Functional neuroimaging has provided a wealth of information on the cerebral localization of
mental functions. In spite of its success, however, several limitations restrict the knowledge that
may be gained from each individual experiment. These include a usually rather small sample size,
limited reliability of an indirect signal like BOLD fMRI and the need to base inference on relative
contrasts between conditions. Such limitations have raised some concerns on the interpretability
and validity neuroimaging results, but have also encouraged the development of quantitative
meta-analysis approaches. Neuroimaging meta-analysis is used to summarize a vast amount of
research findings across a large number of participants and diverse experimental settings. Such
integration then enables statistically valid generalizations on the neural basis of psychological
processes in health and disease. They also permit comparisons of different tasks or processes to
each other and the modeling of interacting networks. Quantitative meta analysis therefore
represents a powerful tool to gain a synoptic view of distributed neuroimaging findings in an
objective and impartial fashion, addressing some of the limitations raised above. The purpose of
this course is to review the theory and practice of meta-analytic modeling and database-driven
syntheses. In order to provide a comprehensive overview, this course spans both basic and
advanced topics and addresses practical tips and tools to conduct meta-analytic studies in
psychological and clinical applications. This broad coverage will thus provide both a deeper
understanding of the methodological underpinnings as well as concrete ideas for how to apply
meta analytic techniques to advance brain science.