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: Beatrijs.Moerkerke(a)UGent.be
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.