CONN includes a user-friendly GUI to manage all aspects of functional connectivity analyses,[1] including preprocessing of functional and anatomical volumes,[2] elimination of subject-movement and physiological noise,[3] outlier scrubbing,[4] estimation of multiple connectivity and network measures, and population-level hypothesis testing.

[5] In addition, the BOLD signal at white matter and ventricles can be used to characterize potential motion and physiological noise sources, and the combined effect of these and other noise sources can be removed from the functional data to improve the robustness of functional connectivity measures.

[6] CONN computes multiple measures of functional connectivity, including Fisher-transformed Pearson correlation coefficients between the BOLD timeseries from different regions of interest (ROIs), as well as with every voxel in the brain.

[8] Analyses that combine functional connectivity measures from multiple ROIs or voxels also incorporate additional multiple comparison corrections such as False Discovery Rate, parametric methods based on the theory of continuous random fields,[9] and non-parametric cluster-level statistics.

[13] It is included in the NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) list of top-10 tools and resources in neuroimaging,[14] and the NITRC forum has indexed to date over 10,000 posts of software support from CONN's developers and community.