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

Vasileios Megalooikonomou, Christos Davatzikos, Edward H. Herskovits

Research output: Contribution to journalArticle

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. (C) 2000 Wiley-Liss, Inc.

Original languageEnglish (US)
Pages (from-to)61-73
Number of pages13
JournalHuman Brain Mapping
Volume10
Issue number2
DOIs
StatePublished - Jun 1 2000

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Keywords

  • Brain mapping
  • Computer simulation
  • Databases
  • Magnetic resonance imaging
  • Monte Carlo method
  • Sample size
  • Statistical distributions
  • Statistical models

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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