Statistical methods for fMRI data analysis

Xia, Jing

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https://hdl.handle.net/2142/24259 Copy

Description

Title

Statistical methods for fMRI data analysis

Author(s)

Xia, Jing

Issue Date

2011-05-25T14:56:20Z

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Michelle Y.

Doctoral Committee Chair(s)

Wang, Michelle Y.

Committee Member(s)

• Liang, Feng
• Simpson, Douglas G.
• Marden, John I.

Department of Study

Statistics

Discipline

Statistics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Date of Ingest

2011-05-25T14:56:20Z

Keyword(s)

• Functional magnetic resonance imaging (fMRI)
• Functional Activation
• Functional Connectivity
• Bayesian

Abstract

Since the early 1990s, functional magnetic resonance imaging (fMRI) has dominated the brain mapping field and it has been proved a powerful tool for mapping human brain functions. The fMRI is a high spatial-temporal resolution medical-imaging modality, which means the data structure is complicated and the data size is huge. These features of fMRI data pose some challenges to traditional statistical methods which focus on data with smal sample size and simple data structure. The functional activation detection and functional connectivity network analysis by using fMRI are two important research topics in the neuroscience. In this work, we present three different statistical methods, corresponding to three chapters, for the activation detection and network discovery. In the first part, we present a spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation; in the second part, we propose a model based clustering method to detect the functional connectivity network; in the third part, we present a general and novel statistical framework for robust and more complete estimation of brain functional connectivity based on correlation analysis and hypothesis testing. The complicated data features are taken into account in the three algorithms.

Graduation Semester

2011-05

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http://hdl.handle.net/2142/24259

Copyright and License Information

Copyright 2011 Jing Xia

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