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 >
Jingyu Liu, PhD
Associate Professor of Translational Neuroscience
Dr. Liu’s research focuses on exploring and identifying genetic/epigenetic effects on brain anomalies related to various mental illnesses and disorders. Her research involves developing methods to test the association of various phenotypes derived from brain function and structure with genetic/epigenetic features across the whole genome. The brain-based phenotypes can be extracted from MRI images, EEG and MEG signal, while the genetic and epigenetic features are from single nucleotide polymorphisms (SNP), copy number variations (CNV) and DNA methylation. Due to complexity of data, her team also specializes in the development of novel methods for multi-model feature extraction and multivariate association. The disorders being studied include, but not limited to, schizophrenia, bipolar disorder, substance addition, and TBI.
Selected Publications //
- Longitudinal epigenetic predictors of amygdala: Hippocampus volume ratio >
- Regional enrichment analyses on genetic profiles for schizophrenia and bipolar disorder. >
- Cognitive Control, Learning, and Clinical Motor Ratings Are Most Highly Associated with Basal… >
- Alterations of Resting State Functional Network Connectivity in the Brain of Nicotine and Alcohol… >
- Multivariate Imaging Genetics Study of MRI Gray Matter Volume and SNPs Reveals Biological Pathways C >
- A pilot study on commonality and specificity of copy number variants in schizophrenia and bipolar… >
- Memory Efficient PCA Methods for Large Group ICA. >
- The association of DNA methylation and brain volume in healthy individuals and scz patients >
- G-protein genomic association with normal variation in gray matter density >
- A group ICA based framework for evaluating resting fMRI markers when disease categories are… >
- CREB-BDNF pathway influences alcohol cue-elicited activation in drinkers >
- Genetic markers of white matter integrity in schizophrenia revealed by parallel ICA >
- An open science resource for establishing reliability and reproducablity in functional connectomics >
- Methylation Patterns in Whole Blood Correlate with Symptoms in Schizophrenia Patients >
- A Review of Multivariate Analyses in Imaging Genetics >
Imaging Genetics on Huntington’s Disease
We will use our imaging genetics analysis methods in the PREDICT HD dataset to identify structural networks and genetic profiles which relate to the predicted time to disease onset. We use cross-sectional data to identify these patterns, and use the longitudinal data to identify which of these imaging genetic patterns predict the rate of loss of function. A recently released Huntington's Disease Signaling Pathway identifies genes which interact with the Huntington gene HTT, and forms a high-priority geneset to examine. We exepect that cumulative effects of many genotypic factors from this pathway in conjunction with structural "fingerprints", identified using independent components analysis ICA, will account for additional variance in disease progression (pre-diagnosis) in the lower-repeat subset of the prodromal HD sample.
Epigenetic Influences on Diseases
DNA methylation as one epigenetic mechanism regulates the gene expression and further gene function without changing DNA sequence. We evaluate the methylation level of individual CpG sites across genome, and study their connections with population, gender, age, exercise and diseases.
Copy Number Variation Effect on Substance Use Disorders and Co-morbidity Studies
Copy number variations as one type of DNA structural variation alter more nucleotides than any other genetic mutations. It has the potential to influence normal development and risk of diseases. In this project we studied the CNV effect on substance use disorders from multiple levels using brain function and structure features. Additionally, we investigate the co-morbidity among disorders involving various substances.
Multivariate Analyses on Genetic Effects on Brain Abnormality Associated with Schizophrenia
Multivariate analyses on genetic effects on brain abnormality associated with schizophrenia: Genetics play an important role in psychiatric disorders including schizophrenia. Not a single gene but many genes interact together, and contribute to the risk of development of schizophrenia. We have developed and continue to develop multivariate methods to assess the complex genetic effect on brain based on phenotypes associated to schizophrenia.
