Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies

By Gholamreza Salimi Khorshidi, Stephen M. Smith, J. Keltner, Tor D. Wager, Thomas E. Nichols. NeuroImage, 2009

401 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/79b79dec1d9aef1cbe0be91310f686c63a6e2290

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Meta-analysis of Functional Neuroimaging Studies of Self- and Other Judgments Reveals a Spatial Gradient for Mentalizing in Medial Prefrontal Cortex (2012, neutral): “mrc-cbu.cam.ac.uk/downloads/MNI2tal/tal2mni.m). Peaks were then convolved with a 10-mm spherical kernel, a consensus kernel size for MKDA (Kober & Wager, 2010; Salimi-Khorshidi, Smith, Keltner, Wager, & Nichols, 2009; Wager et al., 2007; Wager, Jonides, & Reading, 2004).”
Emotional perception: Meta-analyses of face and natural scene processing (2011, background): “However, the simple coordinate locations offer considerably less information (Salimi-Khorshidi et al., 2009), which cannot be quantified.”
Meta-analyzing brain dysfunctions in dyslexic children and adults (2011, positive): “Salimi-Khorshidi et al. (2009) showed that the latter approach is preferable due to the higher amount of information that enters meta-analysis (e.g., extent and magnitude of activation).”
Behavior, Sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation (2016, neutral): “They provide robust insights into the location of psychological and pathological effects (Kober and Wager, 2010; Nickl-Jockschat et al., 2015; Radua and Mataix-Cols, 2012; Schilbach et al., 2012; Yarkoni et al., 2011).”
Making big data open: data sharing in neuroimaging (2014, methodology): “Despite this, coordinate-based meta-analysis can be surprisingly effective at capturing the same patterns present in the original image-based data, though it is weaker than the full statistical maps 33 .”
The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes (2015, neutral): “Collapsing across numerous different baselines or control conditions is a common, indeed inevitable, practice in any large meta-analysis of functional neuroimaging studies (Salimi-Khorshidi et al., 2009; Svoboda et al., 2006; Wager et al., 2007, 2009).”
The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value (2013, positive): “At the same time, it reinforces the importance of meta-analytically aggregating results across large numbers of experiments, and of quantifying the degree of cross-study overlap in a transparently interpretable manner (as we have attempted to do by using the unweighted MKDA statistic).”
Ten simple rules for neuroimaging meta-analysis (2017, methodology): “Image-based meta-analyses allow for the use of hierarchical mixed effects models that account for intra-study variance and random inter-study variation (Salimi-Khorshidi et al., 2009) as the full information required for this is provided in image form.”
The human vestibular cortex revealed by coordinate-based activation likelihood estimation meta-analysis. (2012, neutral): “Several methods are available to compute a meta-analysis of neuroimaging data, including coordinatebased and image-based meta-analyses, and they have previously been applied successfully to different functional neuroimaging studies (Salimi-Khorshidi et al., 2009; Laird et al., 2009b).”
Data sharing in neuroimaging research (2012, background): “…showed that the reliability between a study using the original functional contrast maps and those derived from the coordinates alone was poor (Salimi-Khorshidi et al., 2009), providing an argu-ment for the need to share original and derived images, not only the Talairach coordinates as are…”
A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps (2012, background): “Ingenious peak-probability methods such as activation likelihood estimation (ALE), the first coordinate-based method to be developed [8,23], or multilevel kernel density analysis (MKDA), which first introduced the weighting by sample size [26,27], are (exclusively) based on the regional likelihood o”
From brain maps to cognitive ontologies: informatics and the search for mental structure (2015, background): “…meta-analysis is demonstrably inferior to image-based metaanalysis because it unnecessarily discards the vast majority of usable information in the original maps, thereby reducing sensitivity and typically precluding the estimation of continuous effect sizes (Salimi-Khorshidi et al. 2009).”
The neural bases of social cognition and story comprehension. (2011, methodology): “A limitation also exists in employing the ALE methodology, with meta-analyses based on full statistic images demonstrating superior results to approaches that rely on aggregating peak coordinates (Salimi-Khorshidi et al. 2009).”
Toward open sharing of task-based fMRI data: the OpenfMRI project (2013, positive): “This approach has been very powerful, but at the same time is clearly limited by the coarseness of the data at every level; the shortcomings of coordinate-based meta-analysis in comparison to meta-analysis based on full image data have been shown by Salimi-Khorshidi et al. (2009).”
Investigating the Functional Heterogeneity of the Default Mode Network Using Coordinate-Based Meta-Analytic Modeling (2009, methodology): “Recent results indicate that resting state networks derived via ICA of resting state fMRI data explicitly match activation networks derived via ICA of Brain-Map coordinate data (Smith et al., 2009).”
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain (2014, methodology): “Coordinate-based meta-analysis (CBMA) methods show only modest overlap with image-based meta-analysis (IBMA; meta-analysis based on unthresholded statistical maps) methods and are less powerful (Salimi-Khorshidi et al., 2009).”