Our statistical methodology research and collaborations focus on various biomedical imaging areas. Primarily, we work with brain imaging data, including both functional and structural neuroimaging modalities. We have also worked in cardiac imaging and in cancer applications, including brain tumor, breast and prostate cancer imaging.

Generally, functional neuroimaging is a method for mapping measures of localized brain activity in vivo. We apply our methods to functional neuroimaging applications that seek to characterize changes in distributed neural processing associated with psychiatric disorders, drug cravings, behaviors, emotions, and decision-making. CBIS also conducts research on brain networks and functional connectivity, which provides insights into the relationships between different brain regions when performing a particular task or during resting state. In recent years, the collection of multimodal neuroimaging (fMRI, sMRI and DTI, etc.) has become common practice to provide different views of brain function or structure. CBIS has been working on developing effective analytical tools for fusing multimodal imaging to obtain more accurate and informative results on brain function and connectivity.

Our research seeks state-of-the-art biostatistical methods that are applicable (1) to describe functional associations between brain regions, (2) to determine functional connectivity and hierarchical networks in the brain, (3) to make inferences concerning task-related changes in brain activity that ultimately produce maps revealing distributed patterns of task/activity associations, (4) to address various prediction objectives and (5) to conduct integrative analysis of multimodal imaging data. Specifically, we have applied our statistical methods to help better understand the neural correlates underlying.

  • brain disorders such as depression, PTSD, Alzheimer’s disease, and schizophrenia

  • task-related and resting-state functional connectivity differences in Zen meditators

  • social anxiety disorder and its response to pharmacotherapy

  • the sensitivity to ethical issues related to justice and care

  • cue-induced nicotine craving

  • cocaine dependence

We briefly describe our statistical methodology research below…


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 ... read more


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 ... read more

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... read more

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 ... read more

Prediction Methods

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 the following statistical prediction methods based on brain images ... read more