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application of a data-mining method based on bayesian networks to lesion-deficit analysisAlthough lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.
Document ID
Document Type
Reprint (Version printed in journal)
Herskovits, Edward H.
(Hospital of the University of Pennsylvania 3400 Spruce Street, Philadelphia, PA 19104, United States)
Gerring, Joan P.
Date Acquired
August 21, 2013
Publication Date
August 1, 2003
Publication Information
Publication: NeuroImage
Volume: 19
Issue: 4
ISSN: 1053-8119
Subject Category
Life Sciences (General)
Funding Number(s)
Distribution Limits
NASA Discipline Neuroscience
Non-NASA Center