Statistical methods for fMRI data analysis

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Xia, Jing

Contributors dc:contributor

• Wang, Michelle Y.
• Liang, Feng
• Simpson, Douglas G.
• Marden, John I.

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

Statement dc:rights

• Copyright 2011 Jing Xia

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/24259