PhD studentship in Psychology / Statistics: From peak to meta-analysis map: cumulating knowledge across neuroimaging studies

Responsible:
Ruth Seurinck & Beatrijs Moerkerke, Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium

Functional magnetic resonance imaging (fMRI) is an important research technique in psychology. Since publications of single fMRI studies have flourished, it is increasingly recognized that progress in understanding human brain function will not only require the acquisition of new data but a synthesis and integration of data across studies and labs (Yarkoni, Poldrack, Van Essen & Wager, 2010).  Meta-analysis is a promising tool to achieve this goal.

The current available procedures for fMRI data use coordinate-based techniques where the limited amount of voxels that survive a statistical threshold are combined into one map to determine the location in the brain of the overall observed effect (Laird et al., 2005; Radua et al., 2009; Wager, Lindquist, Nichols, Kober and Van Snellenberg, 2009). However, statistical significance is not an optimal indicator for the true underlying effect and information is only available for these significant peak voxels. Also, there is evidence of publication bias in fMRI studies. Studies that do not reach statistical significance are less likely to be published and included in a meta-analysis, distorting the results of the meta-analysis.  A recent study demonstrated evidence for publication bias in a meta-analysis of the frontal lobe in working memory by summarizing activation in the frontal lobe (> 1000 voxels) in a single effect size for each study (Jennings and Van Horn, 2012).

The main goal of this research proposal is to adapt coordinate-based meta-analysis methods for fMRI data to create an informative set of meta-analysis brain maps with a focus on effect size estimation. We will further develop procedures for the assessment and correction of publication bias.

This project obtained a grant from the Research Foundation Flanders (FWO) and will be conducted in close collaboration with dr. Simone Kühn from the Max Planck Institute for Human Development in Berlin, Germany. 


Candidate

The successful candidate will hold a Master's degree in Psychology (or related discipline) and/or Statistical Data Analysis. She/he will be hosted within a dynamic group of researchers. She/he will be offered excellent training and development opportunities, and will be involved in both  methodological development on meta analysis in fMRI studies as well as the applications thereof.

Duration: 48 months

Date of start: as soon as possible

Please send your application (including a current CV, publication list, letter of recommendation and copies of diplomas and certificates) to Beatrijs Moerkerke.  We encourage candidates to apply early. Applications received before June 2, 2014 will be given full consideration. Applications received after June 2 will be considered as they arrive, until the position is filled.

Contact:
Beatrijs Moerkerke
Department of Data Analysis
Faculty of Psychology and Educational Sciences
Ghent University, Belgium
E-mail: [log in to unmask]



References

Jennings, R. G., & Van Horn, J. D. (2012). Publication bias in neuroimaging research: Implications for 	Meta-analyses. Neuroinformatics, 10, 67-80.
Laird, A. Fox, P.M., Price, C.J., Glahn, D.C., Uecker, A.M., Lancaster, J.L., Turkeltaub, P.E., Kochunov, P., & Fox, P.T. (2005). ALE meta-analysis: Controlling the false discovery rate and performing statistical contrasts. Human Brain Mapping, 25, 155-164.
Radua, J., Mataix-Cols, D., Phillips, M.L., El-Hage, W., Kronhaus, D.M., Cardoner, N., & Surguladze, S. (2009). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry, 27, 605-611.
Wager, T. D., Lindquist, M. A., Nichols, T. E., Kober, H., & Van Snellenberg, J. X. (2009). Evaluating the consistency and specificity of neuroimaging data using meta-analysis. Neuroimage, 45, S210-S221.
Yarkoni, T., Poldrack, R. A., van Essen, D. C., & Wager T. D. (2010). Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends in Cognitive Sciences, 14, 489-496.

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