A likelihood ratio approach for functional localization in fMRI.

BACKGROUND: To increase power when analyzing fMRI data, researchers often define functional regions of interest (fROIs). It is crucial that this fROI is defined with an optimal balance between both false positives and false negatives to ensure maximal spatial accuracy and to avoid potentially biased results in the main fMRI experiment. Additionally, since the fROI is defined in each subject separately, the used method should attune to the general level of activation of the individual. NEW METHOD: We investigate the benefits of the maximized likelihood ratio (mLR) method. This method is based on the likelihood paradigm where likelihood ratios are used to reflect relative statistical evidence in favor of an a priori defined practically relevant alternative hypothesis as compared to the null hypothesis of no activation. RESULTS: Through both simulations and real data, we show that the mLR method provides cumulative evidence for voxels that are active with an effect size that is larger than the one a priori defined in the alternative. Furthermore, an optimal balance between Type I and Type II errors is achieved when the alternative is an underestimation of the true effect size. COMPARISON WITH EXISTING METHODS: The mLR method is compared with false discovery rate corrected null hypothesis significance testing and regular likelihood ratio testing. It performs as good as or outperformed both methods in both detection of practically relevant voxels and the trade- off between false positives and false negatives. CONCLUSIONS: The mLR method provides fROIs that are both spatially accurate and practically relevant.

Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI.

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null.

Automatic effects of no-go instructions.

Previous research has indicated that stimulus-response mappings that have been instructed but never applied overtly before can lead to automatic response biases when they are irrelevant. In the present study, we investigated whether the same applies to no-go instructions, which relate a stimulus to a no-go response. The results of 2 experiments suggest that a no-go instruction that has never been practiced overtly before can automatically bias responding when it is irrelevant. In addition, the automatic effect of a no-go instruction was similar in size to the automatic effect of a go instruction. Finally, the automatic effect of an unpracticed no-go instruction tended to be larger than the automatic effect of an overtly practiced no-go instruction. We propose that (a) associations between a stimulus and the requirement to stop can be formed on the basis of instructions and without overt practice, (b) these associations may be functionally equivalent to associations formed on the basis of go instructions, and (c) overtly practiced no-go instructions and unpracticed no-go instructions are represented in different formats. (PsycINFO Database Record