Coordinate Density Analysis of neuroimaging studies

Abstract

Meta-analysis of published neuroimaging studies testing a common hypothesis is most often performed using coordinate based meta-analysis (CBMA). The locations of spatial clusters of reported coordinates are considered relevant to the hypothesis because multiple studies have reported effects in the same anatomical vicinity. Many algorithms have been implemented, and a common feature is the use of some empirical assumptions that may not be generalisable. Some algorithms require numerical randomisation of coordinates uniformly in an image space to define a statistical threshold, but there is no consensus about how to define the space. Most algorithms also require a smoothing kernel to extrapolate the reported foci to voxel-wise results, but again there is no consensus. Some algorithms utilise the reported statistical effect sizes (Z scores, t statistics, p-values) and require assumptions about their distribution. Beyond these issues thresholding of results, which is necessitated by the potential for false positive results in neuroimaging studies, is performed using a multitude of methods. Whatever the results of these algorithms, interpretation is always conditional on the validity of the assumptions employed. Coordinate density analysis (CDA), detailed here, is new method that aims to perform the analysis with minimal, or easy to interpret, assumptions.

CDA uses only the same data as other CBMA algorithms but uses a model-based assessment of coordinate statistical significance that requires only a characteristic volume, for example the human grey matter (GM) volume, and does not require any randomisation. There is also no requirement for an empirical smoothing kernel parameter. Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA.