Open AccessA simulator for evaluating methods for the detection of lesion‐deficit associations
Author(s) -
Megalooikonomou Vasileios,
Davatzikos Christos,
Herskovits Edward H.
Publication year - 2000
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/(sici)1097-0193(200006)10:2<61::aid-hbm20>3.0.co;2-9
Subject(s) - statistical power , lesion , function (biology) , set (abstract data type) , statistical model , statistics , pattern recognition (psychology) , computer science , probability distribution , artificial intelligence , mathematics , medicine , pathology , biology , evolutionary biology , programming language
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. Hum. Brain Mapping 10:61–73, 2000. © 2000 Wiley‐Liss, Inc.