# Find Significant Clusters Using a Two Sample T-Test

## Usage

For each node, determine if the means of the two input files are
significantly different.

This test is also called a T-Test for Independent Means.

## Inputs

- Name of Metric/Shape File for one group of subjects.
- Name of Metric/Shape File for second group of subjects that will
be compared with the first group of subjects.
- Name of a Fiducial Coordinate File.
- Name of an Open Topology File.
- Area Correction Metric/Shape file.
- Area Correction Metric/Shape File Column number.
- Negative threshold for metric/shape values to be in a negative
cluster.
- Positive threshold for metric/shape values to be in a positive
cluster.
- Number of iterations for the shuffled T-Map.
- P-Value.

## Outputs

- Name for T-Map file.
- Name for shuffled T-Map file.
- Paint file identifying clusters.
- Text report.

## Operation

- Compute a Statistical T-Map for the two
input files.
- Combine the two input metric/shape files so that it now has (N1 +
N2) subjects.
- Compute a Shuffled T-Map
on the combined metric/shape file.
- Find the biggest cluster in each column of the permutation T-Map
metric/shape file and sort them by cluster size.
- Find the largest (alpha)(iterations) clusters in the Permutation
T-Map and use its cluster size as the Significant Cluster Cutoff.
- Find clusters in the Real T-Map file.
- Report all clusters in Real T-Map file that are larger than
Significant Cluster Cutoff.
- Create a paint file showing all significant clusters in Real
T-Map file. Name them plus/minus based upon the threshold they
exceed.