### 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 language | English (US) |
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Pages (from-to) | 61-73 |

Number of pages | 13 |

Journal | Human Brain Mapping |

Volume | 10 |

Issue number | 2 |

DOIs | |

State | Published - 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

### Cite this

*Human Brain Mapping*,

*10*(2), 61-73. https://doi.org/10.1002/(SICI)1097-0193(200006)10:2<61::AID-HBM20>3.0.CO;2-9