title: A simulator for evaluating methods for the detection of lesion-deficit associations.
abstract: Although much has been learned about the functional organization of the human brain through lesion-deficit analysis, the variety of statistical and image-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, each 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 distribution of lesions, to model the structure-function associations, and to model registration error. We used the LDS to evaluate, as examples, the effects of the complexities and strengths of lesion-deficit associations, and of registration error, on the power of lesion-deficit analysis. We measured the numbers of recovered associations from these simulated data, as a function of the number of subjects analyzed, the strengths and number of associations in the statistical model, the number of structures associated with a particular function, and the prior probabilities of structures being abnormal. 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 model. The number of structures associated with a particular function (i.e., the 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 associations discovered by approximately 13% compared to perfect registration. The LDS provides a flexible framework for evaluating many aspects of lesion-deficit analysis.
authors.affiliation: Department of Computer Science, Dartmouth College, Hanover, New Hampshire 03755, USA. vasilis@cs.dartmouth.edu
authors.forename: V
authors.initials: V
authors.lastname: Megalooikonomou
authors.forename: C
authors.initials: C
authors.lastname: Davatzikos
authors.forename: E H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D001921:Brain / Q000473:pathology; D001930:Brain Injuries / Q000175:diagnosis* / Q000523:psychology*; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological