Outputs of NiMARE

NiMARE includes a wide range of tools with a correspondingly large number of possible outputs. Here we outline the rules we apply to NiMARE outputs.

File names

NiMARE-generated files, especially ones made by meta-analyses, follow a naming convention somewhat based on BIDS.

Here is the basic naming convention for statistical maps and tables:



First, the value represents type of data in the map (e.g., z-statistic, t-statistic). Some of the values found in NiMARE include:

  • z: Z-statistic

  • t: T-statistic

  • p: p-value

  • logp: Negative base-ten logarithm of p-value

  • chi2: Chi-squared value

  • prob: Probability value

  • stat: Test value of meta-analytic algorithm (e.g., ALE values for ALE, OF values for MKDA)

  • est: Parameter estimate (IBMA only)

  • se: Standard error of the parameter estimate (IBMA only)

  • tau2: Estimated between-study variance (IBMA only)

  • sigma2: Estimated within-study variance (IBMA only)

  • label: Label map


For one-sided tests, p-values > 0.5 will have negative z-statistics. These values should not be confused with significant negative results. As a result, in NiMARE, these values are replaced by 0.

Next, a series of key/value pairs describe the methods applied to generate the map.

  • desc: Description of the data type. Only used when multiple maps with the same data type are produced by the same method.

  • level: Level of multiple comparisons correction. Either cluster or voxel.

  • corr: Type of multiple comparisons correction. Either FWE (familywise error rate) or FDR (false discovery rate).

  • method: Name of the method used for multiple comparisons correction (e.g., “montecarlo” for a Monte Carlo procedure).

  • diag: Type of diagnostic. Either Jackknife (jackknife analysis) or FocusCounter (focus-count analysis).

  • tab: Type of table. Either clust (clusters table) or counts (contribution table).

  • tail: Sign of the tail for label maps. Either positive or negative.

File contents

NiMARE outputs unthresholded statistical maps. Users may then threshold their results separately.

This may result in some confusion for cluster-level corrected maps, in which each _cluster_ (after applying a voxel-wise cluster-defining threshold) has an overall significance level. As such, cluster-level corrected maps contain zeros for all non-significant voxels after applying the cluster-defining threshold, and each cluster has a single value across all voxels in the cluster. All clusters surviving the cluster-defining threshold will be included in the map, including clusters that have very high p-values.