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.