Principal Investigators //
- David Bridwell, PhD >
- Vince Calhoun, PhD >
- Arvind Caprihan, PhD >
- Zikuan Chen, PhD >
- Vince Clark, PhD >
- Eric D. Claus, PhD >
- Carla Harenski, PhD >
- Kent Hutchison, PhD >
- Kent A. Kiehl, PhD >
- Jeffrey D. Lewine, PhD >
- Jingyu Liu, PhD >
- Andrew R. Mayer, PhD >
- John Phillips, MD >
- Sergey Plis, PhD >
- Matthew Shane, PhD >
- Julia M. Stephen, PhD >
- Jing Sui, PhD
- Jessica Turner, PhD >
- Qingbao Yu, PhD >
Jing Sui, PhD
Assistant Professor of Translational Neuroscience
Dr. Sui specializes in brain imaging data analysis, especially the multimodal data fusion and classification. Her research focuses on combining multiple data types (fMRI, DTI, sMRI, genetics) smartly in a joint analysis so as to identify the potential biomarkers for certain brain diseases, which takes advantage of the fact that each modality provides a limited view of the brain. Her research also involves developing novel methods to better understand and discriminate brain disorders, such as schizophrenia from bipolar disorder. Dr. Sui is also familiar with image/signal processing (infrared video sequence processing), multivariate modeling, machine learning and large-scale data mining.
Selected Publications //
- Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia.. >
- A pilot study on commonality and specificity of copy number variants in schizophrenia and bipolar… >
- Artifact removal in the context of group ICA: a comparison of single-subject and group approaches >
- Resting-state functional network connectivity in prefrontal regions differs between unmedicated… >
- Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Select… >
- A group ICA based framework for evaluating resting fMRI markers when disease categories are… >
- CREB-BDNF pathway influences alcohol cue-elicited activation in drinkers >
- Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy… >
- State-related functional integration and functional segregation brain networks in schizophrenia. >
- Function-structure associations of the brain: Evidence from multimodal connectivity… >
- Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + >
- Disrupted correlation between low frequency power and connectivity strength of resting state brain n >
- Altered small-world brain networks in schizophrenia patients during working memory performance. >
- Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in a CCA+ICA Based Model >
- A Selective Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data >
Fusing multi-task and multi-modal brain imaging data and indentify the potential biological markers
Each brain imaging modality reports on a different aspect of the brain with different strengths and weaknesses and there are now literally thousands of putative imaging biomarkers. This project will develop multivariate methods which use higher order statistics to combine diverse information in a scalable manner, identify correspondence among data types and also provide a sophisticated data sharing and management system.
Exploring similarity and differences among schizophrenia and bipolar disorder by combining fMRI and
This project will develop an exploratory data fusion model which combines 2 multivariate methods and is able to identify correspondence among multiple data types. We aim to apply this model to schizophrenia and bipolar disorder via an fMRI-DTI fusion, which can identify both shared and disease-specific brain abnormalities from multiple perspectives (brain function and structure).
Brain Connectivity Changes in Individual Subjects with Neuropsychological Disease
Methods to provide better characterization of functional and structural brain network connectivity in patients with schizophrenia and addiction are being developed.(Calhoun et al., 2009; Greicius et al., 2007; Lynall et al., 2010; van den Heuvel et al., 2010) The goal is to provide accurate markers of disease progression in individual subjects with neuropsychological diseases associated with brain connectivity alterations.
