate an effect).

**ROI analysis**

- How were ROI's defined
- E.g., functional versus anatomical localizer?

- How was signal extracted within ROI?
- E.g., average parameter estimates, FIR deconvolution?
- If percent signal change reported, how was scaling factor determined (e.g. height of block regressor or height of isolated event regresor)? Is change relative to voxel-mean, or whole-brain mean?