2009 — 2011 |
Wang, Lei |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Schizophrenia Data and Software Tool Federation Using Birn Infrastructure @ Northwestern University At Chicago
DESCRIPTION (provided by applicant): As part of the growing field of Computational Anatomy (CA) (Grenander and Miller 1998;Miller 2004), our group has developed tools to identify and characterize brain structural abnormalities in schizophrenia, some of which meet the criteria for disease endophenotypes. In this effort, we have collected high resolution magnetic resonance (MR) datasets from more than 270 subjects using the same MR scanner platform and sequences. Longitudinal MR data are also available on a majority of these subjects. Using CA tools, we have generated surface maps for all of the deep subcortical structures (i.e., hippocampus, amygdala, thalamus, caudate nucleus, nucleus accumbens, putamen and globus pallidus). In addition, smaller datasets of variables related to the volume, thickness and surface area of cortical structures (e.g., the cingulate gyrus) have been generated. Finally, we have constructed manual segmentation datasets for all these structures, which can be used for the validation of new computational methods. If made publically accessible, these high resolution scans and the associated structural data will be invaluable to the neuroscience community in many ways. First, other groups of scientists will be able to use these data to generate or test new hypotheses related to the maldevelopment of brain structures and neural networks in individuals with schizophrenia. Second, scientists in other groups would be able to rapidly replicate findings produced using their own datasets. Third, the data could be used to test and validate new brain mapping tools. Further, the CA pipeline designed for the analysis of these datasets, which consists of landmarking and diffeomorphic mapping tools along with training and validation datasets, will enable others to study other MR datasets collected from other clinical samples. The specific aims of this application are: Aim 1. To make available imaging data along with demographic, clinical neurocognitive and genotyping data from 139 subjects with schizophrenia and 136 control subjects group matched for age and gender in a federated database. All data will be appropriately de-identified and anonymized to comply with HIPAA regulations. Aim 2. To make available neuroanatomical variable datasets, including data on the volumes and surfaces of subcortical structures, and data on the gray matter volumes, thicknesses and surface areas of cortical structures. Aim 3. To federate software tools and training and validation datasets. A key group of intended users of our distribution is researchers who may have their own imaging data but lack software tools for mapping subcortical brain structures. In that group, our software tools would be needed and used on our training data, therefore enabling these researchers to obtain structural measurements in their images. The software tools will include all that are required by our mapping protocol such as landmarking tools. PUBLIC HEALTH RELEVANCE: We propose to share and federate our schizophrenia research data, including structural magnetic resonance (MR) scans, neuroanatomic data, clinical and cognitive data, all appropriately anonymized, as well as software tools, with the neuroscience community using the infrastructure of the Biomedical Informatics Research Network (BIRN). Other groups of scientists will be able to use these data to generate or test new hypotheses related to abnormalities of brain structures and neural networks in individuals with schizophrenia. They would also be able to rapidly replicate findings produced using their own datasets or test and validate new brain mapping tools. Further, the software tools would enable others to study other MR datasets collected from other clinical samples.
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2012 — 2016 |
Wang, Lei |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Hippopci Hippocampal Predictors of Cognitive Impairment in Breast Cancer Patients @ Northwestern University At Chicago
DESCRIPTION (provided by applicant): Post-surgery adjuvant therapy produces changes in cognitive function in up to 70% women with breast cancer. Our recent work demonstrates that breast cancer patients receiving adjuvant hormonal treatment also exhibit a decline in cognition three months post treatment, providing new evidence that MCI also arises from hormonal therapy. To date, no investigation has assessed the neural correlates of adjuvant hormonal therapy, nor have any studies determined how to identify individuals at risk for treatment-related cognitive impairment. We propose to use longitudinal magnetic resonance imaging (MRI) to identify predictors and mechanisms of cognitive impairment in breast cancer patients receiving hormonal treatment. We will achieve this by using structural and functional assessments that are sensitive to the integrity of the hippocampal-cortical circuitry. Our central hypothesis is tha measures of the hippocampal-cortical circuitry can be used to predict cognitive decline, and that the trajectories of specific domains of cognitive performance in patients receiving adjuvant therapy may be related to trajectories of specific hippocampal-cortical circuitry components. Aims: I. To determine treatment-related cognitive impairment. We will determine different trajectories of cognition over time by analyzing neuropsychological measures at all time points using growth mixture modeling. II. Using baseline hippocampal-cortical circuitry markers to predict treatment-related cognitive impairment at follow-up. We will assess the predictability of structural and functional markers and test that integration of measures of structure and function will provide greater sensitivity to predict cognitive impairment. III. Using longitudinal hippocampal-cortical circuitry markers to determine the neuro-pathophysiology of treatment-related cognitive impairment. We will test for relationships between circuitry structure and function in order to identify and characterize patterns linked to treatment- related cognitive decline. We will test the specificity of structural and functional changes in the circuitry components that are associated with domains of cognitive impairment and trajectories of cognitive decline. IV. To integrate research and clinical data to augment patient care, and to establish longitudinal neuroimaging database.
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2013 — 2016 |
Ambite, Jose-Luis Potkin, Steven G Turner, Jessica Wang, Lei |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Schizconnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation @ Northwestern University At Chicago
DESCRIPTION (provided by applicant): The schizophrenia research community has invested substantial resources to develop methods to collect, manage and share neuroimaging data, along with other meta-data such as clinical, behavioral, cognitive and genetics data. The exploration and analysis of multi-site, multi-dimensional, multi-modal data has improved our understanding of the relationships among abnormalities of brain circuitry, brain function and genetic variability in schizophrenia, as demonstrated by efforts from multi-site consortiums such as the Functional Biomedical Informatics Research Network (FBIRN) and the Mind Research Network (MRN) Clinical Imaging Consortium (MCIC). However, to draw meaningful conclusions about these complex measures, data from large samples are needed, far more than what would be possible at any individual site. Presently, such large-scale data analysis and discovery in schizophrenia research using data from multiple sources would depend upon the effort of separately obtaining data from different places. Practical challenges related to data sharing due to cost of duplication of data, disparate database architecture and querying are preventing many research teams from being able to participate in state-of-the-art neuroscience research. We propose to create a SchizConnect Mediator as a data mediation and integration platform for establishing a true federation of schizophrenia neuroimaging-related databases to support hypothesis generation and testing, and to deliver a web portal, SchizConnect, to interact with the federated databases. In this approach, information from disparate, heterogeneous databases are queried and integrated in a uniform, semantically- consistent structured manner. To a user or a client program the system appears as a single (virtual) database with a uniform schema/model of the domain, but the data remains at the repositories and under the control of the data providers. We will first test and validate this approach on three large data sets and extend this federation to other data sets in the final year. By bringing information from disparate neuroimaging repositories into a common integrated system, we will provide resources for investigators to conduct unique investigations not addressable at any single site. By integrating existing data sources that are native to their own institutions and heterogeneous in the terminology and modeling constructs, we will enable more schizophrenia researchers to share their already-collected existing data with no changes to their existing data repositories, thus greatly promoting discovery related to the mechanisms underlying schizophrenia, a key NIMH strategic objective.
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