Mapping Growth Patterns and Genetic Influences on Early Brain Development in Twins

  • Authors:

  • Yasheng Chen;Hongtu Zhu;Dinggang Shen;Hongyu An;John Gilmore;Weili Lin

  • Affiliations:

  • Dept. of Radiology, Univ. of North Carolina at Chapel Hill, Chapel Hill, USA 27599;Dept. Biostatistics, Univ. of North Carolina at Chapel Hill, Chapel Hill, USA 27599;Dept. of Radiology, Univ. of North Carolina at Chapel Hill, Chapel Hill, USA 27599;Dept. of Radiology, Univ. of North Carolina at Chapel Hill, Chapel Hill, USA 27599;Dept. of Psychiatry, Univ. of North Carolina at Chapel Hill, Chapel Hill, USA 27599;Dept. of Radiology, Univ. of North Carolina at Chapel Hill, Chapel Hill, USA 27599

  • Venue:
  • MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
  • Year:
  • 2009

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Abstract

Despite substantial progress in understanding the anatomical and functional development of the human brain, little is known on the spatial-temporal patterns and genetic influences on white matter maturation in twins. Neuroimaging data acquired from longitudinal twin studies provide a unique platform for scientists to investigate such issues. However, the interpretation of neuroimaging data from longitudinal twin studies is hindered by the lacking of appropriate image processing and statistical tools. In this study, we developed a statistical framework for analyzing longitudinal twin neuroimaging data, which is consisted of generalized estimating equation (GEE2) and a test procedure. The GEE2 method can jointly model imaging measures with genetic effect, environmental effect, and behavioral and clinical variables. The score test statistic is used to test linear hypothesis such as the association between brain structure and function with the covariates of interest. A resampling method is used to control the family-wise error rate to adjust for multiple comparisons. With diffusion tensor imaging (DTI), we demonstrate the application of our statistical methods in quantifying the spatiotemporal white matter maturation patterns and in detecting the genetic effects in a longitudinal neonatal twin study. The proposed approach can be easily applied to longitudinal twin data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.