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Neuroimaging meta-analysisNeuroimaging meta-analysis

Neuroimaging Meta-Analysis

Half day afternoon course

Friday 28th September 2018 / 6th Mehr 1397 (14:00-19:00)




Simon Eickhoff MD, PhD
Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Dusseldorf, Germany



Masoud Tahmasian MD, PhD
Shahid Beheshti University, Tehran, Iran



Functional and structural 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.



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