Principal Investigators //
- 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
Jessica Turner, PhD
Associate Professor of Translational Neuroscience
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.
Selected Publications //
- Methylation Patterns in Whole Blood Correlate with Symptoms in Schizophrenia Patients >
- Prefrontal Inefficiency Is Associated With Polygenic Risk for Schizophrenia >
- Heritability of multivariate gray matter measures in schizophrenia >
- Electronic data capture, representation, and applications for neuroimaging: A Frontiers in Neuroinfo >
- Reliability of the amplitude of low-frequency fluctuations in resting state in chronic schizophrenia >
- The Cognitive Paradigm Ontology: Design and Application >
- Neuroscience Data Integration with Mediation: An (F)BIRN Application and Case Study >
- Voxel-based morphometric multisite collaborative study on schizophrenia >
- Multivariate analyses suggest genetic impacts on neurocircuitry in schizophrenia >
- Neuroimaging for the Diagnosis and Study of Psychiatric Disorders >
- Cerebral and cerebellar sensorimotor plasticity following motor imagery-based mental practice of… >
- An fMRI investigation of hand representation in paraplegic humans >
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.