Independent Component Analysis (ICA)
Independent component analysis (ICA) is the most commonly used computational tool for identifying and characterizing underlying brain functional networks. One of the challenging research topics in ICA is how to perform group ICA for multi-subject imaging studies. Our research on group ICA methodology focused on development of probabilistic group ICA framework for estimating brain functional networks based on multi-subject fMRI data...
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Meta Analysis
In recent years, the number of imaging studies on the structure and function of human brains has grown significantly. Most of these studies recruit a number of subjects for brain scans and apply various statistical methods to assess the association patterns between brain locations and human behavior as well as neurological disease or dysfunction. However, the limited sample sizes in many studies may lead to low statistical power. At the same time, results from different literatures on similar topics could sometimes be inconsistent...
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Multimodality Methods
We have developed several methods that combine modalities of neuroimaging data, namely fMRI and DTI data, to study the relationship between brain structure and function and to investigate the connectivity disruption pathways that characterize certain brain diseases. Resting-state and task-related brain activity, measured by fMRI, reflects the functional connectivity (FC) or associations between different brain regions. Diffusion tensor imaging (DTI), which enables the reconstruction and probabilistic quantification of major fiber tracts in the brain, provides structural connectivity (SC) information that may improve our understanding of FC...
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Brain Networks and Connectivity
Recently, network analyses for fMRI data have emerged that characterize the functional relationships between brain regions. A typical neuroimaging network analysis involves defining brain regions (nodes), quantifying a measure of association between all pairs of brain regions (edges) to produce a connectivity matrix, thresholding these associations to obtain a more sparse connectivity matrix, and then calculating summary statistics that...
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Prediction Methods
In recent years, there has been a strong interest in the neuroimaging community to utilize information in imaging data to predict individual disease status and treatment response. We developed two statistical prediction methods based on brain images. First, a prediction method based on a Bayesian hierarchical model for forecasting future neural activity based on a subject’s baseline brain images and other individual characteristics (Guo, Bowman and Kilts, 2008), which can potentially help select optimal treatment plans for individual patients...
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