A joint project of the Graduate School, Peabody College, and the Jean & Alexander Heard Library

Title page for ETD etd-10232013-102842

Type of Document Dissertation
Author Yang, Xue
URN etd-10232013-102842
Title Robust Statistical Inference in Human Brain Mapping
Degree PhD
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Bennett A. Landman Committee Chair
Benoit Dawant Committee Member
Hakmook Kang Committee Member
Jack H. Noble Committee Member
Richard A. Peters Committee Member
Victoria L. Morgan Committee Member
  • spatial temporal model
  • resilience
  • random regressor
  • robust regression
  • multi-site analysis
  • statistical parametric mapping
  • biological parametric mapping
Date of Defense 2013-10-01
Availability unrestricted
Statistical parametric mapping has been widely used in human brain mapping to explain brain image changes as a function of other factors. The core theory underlying this approach is the general linear model (GLM). Originally introduced for structural magnetic resonance image (MRI) and positron emission tomography analysis, this framework has been extended to resting state functional MRI and multi-modality brain image analysis. Despite the power of the extensions, problems within the traditional GLM assumptions and ordinary least squares (OLS) estimation arise.

The aim of this dissertation is to develop robust and accurate models within the GLM framework for multi-modality brain mapping and functional connectivity analysis. We introduced and modified modern statistical methods, which are established in statistical community, in the context of human brain mapping to obtain robust and accurate estimations. The robust regression and non-parametric mapping were introduced to address outlier problems. Model II regression and regression calibration were introduced to consider the imaging regressors in multi-modality brain image analysis. We developed spatial temporal models to account for spatial and temporal correlations simultaneously for functional connectivity analysis. To evaluate our methods, we proposed a quantitative approach for comparing inference methods on empirical studies. A large multi-site study was conducted to investigate the application of inter-modality human brain mapping using a shared database.

  Filename       Size       Approximate Download Time (Hours:Minutes:Seconds) 
 28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access 
  ThesisXueYang.pdf 6.66 Mb 00:30:49 00:15:51 00:13:52 00:06:56 00:00:35

Browse All Available ETDs by ( Author | Department )

If you have more questions or technical problems, please Contact LITS.