A likelihood ratio approach for functional localization in fMRI

• Published in: Journal of neuroscience methods Vol. 330; p. 108417
• Main Authors: Degryse, Jasper, Moerkerke, Beatrijs
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.01.2020
• Subjects: Alternative hypothesis; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Brain Mapping - standards; Effect size; FMRI; Functional ROI; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Likelihood Functions; Likelihood ratio; Localizer task; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Models, Statistical; Sensitivity and Specificity; Likelihood ratio; Localizer task; Functional ROI; Effect size; Alternative hypothesis; FMRI
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2019.108417

[Display omitted] •The mLR balances Type I and Type II errors, resulting in maximal spatial accuracy.•fROI voxels selected with the mLR method show practically relevant activation.•The mLR automatically adjusts to individual differences in baseline activation.•In conclusion, the mLR method is optimal for the definition of fROIs. 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. 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. 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. 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. The mLR method provides fROIs that are both spatially accurate and practically relevant.