Modeling motor learning connectivity using coordinate-based meta-analysis and TMS/PET

BrainMap is a database of peak activation locations and metadata reported in functional neuroimaging studies, which was designed to develop and promote coordinate-based meta-analysis techniques. Here, we demonstrate the activation likelihood estimation (ALE) method in a meta-analysis of published TMS/PET studies. Using the results of this meta-analysis, we constructed a data-driven model of motor connectivity in TMS/PET data in which stimulation was delivered to RM1 before and after motor skill acquisition. A hybrid motor connectivity model of pre- and post-learning was generated to identify specific pathways most affected by the mechanisms involved in the motor learning process.