Principal Investigators //
- Nathaniel Anderson, PhD >
- David Bridwell, PhD >
- Vince Calhoun, PhD >
- Arvind Caprihan, PhD >
- Zikuan Chen, PhD >
- Jiayu Chen, PhD >
- Vince Clark, PhD >
- Eric D. Claus, PhD >
- Yuhui Du, PhD >
- Flor A. Espinoza, PhD >
- Faith Hanlon, 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 >
- Lori Sanfratello, PhD >
- Matthew Shane, PhD >
- Julia M. Stephen, PhD >
- Jing Sui, PhD >
- Jessica Turner, PhD
- Victor M. Vergara, PhD >
- Claire E. Wilcox, MD >
Jessica Turner, PhD
Associate Professor of Translational Neuroscience at MRN
Associate Professor of Psychology at GSU
Dr. Turner’s research falls under “what can we know from cognitive neuroimaging and genetic data,” and “how can we represent what we know from these experiments?”
The first research program includes the extraction and understanding of multivariate patterns within the combined methods of neuroimaging and genetics, as applied to clinical populations. In particular she is investigating the genetics underlying brain structure changes in chronic schizophrenia, as well as the genetic influences on functional and structural neuroimaging measures in other diseases.
The second research program includes the development of formal representations of cognitive experiments, the experimental variables involved, and the results of the data for automated data sharing and meta-analysis within neurobiology. Dr. Turner’s background is in psychophysics and MRI methodology as applied to a range of clinical populations, with secondary experience in the analysis of genome wide scan (GWS) data, and ontological development.
For more information on Dr. Turner, please refer to her Georgia State University webpage.
Selected Publications //
- Abnormal asymmetries in subcortical brain volume in schizophrenia >
- Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome >
- Independent component analysis of SNPs reflects polygenic risk scores for schizophrenia >
- COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data >
- Modality-Dependent Impact of Hallucinations on Low-Frequency Fluctuations in Schizophrenia >
- Functional MRI Evaluation of Multiple Neural Networks Underlying Auditory Verbal Hallucinations in Schizophrenia Spectrum Disorders >
- Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients >
- Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia >
- Polymorphisms in MIR137HG and microRNA-137-regulated genes influence gray matter structure in schizophrenia >
- Sharing the wealth: Neuroimaging data repositories >
- Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24 >
- Terminology development towards harmonizing multiple clinical neuroimaging research repositories >
- SchizConnect: Mediating Neuroimaging Databases on Schizophrenia and Related Disorders for large-scale integration >
- Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium >
- Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis >
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.
Resting State Brain Networks in Neuropsychiatric Disorders
The amplitude of low frequency fluctuations (ALFF) in fMRI measured while subjects are resting has been shown to be quite reliable in healthy subjects, and to correlate with antipsychotic treatment response in antipsychotic–naïve schizophrenia patients. We are exploring the use of various resting state measures within neuropsychiatric patients undergoing treatment changes to determine any correlations with clinical improvements.
Cognitive Paradigm Ontology
Cognitive Paradigm Ontology (CogPO), a domain ontology of cognitive paradigms, facilitates descriptions of cognitive experimental paradigms in ways that are machine-readable and machine-interpretable, to allow communication and automated data sharing across diverse databases and data sources. The design of CogPO concentrates on what can be observed directly: categorization of each paradigm in terms of (1) the stimulus presented to the subjects, (2) the requested instructions, and (3) the returned response. All paradigms are essentially comprised of these three orthogonal components, and formalizing an ontology around them is a clear and direct approach to describing paradigms. This structured, well-defined, common and controlled vocabulary will be capable of representing the cognitive paradigms across a variety of data repositories. This ontology will be made available for adoption not only by other fMRI databases, but also for archives of other neuroimaging modalities (e.g., EEG or MEG data), such as the Neural ElectroMagnetic Ontologies (NEMO), and literature neuroinformatics efforts such as the Society for Neuroscience's PubMed Plus.
Mining the Genomewide Scan
Genetic profiles of structural loss in schizophrenia: The development of both neuroimaging and genome-wide scanning technologies has created a proliferation of data about neuropsychiatric disorders. It is possible to collect more information in a study about each subject than there are subjects available to study, creating a challenge for standard statistical techniques. We develop an approach already used separately in imaging and genetics, but apply it here to the combination of imaging genetic data on a massive dataset, to determine genetic effects on brain structure in psychiatric disorders. Schizophrenia is a highly heritable neuropsychiatric disorder with significant public health costs. Understanding the contributions of genetic variability in the disease will help identify better predictors of prognosis and treatment response. Current studies are using genome wide scan (GWS) approaches to identify the numerous genes which might play a role in schizophrenia-either in increased risk for the disorder overall, or through modulating the various clinical symptoms. Structural neuroimaging measures implicate gray matter loss in schizophrenia; subjects with schizophrenia tend to have larger ventricles and smaller grey matter volumes than do their healthy counterparts, and regional loss in the medial frontal, temporal and insular gyri have been identified by us and others. Identifying the genetic influences underlying these patterns of gray matter loss is the goal of this project.