Abstract
For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups (<20–27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a “dual-use” fMRI task, n = 39), and the implications of this approach are discussed.
Highlights
IntroductionFunctional magnetic resonance imaging (fMRI) is based on the intrinsic contrast of oxygenated versus de-oxygenated blood
Functional magnetic resonance imaging is based on the intrinsic contrast of oxygenated versus de-oxygenated blood. This effect is observable in the socalled blood-oxygenation level dependent effect (BOLD-fMRI; [1]), which is widely used in neuroscience research to detect brain activations
One common approach to statistical analysis of fMRI-data is employing the general linear model [2] whereby statistical parametrical maps can be generated from the imaging data that allow drawing inferences on different levels
Summary
Functional magnetic resonance imaging (fMRI) is based on the intrinsic contrast of oxygenated versus de-oxygenated blood. One step further is the joint assessment of a small group of subjects, termed fixed-effects analysis This approach only allows to assess the ‘‘typical’’ activation pattern in this group [5,6]; due to the strong influence of single subjects on the resulting group activation maps, no inference above and beyond the particular group of subjects in this analyses can be made. Another approach is to perform conjunction-analyses [7], where the question of ‘‘joint activation’’ between individuals can be posed in different ways [8,9], but again, results from a small group cannot be generalized
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