| Summary: | Functional magnetic resonance data acquired in a task-absent condition ("resting state'') require new data analysis techniques that do not depend on an activation model. Here, we propose a new analysis method called "Connectivity Concordance Mapping (CCM)".The main idea is to assign a label to each voxel based on the reproducibility of its whole-brain pattern of connectivity. Specifically, we compute the correlations across measurements of each voxel's correlation-based functional connectivity map, resulting in a voxelwise map of concordance values. Regions of high interscan concordance can be assumed to be functionally consistent, and may thus be of specific interest for further analysis. Here we present two fMRI studies to test the algorithm. The first is a eyes open/eyes closed paradigm designed to highlight the potential of the method in a relatively simple state-dependent domain. The second study is a longitudinal repeated measurement of a patient following stroke. Longitudinal clinical studies such as this may represent the most interesting domain of applications for this algorithm, as it provides an exploratory means to identify changes in connectivity, such as those during post-stroke recovery.
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