An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping

Lesion symptom mapping (LSM) tools are used on brain injury data to identify the neural structures critical for a given behavior or symptom. Univariate lesion-symptom mapping (ULSM) methods provide statistical comparisons of behavioral test scores in patients with and without a lesion on a voxel by voxel basis. More recently, multivariate lesion-symptom mapping (MLSM) methods have been developed that consider the effects of all lesioned voxels in one model simultaneously. However, very little work has been done to empirically compare the advantages and disadvantages of these two different methods. In the current study, we provide a needed systematic comparison of 5 ULSM and 8 MLSM methods, using both synthetic and real data to identify the potential strengths and weaknesses of both approaches. We tested power and spatial precision of each LSM method for both single and dual (network type) anatomical target simulations across anatomical target location, sample size, noise level, and lesion smoothing. Additionally, we performed false positive simulations to identify the characteristics associated with each method’s spurious findings. Simulations showed no clear superiority of either ULSM or MLSM methods overall, but rather highlighted specific advantages of different methods. No single method produced a thresholded LSM map that exclusively delineated brain regions associated with the target behavior. Thus, different LSM methods are indicated, depending on the particular study design, specific hypotheses, and sample size. Overall, we recommend the use of both ULSM and MLSM methods in tandem to enhance confidence in the results: Brain foci identified as significant across both types of methods are unlikely to be spurious and can be confidently reported as robust results.