A simulator for evaluating methods for the detection of lesion-deficit associations

Although much has been learned about the functional, organization of the hu man brain through lesion-deficit analysis, the variety of statistical and i mage-processing methods developed for this purpose precludes a closed-form analysis of the statistical power of these systems. Therefore, we developed a lesion-deficit simulator (LDS), which generates artificial subjects, eac h of which consists of a set of functional deficits, and a brain image with lesions; the deficits and lesions conform to predefined distributions. We used probability distributions to model the number, sizes, and spatial dist ribution of lesions, to model the structure-function associations, and to m odel registration error. We used the LDS to evaluate, as examples, the effe cts of the complexities and strengths of lesion-deficit associations, and o f registration error, on the power of lesion-deficit analysis. We measured the numbers of recovered associations from these simulated data, as a funct ion of the number of subjects analyzed, the strengths and number of associa tions in the statistical model, the number of structures associated with a particular function, and the prior probabilities of structures being abnorm al. The number of subjects required to recover the simulated lesion-deficit associations was found to have an inverse relationship to the strength of associations, and to the smallest probability in the structure-function mod el. The number of structures associated with a particular function (i.e., t he complexity of associations) had a much greater effect on the performance of the analysis method than did the total number of associations. We also found that registration error of 5 mm or less reduces the number of associa tions discovered by approximately 13% compared to perfect registration. The LDS provides a flexible framework for evaluating many aspects of lesion-de ficit analysis. (C) 2000 Wiley-Liss, Inc.